*Institutional profile was not available or author gender could not be determined through an institutional profile.
†MD, with or without other degrees; a PhD, with or without other degrees (excluding MD); or any other degrees.
The vast majority of high-impact researchers were affiliated with academic centres or hospitals (213/246, 87.6%); 85.8% (211/246) were primarily based in the Americas or Europe ( table 1 ). An institutional profile was identified for almost all (241/246, 98.0%) of the high-impact researchers. The majority of those with an institutional profile (166/241, 68.9%) had an MD and one-third (82/241, 34.0%) of the researchers were women. High-impact researchers had, on average, a publication history of greater than 20 years, an H-Index of 38.5 (median: 38.5, IQR 22.0–64.0), and had published at least four first or senior author manuscripts in 2019 (median: 4, IQR 2–8).
In 2019, the 241 general researchers published 914 first or senior author research or review articles in 598 unique journals. The most common journals were Medicine (15/914, 1.6%) and the International Journal of Molecular Sciences (9/914, 1.0%). Among the 462 journals with a 2018 JCR impact factor, the median impact factor among their articles was 2.65 (IQR 1.69–3.86). The 246 high-impact researchers published 1471 original first or senior author research or review articles in 604 unique journals. The most common journals were NEJM (60/1471, 4.1%), The Lancet (43/1471, 2.9%) and PLOS Medicine (36/1471, 2.5%). Among the 537 journals with a 2018 JCR impact factor, the median impact factor was 5.05 (IQR 3.18–11.05).
Of the 914 first or senior author research or review articles published by the general researchers, 414 (414/914, 45.3%) were indexed in Scopus as open access. There were 384 (41.6%) articles published in an APC-based journal ( table 2 ). Of the 457 (457/914, 50.7%) articles published in journals with a hybrid funding model, 72 (72/457, 15.8%) were indexed as open access. Among the high-impact researchers, 726 (726/1471, 49.4%) of the articles were indexed in Scopus as open access. Just under one-third of all articles were published in an APC-based journal (426/1471, 28.9%). Among the 870 (870/1471, 59.1%) articles published in journals with a hybrid funding model, less than one-third (255/870, 29.3%) were open access.
Journal funding models for the articles published by general and high-impact researchers
| No (%) | |
General researchers | High-impact researchers | |
Total number of journals | 914 | 1471 |
Article processing charge-based journals | 384 (42.0) | 426 (29.0) |
Subscription-based journals | 57 (6.2) | 169 (11.5) |
Hybrid* | 457 (50.0) | 870 (59.1) |
Unknown | 16 (1.8) | 6 (0.4) |
*A traditional subscription-based journal with a fee-based open access publication option.
The journal funding model and any associated APCs could be identified for 94.1% (860/914) of the first or senior research or review articles published by the general researchers and 97.8% (1439/1471) of the articles published by the high-impact researchers. In 2019, the 241 general and 246 high-impact researchers paid an estimated total of US$497716 (£390209) and US$1067869 (£837209) in APCs, respectively, for their first and senior author articles. Although the median APCs paid by general clinical medical researchers was US$191 (IQR US$0–US$2500) (£150, £0–£1960), one researcher was estimated as having paid US$30115 (£23610) in APCs ( table 3 ). The median total APCs per researcher in the high-impact sample was US$2900 (IQR US$0–US$5465) (£2274, £0–£4285); one researcher was estimated as having paid as much as US$34676 (£27186) in APCs.
Article processing charges (APCs) for all first and/or senior research and review articles published in 2019
| Median (IQR) | |
General researchers (n=241) | High-impact researchers (n=246) | |
Standard APCs paid per year, US$ | 191 (0, 2500) | 2900 (0, 5465) |
First author articles only | 0 (0, 0) | 0 (0, 0) |
Senior author articles only | 0 (0, 0) | 2800 (0, 5181) |
APCs paid per year (including hybrid journals)*, US$ | 739 (0, 3950) | 5000 (0, 10879) |
APCs paid per year (minimum)†, US$ | 0 (0, 2500) | 2600 (0, 5465) |
†The minimum APCs paid is defined as the lowest possible APC an author could have paid given the discounts, membership options or licensing options listed on a journal website.
APCs, article processing charges.
In sensitivity analyses, after including potential discounts on standard APCs, the minimum listed APCs general researchers could have paid for their first and senior author publications in 2019 was US$0 (IQR: US$0–US$2500) (£0, £0–£1960) ( table 3 ). However, researchers in the high-impact sample would have paid approximately US$300 less on average (median: US$2600, IQR US$0–US$5465) (£2038, £0–£4285). If all researchers paid the APCs for their first and senior open access published in hybrid journals (as opposed to the articles being made available through delayed open access due to funder requirements, at the discretion of the journal, or through other mechanisms such as self-archiving) the median total APCs paid by the general and high-impact researchers would have been US$739 (IQR US$0–US$3950) (£579, £0–£3097) and US$5000 (IQR US$0–US$10879) (£3920, £0–£8529), respectively.
The estimated median total APCs paid did not vary across index researcher gender, training, H-index and years since first publication ( table 4 ). However, high-impact researchers in the Region of the Americas did have lower median total APCs per researcher than those in other regions of the world (Region of the Americas: US$1695, IQR US$0–US$3935 (£1329, £0–£3085) vs Other regions: US$4800, IQR US$1888–US$8290 (£3763, £1480–£6500); p<0.001).
Standard article processing charges (APCs) for first and senior research and review articles, across researcher characteristics
General researchers (n=241)* | High-impact researchers (n=246)* | ||||||
Total APC per year (US$) | Total APC per year (US$) | ||||||
No | Median (IQR) | P value† | No | Median (IQR) | P value† | ||
Gender | 0.48 | Gender | 0.20 | ||||
Male | 109 | 300 (0–2950) | Male | 159 | 2500 (0–5380) | ||
Female | 70 | 0 (0–3502) | Female | 82 | 3145 (0–6387) | ||
Primary affiliation | 0.36 | Primary affiliation | 0.45 | ||||
Academia/hospital | 236 | 28 (0–2500) | Academia/hospital | 213 | 3000 (0–5526) | ||
Other | 5 | 4420 (3070–6160) | Other | 31 | 1870 (0–4950) | ||
Training‡ | 0.19 | Training‡ | 0.13 | ||||
MD | 120 | 234 (0–2983) | MD | 166 | 2454 (0–5355) | ||
PhD only | 58 | 975 (0–4679) | PhD Only | 61 | 3490 (1695–7150) | ||
Other degrees only | 3 | 0 (0–0) | Other degree | 5 | 0 (0–4800) | ||
Region | 0.32 | Region | <0.001 | ||||
Region of the Americas | 62 | 0 (0–2425) | Region of the Americas | 144 | 1695 (0–3935) | ||
Other | 179 | 225 (0–2865) | Other | 101 | 4800 (1888–8290) | ||
H-Index (median) | 0.14 | H-Index (median) | 0.30 | ||||
≤11.0 | 120 | 0 (0–1390) | ≤38.5 | 123 | 2500 (0–4800) | ||
>11.0 | 121 | 1302 (0–4761) | >38.5 | 123 | 3465 (0–7494) | ||
Years since first publication (quartiles) | 0.22 | Years since first publication (quartiles) | 0.97 | ||||
<7.0 | 62 | 0 (0–925) | <14.3 | 62 | 2625 (0–4800) | ||
7.0–15.0 | 62 | 862 (0–3165) | 14.3–22.5 | 61 | 3000 (0–8000) | ||
15.0–25.0 | 63 | 300 (0–4400) | 22.5–32.0 | 68 | 3000 (0–6500) | ||
>25.0 | 54 | 0 (0–3513) | >32.0 | 55 | 2800 (0–4875) |
*Unknown values were considered as missing for these analyses; therefore, row amounts may not sum to column total.
†Calculated using Mann-Whitney U or Mood’s test as appropriate.
‡MD, with or without other degrees; a PhD, with or without other degrees (excluding MD); or any other degrees.
In this cross-sectional study, 241 and 246 randomly selected general and high-impact medical researchers published a median of 2 and 4 first or senior author research or review articles in 2019, respectively. Approximately one-third of the articles across both samples were published in journals that required an APC. The median total APCs per general and high-impact researcher in 2019 was US$191 (£150) and US$2900 (£2274), respectively, with one researcher who may have incurred as much as US$34676 in APCs (£27186). Across both samples, there were no meaningful differences in APCs paid by gender, affiliation or training. However, in the high-impact sample, researchers from the Region of the Americas had a lower median total APCs paid (US$1695) (£1329) than researchers from all other regions (US$4800) (£3763). As open access publishing with APCs becomes increasingly common, it is important to consider the financial implications for individual researchers across different fields, settings and levels of seniority.
Our study suggests that many general and high-impact researchers could have paid thousands of dollars in APCs to publish their first and senior research and review articles in 2019. Across the 487 index researchers in both samples, which represents only a fraction of all biomedical researchers actively publishing in 2019, the total estimated APCs was approximately US$1500000 (£1 176 000). Given that general researchers published a median of 2 first or senior articles per year (potentially in lower impact factor journals with smaller APCs) 40 41 it may not be surprising that the median total APCs per researcher was relatively low (US$191) (£150). However, among high-impact researchers, who published a median of 4 first or senior research or review articles in 2019, the median total APCs per researcher was US$2900 (£2274). This suggests that these researchers paid an APC for one of every four of their first or senior author articles. Moreover, if we extrapolate our findings, individual researchers could spend a total of US$116000 (£90 944) on publication costs over a 40-year career.
It is important to note that there are numerous benefits to open access publishing. Limiting the amount of science that exists behind a paywall can have clear advantages for individual researchers and the public. 42 43 Open access publishing can enhance equity by improving the ability of researchers, either working in low-resource settings or at institutions that cannot support the hefty cost of journal subscriptions, to access publications. 44 45 Articles published open access can receive a citation boost compared with those behind paywalls, a boon for researchers looking to increase the audience and impact of their work. 46 Furthermore, APCs often serve an important purpose in the publication process. APCs can be used to pay the salaries of journal editors, who are often responsible for screening a large number of manuscript submissions, identifying and soliciting appropriate peer-reviewers (and performing their own peer-review), and helping improve the quality of studies as they transition from submission to eventual publication. Moreover, APCs could be used to pay peer reviewers for their efforts—a service currently provided by researchers for free even in cases where researchers are paying thousands of dollars to publish an article. 47 However, if APCs continue to increase, questions will continue to be raised about journals’ potential profit motives, predatory journals and hybrid journals that receive payments from both institutions and researchers.
However, the rise of the APC-centred open access publishing model poses a number of challenges for researchers. 18 48 Approximately one-third of the first or senior research and review articles published by the general and high-impact researchers were published in an open access journal that required an APC. Although not all open access journals charge APCs, approximately 50% of all articles that are published open access are published in journals that do. 42 When grant money or institutional discretionary funds are used to cover APCs, as is the case for approximately 80% researchers in the health, biological and life sciences, 10 fewer resources are available for other research-related expenses. 48 For instance, the US$2900 (£2274) median amount spent by researchers in our high-impact sample could support the attendance of multiple individuals at a conference or a critical piece of research equipment. Moreover, for researchers spending tens of thousands of dollars a year on APCs, these funds could have covered the tuition of a graduate student or the partial salary of a postdoctoral fellow. Second, the amount of APCs has risen dramatically in recent years—at a rate nearly three times that of the expected inflation rate. 13 49 These increases have raised questions about whether APCs actually reflect the cost of publishing or if publishers are driven by primarily financial motives. 9 48 While there does not appear to be a quality difference between subscription-based and open access journals, 5 50 there is some evidence that journals with higher APCs are perceived to be higher impact. 41 50
Lastly, the amount of APCs can enhance existing inequities in publishing by creating an additional barrier to many researchers based on field, 10 seniority, 14 disparities in research funding 15 16 or setting. 18 48 51 For instance, evidence suggests that researchers from countries with gross domestic products (GDPs) lower than US$25000 (£19600) are more likely to pay APCs out of personal funds compared with researchers from countries with GDPs higher than US$25000 (£19600). 10 It is important to note that certain journals grant fee waivers to researchers from low-income and middle-income countries or to researchers without funding to support publication. However, many researchers may be unaware of the specific journals that do provide waivers. 17 Furthermore, journal waivers do not necessarily address all of the inequities imposed by APCs. For early career researchers with no established grant funding or accumulated discretionary funds, even discounted APCs can be beyond available resources.
As open access publishing becomes the norm, numerous opportunities exist to address the disadvantages that may prevent many researchers from paying for APCs. At the journal level, increased transparency may be necessary to inform researchers from low-income and middle-income countries or at early stages of their careers about the waivers that are available. It is also critical that funders and institutions leverage their influence to restrain the hyperinflation of APCs. In 2018, cOAlition S, an international consortium of research funders, launched ‘Plan S’. This initiative, which aims to make all scientific publications resulting from publicly funded research immediately available open access, 52 has proposed an APC fee cap. 49 52 As more scientific research is available open access, institutions can shift resources from subscriptions to a pool of funds to support the expenses for early career researchers. Among universities in the UK, there is an ongoing commitment to promoting open access publishing by encouraging submission to open access repositories and by assisting researchers in the payment of APCs for immediate open access publication. 13 At the funder level, more agencies could embrace the Gates Foundation or the Charity Open Access Fund model used by the Wellcome Trust, where researchers supported by these funders can request coverage of any associated APCs. 53 54 Individual researchers can also increasingly choose to release their research open access through venues such as pre-print servers, like medRxiv, without undermining their ability to publish their findings in peer-reviewed journals. 55 Furthermore, so-called ‘Green Open Access’ policies, where researchers can elect to post peer-reviewed papers in open access repositories, are available for many journals, although most researchers do not use this option. 44 56 57 Scientific publishing is changing and it will be necessary for all stakeholders to adapt.
This study is subject to certain limitations. First, we recognise the limitations of classifying authors as ‘general’ or ‘high-impact’ based on one senior author research or review article published in one of the 10 highest impact factor medical journals. Second, our estimates do not represent the actual APCs that the index researchers in our sample paid. Without access to the financial records from the index researchers and journals in our sample, we had to make several assumptions about the nature of APC payments, most fundamentally that it was the index author who paid the APCs, rather than a funder or other organisation. In particular, articles for which the index researcher was a middle author were excluded, as we assumed index researchers are less likely to pay associated APCs as a middle author. We also did not account for situations in which APCs may have been paid by coprimary or cosenior authors. Additionally, we used the most recent APCs listed on journal websites, which may not represent the APCs paid in 2019. For our primary analysis, we assumed that researchers in our sample did not pay the optional APCs for open access publications in hybrid journals. Using publicly available information, it is difficult to determine if open access publications in hybrid journals were paid for by researchers or were available open access due to funder requirements or journal discretion. Furthermore, we did not account for any unlisted discounts or fee waivers provided by journals to researcher institutions in our analyses. Although the true minimum APCs per researcher may be lower than our estimate, our results did not change substantially when analyses were repeated using the lowest APCs listed by journals (excluding waivers). Overall, our sensitivity analyses provide a range of what researchers are likely to have paid.
Third, although Scopus provides a comprehensive accounting of a given researcher’s publication history, not all manuscripts published by a researcher may be indexed on Scopus. Furthermore, Scopus may create multiple researcher profiles for the same researcher, due to changing institutions or different permutations of the researcher’s name. However, we attempted to identify and include all researcher profiles for each index researcher. Second, we relied on articles classified as ‘articles’ or ‘reviews’ on Scopus. Although this method allowed us to objectively screen and classify index researcher articles, it is possible that we may have included or excluded articles that were incorrectly classified by Scopus. Lastly, due to the cross-sectional design of our study, we are unable to establish causal relationships between author characteristics (e.g., region) and potential APCs paid.
This cross-sectional analysis suggests that clinical medical researchers could have paid as much as US$34676 (£27186) in total APCs for their first and senior author research and review articles in 2019. Although the total APCs in this study are estimates, it is important to understand the potential cost of open-access publishing to researchers as journals with APCs become more common. In particular, future studies should evaluate the impact of APCs on individuals who may not have the funding or institutional resources to cover these costs.
Twitter: @JoshuaDWallach
Contributors: JDW and JSR first conceived the study idea when arguing about who would have to pay the APC for one of their previous manuscripts. MKE, KN, JSR, and JDW designed this study. MKE, XS and JJS acquired the author, journal, and APC data. MKE conducted the statistical analysis. MKE, JSR and JDW drafted the manuscript. MKE, XS, JJS, KN, RL, JSR and JDW participated in the interpretation of the data and critically revised the manuscript for important intellectual content. MKE and JDW had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. MKE and JDW are guarantors. JDW provided supervision, and despite being the senior author, begged JSR to pay the APCs.
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: In the past 36 months, XS received a scholarship from China Scholarship Council. JSR is a former Associate Editor of JAMA Internal Medicine, a current Research Editor at BMJ, and has received research support through Yale from Johnson & Johnson to develop methods of clinical trial data sharing, from the FDA to establish a Center for Excellence in Regulatory Science and Innovation (CERSI) at Yale University and the Mayo Clinic (U01FD005938), from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology (NEST), from the Agency for Healthcare Research and Quality (R01HS022882), from the National Heart, Lung and Blood Institute of the National Institutes of Health (NIH) (R01HS025164, R01HL144644), and from the Laura and John Arnold Foundation. JDW received research support through the Collaboration for Research Integrity and Transparency from the Laura and John Arnold Foundation and through the Center for Excellence in Regulatory Science and Innovation (CERSI) at Yale University and the Mayo Clinic (U01FD005938).
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication: Not required.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement: The dataset will be made available via a publicly accessible repository on publication. https://osf.io/f6cwu/
Authors using government research funding or university consortium funding may be required to publish in OA journals. In addition, authors may choose to publish open access to gain the largest possible audience for their innovative practical, applied, and theoretical research.
Similar to most gold open access publications, IEEE open access articles are supported by article processing charges (APCs), rather than through subscriptions. APCs may be paid by the author, the author’s institution, or a funding agency.
APC pricing for the various open access options available from IEEE are listed below.
For details on APC pricing related to IEEE institutional open access agreements, please see your institution’s administrator.
Discounts do not apply to undergraduate and graduate students. Discounts cannot be combined or applied to any other fees such as overlength or color page charges.
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A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.
Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.
This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.
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Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.
Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:
Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.
The AI-powered Citation Checker helps you avoid common mistakes such as:
There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.
You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.
You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.
Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:
Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.
Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.
In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”
A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.
The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.
You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.
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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.
A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.
Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:
You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.
Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.
Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.
George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.
It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.
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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.
What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.
Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?
How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.
The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.
One way to stay on track is to use your thesis statement and topic sentences . Check:
Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.
The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.
Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.
You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.
You should not :
There are four main considerations when it comes to the second draft.
The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .
Check the content of each paragraph, making sure that:
Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .
Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading or create an APA title page .
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I have followed all instructions in the assignment sheet.
My introduction presents my topic in an engaging way and provides necessary background information.
My introduction presents a clear, focused research problem and/or thesis statement .
My paper is logically organized using paragraphs and (if relevant) section headings .
Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .
Each paragraph is relevant to my research problem or thesis statement.
I have used appropriate transitions to clarify the connections between sections, paragraphs, and sentences.
My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.
My conclusion shows how my research has contributed to knowledge or understanding of my topic.
My conclusion does not present any new points or information essential to my argument.
I have provided an in-text citation every time I refer to ideas or information from a source.
I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .
I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.
I have followed all formatting guidelines (page numbers, headers, spacing, etc.).
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It might seem mysterious, but the process of getting scientific results to become scientific papers straightforward ... but needs some improvements..
Scientific research! No, it isn't just a bunch of folks in lab coats shouting "eureka!" and then getting handed a Nobel Prize. Lots of scientific research gets done these days in the United States alone. This work is being done by a widely diverse (but maybe not diverse enough) group of people at universities, labs, companies, you name it. In fact, the average scientist likely spends more time writing than "doing" research. The process to go from research to publication is not well known by most of the public, so let's demystify it!
1. Accepted scientific research has been peer-reviewed . This means that scientists independent of the research have vetted and commented on the work and the paper that was written about the work. This is why publication in a peer-reviewed journal or book is the gold standard ... and why random people on the internet claiming grand scientific discoveries (like earthquake prediction) without submitting it to peer review should be handled extremely skeptically.
2. No person is an island. Very little modern scientific research is done by one person. Research is done by teams and collaborations because, in the end, it tends to produce better, more thorough results.
3. Published papers are not the end of the line. So, you published your results! This doesn't mean that the research is done. You or anyone can take the baton and run with it, possibly overturning your results. That's ok! That's part of science. However, if your results are solid, then they should start the test of further research. Most science is a theory, so it is just an idea with evidence for how the some part of the universe works.
Let's get into the nuts and bolts. The process of publishing scientific research starts with results. For some fields, it might be from experiments. In others, it might be field observations. In others, it might be laboratory analyses. In many cases, it is a combination of these with a dollop of data from other researchers. You need to take all these data and make sense of them, either through statistical analyses or comparisons to known results or theoretical modeling.
Once you've built your explanation for your data, you actually need to write the manuscript. I'm not going to get into the gory details, but manuscripts can vary from a few pages to tens of pages depending on the results and your chosen venue to attempt to publish. You'll likely have to make some figures and tables as well. For most scientists, you are doing this all yourself! In many ways, you have to become your own desktop publisher.
Unless you are in the small minority without collaborators, you are likely working together with others on the manuscript. They might write parts of it, analyse the data, offer content. Authorship has different roles in different disciplines. For me, my coauthors tends to be active collaborators, people who played significant roles in the data collecting, or students who worked on the project. In the end, all these people likely get to examine and comment on the manuscript before it heads to a journal.
And journals ... there are so many of them! Some are big for all of science, like Nature and Science . Others are for specific disciplines like Geology or Earth & Planetary Science Letters . Still others are much more specific to sub-disciplines, like the Journal of Volcanology & Geothermal Research . There are also journals for specific geographic regions, for applications of research to teaching and many more. It can be challenging to decide where to submit and there are lots of sketchy, predatory publishers out there as well (more on that later).
Once you've decided what journal, you need to get your manuscript into their formatting. This might mean specific fonts, reference styles (you need to credit the previous papers when you use their information or ideas), image sizes, data appendices and more. This, again, is like being your own publishing firm. It takes time.
How much time? Well, depending on how quickly you write and how quickly your collaborators respond, the average manuscript might take months to years to come together. And that is just from inception to submission ... in a sense, the journey is just beginning.
OK, so you've submitted the manuscript to the Journal of Awesome Earth Science . The journal has decided that your paper meets their criteria for potential publication based on the novel nature of the work or the field of research. What's next? Usually, an associate editor (usually a volunteer from the discipline community) gets assigned the manuscript and they need to find reviewers (also volunteers).
Most journals let authors suggest some (and nix others), but the associate editor might reach out to experts in the field to review. Usually, the review process is supposed to take a few weeks, but more likely it might take a few months (remember, everyone is volunteering). Much of the time, the authors never find out who the reviewers were unless the reviewers specifically say they can be identified.
Once the associate editor gets the comments and recommendations from the reviewers, they need to decide what to report to the authors. Usually, there are four(ish) tiers: accept, accept with minor revisions, major revisions and reject. If your manuscript is rejected it is typically because it was poorly written or the data and interpretation didn't hold up to examination by your peers (the reviewers).
"Major revisions" means that the reviewers seem something of value, but think the manuscript needs work and may need to go through review again before acceptance. "Minor revisions" means that the manuscript has a few things to fix, but after that, it should be good to publish.
So, it's back to the authors! If you weren't rejected (and getting rejected happens, especially if you are submitting to a top level journal like Science or Nature), then you likely need to revise. That might take a few weeks to months. When you resubmit the revisions, you need to provide a point by point response to the reviewers comments, explaining how you fixed things (or chose not to fix). All of this gets sent back to the associate editor to decide the manuscript's fate.
The associate editor will then make a recommendation that will likely need to be approved by the main editor for the journal. If they give it the OK, then it heads to the folks who will give it a stern copyedit and start to format it for publication (print, online or both). This might take another few weeks. Usually, after the copyedit, the manuscript-now-paper can exist as a "pre-print" that hasn't been formatted for the journal but might be available for "early access".
Now, here is the rub. Guess how much scientists get paid by publishers to publish their work? Nothing. Absolutely nothing. In fact, most of the time researchers have to pay the publisher to publish the manuscript and if you want it to be open access, you really have to pay. We're talking hundreds to thousands of dollars just to get your work out to the community.
And remember what else I said? The editors and reviewers are volunteers, too! So there is a huge amount of work that most publishers get for free. There are a handful of journals that don't work in those models, but many of the big, fancy journals are put out by for-profit (and very profitable at that) publishers like Elsevier or Wiley.
The publishers then turn around and charge libraries for subscriptions (print and(or) digital, with those being in the tens to hundreds of thousands of dollars per year. Without those institutional subscriptions, many scientists wouldn't have access to all the published research out there.
That leads to problems in access and equity. If only those who are at institutions that can afford subscriptions have access, what does that do to the scientific community as a whole? And if scientists are donating all this time to publishers, why can they get away with charging so much to both the producers and consumers of research?
In the end, many scientists are judged by the quality, quantity or both of their research. This means that getting tenure, getting promotions, salaries and more are based on working in this model. If that seems problematic to you, well, you're right to think that. Scientists are beholden to a system that is scientifically rigorous but financially imbalanced for the most part.
Most researchers are just excited to get their work out there. We love discovery! We love trying to understand our world better and then share that with other scientists and the public. The process of peer-reviewed publication can take years and cost thousands of dollars, but many millions of papers are published each year across the natural sciences alone. A more equitable and open version of this is needed, but how this cycle is change depends on scientists and universities to look at what is means to be a successful researcher in a new light.
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Making your Research easy
Publication Plays an important role in every researcher’s carrier. if you are a Ph.D. student or doing your master’s or bachelor’s, then as per the University norms, you need to publish your research paper in a good journal. Here in this article, we are going to discuss How much does it cost to publish a paper in a journal .
Before proceeding let us discuss how the Journal operates and its finances. As we know that every organization and institution needs the funds to operate. The Journal publication organization has some costs to operate.
To operate the publication most journal publishers follows the two type of model of publication
1. Open access Method
2. Subscription-based method.
The subscription-based model is a very old model and many publishers adopted this for many years now. But the Open access model is new to the world and becoming popular very rapidly.
So, let us understand why is it getting popular so fast?
In the Subscription-based model if any reader wants to read the published article then he/she has to pay some charges to download the paper or to read the paper for future research.
But in case of open access , the readers can download, read and cite the paper completely free. So the readers and researchers prefer the articles which do not cost them. Due to this, the Open Access model is getting popular.
Now there are two types of models in which the journals that publish the paper from authors can have an income.
As we discussed now The first model is known as the open-access model . In this model, the author of the paper has to pay the publication fees. But now some publishers do not use the Subscription-based model rather they call it Green Open access .
Let us understand what is Gold Open Access and Green Open Access Journals
Must read: How to publish a paper in International Journal
According to Elsevier in this model of publication, the journal article will be freely available for everyone after publication. The publishing costs are covered by the author or by their institution/funding body/society on their behalf, typically in the form of an Article Publishing Charge (APC) or other types of fees.
Elsevier’s APCs range between $150 and c$6000 US Dollars excluding tax, depending on the journal, with prices clearly displayed on the Article Publishing Charge (APC) price list and on journal homepages. Other than these journals you can find journals that charge between 20 USD and to 200USD if it’s a Peer review journals.
Other Publishers like Springer nature, Willey, IEEE, and Hindawi also followed the same.
Must read: How to know if a journal is indexed
In this model, the authors do not need to pay any additional charges for the publication. The publication costs are covered by subscriptions. The reader will pay when he/she wants to download the paper and read it.
Refer to Elsevier Open access policy here
Check here the Springer’s Open access policy here
IEEE Publication charges
Here is the tentative cost to publish a paper in a journal
SCI Indexed Journal: 500USd to 6000USD per article
Scopus Indexed Journal: 200USD to 1500USD per article
Web of Science Indexed Journal: 200USD to 1500USD per article
ABDC Indexed Journal: 200USD to 1000USD per article
Peer reviewed and Google Scholar Indexed Journals: 20 USD to 300USD per article
UGC Care(for India only-Group-1)listed Journal: 50USD to 300USD per article
Other reputed Indexing like Pubmed, IJIFACTOR, GARUDA, DOAJ, EI Compemdex, CNIK Indexed Journal: 100USD to 1000USD per article.
Because of the Publication cost, Open access journals are expensive. But it’s now it is widely accepted that the Open access journal is the future of the publication . The researchers love to read the journal article available freely and cite them.
Generally, people consider the journals which charge APC from authors during publication are fake journals or predatory journals. But that is not true people should understand that the journals that are publishing their papers need some source of income to sustain the journal and to maintain the journal quality.
As here the author needs to pay before publication some people think it’s easy to pay and publish. But it’s not true. There might be some predatory journals that take money to publish but most Journal publishers follow the Global standard of Journal publication. You need to find good journals to publish your research paper.
To know more about the publication fees of any organization and institution one should always visit the website of that organization.
Must read: How to search Scopus indexed journals
Is it good to publish in open-access journals?
Absolutely yes!! As we discussed earlier in this article the researchers love to read the articles which available freely. So, the chance of getting a higher impact on your paper after publication. Most reputed Journal publishers are now having Gold open-access Journal publications. This means they charge APC from the author to get the paper published.
Here is the list you can check
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Open Access
Peer-reviewed
Research Article
Roles Conceptualization, Formal analysis, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing
* E-mail: [email protected] , [email protected]
Affiliations College of Art, Shandong Management University, Jinan, Shandong, China, Shandong Architectural Design and Research Institute Co., Ltd., Jinan, Shandong, China
Roles Conceptualization, Methodology, Project administration, Resources, Software, Writing – original draft, Writing – review & editing
Affiliation Shandong Architectural Design and Research Institute Co., Ltd., Jinan, Shandong, China
"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT’s impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.
Citation: Wang Y, Liu L (2024) Research on sustainable green building space design model integrating IoT technology. PLoS ONE 19(4): e0298982. https://doi.org/10.1371/journal.pone.0298982
Editor: Sathishkumar Veerappampalayam Easwaramoorthy, Sunway University, MALAYSIA
Received: August 8, 2023; Accepted: February 1, 2024; Published: April 29, 2024
Copyright: © 2024 Wang, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
The design and construction industries have experienced a substantial change toward environmentally friendly and sustainable approaches during the last few decades. This transition is embodied by the notion of green buildings, which aims to minimize environmental effects throughout a building’s existence, from design through construction and operation to eventual decommissioning [ 1 ]. Green Building (G.B.) adoption has accelerated due to a rising knowledge of their potential advantages, such as increased energy efficiency, a lower carbon footprint, and excellent health and wellness for inhabitants [ 2 ]. Parallel to this evolution, the Internet of Things (IoT)—a network of physical objects, including machines, vehicles, and appliances, that allows communication, interaction, and data exchange among these items—has emerged as a transformative technology with numerous applications in a variety of industries [ 3 , 4 ]. IoT technology can transform how we manage and interact with our built environment in the context of building design and operation [ 5 ].
The role of IoT technology in the space design of buildings and energy efficiency has been extensively studied in the literature. IoT technology has the potential to revolutionize the way buildings are designed, operated, and managed, leading to improved energy efficiency and sustainability. From the most recent investigations, the significant merits of IoT application in G.B. design can be drawn as follows.
Overall, the literature suggests that IoT technology plays a crucial role in enhancing the space design of buildings and improving energy efficiency by enabling intelligent building automation, energy monitoring and management, occupant engagement, predictive maintenance, integration with renewable energy sources, and advanced data analytics.
Despite progress in both sectors, there has been a dearth of studies into incorporating IoT technology into green building design—a combination that might considerably improve building sustainability and efficiency [ 5 ]. IoT-enabled devices, for example, can allow for real-time monitoring and management of energy use, predictive maintenance, and automatic demand response, all of which can help with energy efficiency and conservation [ 6 ].
Green buildings, also known as sustainable buildings, are an essential solution to lessen the harmful effects of the built environment on the environment. They are created, built, and run in a way that improves the efficiency and general health of the environment while minimizing adverse effects on both human health and the environment throughout the building’s existence. Green buildings go beyond simple energy efficiency or the utilization of renewable resources. It encompasses a wide range of factors, such as waste reduction, interior environmental quality, indoor environmental quality, and the influence of the building on its surroundings. Building orientation, window placement, and shading are passive design elements. Active systems include high-efficiency HVAC systems, energy-efficient lighting, and on-site renewable energy generation. Energy efficiency is still central to green building design [ 7 ].
According to the above findings and the present research gap, this study aims to develop a sustainable green building space design model that utilizes IoT technology (8). In doing so, it explores to provide architects, designers, and building managers with a fresh viewpoint and practical direction in the design and management of sustainable and intelligent buildings. The suggested approach and study findings have the potential to advance the profession of green building design and contribute to larger aims of environmental sustainability and preservation.
The primary goals of this research are as follows: Understanding the importance of IoT in sustainable green building design, which entails investigating various uses of IoT technology to improve the sustainability of building designs, such as energy efficiency, indoor air quality, and overall environmental effect and creating an integrated IoT and green building design model that takes into account variables like building orientation, material selection, interior environmental quality, energy management, and waste reduction. Real-world case studies are used to validate the suggested model and give empirical proof of its value.
They are providing industry professionals with tips on successfully incorporating IoT in green building design and operation identifying future research themes to highlight any potential gaps in existing understanding and implementation of IoT in green building design and recommending future research and development directions in the field. Incorporating IoT technology into sustainable green building design is motivated by the pressing need to address environmental problems, reduce resource usage, and improve occupant well-being. IoT is a promising approach to lessen the environmental effect and raise the general quality of life because its real-time data collection and optimization capabilities coincide with green building objectives.
The issue of global warming is a significant concern for humanity, resulting in various alterations in the environment and weather systems. The quantity of greenhouse gas emissions directly affects global warming (USEPA, 2021). Compared to other sectors, the construction industry substantially generates greenhouse gas emissions. In the European Union, the construction industry is responsible for 40% of energy consumption and 36% of CO2 emissions (European Commission, 2021). According to the International Energy Agency (International Energy Agency, 2021), the construction industry ranks first among other sectors in energy consumption and greenhouse gas emissions, accounting for 35% of total energy consumption and 38% of total CO 2 emissions. Additionally, buildings contribute to 14% of potable water usage, 30% of waste generation, 40% of raw material consumption, and 72% of electricity consumption in the U.S. (Bergman, 2013). Furthermore, it is worth noting that 75% of buildings in the E.U. are energy-inefficient (European Commission, 2021). Researchers have identified green buildings (G.B.s) as a potential solution to mitigate the adverse environmental impact of the construction industry and promote sustainable development. G.B.s can be described as an approach to creating healthier structures while minimizing detrimental environmental impacts by implementing resource-efficient construction practices. Compared to traditional buildings, G.B.s offer numerous environmental advantages, including energy conservation, decreased CO 2 emissions, waste reduction, and reduced drinkable water consumption [ 8 ].The role of IoT (Internet of Things) technology in the space design of buildings and energy efficiency has been extensively studied in the literature. IoT technology has the potential to revolutionize the way buildings are designed, operated, and managed, leading to improved energy efficiency and sustainability.
Another important consideration is water efficiency. Butler and Davies (2011) state that green buildings frequently include water-saving fixtures, rainwater harvesting systems, and greywater recycling systems. Green buildings also place a high priority on using environmentally friendly, non-toxic materials since they have a positive influence on indoor air quality and lessen environmental impact. Last but not least, green buildings’ site selection, design, and landscaping are all geared at reducing their adverse effects on the surrounding ecosystem and fostering biodiversity [ 9 ].
Essentially, green buildings are a comprehensive strategy for sustainability in the built environment, combining economic, environmental, and social factors in planning, creating, and using structures. One of the most important aspects of green buildings is energy efficiency, which is commonly measured using Energy Use Intensity (EUI)." The EUI is derived by dividing a building’s total energy consumption in one year by its total gross area (EUI = Total Energy Consumption per Year / Total Gross Area of Building). Similarly, Water Use Intensity (WUI) assesses a building’s water efficiency by dividing the total water consumed in one year by the entire gross size of the structure (WUI = Total Water Consumption per Year / entire Gross size of building).
Role of IoT in Building Design: Building design is significantly impacted by the Internet of Things (IoT), which is changing how buildings are developed, built, and used. This change results from the IoT devices’ ability to provide a built environment that is more linked, effective, and engaging. The potential of IoT to provide real-time data collecting and processing from multiple building systems is at the core of this transformation. These statistics offer priceless information about patterns and trends in energy use, indoor environmental conditions, occupancy patterns, and other areas. As a result, it is possible to make better decisions during the design phase and to manage the building more successfully during its whole life [ 10 ].
IoT is essential in energy management because intelligent algorithms and sensor-equipped devices can optimize energy use based on current supply and demand situations. According to Morandi et al. (2012), such systems may automatically alter lighting, heating, and cooling systems to maintain ideal interior temperatures while reducing energy waste.
Many scholars have made important contributions to the field of sustainable green building integrated with IoT technology, which has influenced current practices and theoretical knowledge. For example, Smith et al. (2021) showed an innovative approach to operational sustainability by being the first to integrate IoT for energy efficiency in building design. Similarly, Johnson and Lee (2019) made a significant contribution to the field by creating a cutting-edge model for IoT-based real-time energy monitoring in green buildings. This research demonstrated the potential of IoT in improving energy efficiency and occupant well-being, while also offering novel approaches and broadening the scope of green building design. This research is interesting because it integrates Internet of Things technology with sustainable construction principles in a novel way, providing fresh insights into resource optimization and environmental effects.
IoT also supports the shift to design focused more on the user. Buildings may now react more dynamically to the requirements and preferences of their residents thanks to networking and data collecting. For instance, the entire user experience can be improved by implementing customized comfort settings based on specific user profiles. Table 1 presents a global standard of IoT technology. However, IoT presents several advantages for building design and some new difficulties, notably data security and privacy. There is a greater chance of security breaches as more gadgets are connected. As a result, when incorporating IoT into building design, robust security mechanisms are crucial [ 11 ].
https://doi.org/10.1371/journal.pone.0298982.t001
The main contribution of the present research aimed to employ the integration of IoT technology in the construction of sustainable green buildings, with a primary focus on residential and commercial building types due to their significant share of the overall built environment and energy consumption. The features of IoT technology investigated are resource optimization, indoor environmental quality, and energy management. Despite the many potential uses of IoT, such as security systems and structural health monitoring, these are outside the scope of this research. Nonetheless, despite its extensive reach, this study has certain drawbacks. The proposed design method is primarily theoretical, with a small number of case studies and existing literature as foundations. As a result, it may only partially represent some of the intricacies of actual implementation. Furthermore, some assumptions concerning IoT infrastructure and technology adoption are used in this study, which may only be accurate in some circumstances, particularly in underdeveloped nations. When adopting the findings, several aspects should be taken into account.
Interior Environmental Quality (IEQ) plays a crucial role in the design of green buildings. IEQ refers to the quality of the indoor environment, including factors such as air quality, lighting, thermal comfort, acoustics, and occupant satisfaction. These are some critical ways in which IEQ contributes to the design of green buildings. (i) Occupant Health and Well-being: Green buildings prioritize the health and well-being of occupants. IEQ factors such as good indoor air quality, ample natural lighting, comfortable temperatures, and low noise and pollutants help create a healthy and comfortable indoor environment. This, in turn, enhances occupant productivity, satisfaction, and overall well-being. CO2 Monitoring : IoT sensors measure indoor CO2. Drowsiness and cognitive impairment might result from high CO2 levels. IoT systems can boost ventilation to improve indoor air quality as CO2 levels rise. (ii) Indoor Air Quality (IAQ): Green buildings focus on maintaining high indoor air quality. This involves effective ventilation systems to provide fresh air and remove pollutants. Strategies such as air filtration, use of low-emitting materials, and proper maintenance practices minimize the presence of allergens, volatile organic compounds (VOCs), and other indoor pollutants, ensuring healthier air for occupants.
Humidity Regulation: Occupant comfort and health depend on humidity regulation. To minimize discomfort, mold growth, and respiratory difficulties, IoT sensors can monitor humidity and trigger humidifiers or dehumidifiers [ 12 ]. (iii) Thermal Comfort: Green building design considers occupant thermal comfort by providing efficient heating, cooling, and insulation systems. Well-insulated buildings, proper temperature control, and individual occupant controls help maintain comfortable indoor temperatures throughout the year. IoT sensors monitor home temperatures and modify HVAC systems. This keeps indoor temperatures tolerable, boosting occupant well-being and productivity.
This reduces energy consumption and enhances occupant satisfaction. (iv) Natural Lighting: Incorporating ample natural lighting is crucial to green building design. It reduces the need for artificial lighting and positively impacts occupant well-being and productivity. Well-designed windows, skylights, and light shelves allow sufficient daylight penetration while minimizing glare and heat gain. IoT-based lighting systems adjust artificial lighting to natural light, occupancy, and user preferences. This saves energy and makes indoor spaces bright and comfortable.
(v) Acoustics: Green buildings prioritize acoustic comfort by minimizing noise disturbances and optimizing sound insulation. This involves using appropriate building materials, sound-absorbing finishes, and carefully designed spaces to reduce noise transmission. Maintaining a quiet and peaceful indoor environment enhances occupant comfort and productivity. (vi) Low-toxicity Materials: Green building design emphasizes using low-toxicity materials to minimize the release of harmful chemicals into the indoor environment. Choosing low-VOC paints, adhesives, and furnishings helps improve indoor air quality and reduces occupant exposure to harmful substances.
(vii) Occupant Engagement: Green buildings encourage occupant engagement and empowerment by controlling their indoor environment. Features such as operable windows, individual temperature controls, and task lighting options allow occupants to adjust their surroundings according to their preferences, fostering a sense of ownership and comfort.
Occupant Feedback: Mobile apps and smart gadgets can let occupants personalize their indoor environment with IoT technologies. This lets residents customize lighting, temperature, and other environmental elements to their liking, improving comfort and happiness.
Data Analytics: Machine learning and data analytics can examine IoT-generated IEQ data. This research helps to build operators to optimize IEQ by identifying indoor environmental patterns and trends
Considering these IEQ factors, green building design aims to create healthier, more comfortable, and productive indoor environments while minimizing the building’s environmental impact. Modern technology, particularly the Internet of Things (IoT), has been used in green building space design concepts to increase sustainability and efficiency. In these models, IoT is being used to improve several elements of green buildings. Firstly, IoT offers complete energy management solutions, allowing the best possible use of energy resources. Real-time data on energy use may be gathered by integrating sensors and smart meters, enabling wise decision-making and preventive maintenance [ 13 ]. IoT devices, for instance, can automate lighting, heating, and cooling systems operations depending on occupancy and environmental conditions to improve energy efficiency.
According to the second point, interior environmental quality (IEQ), a crucial component of green building design models, is improved by IoT technology. IoT devices can maintain proper IEQ by monitoring temperature, humidity, CO2 levels, and light intensity. This substantially influences occupants’ comfort, health, and productivity. In green buildings, IoT also makes water management more effortless. Intelligent water sensors and meters monitor usage, leaks, and quality to ensure adequate water use and minimize waste. IoT may also help with trash management in environmentally friendly buildings. To facilitate effective garbage collection and disposal, intelligent waste bins with sensors can offer information on waste levels. Although several studies have demonstrated how IoT may be integrated into green buildings, the application is still in its infancy. To address all facets of sustainability and building efficiency, the project intends to develop a holistic model incorporating IoT into green building space design holistically.
Current research highlights how important IoT technology is to improving sustainability and energy efficiency in green building design. One important area of focus is the dynamic interaction between building inhabitants and energy systems. Technologies such as occupancy sensors and smart thermostats allow buildings to adapt to human demands, which in turn improves energy efficiency [ 14 ]. According to Lyu et al. [ 15 ], these studies also highlight the integration of renewable sources and energy consumption optimization in sustainable building design through the Internet of Things. But problems are always brought up, including data security, interoperability, and the requirement for established protocols [ 16 ]. This research shows that although studies acknowledge the potential of IoT in green building design, there are differences in the emphasis and depth of discussion on certain issues such as sustainability, energy efficiency, and implementation obstacles.
4.1. research design.
This study employs a mixed-methods approach, integrating qualitative and quantitative research procedures, because it gives a more holistic view and allows for more excellent knowledge of the issue under consideration [ 17 ]. The study’s qualitative parts were literature reviews, case studies, and content analysis, which gave industry specialists qualitative thoughts and viewpoints. Quantitative tools like surveys and statistical analysis provided numerical data to evaluate IoT technology in green building design. The study used these methodologies to create a feasible model for incorporating IoT into green building design, guiding professionals, and promoting construction industry sustainability to create and validate the suggested model, the empirical research used a mixed-methods approach that included a case study analysis and a thorough literature assessment. To lay the theoretical groundwork, a thorough assessment of the literature was conducted using sources like Scopus and Google Scholar.
Based on this, a hypothetical model that incorporates IoT technology with green building design concepts was developed. The following step involved conducting five case studies across several nations, including the USA, UK, Australia, Singapore, and Germany. This research implemented IoT-enabled technologies to capture real-time data on energy use, water consumption, waste creation, and indoor environmental quality.
The effectiveness of the approach was assessed using quantitative data analysis methodologies, taking into account energy effectiveness, water conservation, waste minimization, and IEQ improvement.
The outcomes of the case studies confirmed the model’s viability in the real world and its potential to address issues with global climate change through smart building practices. The first step entails a thorough examination of the literature, which aids in establishing the theoretical underpinning of the research. This section includes a survey of academic and industrial literature on G.B.s, IoT, and the incorporation of IoT in G.B. design.
Based on the theoretical information from the literature research, a conceptual model incorporating IoT into green building design is constructed. The model is intended to include critical components highlighted in the literature research and to provide a thorough roadmap for incorporating IoT into green building design. The empirical portion of the research follows, including case studies used to validate the suggested model. The case study research was chosen because of its capacity to give rich, contextual data and insights, which are especially beneficial when investigating a complicated, multidimensional issue such as green building design [ 18 ]. Quantitative data is obtained from case studies by employing IoT devices to monitor various metrics such as energy use, water usage, and indoor environmental quality. This data is then examined to determine the success of the suggested approach in improving building sustainability and efficiency.
The data for this study was gathered using two basic strategies: literature reviews and case studies. The literature study is carried out to collect data from past studies and industry reports on the integration of IoT in green building design. Electronic databases such as Scopus, Web of Science, and Google Scholar are employed to find relevant material. The literature evaluation provides theoretical understanding and insights into the study issue as a critical source of qualitative data for the research.
Case studies give factual and quantitative data for the study. Buildings that use IoT technology are chosen as case studies. Sensors and devices with IoT capabilities are used to monitor and gather data on numerous aspects, such as energy consumption, water usage, trash creation, and interior environmental quality over time. Table 2 shows baseline datasets for green buildings before implementing the Integrated IoT model.
https://doi.org/10.1371/journal.pone.0298982.t002
As seen in Table 1 , the quantitative performance of each building was effectively assessed by factors such as energy consumption, water usage, and trash creation. Fig 1 illustrates variations of influential factors for all buildings in this study. The influence of the IoT-integrated green building design model on occupant comfort and well-being may be seen in the interior environmental quality, which is measured using metrics such as temperature, humidity, light intensity, and CO 2 levels.
https://doi.org/10.1371/journal.pone.0298982.g001
Several aspects and their interrelationships are considered while analyzing case study data. Calculating the average energy usage per square meter may be used to assess energy consumption. This is accomplished by dividing total energy use by building size. Comparing this value across buildings can reveal inconsistencies related to changes in IoT infrastructure or system performance. Another critical element to consider is water usage. Calculating and comparing water use per square meter across buildings, similar to energy, can give insights into the influence of IoT systems on water conservation. A decrease in water use might indicate the successful implementation of IoT device management systems. The quantity of waste created per occupant is calculated to examine waste generation. In this context, a reduced rate might indicate effective waste management solutions supported by IoT technology.
Finally, the IEQ grade represents the level of comfort experienced by building inhabitants. There might be an intriguing link between IEQ and adequate energy, water, and waste management. Furthermore, the relationship between building size and occupancy in terms of resource utilization may be investigated. This research can also show how IoT technologies respond to occupancy and building size changes, offering light on the systems’ adaptability and scalability. In Fig 2 , a graphical illustration of buildings was depicted.
https://doi.org/10.1371/journal.pone.0298982.g002
From the above-given data in Table 2 , we can calculate Energy Consumption per sq. m Water Usage per sq. m., and Waste Generation per occupant:
The overall energy consumption in Building A was 50,000 kWh dispersed over an area of 10,000 sq. m., resulting in an energy consumption rate of 5.0 kWh per sq. m. Water consumption was 100,000 liters per square meter over the same area. With 200 passengers, the total waste output of 500 kg equals 2.5 kilograms per person. Similar computations can be performed for various structures. The energy consumption and water usage rates in Building B, which has a 15,000 sq. m. area and 300 inhabitants, are the same as in Building A, 5.0 kWh per sq. m. and 10.0 liters per sq. m., respectively. At the same time, waste generation per occupant is still 2.5 kg. Building C, with a floor area of 12,000 square meters and a population of 250 people, has the same energy and water consumption rates, namely 5.0 kWh per square meter and 10.0 liters per square meter. The waste generation per passenger, however, is lower at 2.4 kg. Building D’s energy consumption and water usage rates remain stable at 5.0 kWh per square meter and 10.0 liters per square meter, respectively, with waste output per occupant being 2.5 kg. Finally, with a 14,000 sq. m. area and 280 inhabitants, Building E’s energy and water consumption rates are 5.0 kWh per sq. m. and 10.0 liters per sq. m., respectively. At the same time, waste output per occupant is 2.5 kg, echoing the trends found in the previous buildings.
Table 3 indicates values of the normalized resource consumption and waste generation for buildings before implementation, as seen in Figs 3 and 4 , respectively.
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5.1. framework development.
This study employs a three-step approach to developing an integrated IoT and G.B. design model. To begin, green building design concepts must be defined. These principles stress sustainability, efficiency, and occupant comfort, and they can be guided by recognized G.B. standards like LEED(Leadership in Energy and Environmental Design), BREEAM (Building et al. Method), or Green Star [ 19 ]. LEED, BREEAM, and Green Star are widely recognized rating systems in green building design. LEED is a rating system developed by the U.S. Green Building Council (USGBC). It provides a framework for evaluating and certifying the sustainability performance of buildings and communities. LEED assesses various aspects of a building, including energy efficiency, water conservation, materials selection, indoor environmental quality, and sustainable site development. Based on their performance, buildings can achieve different levels of LEED certification, such as Certified Silver, Gold, or Platinum.
Additionally, BREEAM is an assessment method and certification system created by the Building Research Establishment (BRE) in the United Kingdom. Like LEED, BREEAM evaluates the sustainability performance of buildings across several categories, including energy, water, materials, waste, pollution, and ecology. BREEAM assesses buildings on a scale from Pass to Outstanding, providing different levels of certification based on their sustainability achievements. Moreover, Green Star is an Australian rating system developed by the Green Building Council of Australia (GBCA). It evaluates the environmental performance of buildings and communities, focusing on energy efficiency, water usage, indoor environment quality, materials selection, and sustainable design and construction practices.
Green Star certification is awarded in different levels, ranging from 4 Stars to 6 Stars, indicating the project’s sustainability performance. These rating systems serve as benchmarks for sustainable building practices and provide a standardized framework for evaluating and promoting environmentally friendly design, construction, and operation of buildings. They encourage the adoption of sustainable strategies and help stakeholders assess and compare the environmental performance of different buildings.
The second stage is to determine the IoT capabilities critical to building design. Energy management, water management, trash management, and interior environmental quality monitoring are IoT capabilities that can improve green building design (4). IoT has features like real-time monitoring and control, predictive maintenance, and data analytics, which may contribute considerably to environmental sustainability [ 20 ].
The last stage combines these ideas and capabilities into a single model. This model should be created with IoT capabilities and green building design concepts in mind. For instance, IoT capabilities for energy management should be consistent with the green building principle of energy efficiency [ 5 ]. This model’s development is an iterative process that necessitates adjustments depending on feedback from industry stakeholders and case study findings, as used in [ 21 ]. The collected data were subjected to analysis using IBM SPSS v23.0 software. Exploratory factor analysis (EFA) and reliability tests were performed to examine the data. Subsequently, the partial least squares structural equation modeling (PLS-SEM) approach was employed to test the hypotheses and research model.
Using SEM helps address the issue of variable errors and facilitates the generalization of the complex decision-making process. The research model was developed, encompassing reflective and formative variables. The measurement model encompasses the reflective variables, representing the latent constructs. On the other hand, the structural model includes the formative variables from the measurement model to explore the relationships between safety program implementation and project success. Incorporating IoT into G.B. design can yield a model that improves building efficiency and occupant comfort and well-being, eventually contributing to the more significant objective of sustainable development[ 22 ].
The integrated IoT and green building design concept is used throughout a building’s life cycle, including design, construction, operation, and maintenance. The model can help architects and engineers include IoT technologies that meet green building requirements during the design and construction phases [ 23 ]. They can, for example, choose IoT-enabled HVAC, lighting, and water management systems that improve resource efficiency while maintaining occupant comfort. Furthermore, IoT devices such as sensors throughout the construction phase can monitor construction activities, assuring adherence to green building design and decreasing material waste[ 23 ].
The model’s value endures during the operation and maintenance period. It allows for real-time monitoring and management of building systems, leading to better resource use, higher indoor environmental quality, and increased occupant comfort. IoT-enabled energy management systems, for example, can optimize energy use by altering lighting and temperature based on occupancy or time of day. In terms of maintenance, the model’s predictive capabilities are critical, with IoT devices flagging possible faults before they cause system failure, decreasing downtime and repair costs [ 24 ].
Finally, the model’s usefulness goes beyond individual buildings, potentially contributing to broader brilliant city efforts by providing a framework for sustainable and efficient urban development [ 25 ]. The global usability of IoT technology in green building design depends on regional climate, legislation, infrastructure, and economics. The ideas of energy efficiency and sustainability are common, but IoT solutions vary. Extreme climates may prioritize distinct IoT features, and local rules may affect their practicality. Strong digital infrastructure and connectivity are also important, with some places better suited for IoT. Economic factors and finance affect integration speed [ 8 ]. Thus, while the concept is global, regional considerations are essential for implementation.
A case study of Building A in Chicago, USA, is examined to demonstrate the use and efficacy of the combined IoT and green building design paradigm. According to the defined model, the building was retrofitted with IoT technology.
Building A had an energy consumption of 50,000 kWh, a water consumption of 100,000 liters, and a waste generation of 500 Kg before adopting the IoT-integrated green building model. Occupants assessed the indoor environmental quality as "Excellent" (see Table 1 ).
Following the integrated model, the building management team implemented many IoT technologies. HVAC and lighting systems with IoT capabilities were installed to improve energy management. Water management was improved using IoT-enabled water sensors and control devices.–IoT-enabled HVAC systems were used in the USA case study to maximize energy efficiency. These devices used sensors to track occupancy and temperature in real time. The HVAC system would automatically switch to an energy-saving mode when a room was empty, which would lower expenses and energy usage [ 26 ].
UK Case Study : IoT-Based Lighting Systems . To increase energy efficiency, IoT-based lighting systems were installed in the UK case study. Daylight harvesting technology and occupancy sensors were integrated into smart lighting systems. Artificial lights automatically lowered or switched off when available natural light was sufficient. Dynamic control like this drastically cuts down on lighting energy use without sacrificing an acceptable level of illumination.
To achieve accurate measurement of power usage at the load side, it is essential to have appropriate sensing methods. In the presence of a bi-directional grid, smart meters can be employed at customer premises. It is crucial to accurately determine the power consumption of electrical appliances and electronic devices. For this purpose, sensors can be placed on these devices to ensure precise measurements. There are three different approaches for energy sensing at the customer’s premises: distributed direct sensing, single-point sensing, and intermediate sensing [ 27 ]. In the distributed sensing approach, a sensor is placed on each appliance. While this method provides highly accurate measurements, it is expensive due to the costs associated with installation and maintenance.
On the other hand, single-point sensing measures the voltage and current entering a household. Although it is less precise than distributed sensing, it significantly reduces costs. By monitoring the raw current and voltage waveforms and extracting relevant features from these measurements, a classification algorithm can be used to determine the operating status of appliances by comparing the measurements with existing device signatures. Intermediate sensing falls between direct and single-point sensing.
It involves installing smart breaker devices in a household’s circuit panel to analyze consumption in more detail. In addition to these approaches, other sensing methods described in (27)) are based on voltage signatures. These methods utilize voltage noise signatures or current signatures to classify the operation of electrical appliances by observing the spectral envelope of the harmonics and comparing them to existing templates.
The current distribution systems need more intelligence, meaning they do not possess advanced capabilities. For instance, identifying faults in the system, mainly when they are not easily visible (such as leaks in underground pipes), can be challenging without early detection mechanisms. Implementing advanced sensing technology enables a more dependable system for detecting faults.
Australian Case Study : Water Sensors and Control Devices . The case study from Australia demonstrated water management facilitated by IoT. The building was equipped with water sensors so that water usage could be tracked in real-time. Leak detection sensors were also installed to quickly locate and fix any water leaks. Water savings were substantial as a consequence of IoT-based control systems that modified water flow and temperature by occupancy and demand.
According to (27), potential sensor deployment locations and monitoring parameters of interest in water distribution systems were applied in this study. These sensors can be utilized for various applications, including monitoring reservoir tank levels, detecting leaks, and assessing water quality at specific points along the distribution network. In Metje et al.’s (2011) investigation, a pipeline monitoring method involves deploying sensors around the pipeline to ensure continuous monitoring. Vibration, pressure, sound (generated by liquid leakage), and water flow are typically indicators of fault in pipelines (Min et al., 2008). The water distribution system is depicted in Fig 5 . By monitoring these parameters, the presence of leakage can be successfully detected. In Stoianov et al.’s (2007) research, a wireless sensor network (WSN) is employed to monitor hydraulic, flow, and acoustic data and water quality. Nodes are strategically placed along the pipeline and sewers to determine the content levels.
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Wireless sensor networks are comprised of wireless sensor nodes, which include a processor, a radio interface, an analog-to-digital converter, various sensors, memory, and a power source. The overall structure of a wireless sensor node is depicted in Fig 6 .
https://doi.org/10.1371/journal.pone.0298982.g006
Singapore Case Study on IoT-Based Water Quality Assurance . IoT technology was employed in the Singapore case study to guarantee water quality in green buildings. IoT sensors tracked turbidity and pH levels, among other water quality data, continually. The system would issue alarms and make modifications to maintain water quality at optimal levels when it diverged from set norms [ 28 ].
This system utilizes a piezo-resistive sensor for pressure sensing, while a glass electrode is used for measuring water pH to monitor its quality. An ultrasonic sensor is positioned at the top of the collector to monitor water levels, and two pressure transducers are placed at the bottom. Vibration data is collected using dual-axis accelerometers.
The gathered data is then subjected to analysis to detect leaks. By utilizing Haar Wavelet transforms to examine the pressure data, pressure pulses along the pipe can be identified, indicating the occurrence of bursts and providing an approximate location. Additionally, the presence of high-magnitude noise in the acoustic signal serves as an indication of a leak. Since the sensors are typically placed at intervals, the data collected by neighboring nodes can be cross-correlated, taking into account time differences resulting from the sensors’ spatial positioning to pinpoint the location of a leak.
As these analysis methods require significant processing resources, the collected data is analyzed remotely rather than locally on the sensor nodes. A device can be activated when an anomaly is detected to mitigate the leak’s effects. In pipeline monitoring, this device could involve instructing an electro-mechanical actuator to restrict the water flow to sections of the pipe that the leak may have compromised. Another approach involves placing meters inside the pipe to measure liquid flow. Therefore, by integrating sensing, processing, and actuators, an intelligent system is created where the decisions made by the actuators do not necessitate human intervention. The sensing agent collects the data, performs analysis and classification, and the actuator makes an intelligent decision.
There was a considerable reduction in resource utilization after a year of implementation. The energy usage was reduced to 40,000 kWh, a 20% decrease. Water consumption has also lowered by 15% to 85,000 liters. Waste generation has been reduced by 10% to 450 Kg. Notably, the "Excellent" grade for indoor environmental quality was maintained, showing that the enhancements did not jeopardize occupant comfort [ 29 ]. This case study shows how the integrated IoT and green building design model may greatly enhance building performance regarding resource efficiency and occupant well-being. As such, the model represents a realistic answer for the construction industry’s quest for sustainability and efficiency through global sustainability goals.
Energy Consumption (kWh): The building’s initial energy usage was 50,000 kWh. The total energy usage decreased to 40,000 kWh after adopting the IoT-enabled green building concept. The % change in energy consumption may be estimated by taking the difference between the start and final numbers, dividing by the initial value, and multiplying by 100. Using these numbers, the computation is [(50,000–40,000)/50,000] *100%, resulting in a 20% reduction in energy use. An overview of accumulated datasets is presented in Table 4 .
Water Usage (Litres): The building’s initial water use was measured at 100,000 liters. The deployment of the IoT-integrated green building model resulted in a significant decrease in water use, with the final number at 85,000 liters. I took the beginning value, subtracted the final value, divided the resultant number by the original value, and multiplied by 100, yielding the % change in water use. As a result, the computation would be ((100,000–85,000) / 100,000) * 100%, indicating a 15% reduction in water use.
Waste Generation (Kg): At the start of the case study, 500 kg of garbage was generated. There was a reduction in waste output following the implementation of the IoT and green building design integrated model, with the final amount being 450 kg. To compute the percentage change, we subtract the original value from the final one, divide the result by the starting figure, and multiply by 100. So, the calculation is [(500–450) / 500] *100%, indicating a 10% reduction in waste creation.
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6.1 interpretation of results.
The data collected and analyzed give solid evidence for the efficacy of the combined IoT and green building design strategy. Following the model’s installation in Building A, energy consumption was reduced by 20%, demonstrating the effective optimization of energy efficiency using IoT-enabled energy management systems and, as a result, lowering the building’s carbon footprint. Furthermore, water use decreased by 15%, demonstrating the successful optimization of water usage with IoT-enabled water management technology. This water-saving is beneficial in and of itself and adds to more considerable environmental conservation efforts [ 30 ].
Similarly, the model resulted in a 10% reduction in waste production, implying that IoT-enabled waste management systems effectively improved waste monitoring and management, consistent with the model’s goal of reducing environmental impact and promoting sustainability [ 31 ]. Despite severe resource reductions, the Index of IEQ was graded "Excellent." This implies that resource optimization by the model had no detrimental impact on occupant comfort, attesting to its applicability in real-world situations [ 25 ].
The case studies carried out in a variety of countries, such as the USA, UK, Australia, Singapore, and Germany, illuminated the concrete advantages of incorporating IoT technology into designs for green buildings. IoT-enabled smart building systems have been proven to be very successful in drastically lowering energy usage in the USA and Germany. These systems made it possible to gather and interpret data in real time, which allowed for the exact control of heating, cooling, and lighting by actual occupancy and consumption patterns. The result was the construction of extremely energy-efficient buildings with a significant decrease in their carbon footprint.
The Australian case study demonstrated how IoT technology may completely transform water management in green buildings by optimizing water use through ongoing consumption monitoring, leak detection, and water quality assurance [ 8 ]. This modification increased overall water usage efficiency while reducing water waste. Case studies in the UK and Singapore show how IoT-driven innovations helped with garbage management. Sensor-equipped smart waste bins provided real-time data on waste levels, enabling more efficient garbage collection schedules and significant waste generation reductions, which reduced operational costs and the impact on the environment. Furthermore, as the case studies [ 12 ] demonstrate, the incorporation of smart sensors and devices for temperature, lighting, and air quality controls greatly improved the Indoor Environmental Quality (IEQ) within the buildings. Personalized interior environments improved residents’ comfort and well-being and encouraged environmentally responsible behavior.
Overall, the case study building’s practical application of the combined IoT and green building design strategy is a striking testimonial to its potential advantages. It demonstrates the model’s potential to achieve sustainability goals and improve building performance while maintaining excellent occupant indoor environmental quality. Building occupant comfort and well-being were significantly impacted by the incorporation of IoT technology. Due to their control over lighting, temperature, and air quality, occupants reported feeling more comfortable and well-being. Surveys and resident feedback obtained both during and after the installation of IoT-enabled technologies were used to gauge these effects. Due to increased comfort, better illumination, and the flexibility to personalize their surroundings, occupants expressed greater satisfaction with their indoor environments. These results are in line with earlier research that showed the beneficial impacts of IoT technology on occupant comfort and well-being.
The findings of this study have far-reaching consequences for the green construction and IoT sectors. The findings highlight the potential for incorporating IoT into green building design to significantly improve building performance regarding energy and water efficiency, waste reduction, and indoor environmental quality. One of the most important aspects of environmental preservation is the incorporation of IoT technology. Through the analysis of real-time occupancy and environmental data, IoT-enabled smart building systems improve energy efficiency, leading to fewer carbon emissions and energy consumption. Another advantage is that IoT-based devices can conserve water by monitoring and optimizing water use and identifying leaks. This lessens the impact of water waste on the environment.
Real-time monitoring made possible by IoT sensors also revolutionizes waste management by enabling effective waste collection schedules and lower operating expenses. Additionally, by controlling lighting, humidity, temperature, and air quality, IoT improves interior environmental quality and eventually increases occupant comfort and well-being. Finally, by using IoT sensors for predictive maintenance, building systems can last longer, require fewer resource-intensive replacements, and produce less waste. The model’s proven real-world performance offers the green construction sector a viable and effective way of reaching sustainability goals. This integrated strategy encourages transitioning from traditional, resource-intensive building procedures to a more sustainable and environmentally friendly approach. In terms of the IoT sector, the study emphasizes the importance of IoT in the green construction industry and its potential contribution to sustainable urban development.
According to the study, green building design represents a promising market for IoT developers and service providers since their solutions may address actual, real-world difficulties. Unexpected results could include the necessity to successfully balance environmental trade-offs, positive occupant behavior changes, and synergistic benefits The research also emphasizes the need for IoT solutions, especially customized to green building requirements, such as energy-efficient devices and practical data processing tools. Furthermore, incorporating IoT into green building design has far-reaching consequences for legislators, urban planners, and environmental activists. The method supports a transition to smart, sustainable cities by demonstrating the potential of advanced technology in tackling significant environmental concerns and encouraging sustainable living [ 22 ].
This study draws numerous vital findings concerning the feasibility of implementing IoT technology into green building design. Resource optimization is one of the most successful outcomes. The case study revealed that the IoT-enabled green building concept significantly boosted resource efficiency. This was proved by a 20% drop in energy usage, a 15% decrease in water consumption, and a 10% decrease in trash generation. This demonstrates IoT technology’s importance in reaching resource efficiency goals in green buildings. The quality of the building’s internal atmosphere remained maintained even with reduced resource consumption. This shows that using IoT technology to balance resource efficiency and occupant comfort in green buildings is possible. Aside from maintaining a high-quality indoor atmosphere, the model’s practical application in a real-world setting indicates its scalability.
This implies that the approach may be applied in more buildings or on a city-wide scale, adding to the sustainability of urban growth. The results have consequences for the industry as well. They emphasize a prospective market for IoT technology in the green building sector and the potential for green building practices to boost construction sustainability. Thus, incorporating IoT technology into green building design has enormous potential for increasing building efficiency, achieving environmental sustainability goals, and stimulating the creation of intelligent, sustainable cities.
The research has practical implications in two main areas. Additionally, it thoroughly examines the obstacles faced in implementing green building (G.B.) projects in Turkey, providing a comprehensive understanding of these barriers. Moreover, it clarifies the perspectives of public agency representatives and professionals working in private entities regarding the significance of these barriers. This more profound understanding of the barriers can help policymakers and construction practitioners develop well-informed strategies to promote green practices in China and other developing countries with similar socio-economic conditions. Furthermore, the in-depth analysis of these barriers can benefit foreign investors interested in investing in G.B. projects in China. By better understanding the G.B. industry in China, they can make more realistic investment decisions.
However, it is essential to note that the study has limitations. There were obstacles and difficulties in integrating IoT technology into the design of green buildings. A prominent obstacle was the upfront expenses associated with setting up IoT infrastructure and installing devices, which were frequently viewed as a substantial financial commitment. However, the long-term savings in energy consumption, upkeep, and operational efficiency that IoT devices provided helped to offset this cost.
Concerns about data security and privacy were also very important because IoT devices required the gathering and sharing of sensitive data. Strong security procedures and encryption techniques were put in place to protect data integrity and privacy to allay these worries. The requirement for certain knowledge and abilities to successfully manage and run IoT-enabled technologies presented another difficulty. Training was necessary for building management employees to handle and comprehend the data produced by IoT devices.
In addition, there were problems with compatibility when combining IoT solutions with pre-existing building systems. Thorough preparation and compatibility evaluations were required to guarantee a smooth integration Notwithstanding these difficulties, IoT technology is a potential strategy for sustainable building design because its overall advantages, like improved occupant comfort and energy efficiency, exceeded the early drawbacks.
Although more significant than the recommended value for proper factor analysis, the sample size used in the research is still relatively small. Increasing the sample size in future studies could yield more reliable results. Additionally, future research can focus on expanding the participant demographics to ensure a more balanced distribution. While this study primarily focused on barriers to G.B. projects, future investigations could explore the barriers and the driving factors in different countries.
Furthermore, influential factors on IEQ will be analyzed by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Ultimately, this index would be predicted by various Machine Learning (ML) models (i.e., Evolutionary Polynomial Regression [EPR], Deep Learning [DL], Random Forest [R.F.], Support Vector Machine [SVM]) through the process of G.B. design by IoT.
Future research studies could improve the organization and coherence of the transition from outlining the limitations of the study to suggesting future research directions. Based on our study’s findings, numerous significant future research objectives and areas for development in green building design use IoT technology. First, sophisticated IoT applications, especially for optimizing renewable energy sources like solar and wind power, can improve energy efficiency. Understanding how IoT affects occupant behavior and well-being, especially in personalized IoT-driven settings, can inform human-centric design
To secure building systems and tenant data, IoT data collection and processing must be thoroughly investigated for cybersecurity and privacy issues. Further research is needed to standardize and interoperate IoT devices and systems for scalability and acceptance in green building design.
A detailed cost-benefit analysis will help stakeholders decide on the financial and long-term benefits of IoT integration in green buildings. Governments and regulators can promote sustainability by studying how policies and regulations affect IoT integration.
Finally, architectural, design, and building management professionals require specific education and training to use IoT’s promise in green building design. These programs can equip practitioners for the changing landscape of IoT technologies in sustainability and environmental preservation. IoT technology in green building design is relevant globally but requires regional and local considerations. Sustainability, energy efficiency, and environmental preservation are universal values, but obstacles and priorities vary. Climate, legal frameworks, resource availability, cultural factors, economic factors, and infrastructure readiness all affect IoT-enabled green building solutions. Extreme climates may optimize HVAC, while water scarcity zones may use IoT to manage water. Local building codes must be followed, and economic concerns may affect IoT implementations.
S1 dataset..
https://doi.org/10.1371/journal.pone.0298982.s001
If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Join Lareina Yee and Roger Roberts on Tuesday, July 30, at 12:30 p.m. EDT/6:30 p.m. CET as they discuss the future of these technological trends, the factors that will fuel their growth, and strategies for investing in them through 2024 and beyond.
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
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Data Sharing Statement
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Chartock BL , Simon K , Whaley CM. Transparency in Coverage Data and Variation in Prices for Common Health Care Services. JAMA Health Forum. 2023;4(10):e233663. doi:10.1001/jamahealthforum.2023.3663
© 2024
Over half of the US population receives health insurance from private insurers, and prices are negotiated rather than set administratively (eg, Medicare). This negotiation process contributes to a landscape in which private insurance prices are both higher than Medicare rates and highly variable. 1 The private market lacked meaningful price transparency for patients and purchasers until the recent implementation of Hospital Price Transparency and Transparency in Coverage (TiC) rules. 2 Lack of transparency limits the ability of regulators to monitor prices and of employers, patients, and purchasers to impose market discipline on prices. We examined TiC price data for common services from Humana, a large national insurer, and highlighted use cases of such novel data for future research. New TiC payer data are released each month by all payers. Informed health care consumerism is a potential lever for managing costs and improving patient satisfaction.
We obtained October 2022 TiC data from Humana’s public-facing portal and downloaded data in batches (Python Software). 3 Indiana University Institutional Review Board deemed this cross-sectional study exempt from ethics review and informed consent because it was not human participant research.
Humana rates were chosen because of its largely national coverage of clinicians and facilities and our ability to speedily parse the data files. While mostly a provider of Medicare Advantage benefits, Humana covers approximately 1 million individuals with commercial insurance. 4 We restricted analyses to in-network clinicians and facilities and used the mean posted price when the data included multiple contracted rates for the same procedure and clinician or facility within the same network.
We focused on 7 procedures, including more shoppable codes (computed tomography [CT] scan of head or brain without contrast) and less shoppable codes (high-severity emergency department [ED] visit). A key challenge was that TiC data reported rates for clinicians and facilities regardless of whether they actually performed a given service. To identify those who performed the selected services, we used both 2019 100% Medicare fee-for-service claims data and commercial claims data from the RAND hospital price transparency project 5 to match clinicians and facilities who performed these services by their National Provider Identifiers. We analyzed distributional differences in prices (mean, median, and percentiles) and coefficients of variation. Data analysis was performed using Stata 17.0 (StataCorp LLC).
The Table presents descriptive characteristics of the study sample and price variation. The number of clinicians and facilities with Humana prices ranged from 4192 for hip arthroplasty to 189 471 for established patient office visit. Coefficients of variation were similar for both more and less shoppable services (0.51 for CT of head or brain without contrast; 0.53 for high-severity ED visit).
The Figure maps the variation in prices for established patient office visits across US counties. The mean (IQR) county-level price was $86 ($69-$93). Generally, mean county-level prices were lowest in the central US and Florida. Prices were higher in the upper-Midwest and Southeast. Importantly, many higher-priced counties bordered lower-priced counties. Similar geographic patterns were observed for other procedures.
This study revealed how novel data can inform policies that improve the efficiency of the US health care system. The study was limited to a single insurer and 7 procedures; however, it opens the door to using TiC data in other, broader settings.
Future work may examine the underlying causes of price variation in health care, as it is unclear whether prices are meaningfully associated with value as in nearly every market, or whether prices reflect imbalances in market power and negotiation leverage. If price variation reflects clinical or perceived quality variation, purchasers and policymakers need to find balance between receiving higher-quality care and spending financial resources elsewhere. However, if price variation is driven by consolidation or anticompetitive contracting, then regulators should design policies that ensure competitive health care markets. The factors determining price variation are likely in the middle of these 2 possibilities.
Accepted for Publication: August 23, 2023.
Published: October 27, 2023. doi:10.1001/jamahealthforum.2023.3663
Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Chartock BL et al. JAMA Health Forum .
Corresponding Author: Benjamin L. Chartock, PhD, Bentley University, 175 Forest St, AAC 179, Waltham, MA 02452 ( [email protected] ).
Author Contributions: Dr Chartock had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: Chartock, Whaley.
Drafting of the manuscript: All authors.
Critical review of the manuscript for important intellectual content: All authors.
Statistical analysis: Chartock, Whaley.
Obtained funding: Whaley.
Administrative, technical, or material support: Chartock, Whaley.
Supervision: Simon, Whaley.
Conflict of Interest Disclosures: Dr Whaley reported receiving funding from Patient Rights Advocates for this study and funding from the National Institute on Aging and Robert Wood Johnson Foundation during the conduct of the study. No other disclosures were reported.
Data Sharing Statement: See the Supplement .
Additional Contributions: We are grateful to Raman Singh, MS, and Rosie Kerber, MPP, for excellent data and programming assistance. They received no additional compensation beyond their usual salaries for their contributions to this work.
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The fundamental costs of publishing a research paper include submission fees, page rates, Article Processing Charges (APCs), and potential overage charges. These costs play a crucial role in disseminating research findings to the academic and scientific community. Rate this post.
A typical research paper follows the following format: Introduction>> Methods>> Results>>Discussion. This is popularly known as the IMRaD structure (Introduction, Methods, Results and Discussion). You must follow this structure when you write you research paper. In addition, you need to know what you should include in each of these sections.
In 2021, we received 2.5 million research papers from authors. These were carefully reviewed by our 2,000-strong in-house editorial teams in collaboration with 29,000 editors and 1.4 million expert reviewers around the world, resulting in over 600,000 articles being enhanced, indexed, published and promoted following a peer review. ...
Estimating the final cost of publication per paper based upon revenue generated and the total number of published articles, they estimate that the average cost to publish an article is around $3500 to $4000. This estimate is most likely very high, especially for open access journals that typically only publish digital copies.
Data from the consulting firm Outsell in Burlingame, California, suggest that the science-publishing industry generated $9.4 billion in revenue in 2011 and published around 1.8 million English-language articles — an average revenue per article of roughly $5,000. Analysts estimate profit margins at 20-30% for the industry, so the average ...
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To cover the cost of printing, and particularly color printing, certain traditional journals charge per page (often $100-250 each) and/or per color figure (about $150-1,000 each). In rare cases, supplementary materials may also incur a flat charge or a charge per item or page, with fees usually ranging from $150-500. Publication fees.
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Research assistant: 1 day per week for a year at Level B1, plus 25.91% salary on-costs. Overheads at 125% of total cash request, as per rule 3H. 3. Cost each item. For each item on your list, find a reasonable cost for it. Are you going to interview the fifty people and do the statistical analysis yourself? If so, do you need time release from ...
Today, a one year personal subscription to Science costs $149 for a member and $75 for a student. A personal subscription for one year of Nature costs $199. We subscribe to both and pay $350 a ...
The charges may depend on the type of engagement. Publication fees: this is the most commonly understood charge, also known as author publishing charges or article processing charges (both read as APC), that covers the actual cost of publication. A peer-reviewed article may charge all or a combination of these charges for a research paper.
Here is the tentative cost to publish a paper in a journal. SCI Indexed Journal: 500USd to 6000USD per article. Scopus Indexed Journal: 200USD to 1500USD per article. Web of Science Indexed ...
Currently, at the British Library, medium-resolution images cost £29.95 for the first image, and £8 for each subsequent image from the same manuscript. At the Fitzwilliam, new photography costs £35, and obtaining existing high-resolution images £40 per shot. The cost of buying study images from these collections is holding back my research.
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For IEEE Access, the article processing charge is $1,995 USD. For hybrid journals, the article processing charge will be $2,495 USD. Please note that some journals charge additional fees (e.g. overlength and color page charges). See individual journal author instructions for specific details. For the majority of IEEE magazines offering open ...
Choose a research paper topic. Conduct preliminary research. Develop a thesis statement. Create a research paper outline. Write a first draft of the research paper. Write the introduction. Write a compelling body of text. Write the conclusion. The second draft.
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Here is the tentative cost to publish a paper in a journal. SCI Indexed Journal: 500USd to 6000USD per article. Scopus Indexed Journal: 200USD to 1500USD per article. Web of Science Indexed Journal: 200USD to 1500USD per article. ABDC Indexed Journal: 200USD to 1000USD per article.
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About the research. The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and ...
We obtained October 2022 TiC data from Humana's public-facing portal and downloaded data in batches (Python Software). 3 Indiana University Institutional Review Board deemed this cross-sectional study exempt from ethics review and informed consent because it was not human participant research. Humana rates were chosen because of its largely national coverage of clinicians and facilities and ...