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Search Results (3,119)

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27 pages, 720 KiB  
Review
Associations of the MIND Diet with Human Health Outcomes: A Scoping Review
by Katherine Hope Morgan, Michelle Lanphere Lee, Cristina S. Barroso, Joel G. Anderson, Shelley Lott, Danielle Reth, Chelsea Horn and Melanie Dixson
Nutrients 2025, 17(16), 2687; https://doi.org/10.3390/nu17162687 - 20 Aug 2025
Abstract
The MIND diet was designed as an intervention to delay neurodegeneration and has been explored by systematic reviews for associations with cognition and, more recently, cardiometabolic disease. Comparatively less is known about how the MIND diet is associated with other health outcomes (e.g., [...] Read more.
The MIND diet was designed as an intervention to delay neurodegeneration and has been explored by systematic reviews for associations with cognition and, more recently, cardiometabolic disease. Comparatively less is known about how the MIND diet is associated with other health outcomes (e.g., all-cause mortality, anxiety, insomnia). This scoping review included studies exploring associations between the MIND diet and health outcomes other than cognition and cardiometabolic disease. Online databases were used to identify 4090 studies published between January 2015 and April 2024, from which 47 publications were included for review. Associations between the MIND diet and health outcomes were assessed as either favorable, unfavorable, or having no statistically significant association. Overall, 47 studies were included in this scoping review, 46 were observational, and several were conducted in large, established cohort studies. Across the 47 studies, 18 different topics were explored. Higher adherence to the MIND diet was mostly associated with favorable health outcomes (65%), while roughly one-third (33%) of studies found no statistically significant associations. One study, in Italy, found that increased adherence to the MIND diet was associated with increased exposure to cadmium, a heavy metal. In populations that may benefit from the MIND diet, we recommend additional observational and exploratory studies to identify health associations. Studies exploring educational interventions would help to identify facilitators and barriers to adopting the MIND diet. This scoping review provides some evidence that higher adherence to the MIND diet is associated with risk reduction for many diseases. Further research on environmental exposures (e.g., cadmium) and other deleterious substances absorbed by food crops will be crucial as we strive to enhance health and food security through plant-rich dietary patterns. Full article
(This article belongs to the Special Issue Therapeutic Potential of Phytochemicals in Neurodegenerative Diseases)
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33 pages, 2072 KiB  
Article
Airline Ranking Using Social Feedback and Adapted Fuzzy Belief TOPSIS
by Ewa Roszkowska and Marzena Filipowicz-Chomko
Entropy 2025, 27(8), 879; https://doi.org/10.3390/e27080879 (registering DOI) - 19 Aug 2025
Abstract
In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal [...] Read more.
In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article
(This article belongs to the Special Issue Dynamics in Biological and Social Networks)
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18 pages, 1111 KiB  
Systematic Review
Comparison with Dietary Groups of Various Macronutrient Ratios on Body Weight and Cardiovascular Risk Factors in Adults: A Systematic Review and Network Meta-Analysis
by Yiling Lou, Hengchang Wang, Linlin Wang, Shen Huang, Yulin Xie, Fujian Song, Zuxun Lu, Furong Wang, Qingqing Jiang and Shiyi Cao
Nutrients 2025, 17(16), 2683; https://doi.org/10.3390/nu17162683 - 19 Aug 2025
Abstract
Background: This network meta-analysis aimed to assess the relative efficacy of macronutrient dietary groups with varying carbohydrate, fat, and protein ratios on weight control and cardiovascular risk factors improvement in adults. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), [...] Read more.
Background: This network meta-analysis aimed to assess the relative efficacy of macronutrient dietary groups with varying carbohydrate, fat, and protein ratios on weight control and cardiovascular risk factors improvement in adults. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Web of Science Core Collection, and ClinicalTrials.gov from inception to 30 November 2024, as well as reference lists of related systematic reviews. Eligible randomized controlled trials (RCTs) were included. Literature screening, data extraction, and risk of bias assessment were conducted independently by two reviewers. The changes in body weight, blood glucose, systolic blood pressure, diastolic blood pressure, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, triglycerides, and total cholesterol were the study outcomes. Utilizing a Bayesian framework, a series of random-effects network meta-analyses were conducted to estimate mean difference (MD) with 95% credible interval (CrI) and determine the relative effectiveness of the macronutrient dietary groups. The quality of evidence for each pair of dietary groups was assessed based on the online tool called confidence in network meta-analysis (CINeMA). Results: This study initially identified 14,988 studies and ultimately included 66 eligible RCTs involving 4301 participants in the analysis. The very low carbohydrate–low protein (VLCLP, MD −4.10 kg, 95% CrI −6.70 to −1.54), the moderate carbohydrate–high protein (MCHP, MD −1.51 kg, 95% CrI −2.90 to −0.20), the very low carbohydrate–high protein (VLCHP, MD −1.35 kg, 95% CrI −2.52 to −0.26) dietary groups might lead to weight loss compared with the moderate fat–low protein (MFLP) dietary group. Among the dietary groups relative to the MFLP dietary group, the moderate carbohydrate–low protein (MCLP, MD 0.09 mmol/L, 95% CrI 0.02 to 0.16) and VLCHP (MD 0.16 mmol/L, 95% CrI 0.08 to 0.24) dietary groups were less effective in lowering HDL cholesterol, and the VLCHP (MD 0.50 mmol/L, 95% CrI 0.26 to 0.75) dietary group was less effective in lowering LDL cholesterol. In terms of triglyceride reduction, the MCLP (MD −0.33 mmol/L, 95% CrI −0.44 to −0.22), VLCHP (MD −0.31 mmol/L, 95% CrI −0.42 to −0.18), VLCLP (MD −0.14 mmol/L, 95% CrI −0.25 to −0.02), and moderate fat–high protein (MFHP, MD −0.13 mmol/L, 95% CrI −0.21 to −0.06) dietary groups were more efficacious than the MFLP dietary group, while any pair of dietary group interventions showed minimal to no difference in the effects on blood glucose, blood pressure, and total cholesterol. Conclusions: High or moderate certainty evidence reveals that the VLCLP dietary group is the most appropriate for weight loss, while the MCLP dietary group is best for reducing triglycerides. For control of blood glucose, blood pressure, and cholesterol levels, there is little to no difference between macronutrient dietary groups. Additionally, future studies in normal-weight populations are needed to verify the applicability of our findings. Full article
(This article belongs to the Section Nutrition and Public Health)
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33 pages, 2477 KiB  
Systematic Review
Patient-Oriented Smart Applications to Support the Diagnosis, Rehabilitation, and Care of Patients with Parkinson’s: An Umbrella Review
by Rute Bastardo, João Pavão, Ana Isabel Martins, Anabela G. Silva and Nelson Pacheco Rocha
Future Internet 2025, 17(8), 376; https://doi.org/10.3390/fi17080376 - 19 Aug 2025
Abstract
This umbrella review aimed to identify, analyze, and synthesize the results of existing literature reviews related to patient-oriented smart applications to support healthcare provision for patients with Parkinson’s. An electronic search was conducted on Scopus, Web of Science, and PubMed, and, after screening [...] Read more.
This umbrella review aimed to identify, analyze, and synthesize the results of existing literature reviews related to patient-oriented smart applications to support healthcare provision for patients with Parkinson’s. An electronic search was conducted on Scopus, Web of Science, and PubMed, and, after screening using predefined eligibility criteria, 85 reviews were included in the umbrella review. The included studies reported on smart applications integrating wearable devices, smartphones, serious computerized games, and other technologies (e.g., ambient intelligence, computer-based objective assessments, or online platforms) to support the diagnosis and monitoring of patients with Parkinson’s, improve physical and cognitive rehabilitation, and support disease management. Numerous smart applications are potentially useful for patients with Parkinson’s, although their full clinical potential has not yet been demonstrated. This is because the quality of their clinical assessments, as well as aspects related to their acceptability and compliance with requirements from regulatory bodies, have not yet been adequately studied. Future research requires more aligned methods and procedures for experimental assessments, as well as collaborative efforts to avoid replication and promote advances on the topic. Full article
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27 pages, 2395 KiB  
Article
I Can’t Get No Satisfaction? From Reviews to Actionable Insights: Text Data Analytics for Utilizing Online Feedback
by Ioannis C. Drivas, Eftichia Vraimaki and Nikolaos Lazaridis
Digital 2025, 5(3), 35; https://doi.org/10.3390/digital5030035 - 19 Aug 2025
Abstract
Cultural heritage institutions, such as museums and galleries, today face the challenge of managing an increasing volume of unsolicited visitor feedback generated across online platforms. This study offers a practical and scalable methodology that transforms 5856 multilingual Google reviews from 59 globally ranked [...] Read more.
Cultural heritage institutions, such as museums and galleries, today face the challenge of managing an increasing volume of unsolicited visitor feedback generated across online platforms. This study offers a practical and scalable methodology that transforms 5856 multilingual Google reviews from 59 globally ranked museums and galleries into actionable insights through sentiment analysis, correlation diagnostics, and guided Latent Dirichlet Allocation. By addressing the limitations of prior research, such as outdated datasets, monolingual bias, and narrow geographical focus, the authors analyze a current and diverse set of recent reviews to capture a timely and globally relevant perspective on visitor experiences. The adopted guided LDA model identifies 12 key topics, reflecting both operational issues and emotional responses. The results indicate that while visitors generally express overwhelmingly positive sentiments, dissatisfaction tends to be concentrated in specific service areas. Correlation analysis reveals that longer, emotionally rich reviews are more likely to convey stronger sentiment and receive peer endorsement, highlighting their diagnostic significance. From a practical perspective, the methodology empowers professionals to prioritize improvements based on data-driven insights. By integrating quantitative metrics with qualitative topics, this study supports operational decision-making and cultivates a more empathetic and responsive data management mindset for museums. The reproducible and adaptable nature of the pipeline makes it suitable for cultural institutions of various sizes and resources. Ultimately, this work contributes to the field of cultural informatics by bridging computational precision with humanistic inquiry. That is, it illustrates how intelligent analysis of visitor reviews can lead to a more personalized, inclusive, and strategic museum experience. Full article
(This article belongs to the Special Issue Advances in Data Management)
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27 pages, 982 KiB  
Article
Depression in Romanian Medical Students—A Study, Systematic Review, and Meta-Analysis
by Corina Lavinia Duica, Silvius Ioan Negoita, Alina Pleșea-Condratovici, Lavinia-Alexandra Moroianu, Mariana Daniela Ignat, Pantelie Nicolcescu, Anamaria Ciubara, Karina Robles-Rivera, Liliana Mititelu-Tartau and Catalin Pleșea-Condratovici
J. Clin. Med. 2025, 14(16), 5853; https://doi.org/10.3390/jcm14165853 - 19 Aug 2025
Abstract
Background: Depression is a significant global mental health concern, especially among medical students. This study combines two components: (1) a cross-sectional assessment of depression and related psychological and demographic factors among students at “Dunărea de Jos” University of Galați, and (2) a systematic [...] Read more.
Background: Depression is a significant global mental health concern, especially among medical students. This study combines two components: (1) a cross-sectional assessment of depression and related psychological and demographic factors among students at “Dunărea de Jos” University of Galați, and (2) a systematic review and meta-analysis of published Romanian studies on depression in medical students. Methods: For the cross-sectional component, 495 students (Years I–III) completed online questionnaires assessing depressive symptoms (PHQ-9), personality traits, procrastination, and sociodemographic factors. In the systematic review, studies from PubMed and Web of Science were synthesized following PRISMA guidelines, with prevalence data being pooled via random-effects meta-analysis. Results: In the Galați sample, 34.0% of students had clinically significant depressive symptoms (PHQ-9 ≥ 10). Depression was associated with female gender, being in the third year of study, low social support, high neuroticism, and procrastination. The meta-analysis (six studies, N = 1546) yielded a pooled national prevalence of 19.99% (95% CI: 18.24–21.73%). Conclusions: Depression is highly prevalent among Romanian medical students, particularly in Galați. The findings support the need for targeted mental health interventions in Romanian universities. Registration: This systematic review has been registered in the Prospero database (registration number CRD420251056873). Full article
(This article belongs to the Section Mental Health)
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24 pages, 1263 KiB  
Article
Unveiling a Hidden Driver of Online Rating Bias: The Role of Consumer Variety-Seeking Behavior
by Shida Ni, Basak Denizci Guillet, Yixing Gao, Rob Law and Baiqing Sun
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 216; https://doi.org/10.3390/jtaer20030216 - 19 Aug 2025
Abstract
Variety-seeking is a fundamental motivation in consumer decision making, yet its subsequent effect on consumer behavior is not fully understood. Thus, this study aims to investigate how consumers’ variety-seeking behaviors influence their subsequent ratings on online reputation platforms. We proposed a framework and [...] Read more.
Variety-seeking is a fundamental motivation in consumer decision making, yet its subsequent effect on consumer behavior is not fully understood. Thus, this study aims to investigate how consumers’ variety-seeking behaviors influence their subsequent ratings on online reputation platforms. We proposed a framework and constructed econometric models to validate it based on large-scale restaurant-review data from an online reputation platform. Several robustness-check methods were employed to ensure the reliability of our results. The empirical results demonstrate that consumers exhibit a positive rating bias in their reviews for variety-seeking options, compared to regular ones. Further analysis reveals that the influence of variety-seeking dynamically changes with the time-varying characteristics of consumers and restaurants. Specifically, as consumers accumulate a larger number of similar experiences and as restaurants age, the observed rating bias gradually diminishes. This study found a previously undocumented but widely prevalent factor causing rating bias on online reputation platforms, and its significant impact warrants attention. The findings also extend the theoretical application scope of variety-seeking in the field of consumer behavior and offer practical implications for managers and platform designers. Full article
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27 pages, 23044 KiB  
Review
Sensor-Based Monitoring of Bolted Joint Reliability in Agricultural Machinery: Performance and Environmental Challenges
by Xinyang Gu, Bangzhui Wang, Zhong Tang and Haiyang Wang
Sensors 2025, 25(16), 5098; https://doi.org/10.3390/s25165098 - 16 Aug 2025
Viewed by 317
Abstract
The structural reliability of agricultural machinery is critically dependent on bolted joints, with loosening being a significant and prevalent failure mode. Harsh operational environments (intense vibration, impact, corrosion) severely exacerbate loosening risks, compromising machinery performance and safety. Traditional periodic inspections are inadequate for [...] Read more.
The structural reliability of agricultural machinery is critically dependent on bolted joints, with loosening being a significant and prevalent failure mode. Harsh operational environments (intense vibration, impact, corrosion) severely exacerbate loosening risks, compromising machinery performance and safety. Traditional periodic inspections are inadequate for preventing sudden failures induced by loosening, leading to impaired efficiency and safety hazards. This review comprehensively analyzes the unique challenges and opportunities in monitoring bolted joint reliability within agricultural machinery. It covers the following: (1) the status of bolted joint reliability issues (failure modes, impacts, maintenance inadequacies); (2) environmental challenges to joint integrity; (3) evaluation of conventional detection methods; (4) principles and classifications of modern detection technologies (e.g., vibration-based, acoustic, direct measurement, vision-based); and (5) their application status, limitations, and techno-economic hurdles in agriculture. This review identifies significant deficiencies in current technologies for agricultural machinery bolt loosening surveillance, underscoring the pressing need for specialized, dependable, and cost-effective online monitoring systems tailored for agriculture’s demanding conditions. Finally, forward-looking research directions are outlined to enhance the reliability and intelligence of structural monitoring for agricultural machinery. Full article
(This article belongs to the Section Smart Agriculture)
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21 pages, 642 KiB  
Review
Prehabilitation Prior to Chemotherapy in Humans: A Review of Current Evidence and Future Directions
by Karolina Pietrakiewicz, Rafał Stec and Jacek Sobocki
Cancers 2025, 17(16), 2670; https://doi.org/10.3390/cancers17162670 - 15 Aug 2025
Viewed by 290
Abstract
Background/Objectives: Chemotherapy is an aggressive form of oncological treatment often accompanied by numerous adverse effects. A patient’s baseline status significantly influences the course of therapy, its efficacy, quality of life, and overall survival. This review aims to analyze the published peer-reviewed studies in [...] Read more.
Background/Objectives: Chemotherapy is an aggressive form of oncological treatment often accompanied by numerous adverse effects. A patient’s baseline status significantly influences the course of therapy, its efficacy, quality of life, and overall survival. This review aims to analyze the published peer-reviewed studies in this area and to assess whether they permit the formulation of preliminary recommendations for future prehabilitation protocols. Methods: An integrative review was conducted due to the limited number of relevant studies. Four databases—MEDLINE/PubMed (Medical Literature Analysis and Retrieval System Online/National Library of Medicine), Scopus, Cochrane, and Web of Science—were systematically searched for English-language articles published between 2010 and 13 January 2025, using the terms “prehabilitation,” “chemotherapy,” “drug therapy,” and “neoadjuvant.” A total of 162 records were retrieved. After duplicate removal, titles and abstracts were screened. The remaining papers were subjected to detailed analysis, resulting in ten studies with diverse methodologies being included. Results: We reviewed ten (n = 10) studies, most of which were reviews focused on breast cancer, indicating variation in the state of knowledge across different cancer types. A protein intake of 1.4 g/kg body mass helps preserve fat-free mass, with whey being more effective than casein. Omega-3 fatty acid supplementation at a dose of 2.2 g/kg may prevent chemotherapy-related neurotoxicity and support appetite and weight maintenance. Physical activity, especially when it includes strength training, improves VO2max, preserves fat-free mass, and may reduce stress and anxiety. We identified one randomized controlled trial in which a single exercise session before the first dose of doxorubicin resulted in a smaller reduction in cardiac function. Continuous psychological support should be available. A combined behavioural and pharmacological approach appears to be the most effective strategy for smoking cessation. Conclusions: No official guidelines exist for prehabilitation before chemotherapy, and the availability of studies on this topic is very limited. The pre-treatment period represents a critical window for interventions. Further research is needed to evaluate the effectiveness and applicability of particularly single-component interventions. Full article
(This article belongs to the Special Issue Rehabilitation Opportunities in Cancer Survivorship)
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32 pages, 4222 KiB  
Article
AI-Driven Anomaly Detection in E-Commerce Services: A Deep Learning and NLP Approach to the Isolation Forest Algorithm Trees
by Pascal Muam Mah, Iwona Skalna and Tomasz Pelech-Pilichowski
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 214; https://doi.org/10.3390/jtaer20030214 - 14 Aug 2025
Viewed by 312
Abstract
The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible [...] Read more.
The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers. Full article
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19 pages, 448 KiB  
Systematic Review
Preventive Psychological Interventions for the Management of Perinatal Anxiety: A Systematic Review
by Alba Val, Cristina M. Posse and M. Carmen Míguez
Brain Sci. 2025, 15(8), 861; https://doi.org/10.3390/brainsci15080861 - 13 Aug 2025
Viewed by 356
Abstract
Introduction: Anxiety is a common problem during pregnancy and postpartum that can have important consequences for mothers and their babies. Having preventive psychological interventions to apply during the perinatal stage could help to reduce its adverse effects. The aim of this study was [...] Read more.
Introduction: Anxiety is a common problem during pregnancy and postpartum that can have important consequences for mothers and their babies. Having preventive psychological interventions to apply during the perinatal stage could help to reduce its adverse effects. The aim of this study was to find out which psychological interventions have been applied for the prevention of perinatal anxiety, what therapeutic approach and application format have been most commonly used, and which interventions have proven to be most effective. Methods: A literature review was conducted in the PsycInfo, Medline, and SCOPUS databases to identify articles published between March 2015 and March 2025. Results: Twenty studies were selected that met the inclusion criteria. Twelve of the interventions analyzed were indicated prevention programs and eight were universal prevention programs, with most taking place in pregnancy (n = 18). Mindfulness and cognitive behavioral therapy were the most commonly employed approaches. Regarding the application format, interventions conducted face-to-face and online were equally frequent, as well as those carried out individually or in groups. The duration ranged from 4 to 14 sessions. Cognitive behavioral therapy interventions, applied face-to-face and in groups, proved to be the most effective. Conclusions: Preventive psychological interventions are effective in reducing anxiety during pregnancy. Further research is needed to draw conclusive results on their long-term effects and efficacy in the postpartum period. Full article
(This article belongs to the Section Cognitive, Social and Affective Neuroscience)
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16 pages, 579 KiB  
Systematic Review
Addressing the Leadership Gap: A Systematic Review of Asian American Underrepresentation in Orthopaedic Surgery
by Ahmed Nadeem-Tariq, Matthew Michelberger, Christopher J. Fang, Jeffrey Lucas Hii, Sukanta Maitra and Brock T. Wentz
Healthcare 2025, 13(16), 1987; https://doi.org/10.3390/healthcare13161987 - 13 Aug 2025
Viewed by 241
Abstract
Background: While Asian American individuals are well represented in medical schools in the United States, their advancement to senior positions within the field of orthopaedic surgery is disproportionately low. This underrepresentation not only limits diversity in leadership but also constrains the development [...] Read more.
Background: While Asian American individuals are well represented in medical schools in the United States, their advancement to senior positions within the field of orthopaedic surgery is disproportionately low. This underrepresentation not only limits diversity in leadership but also constrains the development of people-centred systems that reflect the needs of an increasingly diverse patient population. Objectives: This study systematically examines Asian American representation across the orthopaedic surgery professional pipeline, focusing on disparities between training-level representation and advancement into both faculty and leadership positions., and framing these gaps as a health equity concern. Methods: A comprehensive literature search for peer-reviewed original research articles was conducted via PubMed, EBSCO Open Research, Wiley Online Library, Google Scholar, and ScienceDirect. The potential articles were screened against prespecified eligibility criteria, and risk of bias was assessed using the Newcastle–Ottawa Scale (NOS). Data were then systematically extracted and analysed. Results: This analysis included 20 research articles investigating Asian American representation in orthopaedic surgery. The results demonstrated an underrepresentation of Asian Americans in orthopaedic leadership positions despite improvements in training programme representation with subspecialty clustering in adult reconstruction and spine. Asian American surgeons were less likely to occupy academic and leadership roles than their non-Asian American peers. Across studies, underrepresentation was consistently observed, with effect size estimates indicating a substantial disparity (e.g., pooled risk difference = 0.19; 95% CI [0.12, 0.28]) in those studies reporting comparative outcomes. Similarly, while Asian Americans in residency programmes increased, this growth did not translate proportionally to faculty advancement. In contrast, Asian women face compounded barriers, particularly in subspecialties like spine surgery. These inequities undermine workforce inclusivity and may reduce cultural and linguistic concordance with patients. Conclusions: Despite having strong representation in orthopaedic training programmes, Asian Americans are disproportionately absent from leadership positions. This poses a challenge to equity in surgical education and patient-centred care. To promote equity in leadership, focused mentorship, clear promotion processes, and institutional reform are necessary to address structural barriers to career advancement, this will reflect the diversity of both the workforce and populations served. Full article
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22 pages, 1780 KiB  
Systematic Review
The Future of Education: A Systematic Literature Review of Self-Directed Learning with AI
by Carmen del Rosario Navas Bonilla, Luis Miguel Viñan Carrasco, Jhoanna Carolina Gaibor Pupiales and Daniel Eduardo Murillo Noriega
Future Internet 2025, 17(8), 366; https://doi.org/10.3390/fi17080366 - 13 Aug 2025
Viewed by 302
Abstract
As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and [...] Read more.
As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and dynamic educational environments. This systematic review examines how artificial intelligence (AI) tools enhance SDL by offering personalized, adaptive, and real-time support for learners in online environments. Following the PRISMA 2020 methodology, a literature search was conducted to identify relevant studies published between 2020 and 2025. After applying inclusion, exclusion, and quality criteria, 77 studies were selected for in-depth analysis. The findings indicate that AI-powered tools such as intelligent tutoring systems, chatbots, conversational agents, and natural language processing applications promote learner autonomy, enable self-regulation, provide real-time feedback, and support individualized learning paths. However, several challenges persist, including overreliance on technology, cognitive overload, and diminished human interaction. These insights suggest that, while AI plays a transformative role in the evolution of education, its integration must be guided by thoughtful pedagogical design, ethical considerations, and a learner-centered approach to fully support the future of education through the internet. Full article
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36 pages, 3139 KiB  
Article
Blockchain Technology Adoption for Sustainable Construction Procurement Management: A Multi-Pronged Artificial Intelligence-Based Approach
by Atul Kumar Singh, Saeed Reza Mohandes, Pshtiwan Shakor, Clara Cheung, Mehrdad Arashpour, Callum Kidd and V. R. Prasath Kumar
Infrastructures 2025, 10(8), 207; https://doi.org/10.3390/infrastructures10080207 - 12 Aug 2025
Viewed by 393
Abstract
While blockchain technology (BT) has gained attention in the construction industry, limited research has focused on its application in sustainable construction procurement management (SCPM). Addressing this gap, the present study investigates the key drivers influencing BT adoption in SCPM using a hybrid methodological [...] Read more.
While blockchain technology (BT) has gained attention in the construction industry, limited research has focused on its application in sustainable construction procurement management (SCPM). Addressing this gap, the present study investigates the key drivers influencing BT adoption in SCPM using a hybrid methodological approach. This study includes a systematic review of academic and grey literature, expert consultations, and quantitative analysis using advanced fuzzy-based algorithms, k-means clustering, and social network analysis (SNA). Data were collected through an online survey distributed to professionals experienced in SCPM and blockchain implementation. The Fuzzy DEMATEL results identify “high quality”, “decentralization and data security”, and “cost of the overall project” as the most critical drivers. Meanwhile, SNA highlights “stability of the system”, “overall performance of the project”, and “customer satisfaction” as the most influential nodes within the network. These insights provide actionable guidance for industry stakeholders aiming to advance SCPM through blockchain integration and contribute to theoretical advancements by proposing novel analytical frameworks. Full article
(This article belongs to the Special Issue Modern Digital Technologies for the Built Environment of the Future)
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28 pages, 1873 KiB  
Article
Optimizing Innovation Decisions with Deep Learning: An Attention–Utility Enhanced IPA–Kano Framework for Customer-Centric Product Development
by Xuehui Wu and Zhong Wu
Systems 2025, 13(8), 684; https://doi.org/10.3390/systems13080684 - 12 Aug 2025
Viewed by 258
Abstract
This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit [...] Read more.
This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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