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Search Results (4,136)

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19 pages, 2158 KB  
Systematic Review
Mitral Valve Prolapse in Athletes: Prevalence, Arrhythmic Associations, and Clinical Implications—A Systematic Review
by Andrea Sonaglioni, Gian Luigi Nicolosi, Michele Lombardo and Massimo Baravelli
J. Clin. Med. 2025, 14(21), 7475; https://doi.org/10.3390/jcm14217475 (registering DOI) - 22 Oct 2025
Abstract
Background: Mitral valve prolapse (MVP) is the most common valvular abnormality in the general population and has been linked to mitral regurgitation, arrhythmias, and sudden cardiac death. Its prevalence and prognostic significance in athletes remain uncertain, raising important questions for pre-participation screening, [...] Read more.
Background: Mitral valve prolapse (MVP) is the most common valvular abnormality in the general population and has been linked to mitral regurgitation, arrhythmias, and sudden cardiac death. Its prevalence and prognostic significance in athletes remain uncertain, raising important questions for pre-participation screening, eligibility for competition, and long-term follow-up. Methods: We systematically searched PubMed, Scopus, and EMBASE databases from inception through August 2025 for original studies reporting MVP prevalence in athletes, diagnosed by echocardiography or pathological assessment. Data on study characteristics, diagnostic definitions, prevalence, arrhythmias, and outcomes were independently extracted by three reviewers. Methodological quality was appraised using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: Twelve studies published between 1987 and 2024 met inclusion criteria, enrolling 19,463 athletes from diverse sports and competitive levels. A total of 407 MVP cases were identified, corresponding to a crude pooled prevalence of 2.4%. Prevalence estimates varied substantially (0.2–20%), reflecting heterogeneity in study populations and diagnostic definitions. When all studies were pooled using a random-effects model, the overall prevalence was 2.0% (95% CI 1.2–2.8%). A sensitivity analysis restricted to contemporary, unselected athletic cohorts yielded a prevalence of 1.1% (95% CI 0.4–1.9%), closely aligning with population-based estimates. Ventricular arrhythmias were more frequent than supraventricular arrhythmias, particularly in association with bileaflet prolapse, leaflet thickening, or significant mitral regurgitation. Most athletes were asymptomatic, and only one prospective study provided long-term follow-up, confirming a generally benign prognosis, though rare adverse events (atrial fibrillation, valve surgery) were documented. Conclusions: MVP is relatively uncommon in athletes and occurs at rates similar to the general population. In most cases, prognosis is favorable and should not preclude sports participation. Nonetheless, recognition of high-risk phenotypes with arrhythmogenic potential highlights the need for individualized evaluation and tailored surveillance strategies in sports cardiology practice. Full article
(This article belongs to the Special Issue Advancements in Diagnostic Innovations in Sports Cardiology)
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22 pages, 376 KB  
Article
CSCVAE-NID: A Conditionally Symmetric Two-Stage CVAE Framework with Cost-Sensitive Learning for Imbalanced Network Intrusion Detection
by Zhenyu Wang and Xuejun Yu
Entropy 2025, 27(11), 1086; https://doi.org/10.3390/e27111086 - 22 Oct 2025
Abstract
With the increasing complexity and diversity of network threats, developing high-performance Network Intrusion Detection Systems (NIDSs) has become a critical challenge. A primary obstacle in this domain is the pervasive issue of class imbalance, where the scarcity of minority attack samples and the [...] Read more.
With the increasing complexity and diversity of network threats, developing high-performance Network Intrusion Detection Systems (NIDSs) has become a critical challenge. A primary obstacle in this domain is the pervasive issue of class imbalance, where the scarcity of minority attack samples and the varying costs of misclassification severely limit the effectiveness of traditional models, often leading to a difficult trade-off between high False Positive Rates (FPRs) and low Recall. To address this challenge, this paper proposes a novel, conditionally symmetric two-stage framework, termed CSCVAE-NID (Conditionally Symmetric Two-Stage CVAE for Network Intrusion Detection). The framework operates in two synergistic stages: Firstly, a Data Augmentation Conditional Variational Autoencoder (DA-CVAE) is introduced to tackle the data imbalance problem at the data level. By conditioning on attack categories, the DA-CVAE generates high-quality and diverse synthetic samples for underrepresented classes, providing a more balanced training dataset. Secondly, the core of our framework, a Cost-Sensitive Multi-Class Classification CVAE (CSMC-CVAE), is proposed. This model innovatively reframes the classification task as a probabilistic distribution matching problem and integrates a cost-sensitive learning strategy at the algorithm level. By incorporating a predefined cost matrix into its loss function, the CSMC-CVAE is compelled to prioritize the correct classification of high-cost, minority attack classes. Comprehensive experiments conducted on the public CICIDS-2017 and UNSW-NB15 datasets demonstrate the superiority of the proposed CSCVAE-NID framework. Compared to several state-of-the-art methods, our approach achieves exceptional performance in both binary and multi-class classification tasks. Notably, the DA-CVAE module is designed to be independent and extensible, allowing the effective data that it generates to support any advanced intrusion detection methodology. Full article
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21 pages, 551 KB  
Systematic Review
Cognitive Remediation as a Tool for Enhancing Treatment Dimensions of Schizophrenic Symptomatology: A Systematic Review of Randomized Controlled Trials
by Maria Skokou, Panagiotis-Diogenis Stavridis, Aikaterini Ntoskou-Messini and Lambros Messinis
Brain Sci. 2025, 15(10), 1130; https://doi.org/10.3390/brainsci15101130 - 21 Oct 2025
Abstract
Background/Objectives: Despite efforts, schizophrenia remains a difficult disease to treat for cognitive, positive, negative, and mood symptoms. In the present review, we explore existing data on the ameliorating effects of neurocognitive rehabilitation and the diverse symptomatology of the disorder. Methods: This [...] Read more.
Background/Objectives: Despite efforts, schizophrenia remains a difficult disease to treat for cognitive, positive, negative, and mood symptoms. In the present review, we explore existing data on the ameliorating effects of neurocognitive rehabilitation and the diverse symptomatology of the disorder. Methods: This systematic review has been registered with PROSPERO (registration number: CRD 420251154674). Following PRISMA guidelines, we conducted a search in PubMed, Scopus, and Science Direct database from inception to 14 July 2025. The methodological quality assessment was made by applying the Joanna Briggs Institute (JBI) critical appraisal tool for systematic reviews. Results: Of the 1001 records screened for eligibility, thirty-five studies were identified for data extraction and synthesis. Of these, seven had a low risk of bias, and seven had a high bias risk. The effects of cognitive remediation on the symptoms of schizophrenia were varied. There are consistently positive effects on negative symptoms, but the findings are mixed regarding other domains of symptomatology. The therapeutic effect on positive psychotic symptoms correlated with the severity of symptoms at baseline. Efficacy for mood and anxiety symptoms is controversial, with a comparable number of studies yielding contradicting results. Conclusions: Cognitive remediation has been shown to represent a significant therapeutic tool for schizophrenia symptoms. The method‘s efficacy seems well-established for negative symptoms, whereas the effects on positive psychotic, mood, and anxiety symptoms, although promising, are currently mixed. More high-quality research targeting patient populations where the symptoms studied are more prominent is needed to clarify the effectiveness of the intervention for distinct dimensions of schizophrenic symptomatology. Full article
(This article belongs to the Special Issue Advancements and Future Directions in Clinical Psychosis)
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33 pages, 2631 KB  
Systematic Review
Battery Sizing and Composition in Energy Storage Systems for Domestic Renewable Energy Applications: A Systematic Review
by Ludovica Apa, Livio D’Alvia, Zaccaria Del Prete and Emanuele Rizzuto
Energies 2025, 18(20), 5536; https://doi.org/10.3390/en18205536 - 21 Oct 2025
Abstract
Renewable energy sources, such as photovoltaic panels and wind turbines, are increasingly integrated into domestic systems to address energy scarcity, rising demand, and climate change. However, their intermittent nature requires efficient energy storage systems (ESS) for stability and reliability. This systematic review, conducted [...] Read more.
Renewable energy sources, such as photovoltaic panels and wind turbines, are increasingly integrated into domestic systems to address energy scarcity, rising demand, and climate change. However, their intermittent nature requires efficient energy storage systems (ESS) for stability and reliability. This systematic review, conducted in accordance with PRISMA guidelines, aimed to evaluate the size and chemical composition of battery energy storage systems (BESS) in household renewable energy applications. A literature search was conducted in Scopus in August 2025 using predefined keywords, and studies published in English from 2015 onward were included. Exclusion criteria included book chapters, duplicate conference proceedings, geographically restricted case studies, systems without chemistry or size details, and those focusing solely on electric vehicle batteries. Of 308 initially retrieved records, 83 met the eligibility criteria and were included in the analysis. The majority (92%) employed simulation-based approaches, while 8% reported experimental setups. No formal risk-of-bias tool was applied, but a methodological quality check was conducted. Data were synthesized narratively and tabulated by chemistry, nominal voltage, capacity, and power. Lithium-ion batteries were the most prevalent (49%), followed by lead–acid (13%), vanadium redox flow (3.6%), and nickel–metal hydride (1.2%), with the remainder unspecified. Lithium-ion dominated due to high energy density, long cycle life, and efficiency. Limitations of the evidence include reliance on simulation studies, heterogeneity in reporting, and limited experimental validation. Overall, this review provides a framework for selecting and integrating appropriately sized and composed BESS into domestic renewable systems, offering implications for stability, efficiency, and household-level sustainability. The study was funded by the PNRR NEST project and Sapienza University of Rome Grant. Full article
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19 pages, 3418 KB  
Article
Effect of Performance Packages on Fuel Consumption Optimization in Heavy-Duty Diesel Vehicles: A Real-World Fleet Monitoring Study
by Maria Antonietta Costagliola, Luca Marchitto, Marco Piras and Alessandra Berra
Energies 2025, 18(20), 5542; https://doi.org/10.3390/en18205542 - 21 Oct 2025
Abstract
In line with EU decarbonization targets for the heavy-duty transport sector, this study proposes an analytical methodology to assess the impact of diesel performance additives on fuel consumption in Euro 6 heavy-duty vehicles, the prevailing standard in the circulating European road tractor fleet. [...] Read more.
In line with EU decarbonization targets for the heavy-duty transport sector, this study proposes an analytical methodology to assess the impact of diesel performance additives on fuel consumption in Euro 6 heavy-duty vehicles, the prevailing standard in the circulating European road tractor fleet. A fleet of five N3-category road tractors equipped with tanker semi-trailers was monitored over two phases. During the first 10-month baseline phase, the vehicles operated with standard EN 590 diesel (containing 6–7% FAME); in the second phase, they used a commercially available premium diesel containing performance-enhancing additives. Fuel consumption and route data were collected using a GPS-based system interfaced with the engine control unit via the OBD port and integrated with the fleet tracking platform. After applying data filtering to exclude low-quality or non-representative trips, a 1% reduction in fuel consumption was observed with the use of fuel with additives. Route-level analysis revealed higher savings (up to 5.1%) in high-load operating conditions, while most trips showed improvements between −1.6% and −3.4%. Temporal analysis confirmed the general trend across varying vehicle usage patterns. Aggregated fleet-level data proved to be the most robust approach to mitigate statistical variability. To evaluate the potential impact at scale, a European scenario was developed: a 1% reduction in fuel consumption across the 6.75 million heavy-duty vehicles in the EU could yield annual savings of 2 billion liters of diesel and avoid approximately 6 million tons of CO2 emissions. Even partial adoption could lead to meaningful environmental benefits. Alongside emissions reductions, fuel additives also offer economic value by lowering operating costs, improving engine efficiency, and reducing maintenance needs. Full article
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24 pages, 468 KB  
Article
Mining User Perspectives: Multi Case Study Analysis of Data Quality Characteristics
by Minnu Malieckal and Anjula Gurtoo
Information 2025, 16(10), 920; https://doi.org/10.3390/info16100920 - 21 Oct 2025
Abstract
With the growth of digital economies, data quality forms a key factor in enabling use and delivering value. Existing research defines quality through technical benchmarks or provider-led frameworks. Our study shifts the focus to actual users. Thirty-seven distinct data quality dimensions identified through [...] Read more.
With the growth of digital economies, data quality forms a key factor in enabling use and delivering value. Existing research defines quality through technical benchmarks or provider-led frameworks. Our study shifts the focus to actual users. Thirty-seven distinct data quality dimensions identified through a comprehensive review of the literature provide limited applicability for practitioners seeking actionable guidance. To address the gap, in-depth interviews of senior professionals from 25 organizations were conducted, representing sectors like computer science and technology, finance, environmental, social, and governance, and urban infrastructure. Data are analysed using content analysis methodology, with 2 level coding, supported by NVivo R1 software. Several newer perspectives emerged. Firstly, data quality is not simply about accuracy or completeness, rather it depends on suitability for real-world tasks. Secondly, trust grows with data transparency. Knowing where the data comes from and the nature of data processing matters as much as the data per se. Thirdly, users are open to paying for data, provided the data is clean, reliable, and ready to use. These and other results suggest data users focus on a narrower, more practical set of priorities, considered essential in actual workflows. Rethinking quality from a consumer’s perspective offers a practical path to building credible and accessible data ecosystems. This study is particularly useful for data platform designers, policymakers, and organisations aiming to strengthen data quality and trust in data exchange ecosystems. Full article
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23 pages, 746 KB  
Article
Modeling Viewing Engagement in Long-Form Video Through the Lens of Expectation-Confirmation Theory
by Yingjie Chen and Jin Zhang
Appl. Sci. 2025, 15(20), 11252; https://doi.org/10.3390/app152011252 - 21 Oct 2025
Abstract
Existing long-form video recommendation systems primarily rely on rating prediction or click-through rate estimation. However, the former is constrained by data sparsity, while the latter fails to capture actual viewing experiences. The accumulation of mid-playback abandonment behaviors undermines platform stickiness and commercial value. [...] Read more.
Existing long-form video recommendation systems primarily rely on rating prediction or click-through rate estimation. However, the former is constrained by data sparsity, while the latter fails to capture actual viewing experiences. The accumulation of mid-playback abandonment behaviors undermines platform stickiness and commercial value. To address this issue, this paper seeks to improve viewing engagement. Grounded in Expectation-Confirmation Theory, this paper proposes the Long-Form Video Viewing Engagement Prediction (LVVEP) method. Specifically, LVVEP estimates user expectations from storyline semantics encoded by a pre-trained BERT model and refined via contrastive learning, weighted by historical engagement levels. Perceived experience is dynamically constructed using a GRU-based encoder enhanced with cross-attention and a neural tensor kernel, enabling the model to capture evolving preferences and fine-grained semantic interactions. The model parameters are optimized by jointly combining prediction loss with contrastive loss, achieving more accurate user viewing engagement predictions. Experiments conducted on real-world long-form video viewing records demonstrate that LVVEP outperforms baseline models, providing novel methodological contributions and empirical evidence to research on long-form video recommendation. The findings provide practical implications for optimizing platform management, improving operational efficiency, and enhancing the quality of information services in long-form video platforms. Full article
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9 pages, 2524 KB  
Proceeding Paper
Sentiment Analysis of Air Pollution in Jakarta Using the Bidirectional Encoder Representations from Transformers (BERT) Method
by Shiva Aulia Anjani, Mya Septiani, Fawwaz Alfauzi, Sudin Saepudin, Muhamad Muslih and Carti Irawan
Eng. Proc. 2025, 107(1), 131; https://doi.org/10.3390/engproc2025107131 - 20 Oct 2025
Abstract
Air pollution represents a critical environmental challenge in Jakarta, significantly affecting public health and overall quality of life. This study aims to examine public sentiment regarding air pollution in Jakarta through the application of the Bidirectional Encoder Representations from Transformers (BERT) methodology. The [...] Read more.
Air pollution represents a critical environmental challenge in Jakarta, significantly affecting public health and overall quality of life. This study aims to examine public sentiment regarding air pollution in Jakarta through the application of the Bidirectional Encoder Representations from Transformers (BERT) methodology. The selection of this method is based on its proficiency in comprehending contextual nuances within textual data, thereby facilitating a more precise sentiment analysis. The data utilized in this research is sourced from social media platforms, particularly Twitter, which serves as a vibrant and informative repository of public opinion. The findings of the analysis indicate a predominance of negative sentiments concerning air pollution, influenced by various factors such as governmental policies and prevailing environmental conditions. This research aspires to enhance understanding of public perceptions related to air pollution and contribute to more informed decision-making in environmental policy formulation. Full article
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27 pages, 1960 KB  
Review
AI and Machine Learning in Biology: From Genes to Proteins
by Zaw Myo Hein, Dhanyashri Guruparan, Blaire Okunsai, Che Mohd Nasril Che Mohd Nassir, Muhammad Danial Che Ramli and Suresh Kumar
Biology 2025, 14(10), 1453; https://doi.org/10.3390/biology14101453 - 20 Oct 2025
Abstract
Artificial intelligence (AI) and machine learning (ML), especially deep learning, have profoundly transformed biology by enabling precise interpretation of complex genomic and proteomic data. This review presents a comprehensive overview of cutting-edge AI methodologies spanning from foundational neural networks to advanced transformer architectures [...] Read more.
Artificial intelligence (AI) and machine learning (ML), especially deep learning, have profoundly transformed biology by enabling precise interpretation of complex genomic and proteomic data. This review presents a comprehensive overview of cutting-edge AI methodologies spanning from foundational neural networks to advanced transformer architectures and large language models (LLMs). These tools have revolutionized our ability to predict gene function, identify genetic variants, and accurately determine protein structures and interactions, exemplified by landmark milestones such as AlphaFold and DeepBind. We elaborate on the synergistic integration of genomics and protein structure prediction through AI, highlighting recent breakthroughs in generative models capable of designing novel proteins and genomic sequences at unprecedented scale and accuracy. Furthermore, the fusion of multi-omics data using graph neural networks and hybrid AI frameworks has provided nuanced insights into cellular heterogeneity and disease mechanisms, propelling personalized medicine and drug discovery. This review also discusses ongoing challenges including data quality, model interpretability, ethical concerns, and computational demands. By synthesizing current progress and emerging frontiers, we provide insights to guide researchers in harnessing AI’s transformative power across the biological spectrum from genes to functional proteins. Full article
(This article belongs to the Special Issue Artificial Intelligence Research for Complex Biological Systems)
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22 pages, 2895 KB  
Article
Determination of the Possibilities of Using Wood and Hazelnut Vinegar in the Control of Harmful Mealy Lice Planococcus ficus Signoret (Hemiptera: Pseudococcidae) in Vineyards of Elazig Province
by Sevcan Aytaç and Veysel Çakir
Sustainability 2025, 17(20), 9312; https://doi.org/10.3390/su17209312 - 20 Oct 2025
Abstract
The background of this study is grounded in the economic importance of Planococcus ficus (P. ficus) Signoret (Hemiptera: Pseudococcidae), commonly known as the vine mealybug, which is a major pest in vineyards across South Africa, the Mediterranean region, the Middle East, Argentina, [...] Read more.
The background of this study is grounded in the economic importance of Planococcus ficus (P. ficus) Signoret (Hemiptera: Pseudococcidae), commonly known as the vine mealybug, which is a major pest in vineyards across South Africa, the Mediterranean region, the Middle East, Argentina, California, and Mexico. This pest causes both direct damage to grapevines and indirect damage by promoting the development of sooty mold, which reduces fruit quality and marketability. The limited effectiveness of conventional pesticides—due to the pest’s concealed habitats and biological resistance—combined with their negative impacts on beneficial arthropods, underscores the need for alternative and environmentally sustainable pest management approaches. The methodology of this study involved a field trial conducted in Koruk Village, Elazığ Province, Turkey, from March to October 2022. The aim of the study is to determine the repellent and toxic effects of two types of wood vinegar (WV) and hazelnut vinegar (HV) on P. ficus populations under natural vineyard conditions by using statistical data analysis methods used in basic engineering. Various concentrations of each vinegar wereapplied to the vines, and pest population dynamics were monitored. Additionally, the potential repellent effects of the vinegars on beneficial predatory insects, particularly members of the Coccinellidae family, were assessed. The results of the study indicated that wood vinegar (WV) was more effective than hazelnut vinegar (HV) in reducing P. ficus populations. Both vinegars demonstrated statistically significant, dose-dependent reductions in pest numbers compared to the untreated control. Although both treatments also exhibited repellent effects on Coccinellidae species, these effects were not statistically significant when compared with the positive control group. These findings support the potential application of vinegar-based products in integrated pest management. The conclusion of this study is that wood vinegar (WV) and hazelnut vinegar (HV), as natural carbonization-derived products, can serve as environmentally friendly alternatives for controlling P. ficus in vineyards. Their application may reduce reliance on synthetic pesticides, contribute to sustainable viticulture practices, and minimize negative impacts on non-target beneficial organisms. This research introduces an innovative, eco-compatible control that could be effectively integrated into broader Integrated Pest Management (IPM) strategies. Full article
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36 pages, 52741 KB  
Article
Interventions in Historic Urban Sites After Earthquake Disasters
by Hatice Ayşegül Demir and Mine Hamamcıoğlu Turan
Architecture 2025, 5(4), 96; https://doi.org/10.3390/architecture5040096 - 20 Oct 2025
Abstract
Earthquakes, fires, and climate change-related hazards increasingly threaten cultural heritage. Documenting and identifying the significance of heritage sites before disasters is essential for archival purposes and for guiding post-disaster interventions such as consolidation, reconstruction, or redesign. Although various post-disaster strategies exist in the [...] Read more.
Earthquakes, fires, and climate change-related hazards increasingly threaten cultural heritage. Documenting and identifying the significance of heritage sites before disasters is essential for archival purposes and for guiding post-disaster interventions such as consolidation, reconstruction, or redesign. Although various post-disaster strategies exist in the literature, they often lack consideration of pre-disaster values and authentic qualities, limiting their effectiveness in value-based regeneration. This study proposes a framework for managing post-disaster interventions grounded in pre-disaster documentation of heritage values, authenticity, and integrity. The methodology includes seven phases: case selection; site survey and documentation; thematic analysis and mapping; quantification of qualitative data; synthesis of pre-disaster analysis results to define values, problems, and potentials; post-disaster assessment using aerial and terrestrial imagery; and development of targeted intervention strategies. This study focuses on two areas in Antakya, Türkiye: Kurtuluş Street and Kuyulu Neighborhood, affected by the 2023 earthquake (M 7.7). These areas represent different historical layers: a Hellenistic grid plan with French-style buildings, and an organic Ottoman settlement morphology, respectively. Conservation data collected in 2019 inform the analysis. Mapping techniques evaluate attributes such as spatial characteristics, typologies, and structural systems. The study concludes that traces of pre-disaster spatial patterns and building features should inform post-disaster designs, ensuring sustainable, earthquake-resistant, and value-based interventions. Full article
(This article belongs to the Special Issue Strategies for Architectural Conservation and Adaptive Reuse)
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29 pages, 317 KB  
Article
Strategic ESG Integration and Sustainability Reporting in the Greek Banking Sector: A Comparative Assessment
by Stavros Garefalakis, Maria Katsougri, Erasmia Angelaki, Konstantinos Spinthiropoulos and Alexandros Garefalakis
Adm. Sci. 2025, 15(10), 401; https://doi.org/10.3390/admsci15100401 - 20 Oct 2025
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Abstract
This study investigates the integration of ESG principles in the Greek banking sector through a comparative analysis of the four systemic banks—National Bank of Greece, Eurobank, Piraeus Bank, and Alpha Bank—during 2019–2023. Using a qualitative approach based on secondary data, including sustainability reports, [...] Read more.
This study investigates the integration of ESG principles in the Greek banking sector through a comparative analysis of the four systemic banks—National Bank of Greece, Eurobank, Piraeus Bank, and Alpha Bank—during 2019–2023. Using a qualitative approach based on secondary data, including sustainability reports, GRI-aligned indicators, and the ATHEX ESG Index, the research evaluates ESG performance across governance, environmental, and social dimensions. Findings highlight disparities in maturity: Eurobank and Alpha Bank demonstrate more advanced and transparent governance and environmental practices, while social indicators remain inconsistently reported. The ATHEX ESG Index is shown to enhance disclosure, though its methodology poses challenges for data quality and comparability. Results suggest that effective ESG integration strengthens resilience, transparency, and long-term competitiveness within the banking sector. However, greater standardization and innovative approaches are needed for Greek banks to align fully with international sustainability frameworks and the UN Sustainable Development Goals. Full article
25 pages, 4152 KB  
Systematic Review
Mapping the AI Landscape in Project Management Context: A Systematic Literature Review
by Masoom Khalil, Alencar Bravo, Darli Vieira and Marly Monteiro de Carvalho
Systems 2025, 13(10), 913; https://doi.org/10.3390/systems13100913 - 17 Oct 2025
Viewed by 261
Abstract
The purpose of this research is to systematically map and analyze the use of AI technologies in project management, identifying themes, research gaps, and practical implications. This study conducts a systematic literature review (SLR) that combines bibliometric analysis with qualitative content evaluation to [...] Read more.
The purpose of this research is to systematically map and analyze the use of AI technologies in project management, identifying themes, research gaps, and practical implications. This study conducts a systematic literature review (SLR) that combines bibliometric analysis with qualitative content evaluation to explore the present landscape of AI in project management. The search covered literature published until November 2024, ensuring inclusion of the most recent developments. Studies were included if they examined AI methods applied to project management contexts and were published in peer-reviewed English journals as articles, review articles, or early access publications; studies unrelated to project management or lacking methodological clarity were excluded. It follows a structured coding protocol informed by inductive and deductive reasoning, using NVivo (version 12) and Biblioshiny (version 4.3.0) software. From the entire set of 1064 records retrieved from Scopus and Web of Science, 27 publications met the final inclusion criteria for qualitative synthesis. Bibliometric clusters were derived from the entire set of 885 screened records, while thematic coding was applied to the 27 included studies. This review highlights the use of Artificial Neural Networks (ANN), Case-Based Reasoning (CBR), Digital Twins (DTs), and Large Language Models (LLMs) as central to recent progress. Bibliometric mapping identified several major thematic clusters. For this study, we chose those that show a clear link between artificial intelligence (AI) and project management (PM), such as expert systems, intelligent systems, and optimization algorithms. These clusters highlight the increasing influence of AI in improving project planning, decision-making, and resource management. Further studies investigate generative AI and the convergence of AI with blockchain and Internet of Things (IoT) systems, suggesting changes in project delivery approaches. Although adoption is increasing, key implementation issues persist. These include limited empirical evidence, inadequate attention to later project stages, and concerns about data quality, transparency, and workforce adaptation. This review improves understanding of AI’s role in project contexts and outlines areas for further research. For practitioners, the findings emphasize AI’s ability in cost prediction, scheduling, and risk assessment, while also emphasizing the importance of strong data governance and workforce training. This review is limited to English-language, peer-reviewed research indexed in Scopus and Web of Science, potentially excluding relevant grey literature or non-English contributions. This review was not registered and received no external funding. Full article
(This article belongs to the Special Issue Project Management of Complex Systems (Manufacturing and Services))
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33 pages, 2025 KB  
Article
Territorial Variation of Energy Poverty and Good Health and Well-Being in European Union Countries—A Spatial Analysis
by Agnieszka Sompolska-Rzechuła, Aneta Becker and Anna Oleńczuk-Paszel
Energies 2025, 18(20), 5491; https://doi.org/10.3390/en18205491 - 17 Oct 2025
Viewed by 178
Abstract
Energy poverty (EP) is a complex socio-economic phenomenon of growing importance in European Union (EU) countries. The level of EP, along with the health of the population and the level of perceived well-being (H&W), is a fundamental element of socioeconomic development and a [...] Read more.
Energy poverty (EP) is a complex socio-economic phenomenon of growing importance in European Union (EU) countries. The level of EP, along with the health of the population and the level of perceived well-being (H&W), is a fundamental element of socioeconomic development and a determinant of the quality of life of individuals and entire societies. In this study, two main research objectives were set: a comparison of country classification results obtained using a classical method (QGIS) and a method based on artificial intelligence (SOM) and assessment of the complementarity of both approaches in studying the diversity of EU countries in terms of EP and H&W. The classification results made it possible to demonstrate changes in the studied phenomena over time. The analysis was carried out using data from the Eurostat database from 2019 and 2023. The results presented in this study indicate that countries with the highest EP levels are located in two distinct regions: Eastern and Southern Europe. Countries with the lowest EP levels are located in Northern and Central Europe. In the case of H&W, higher levels were observed in northern and western European countries, while lower levels were observed in eastern and central European countries. The use of an AI-based method in socio-economic research and the comparison of the results with those obtained using the traditional method provides a more complete picture of the diversity of EU countries in terms of EP and H&W, broadening knowledge in empirical and methodological terms. Full article
(This article belongs to the Special Issue Advances in Sustainable Power and Energy Systems: 2nd Edition)
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17 pages, 874 KB  
Article
Analysis of the Neighborhood Effect in School Performance and Impact on Inequality
by Francisco A. Gálvez-Gamboa and Leidy Y. García
Educ. Sci. 2025, 15(10), 1391; https://doi.org/10.3390/educsci15101391 - 17 Oct 2025
Viewed by 222
Abstract
Although Latin American countries have seen major advances in coverage and school attendance, there are still important geographical differences in educational quality, leading to inequalities. The objective of this study is to determine the influence of geographical context on academic achievement among primary [...] Read more.
Although Latin American countries have seen major advances in coverage and school attendance, there are still important geographical differences in educational quality, leading to inequalities. The objective of this study is to determine the influence of geographical context on academic achievement among primary school students in Chile. The methodology involves the estimation of spatial econometric models, specifically, an analysis of spatial dependence including the Moran index, New-GI tests and substantive and residual autocorrelation tests. The data used correspond to standardized test scores obtained from 4030 schools in Chile between 2014 and 2017. The results demonstrate the existence of spatially dependent effects on academic performance for both reading and math. The main indirect spatial effects arise from the concentration of indigenous and immigrant populations. There is also evidence of high spatial inequality in educational quality, as measured through Education Quality Measurement System (SIMCE) tests. Full article
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