Support Systems Research Research Articles

The Impact of R&D Expenditures on Regional Energy Intensity in Turkey

Energy efficiency measures are effective solutions for addressing environmental and economic issues, including global warming, energy supply security, and trade deficits from imported energy sources. Turkey through an ambitious policy scheme in the last decades, have reduced energy intensity level significantly, though, still higher than developed countries. In this regard, this study investigates the relationship between R&D expenditures and energy intensity in Turkey through NUTS-I regional data ranging from 2010 to 2021. According to both FMOLS and DOLS technique findings increasing R&D expenditures may significantly decreases energy intensity. Furthermore, economic growth appears to correlate with reduced energy intensity. Additionally, FMOLS findings suggest that industrialization may increases energy intensity, while DOLS results indicate that population growth can also significantly increase energy intensity. Therefore, a customized research and development (R&D) incentivization support system could offer various benefits to Turkey and other developing nations facing similar challenges.

Blended Learning: A Disruptive Innovation" by Tony Bates, which Explores the Impact and Effectiveness of Blended Learning in Educational Settings

Background and Aims: The impact and effectiveness of blended learning are critical because they show how combining online and in-person instruction can improve student engagement, academic performance, and flexibility. This approach provides a comprehensive educational experience that adapts to a variety of learning needs and environments, ultimately improving overall educational outcomes. The study's goal was to investigate how blended learning disrupts traditional education models and what benefits it may offer. Methodology: Bates' research integrates academic literature, case studies, and empirical data to assess the impact of blended learning, with surveys, interviews, and case study protocols used to collect comprehensive data. The analysis brings these findings together to reveal key themes and gaps, providing a thorough understanding of blended learning's effectiveness and implementation. Results: The finding found that Blended learning has been shown to improve student engagement, academic performance, and flexibility through successful implementations at institutions such as the University of Central Florida and Georgia State University. Addressing technical and pedagogical challenges, as well as incorporating emerging technologies, is critical for reaping the benefits. Continued research and support systems will be critical for optimizing blended learning and meeting the changing needs of students and teachers. Conclusion: Blended learning improves student engagement, performance, and flexibility, as demonstrated by successful models at UCF and Georgia State University. To fully realize its potential, it must address technical and pedagogical challenges while also embracing emerging technologies, with ongoing research and strong support systems critical to future success.

Explainable Learning Analytics: Assessing the stability of student success prediction models by means of explainable AI

Beyond managing student dropout, higher education stakeholders need decision support to consistently influence the student learning process to keep students motivated, engaged, and successful. At the course level, the combination of predictive analytics and self-regulation theory can help instructors determine the best study advice and allow learners to better self-regulate and determine how they want to learn. The best performing techniques are often black-box models that favor performance over interpretability and are heavily influenced by course contexts. In this study, we argue that explainable AI has the potential not only to uncover the reasons behind model decisions, but also to reveal their stability across contexts, effectively bridging the gap between predictive and explanatory learning analytics (LA). In contributing to decision support systems research, this study (1) leverages traditional techniques, such as concept drift and performance drift, to investigate the stability of student success prediction models over time; (2) uses Shapley Additive explanations in a novel way to explore the stability of extracted feature importance rankings generated for these models; (3) generates new insights that emerge from stable features across cohorts, enabling teachers to determine study advice. We believe this study makes a strong contribution to education research at large and expands the field of LA by augmenting the interpretability and explainability of prediction algorithms and ensuring their applicability in changing contexts.

Embedding planning support systems in spatial planning practice: the role of drift in implementing the Spatial Development Framework methodology

ABSTRACT Planning Support Systems (PSS) research has explored ways to improve PSS use and embeddedness. However, there is little knowledge of how unplanned changes during implementation impact outcomes. This paper answers the question, ‘How can drift influence PSS implementation and use?’ It applies the concept of drift (changes in new technology, user behaviour, and/or existing practice during ICT implementation) to explain how PSS users (GIS specialists, planners) initiate unplanned changes during implementation and use. Following a qualitative approach, we investigate a PSS implementation case study in Rwanda – the Spatial Development Framework (SDF) methodology – to establish the role of drift in PSS use and embeddedness. Our study reveals that a) user understanding and perception of PSS can initiate drift during implementation and use, and b) drift can influence outcomes of PSS use or embeddedness. This study confirms the role of drift in PSS use and embeddedness in Rwanda’s spatial planning process.

Cost-effective Digital Prescription using Pharmaceutical Knowledge Graph

In the current era of the advancing medical domain and the ever-evolving use of technology in fields of pharmaceutical research, remote monitoring, and decision support systems in healthcare, prescription management has transformed from handwritten prescriptions to digital ones. This transition however does not imply that these prescriptions are comprehensive and provide optimized treatment outcomes. These digital prescriptions still reflect the formerly used handwritten prescriptions. Thus, recipients face the daunting challenge of making cost-effective, holistic, informed, and personalized decisions without compromising the legitimacy and authenticity of the original prescription, all due to a lack of readily available alternative medicine options. This problem can be addressed by utilizing the knowledge graph that we built, which contains carefully curated medical information collected from reliable and diverse sources, ensuring the authenticity and relevance of the information. Delving into the intricate interconnections among diverse medical entities and their properties, the medical knowledge graph presents an invaluable solution, empowering the generation of smart digital prescriptions in a fast and precise manner. Specifically, this study focuses on the transformative potential of digital prescriptions, elucidating their role in streamlining healthcare processes and enhancing communication between healthcare providers and patients. By leveraging the insights derived from our medical knowledge graph, we aim to contribute to the advancement of digital prescription systems, fostering more effective, personalized, and technology-driven healthcare solutions.

차이나메디컬보드(CMB)와 한국 의학연구 지원체계의 변화

Foreign aid has had critical impact on the development of Korean medicine since liberation in 1945. In particular, U.S. aid to Korean medical education, represented by the Minnesota Project, is often marked as a major turning point from Japanese-style to American-style medical education in Korea. However, there has been limited analysis of research, which has been a key function of medical schools and their affiliated hospitals. This paper examines the establishment of a system of medical research support in South Korea in the 1970s, focusing on the case of the China Medical Board (CMB). It explores how donor agencies transferred responsibility and authority to Korean counterparts through policy called matching-fund, and how this exit strategy affected the funding and utilization of medical research in Korea. As a backdrop to this policy shift, this study also examine the geopolitical context of U.S. aid policy shifts in the 1970s to show how aid was not only introduced but also exited in shaping the Korean medical research system. The case of the China Medical Board shows how a private aid organization formulated and implemented an exit strategy in the macro geopolitical context of the Nixon Doctrine and changes in U.S. foreign aid policy, and in the process shaped the Korean medical research funding system and its own efforts to secure funding. Rather than a binary narrative of aid versus post-aid, examining the accumulation and manifestation of Korean actors’ own experiences in foreign donor-driven decision-making provides an opportunity to look more critically at the process of shaping the contemporary Korean medical research funding system.

IRB 지속심사와 연구 미준수 연관성 분석

The continuing review of ongoing research approved by the Institutional Review Boards(IRB) is one of the ways to protect human subjects. Though the approval expiration date for new tasks depends on the risk assessment of said task, it is generally limited by law not to exceed a maximum of one year. Continuing review is required to extend the approval expiration date. Through continuing review, the IRB determines whether the research is conducted under the approved protocol, whether any side effects were reported timely, and whether the risk-to-benefit ratio remains favorable. This research used 784 cases of data reviewed by the C University Hospital's clinical research support system from 2019 to 2021 in a retrospective matter and analyzed the characteristics of research and researchers in non-compliance and compliance groups, and types of non-compliances, finding significant differences. Non-compliance groups were highly frequent in investigator initiated trials(IIT), non-clinical trials, and studies without clinical research coordinators, while compliance groups were more frequent in sponsor initiated trials(SIT), studies with co-investigator, and studies with clinical research coordinators. In addition, after analyzing the types of non-compliance, the non-compliance with the IRB reporting and non-compliance with the consent process were high in non-compliance groups. Therefore, we analyzed the relationship between non-compliance and compliance groups in continuing review to find a way to contribute for the ultimate purpose of IRB, protection of human subjects.

Feed Selection Decision Support System The Best Cat uses the Analytical Hierarchy Process Method

With the development of technology and increasingly rapid competition in the business world, just opening a Cat Shop is not enough. To win the competition, technology and research are used to overcome the problem of consumers who are confused about choosing the type of food for their cats, because there are many types of cats and cat food available. circulating in the market today.. Cat Shop is a business that provides various kinds of pet needs, such as pet food, animal equipment and animal accessories. The Cat Shop sells various products to the public. Cat Shop currently has a share of the pet market in big cities in Indonesia. This decision support system research uses the Analytical Hierarchy Process (AHP) method. The AHP method is a method that is widely used in the case of weighting criteria and prioritizing each criterion. After this research was applied, the results were able to answer the information needs regarding the selection of the best cat food, it has been able to answer and assist in solving the problem of choosing the best cat food purchase

A new version of the binary star database BDB: Challenges and directions

Abstract In accordance with the principles of open science, the results of the research should remain available for further investigations and practical interaction with them in the research community. Such results include collected initial and obtained data, specifications and implementations of methods applicable to them including data integration rules, and other possible resources. At the same time, the results of research on data should be curated to maintain their relevance, compliance with domain development, and standards in order to make them as useful as possible in the long term. A research support system in the research domain of physics of stellar systems is being developed to provide relevant data and methods, classify them, make them findable, avoid multiple integration of heterogeneous data, and integrate them into ongoing research in the astrophysical community. New catalogues are being added on regular basis, and this process is accompanied by correct cross-identification of the entities. New observational types of binary stars with their features are added to binary star database (https://bdb.inasan.ru).

A decision support framework to incorporate textual data for early student dropout prediction in higher education

Managing student dropout in higher education is critical, considering its substantial impacts on students' lives, academic institutions, and society as a whole. Using predictive modeling can be instrumental for this task, as a means to identify dropouts proactively on the basis of student characteristics and their academic performance. To enhance these predictions, textual student feedback also might be relevant; this article proposes a hybrid decision support framework that combines predictive modeling with student segmentation efforts. A real-life data set from a French higher education institution, containing information of 14,391 students and 62,545 feedback documents, confirms the superior performance of the proposed framework, in terms of the area under the curve and top decile lift, compared with various benchmarks. In contributing to decision support system research, this study (1) proposes a new framework for automatic, data-driven segmentation of students based on textual data; (2) compares multiple text representation methods and confirms that incorporating student textual feedback data improves the predictive performance of student dropout models; and (3) establishes useful insights to help decision-makers anticipate and manage student dropout behaviors.

Modeling SCOPUS Trend of Decision Support Systems in Energy: A Bibliometric Analysis

The purpose of this article is to provide a bibliometric analysis from SCOPUS database for identifying the current trending of decision support system (DSS) research on energy . The search resulted in 1458 documents that had been published in field computer science, 569 documents in field engineering, 437 documents in field mathematics, 223 documents in field decision sciences and 105 documents in field energy . The development of scientific research in the area of DSS in the context of energy is consistently growing since 2001 until recent year. It is of utmost necessity to identify the potential areas as well as the severity of this research. Thus, the aim of this study is to model the research trend on DSS for energy by conducting bibliometric analysis in Scopus database. The analysis was performed by using the VOSviewer software and data analysis tool available in the Scopus base. A total of 1458 publications in relation to DSS related energy, were extracted from Scopus database ranging from 2001 to 2021 for further modeling. Co-citation analysis and co-word analysis were conducted to model the evolution of research themes in this field. The findings of this study may help researchers understand the nature of DSS research related to energy from across the world and suggest future research directions.

A Novel Puff Recording Electronic Nicotine Delivery System for Assessing Naturalistic Puff Topography and Nicotine Consumption During Ad Libitum Use: Ancillary Study.

Assessing the naturalistic puff topography and associated nicotine consumption during e-cigarette use is important as such information will not only unveil how these products are being consumed in real-world conditions, but also enable investigators and regulatory bodies to conduct quantitative, accurate, and realistic harmful exposure and nicotine abuse liability risk assessments based on actual e-cigarette use. Conventional approaches cannot accurately, conveniently, and noninvasively determine e-cigarette puff topography in a natural use environment. Thus, novel technology-enabled systems that do not primarily rely on self-report mechanisms or intrusive measurements to monitor e-cigarette product use behaviors are highly desired. This study aimed to explore and demonstrate the feasibility of a novel puff recording electronic nicotine delivery system (PR-ENDS) device for measuring naturalistic puff topography and estimating nicotine consumption during the ad libitum use of products among smokers and vapers. An ancillary data analysis based on a completed parent study was conducted. The parent study was a 1-way randomized controlled open-label puff topography and nicotine pharmacokinetic assessment carried out in 24 healthy adults (12 smokers and 12 vapers). Participants were assigned a randomized product use sequence of a PR-ENDS device within 5 site visits for both controlled and ad libitum product use sessions. Blood samples were obtained for plasma nicotine analysis, and questionnaires were administered at various time points. During the ad libitum use session, puff topography was measured using a Clinical Research Support System (CReSS) device as a benchmark, as well as the PR-ENDS device with a built-in puff recording feature. There were no significant differences in representative puff topography parameters (number of puffs, total puff duration, and average puff duration) between the PR-ENDS and CReSS devices at the populational level across different device powers, e-liquid nicotine strengths, and flavors. The nicotine consumption estimated by the PR-ENDS device suggested that this device can be employed as a convenient monitoring tool for estimating nicotine use without measuring e-liquid weight loss between puffs. The linear relationship between nicotine consumption estimated by the PR-ENDS device and the pharmacokinetic parameter AUCad lib (plasma concentration-time curve for 1-hour ad libitum use) substantiated the potential of using this device as a pragmatic, noninvasive, and convenient means for estimating nicotine intake in the human body without blood collection. The novel PR-ENDS device was feasible for assessing naturalistic puff topography and estimating nicotine consumption and intake in the human body during ad libitum use. Several key factors can influence users' puff topography, including device power levels, e-liquid nicotine strengths, and flavors. The study results pave the way for further research in the real-time measurement of naturalistic puff topography and puffing behaviors in the real world.

Application of team science best practices to the project management of a large, multi-site lung cancer screening research consortium.

Research is increasingly conducted through multi-institutional consortia, and best practices for establishing multi-site research collaborations must be employed to ensure efficient, effective, and productive translational research teams. In this manuscript, we describe how the Population-based Research to Optimize the Screening Process Lung Research Center (PROSPR-Lung) utilized evidence-based Science of Team Science (SciTS) best practices to establish the consortium's infrastructure and processes to promote translational research in lung cancer screening. We provide specific, actionable examples of how we: (1) developed and reinforced a shared mission, vision, and goals; (2) maintained a transparent and representative leadership structure; (3) employed strong research support systems; (4) provided efficient and effective data management; (5) promoted interdisciplinary conversations; and (6) built a culture of trust. We offer guidance for managing a multi-site research center and data repository that may be applied to a variety of settings. Finally, we detail specific project management tools and processes used to drive collaboration, efficiency, and scientific productivity.

An Analysis of Clustering the Decision Support Systems in Logistics for Supply Chain Management

Decision-making in logistics is becoming increasingly complex task for businesses such as agriculture, food production, and construction, as it requires decisions at the strategic, tactical, and operational levels, as well as consideration of the triple bottom line. Undoubtedly, decision support systems (DSS) played a crucial role in resolving the difficulties related with decision-making in sustainable logistics. The objective of this work is to investigate the status of research in the domain of DSS for logistics, taking sustainability into account. It is of the utmost importance to determine the prospective areas and significance of this research. By doing a bibliometric analysis, the purpose of this study is to illustrate the scientific research on the Decision Support System in Logistics. Using the Scopus database's VOSviewer programme and data analysis tool, the analysis was conducted. From 1985 to 2022, a total of 1091 papers on DSS and logistics connected to consumers were retrieved from the Scopus database. To show the progression of research ideas in this discipline, co-citation analysis and co-word analysis were undertaken. Transportation decision-making, decision analysis, and intelligence algorithm have been identified as the three primary study clusters relevant to the current research interest. This study's findings may assist researchers in comprehending the nature of Decision Support System research pertaining to logistics from around the world and in determining future research directions.

From diagnosis to post-treatment: biopsychosocial predictors of sexual and mental health in prostate cancer survivors

The increasing incidence of prostate cancer, along with earlier detection and longer survival years has been raising clinician's questions on how to better treat this disease. Radical prostatectomy is one of the most common treatments, although it can have severe consequences, such as changes in sexual functioning and sexual satisfaction, thus affecting the quality of life and mental health. These difficulties can significantly decrease the adherence to treatment protocols and lead to drop-outs. Under the Horizon Europe funding programme for research and innovation, this multicentric project, combining the specialities of urology, psychology, and neuroscience, aims to investigate (1) the biopsychosocial predictors of mental and sexual health in prostate cancer and (2) the neural correlates of sexual functioning in prostate cancer, so as to develop a predictive model regarding post-treatment adjustment and sexual well-being. 300 Men with prostate cancer indicated to radical prostatectomy will be assessed before surgery and nine months afterwards. A subset of 250 individuals will participate in semi-structured interviews and complete self-report psychological measures so as to monitor sexual and mental health. The remainder will take part in a laboratory experiment, with neuroimaging techniques and other physiological measures, in order to identify the neural underpinnings of sexual response in relation to prostate cancer and radical prostatectomy. Results will contribute to the development of a mathematical algorithm, built by Machine Learning experts, that will help predict the biopsychosocial risk and protective factors for sexual health and mental well-being in men with prostate cancer. This study tackles an important health issue in an innovative way, by combining a longitudinal and a laboratorial/experimental design and integrating a wide range of procedures. Furthermore, the development of a prediction model that covers such broad sexual health determinants, is a promising tool that can help clinicians improve their intervention strategies. All the authors declate no known conflict of interest. This project is funded by Horizonte Europa (NORTE-01-0145-FEDER-000057), via the Support System for Scientific and Tecnological Research NORTE-45-2020-75, CCDRN.

Oncology Informatics for Lynch Syndrome Research and Care: A Literature Review.

This study aims to review and evaluate available informatics platforms for research and management purposes of Lynch syndrome (LS) to identify gaps and needs for future development. LS informatics tools were identified through literature search in four publication databases (1 and Scopus). First, the LS and functional elements of every informatics tools for LS were introduced. Then, current existing LS informatics tools were reviewed and explained. A detailed review of implemented studies shows that many types of informatics platforms are available for LS management (ie, prediction model, clinical decision support system, database website, and other tools for research and management purposes of LS). Moreover, several dimensions of existing LS informatics tools were discussed and features and positive findings were reported. Reviewing the literature reveals that several LS informatics tools were focused on gene-specific estimate, cancer risk prediction, identifying/screening patients, supporting personalized care of individuals with LS, and storing mismatch repair mutations information. Nevertheless, these platforms do not fully cover the care and research purposes. For instance, future developments of LS tools require more attention to dynamic knowledgebase, extra-colonic lynch-related cancers on the basis of precision medicine, variants of unknown significance, and support from diagnosis to surveillance for patient follow-up. Insights and recommendations provided in this study could help researchers and developers to meet the existing challenges in future developments.

From diagnosis to post-treatment: biopsychosocial predictors of sexual and mental health in prostate cancer survivors

<h3>Objectives</h3> The increasing incidence of prostate cancer, along with earlier detection and longer survival years has been raising clinician's questions on how to better treat this disease. Radical prostatectomy is one of the most common treatments, although it can have severe consequences, such as changes in sexual functioning and sexual satisfaction, thus affecting the quality of life and mental health. These difficulties can significantly decrease the adherence to treatment protocols and lead to drop-outs. Under the Horizon Europe funding programme for research and innovation, this multicentric project, combining the specialities of urology, psychology, and neuroscience, aims to investigate (1) the biopsychosocial predictors of mental and sexual health in prostate cancer and (2) the neural correlates of sexual functioning in prostate cancer, so as to develop a predictive model regarding post-treatment adjustment and sexual well-being. <h3>Methods</h3> 300 Men with prostate cancer indicated to radical prostatectomy will be assessed before surgery and nine months afterwards. A subset of 250 individuals will participate in semi-structured interviews and complete self-report psychological measures so as to monitor sexual and mental health. The remainder will take part in a laboratory experiment, with neuroimaging techniques and other physiological measures, in order to identify the neural underpinnings of sexual response in relation to prostate cancer and radical prostatectomy. <h3>Results</h3> Results will contribute to the development of a mathematical algorithm, built by Machine Learning experts, that will help predict the biopsychosocial risk and protective factors for sexual health and mental well-being in men with prostate cancer. <h3>Conclusions</h3> This study tackles an important health issue in an innovative way, by combining a longitudinal and a laboratorial/experimental design and integrating a wide range of procedures. Furthermore, the development of a prediction model that covers such broad sexual health determinants, is a promising tool that can help clinicians improve their intervention strategies. <h3>Conflicts of Interest</h3> All the authors declate no known conflict of interest. This project is funded by Horizonte Europa (NORTE-01-0145-FEDER-000057), via the Support System for Scientific and Tecnological Research NORTE-45-2020-75, CCDRN.

Data Visualization of Decision Support Systems Research in Sustainability: A Bibliometric Analysis

The development of scientific research for decision support system in the area of sustainability is growing steadily from 1991 with an increasing trend up to 2022. It is of utmost necessity to identify the potential areas as well as the severity of this research. Thus, the aim of this study is to visualize the scientific research on decision support system on sustainability by conducting bibliometric analysis. The data visualization was performed using the VOSviewer software and data analysis tool available in the Scopus database. A total of 1736 publications in relation to decision support system review related to sustainability, were extracted from Scopus database ranging from 1991 to 2022. Co-citation analysis and co-word analysis were conducted to visualize the evolution of research themes in this field. Four main clusters were identified which mainly related to decision support system applications, decision approach, environmental aspect and water management. The findings of this study help researchers to understand the nature of decision support system research in sustainability from across the world and suggest future research directions.

Health research-strengthening and capacity development: Research support system model in an academic healthcare system.

Introduction: Healthcare research contributes to the well-being of a population; hence, it is important to use the right system to ensure that junior researchers develop the required skills. Current research-strengthening and capacity development programs might lack a research process-based common framework or model leading to variable and suboptimal outcomes. This study aimed to describe the development and evaluation of a model for health research-capacity development at both individual and institutional levels in a Joint Commission International-accredited governmental healthcare organization in Qatar.Methods: This retrospective observational study evaluated a research support system employed in Qatar for 1 year and constituted of16 stations, each covering a different topic and supported by an experienced faculty member. We recorded how many faculty members were involved and how many people accessed which stations. We developed an outcomes logistic model and obtained feedback about their experience of using the research support system through a short survey.Results: Twenty-one faculty members supported a total of 77 participants, representing various professions and specialties. The majority of the participants received support on multiple stations, and the most solicited were study design and methodology (n = 45, 58.4%) and research idea (n = 29, 37.7%). The most common type of research that participants required support for was clinical research (n = 65, 84.4%). Moreover, 58.4% of the participants answered the survey, and their responses attested to their perceived benefit of making use of the research support system.Conclusion: The research support system presented was positively evaluated by participants and promoted networking. Such aspects are favorable to the development of a research culture within an organization and would be a good addition for implementation in universities running healthcare programs and hospitals with residency programs and a large and varied healthcare workforce. This would contribute to the development of health-related research capacity and quality of research outputs in these institutions.

Med-BERT: A Pretraining Framework for Medical Records Named Entity Recognition

A large amount of data is generated every day with the development of Internet medical care, which is of great significance for the clinical decision support system and medical real-world research. Medical records named entity recognition (NER) is important on the aforementioned research topics under the premise of protecting patients’ private information. In this article, we propose a medical dictionary enhanced bidirectional encoder representations from transformers (BERT), dubbed Med-BERT, to achieve better representations of long medical entities. On Med-BERT, we propose a span flat-lattice transformer (Span-FLAT) method on medical records NER, and the entity types include private information such as names and addresses, as well as medical information such as patient symptoms, signs, and diseases. Experimental results on two benchmark medical datasets show the effectiveness of Med-BERT, and the proposed Med-BERT-based Span-FLAT method remarkably outperforms the state-of-the-art methods on medical NER task.