Research Gaps Research Articles

Assessing the impact of wheat varieties and processing methods on diabetes risk: A systematic review

Diabetes mellitus remains a major global health challenge, with diet playing a critical role in both its management and prevention. Among dietary factors, wheat-based products are staple foods worldwide, yet their impact on diabetes risk is influenced by the variety of wheat and the methods used in its processing. This systematic review aims to synthesize existing research on how different wheat varieties and their processing methods affect the risk of diabetes. We conducted a comprehensive search of multiple databases, selecting studies that met predefined inclusion criteria focused on wheat characteristics and diabetes outcomes. Our review categorizes wheat varieties based on their genetic profiles and glycemic indices, and examines how traditional and modern processing methods, such as milling and fermentation, alter these properties and influence health outcomes. Preliminary findings suggest that whole grains and ancient wheat varieties often have lower glycemic responses compared to refined and genetically modified strains. Additionally, processes like stone grinding and fermentation have been shown to improve glycemic profiles and potentially lower diabetes risk. This review highlights significant gaps in current research, particularly in long-term clinical outcomes and comparisons of genetic variations. We discuss the implications for dietary guidelines and propose directions for future research to better understand and utilize wheat's nutritional potential in diabetes prevention.

Accessibility of COVID-19-related Public Websites in Japan

ABSTRACT Background During the COVID-19 pandemic, accessible websites and online content have become more critical worldwide as patients seek information about their medical concerns with limited access to care. However, there are still research gaps regarding the accessibility to COVID-19 information in Japan. Purpose This study systematically and quantitatively analyzed COVID-19-related local government websites from official COVID-19 for compliance with WCAG guidelines. Methods On June 25, 2022, we analyzed COVID-19 web portals by Japanese local governments using AChecker, WAVE, and miChecker. These tools are automated accessibility evaluation tool based on Web Content Accessibility Guidelines (WCAG), which are widely used international standards in accessibility. Results Of the 72 websites included, the average number of errors per page was 2.7 (range 0–38) for WAVE and 8.7 (range 0–104) for WCAG 2.0 AA, respectively. The number of errors was lower than the number of accessibility errors on general websites. “Perceivable” errors, such as ambiguous colors/contrasts and missing alternative text, were prominently displayed at the top of the list of web page accessibility errors. Discussion The accessibility of the COVID-19 website of the Japanese local government was deemed satisfactory compared with the average number of accessibility errors on the top 1,000,000 home pages worldwide (50.8 per page). However, the websites lacked support in images (color and text) and audio guidance for users with visual and cognitive impairments. Translation to Health Education Practice For proper user perception, COVID-19 information had to be presented with clear contrast, alternative text, and informative captions.

A systematic literature review with bibliometric analysis of Quality 4.0

PurposeThis study aims to investigate and provide comprehensive insights into the state of research on Quality 4.0 across various sectors. The investigation focusses on studies conducted in different countries and is motivated by the perceived benefits of adopting Quality 4.0 and the existing research gap in this domain. The literature review examines two main aspects of Quality 4.0: implementation ingredients and its applications.Design/methodology/approachThis study employed a comprehensive systematic literature review (SLR) and bibliometric analysis of Quality 4.0 publications to identify the benefits, challenges and trends of adopting Quality 4.0.FindingsThe applications of Quality 4.0 are centred on six key areas, including agile product development, predictive quality algorithms, cloud databases, automation, reduced cost of quality, streamlined quality processes, accurate demand forecasting and monitoring suppliers and quality metrics using digital dashboards. Geographical disparities in research were identified, with India and the United States of America emerging as the top publishing countries. The dominance of the subjects of business, management and accounting underscored the importance of quality management across diverse sectors.Research limitations/implicationsThe limitations of this study include the novelty of the Quality 4.0 topic, which posed a challenge in finding academic material, and the analysis period of six years, which could potentially limit the coverage of recent developments.Practical implicationsThe insights and observations from this research offer valuable guidance to academics, practitioners and professionals seeking to implement Quality 4.0 in diverse sectors.Originality/valueThis study contributes a novel perspective to the field by examining the application, utilisation and evaluation of Quality 4.0, providing valuable insights for effectively managing its implementation.

Sustainable water resource management by business organisations: empirical investigation applying stimulus-organism-response theoretical perspective

PurposeThe purpose of this research was to study sustainable water resource management using a stimulus-organism-response (SOR) perspective.Design/methodology/approachThis research study was an exploratory qualitative study. Thematic content analysis was used based on semi-structured interviews with 30 experts operating in the USA, representing 26 water-intensive organisations across different industries. The study was anchored in the theoretical foundations of SOR perspectives.FindingsThe results of this study revealed several fundamental factors, processes and forces that were considered by organisations for sustainable water resource management. Managers evaluated risks relative to water resources and developed strategic initiatives regarding water management. The authors found that often organisations considered water resources management aspects while deciding business operations. This was especially true for substantive water resource-consuming organisations with wide geographical operations.Research limitations/implicationsThrough this study, the authors explained how the interrelationship between organisations and water resources presented risks and challenges. The authors applied SOR theoretical perspective in this research study. This was while factoring in an organisation’s present considerations and future plans regarding sustainable water resource management. Thus, the study findings were expected to further interdisciplinary research at the intersection of organisational and environmental studies.Practical implicationsThe finding that water sustainability challenges and efforts could act as strong motivating forces for innovation and technology was significant. Water sustainability challenges could also be a catalyst for synergistic collaborations amongst organisations and diverse groups of institutions. The study insights were relevant to organisational scholars, the water management industry regulators and managers involved with organisational sustainability programmes.Originality/valueOrganisational challenges regarding sustainable water resource management have been influenced by growing populations and climate change. Furthermore, the increasing context of scarcity was compounded by increased pressures from numerous stakeholders. Although critical water management issues were recognised by organisations, relatively little was known about how organisational managers were planning for and responding to these issues. This research study contributed towards addressing the mentioned research gap.

Social Determinants of Health Among American Indians and Alaska Natives and Tribal Communities: Comparison with Other Major Racial and Ethnic Groups in the United States, 1990–2022

Background and Objective Limited research exists on health inequities between American Indians and Alaska Natives (AIANs), tribal communities, and other population groups in the United States. To address this gap in research, we conducted time-trend analyses of social determinants of health and disease outcomes for AIANs as a whole and specific tribal communities and compared them with those from the other major racial/ethnic groups. Methods We used data from the 1990–2022 National Vital Statistics System, 2015–2022 American Community Survey, and the 2018–2020 Behavioral Risk Factor Surveillance System to examine socioeconomic, health, disability, disease, and mortality patterns for AIANs. Results In 2021, life expectancy at birth was 70.6 years for AIANs, lower than that for Asian/Pacific Islanders (APIs) (84.1), Hispanics (78.8), and non-Hispanic Whites (76.3). All racial/ethnic groups experienced a decline in life expectancy between the pre-pandemic year of 2019 and the peak pandemic year of 2021. However, the impact of COVID-19 was the greatest for AIANs and Blacks whose life expectancy decreased by 6.3 and 5.8 years, respectively. The infant mortality rate for AIANs was 8.5 per 1,000 live births, 78% higher than the rate for non-Hispanic Whites. One in five AIANs assessed their physical and mental health as poor, at twice the rate of non-Hispanic Whites or the general population. COVID-19 was the leading cause of death among AIANs in 2021. Risks of mortality from alcohol-related problems, drug overdose, unintentional injuries, and homicide were higher among AIANs than the general population. AIANs had the highest overall disability, mental and ambulatory disability, health uninsurance, unemployment, and poverty rates, with differences in these indicators varying markedly across the AIAN tribes. Conclusion and Global Health Implications AIANs remain a disadvantaged racial/ethnic group in the US in many health and socioeconomic indicators, with poverty rates in many Native American tribal groups and reservations exceeding 40%.

Critical Review on the Contribution of Machine Learning to Health Science

Background: The field of machine learning in health science is evolving exponentially, with a focus on accelerating scientific discoveries, improving holistic well-being, and advancing personalized healthcare. Aim: In this same spirit, this critical review article aims to provide a comprehensive understanding of the role, challenges, opportunities, and ethical considerations of integrating machine learning into health science, with an emphasis on healthcare research and practice. Methods: To base its critiques on previous literature, the elucidative survey considered specific criteria, such as the significance and contribution of each source to the field, methodology or approach, and argument, as well as the use of evidence. Results: The study results indicate that machine learning holds great promise to improve evidence-based health science, but significant work is needed to ensure the technology is developed and deployed in a way that is trustworthy and ethical. Conclusion: In conclusion, the literature review presents a balanced assessment of the strengths, weaknesses, and notable features of the current state of machine learning in health science. The key takeaway point is that while machine learning has demonstrated significant potential to improve health science outcomes and strategic management, there are still important challenges, limitations, and research gaps that need to be addressed to facilitate widespread adoption and trust in these technologies.

The Database Generation for Planar 2-DOF Fractionated Multiple-Joint Kinematic Chains

Abstract According to the fractionation concept and whether containing a multiple-joint, planar kinematic chains (KCs) are primarily categorized as non-fractionated simple-joint kinematic chains (NFS-KCs), fractionated simple-joint kinematic chains (FS-KCs), non-fractionated multiple-joint kinematic chains (NFM-KCs), and fractionated multiple-joint kinematic chains (FM-KCs). Currently, there is a noticeable research gap on the enumeration of FM-KCs, whereas comprehensive investigations have been conducted on the enumeration of the other three types of KCs. Aiming at addressing this research gap, the present study develops a heuristic approach for enumerating all planar 2-DOF FM-KCs. To this end, the composition of 2-DOF FM-KCs is initially analyzed to acquire their two basic classifications. Then, utilizing the characteristic graph generation methods for NFM-KCs and NFS-KCs, the combined characteristic graph and its corresponding combined characteristic number string of a 2-DOF FM-KC are created for solving the isomorphic problem in enumeration procedure. Finally, all non-isomorphic 2-DOF FM-KCs with 9, 11, 13, 15, and 17 links are enumerated, and the corresponding database is established.

Design and construction of small-scaled variable modular installation (SVMI) in the context of Chinese street renewal (CSR) : A case study of the "Jingzhang Box" (JZB) service installation in Beijing

China's urban areas are entering an era of stock renewal, where street renewal is receiving policy attention and practical project implementation. However, the challenges posed by spatial constraints and insufficient functional activities on the streets have led to street renewal failing to meet public demands. Small Variable Modular Installations (SVMI) possess a high degree of adaptability to constrained spaces and a flexible capacity to shape diverse functions, making them a targeted strategy in theory to address the challenges of street renewal. Nevertheless, there is a research gap in the context of Chinese street renewal (CSR) concerning the design, and construction of SVMI: an insufficient exploration of combined variable and modular technology solutions and practical construction techniques and methods. In response to this research gap, this study focuses on the "Jing-Zhang Box" (JZB) service installation in Beijing, providing specific analysis of its design concept, design technical points, application functions, spatial structure and connection methods, construction methods, and construction outcomes. This study aims to provide specific supplementation to the research on SVMI design and construction in the context of CSR and to offer theoretical and engineering foundations for the future design and practical construction of SVMI in various scenarios.

A systematic literature review of the stereotype content model in the fields of psychology and marketing: main themes examined in the literature and an agenda for future research in marketing

The stereotypes content model indicates that two traits (i.e., warmth and competence) govern individuals’ impression formation. The great variety of research that has used this theory since the early 2000s leads to a need for a structured overview of prior findings. The goal of this study is to provide a concise map of research streams and present a research agenda. We conducted a systematic literature review of 955 articles. A bibliographic coupling analysis showed four clusters, i.e., (1) the general theoretical foundations of the SCM, (2) the societal impact of key stereotypes (with emphasis on gender), (3) research in clinical psychology and child development, and (4) marketing. Taking a closer look at research in marketing (using co-occurrence analysis), six research streams were identified, including research on branding, country-of-origin, front-line service providers, prosocial consumer behavior, perception of endorsers, and, more recently, on artificial intelligence (AI). The review presents key findings and research gaps across these topics. Finally, we reviewed the few articles that, although not falling into these streams, opened important research veins. This process provided the essential information to present a promising and complete research agenda, to continue building knowledge with impactful implications in different contexts.

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

Adverse events associated with chimeric antigen receptor T-Cell therapy in ophthalmology: a narrative review

Chimeric antigen receptors are synthetically produced receptors engineered to engage with target cells with high specificity. These cells are created by inserting an artificial T-cell receptor into an immunoglobulin’s antigen-binding region, allowing the cells to combine and target specific antigens. The use of chimeric antigen receptor (CAR) T-cell therapy has been a remarkable achievement in the field of immunotherapy, particularly in the treatment of ophthalmic tumors like retinoblastoma and uveal melanoma. However, there are some documented side effects, such as cytokine release syndrome (CRS) and immunological effector cell-associated neurotoxicity syndrome (ICANS). Additionally, ocular side effects such as blurred vision, vision impairment, and intraocular infections are also concerning and require further evaluation. This review highlights the advances made in chimeric antigen receptor (CAR) immunotherapy, including its structure and manufacture, as well as relevant clinical discoveries and associated adverse effects. By identifying the gaps in current research, this analysis provides insights into potential strategies and solutions for addressing some of the most severe side effects.

A bibliometric analysis of literature published on ChatGPT and GPT

Purpose This study aims to analyse the literature published on ChatGPT and generative pre-trained transformer (GPT) available through Scopus to identify the top countries, institutions, authors, journals and titles in terms of publishing and citations in the area.The research also intends to determine the collaborative trends among countries and authors as well as top-used keywords on the topic identified from the analysed literature. Design/methodology/approach The researchers searched the Scopus database to collect and assess the literature on the topic. The paper used six applications such as Biblioshiny, VosViewer, Python, MS Access and Excel and Endnote to collect and analyse the literature. Findings It is found that European countries such as the USA, the UK and Germany took the lead in terms of publishing and impact in the area but the USA stood first with 90 publications and 1,720 citations in this connection. Likewise, the organization “Rheinisch-Westfälische Technische Hochschule Aachen” scored the top position regarding publishing, but Open AI remained on top for getting the highest citations (1,384). Apropos, the author “Choi, Y” produced the highest number of publications. The research also shares the collaborative patterns, top journals and publications in the area, as well as the top-used keywords on the topic. Originality/value To the best of the authors’ knowledge, this is the first study that shares a bibliometric analysis of literature published on GPT and ChatGPT. The research not only fills the research gap on the topic but also shares implications for relevant stakeholders and future research directions for future researchers.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Cost prediction for water reuse equipment using interpretable machine learning models

During the initial stages of engineering projects management, the accurate estimation of equipment costs plays a crucial role in determining project approval decisions. However, there is a significant research gap regarding the estimation of investment costs for water reuse equipment. Additionally, cost predictions for such construction projects often suffer from limitations, including low accuracy, limited generalizability, and inefficiency. Advanced machine learning (ML) methods, renowned for their ability to model complex decision-making processes, offer powerful solutions. In this study, four traditional models and four ensemble models were employed to predict the cost of water reuse equipment. The results demonstrated that the ensemble models exhibited significantly superior predictive performance to that of traditional models with the three boosting ensemble models achieving the best performance (traditional models: training R2 = 29.77 %–94.85 %, testing R2 = 30.62 %–71.72 %; boosting ensemble models: training R2 = 97.42 %, testing R2 = 82.16 %–93.79 %). Furthermore, this study simplified the features of predictive models and identified the key variables that influence the cost of water reuse equipment using Shapley additive explanations (SHAP) method. The retrained ensemble models re-constructed based on the selected variables achieved significant predictive performance, with the Gradient Boosting Decision Tree (GBDT) outperforming the other models (training set R2 = 97.37 %, testing set R2 = 93.86 %). The water quantity, inflow conductivity, outflow conductivity, and recovery rate emerged as critical factors affecting the cost of water reuse equipment. Overall, the methods proposed in this study can enhance the versatility of cost prediction processes in environmental engineering scenarios, particularly those concerning the construction costs of water treatment equipment.

Timing Matters: A Longitudinal Study Examining the Effects of Physical Activity Intensity and Timing on Adolescents' Mental Health Outcomes.

Physical activity is universally acknowledged for its benefits to mental health; however, the specific intensities and timings that best benefit adolescents' mental health, crucial due to their significant influence on daily schedules, have not been thoroughly investigated. This study addresses the substantive research gap by exploring the varied effects of physical activity intensity (light versus moderate to vigorous) and timing (weekdays versus weekends and holidays) on adolescent mental health. Utilizing a large-scale longitudinal dataset (NT1 = 84,054; NT2 = 44,623) from 158 schools, this research describes the current state of adolescent physical activity and investigates the effects of physical activity on mental health outcomes, including positive (i.e., life satisfaction, positive mental health) and negative indicators (depression, anxiety), over a 6-month period. Participants were adolescents aged 9-19 years (mean age = 12.73 ± 2.43 years, 48.9% female), with the analysis adjusted for potential confounding factors. Results showed that as adolescents grow older, their engagement in moderate-to-vigorous physical activity during both weekdays and weekends/holidays tends to decrease, while light physical activity during weekdays increases. Multilevel regression analysis indicated that moderate-to-vigorous physical activity during weekends/holidays at Time 1 positively correlated with better mental health outcomes at Time 2 (six months later), featuring enhanced positive indicators and reduced negative ones. Moderate-to-vigorous physical activity on weekdays at Time 1 was positively linked to mental health at Time 2. Light physical activity during weekends/holidays at Time 1 positively predicted life satisfaction and positive mental health at Time 2. In contrast, light physical activity on weekdays at Time 1 negatively correlated with life satisfaction at Time 2. The study underscores the importance of promoting moderate-to-vigorous physical activity, particularly on weekends and holidays, to improve mental health outcomes among adolescents.

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

Understanding the Influence of Host Radiation on Symbiont Speciation through Parasites of Species Flocks.

(Adaptive) radiations have attracted evolutionary biologists for a long time as ideal model systems to study patterns and processes of often rapid speciation. However, whereas a wealth of (sometimes already genome-scale) data is available for host radiations, very few studies target the patterns of diversification in their symbionts, even though they would be excellent models to study symbiont speciation. Our review summarizes what little is known about general patterns of symbiont diversification in often iconic adaptive host radiations and to what extent these patterns are dependent on the evolutionary trajectories of their hosts. We identify research gaps that need to be addressed in the future and discuss the potential of approaches not yet typically used in these study systems, such as epidemiological disease modeling and new omics technologies, for significantly advancing our understanding of these complex eco-evolutionary relationships.

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.

Examining the Link Between Entrepreneurialism and Employability: A Literature Review

Policymakers and higher education institutions share global concerns about unemployment and equipping companies with a quality workforce. The dynamism of today’s business world has changed the nature of employability. In order to survive in this frequently changing business world, graduates must not only focus on their employability skills but also on entrepreneurial attributes. This article tries to understand the connection between entrepreneurship and employability. In like manner, this article carries out a review of literature in these two fields relying upon the Scopus database. This study extracted the final corpus of 236 articles and processed it further in Vos Viewer to know the most influential authors, countries, organisations and articles followed by an extensive review of 40 articles. This study reveals that the employability literature and entrepreneurial literature are mainly connected through entrepreneurship education and entrepreneurial skills. This study also offers future research direction by exposing some research gaps. This article will help higher education institutions understand the need to develop entrepreneurialism among graduates to enhance their employability.

Social Media Use and Impact on Voters and Candidates in Kerala

This article presents a comprehensive survey of social media usage among Kerala voters across three parliamentary constituencies ( N = 1500). It explores the socio-economic factors influencing both online and offline political engagement and sheds light on how social media empowers younger voters by reducing their reliance on traditional influencers. Kerala’s social media trends align with those of developed nations like the USA, in contrast to broader Indian digital and gender divide indicators. The study also delves into the social media strategies of 280 successful candidates in the April 2019 Kerala Legislative Assembly elections, identifying the key factors influencing their electoral success. Notably, Facebook engagement metrics, such as shares and followers, emerge as more significant predictors of candidate winnability than Twitter metrics. The research highlights the profound impact of social media on political communication, perception, youth participation and candidate strategies, ultimately shaping electoral outcomes in Kerala. This study addresses a critical research gap regarding social media use among Kerala voters and the reciprocal strategies adopted by candidates, including the predictive impact on candidates’ performance.