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The widespread adoption of dynamic adaptive streaming (DAS) has revolutionized the delivery of high-quality internet multimedia content by enabling dynamic streaming quality adjustments based on network conditions and playback capabilities. While numerous reviews have explored DAS technologies, this study differentiates itself by focusing on Quality of Experience (QoE)-oriented optimization in cloud-edge collaborative environments. Traditional DAS optimization often overlooks the asymmetry between cloud and edge nodes, where edge resources are typically constrained. This review emphasizes the importance of dynamic task and traffic allocation between cloud and edge nodes to optimize resource utilization and maintain system efficiency, ultimately improving QoE for end users. This comprehensive analysis explores recent advances in QoE-driven DAS optimization strategies, including streaming models, implementation mechanisms, and the integration of machine learning (ML) techniques. By contrasting ML-based DAS approaches with traditional methods, this study highlights the added value of intelligent algorithms in addressing modern streaming challenges. Furthermore, the review identifies emerging research directions, such as adaptive resource allocation and hybrid cloud-edge solutions, and underscores potential application areas for DAS in evolving multimedia systems. With the aim of serving as a valuable resource for researchers, practitioners, and decision-makers in addressing the challenges of resource-constrained edge environments and the need for QoE-centric solutions, this comprehensive analysis endeavors to promote the development, implementation, and application of DAS optimization. Acknowledging the crucial role of DAS optimization in improving the overall QoE for the end users, we hope to facilitate the continued advancement of video streaming experiences in the cloud-edge collaborated environment. Full article

Excessive use of fossil fuel-powered vehicles is a major problem for the entire world today, because of which greenhouse gases are increasing day by day. As a result, climate change and global warming have grown to be serious problems that affect both the environment and life on Earth. However, the effective way of reducing greenhouse gases is to use electric vehicles for commuting. The assessment and selection of the best possible way of charging an electric vehicle is a convoluted decision-making challenge due to the presence of assorted contradictory criteria. Additionally, individual decision makers’ minds and insufficient data are obstacles to doing this. In this regard, interval-valued picture fuzzy sets have been considered as a compatible tool to handle vagueness. In this paper, a multi-attribute group decision-making problem with the bidirectional projection-based VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is considered where the weights are partially known. The objective weights of the attributes in this model are determined using the deviation-based approach. The compromised solution is also assessed using the VIKOR approach. Both the interval-valued image fuzzy Schweizer–Sklar power weighted geometric operator and the interval-valued picture fuzzy Schweizer–Sklar power weighted averaging operator are used in this process. Lastly, a numerical example showing the most suitable way to charge an electric vehicle is given to demonstrate the suggested methodology. To evaluate the robustness and efficacy of the suggested strategy, a comparative analysis with current techniques and a sensitivity analysis of the parameters are also carried out. Full article

The significant environmental impact of the built environment, particularly concerning energy use, carbon emissions, and material consumption, coupled with its economic and social implications, has driven the demand for sustainable buildings. Life Cycle Sustainability Assessment (LCSA) offers a comprehensive approach to evaluating sustainability performance by integrating environmental, economic, and social dimensions across the building life cycle. However, the application of LCSA frameworks in the buildings sector remains limited due to the challenges in harmonizing different sustainability dimensions and addressing methodological inconsistencies. This study employs a scientometric analysis to systematically examine the research landscape on LCSA for buildings. Bibliographic records from the Scopus and Web of Science databases (1999–2024) were systematically analyzed using science mapping techniques and tools, including VOSviewer, CiteSpace, and Gephi. The analysis identifies key research trends, conceptual developments, influential academic sources, and collaboration patterns at the country level. The findings reveal a multi-faceted research landscape characterized by a predominance of environmental assessments, increasing attention to economic and social dimensions, the development of BIM-related methodologies, and emerging trend towards dynamic LCSA. Persistent barriers include insufficient standardization of methodologies, limited data availability, and the fragmented incorporation of the environmental, economic, and social dimensions of sustainability. The findings emphasize the need for advancing LCSA frameworks to achieve more effective integration of the triple bottom line, enabling robust decision-making and advancing sustainability in the built environment. Full article

This study introduces an integrated Multi-Criteria Decision Making (MCDM) methodology combining the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize the selection of municipal water supply pipeline materials. A comprehensive evaluation system encompassing thirteen criteria across technical, economic, and safety dimensions was developed to ensure balanced decision-making. The method employs a weight determination model based on Jaynes’ maximum entropy theory to harmonize subjective AHP-derived weights with objective EWM-derived weights, addressing inconsistencies in traditional evaluation approaches. This framework was validated in a case study involving a DN400 pipeline project in Jiaxing, Zhejiang Province, China, where five materials—steel, ductile iron, reinforced concrete, High-Density Polyethylene (HDPE), and Unplasticized Polyvinyl Chloride (UPVC)—were assessed using quantitative and qualitative criteria. Results identified HDPE as the most suitable material, followed by UPVC and reinforced concrete, with steel ranking lowest. Comparative analysis with alternative MCDM techniques demonstrated the robustness of the proposed method in balancing diverse factors, dynamically adjusting to project-specific priorities. The study highlights the flexibility of this approach, which can extend to other infrastructure applications, such as drainage systems or the adoption of innovative materials like glass fiber-reinforced plastic (GFRP) mortar pipes. By integrating subjective and objective perspectives, the methodology offers a robust tool for designing sustainable, efficient, and cost-effective municipal water supply networks. Full article

Many of the articles on AI in education compare the performance and fairness of different models, but few specifically focus on quantitatively analyzing their explainability. To bridge this gap, we analyzed key evaluation metrics for two machine learning models—ANN and DT—with a focus on their performance and explainability in predicting student outcomes using the OULAD. The methodology involved evaluating the DT, an intrinsically explainable model, against the more complex ANN, which requires post hoc explainability techniques. The results show that, although the feature-based and structured decision-making process of the DT facilitates natural interpretability, it struggles to model complex data relationships, often leading to misclassification. In contrast, the ANN demonstrated higher accuracy and stability but lacked transparency. Crucially, the ANN showed great fidelity in result predictions when it used the LIME and SHAP methods. The results of the experiments verify that the ANN consistently outperformed the DT in prediction accuracy and stability, especially with the LIME method. However, improving the interpretability of ANN models remains a challenge for future research. Full article

Background: Breast cancer screening through mammography interpretation is crucial for early detection and improved patient outcomes. However, the manual classification of mammograms using the BIRADS (Breast Imaging-Reporting and Data System) remains challenging due to subtle imaging features, inter-reader variability, and increasing radiologist workload. Traditional computer-aided detection systems often struggle with complex feature extraction and contextual understanding of mammographic abnormalities. To address these limitations, this study proposes MammoViT, a novel hybrid deep learning framework that leverages both ResNet50’s hierarchical feature extraction capabilities and Vision Transformer’s ability to capture long-range dependencies in images. Methods: We implemented a multi-stage approach utilizing a pre-trained ResNet50 model for initial feature extraction from mammogram images. To address the significant class imbalance in our four-class BIRADS dataset, we applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for minority classes. The extracted feature arrays were transformed into non-overlapping patches with positional encodings for Vision Transformer processing. The Vision Transformer employs multi-head self-attention mechanisms to capture both local and global relationships between image patches, with each attention head learning different aspects of spatial dependencies. The model was optimized using Keras Tuner and trained using 5-fold cross-validation with early stopping to prevent overfitting. Results: MammoViT achieved 97.4% accuracy in classifying mammogram images across different BIRADS categories. The model’s effectiveness was validated through comprehensive evaluation metrics, including a classification report, confusion matrix, probability distribution, and comparison with existing studies. Conclusions: MammoViT effectively combines ResNet50 and Vision Transformer architectures while addressing the challenge of imbalanced medical imaging datasets. The high accuracy and robust performance demonstrate its potential as a reliable tool for supporting clinical decision-making in breast cancer screening. Full article

The processing and disposal of municipal solid waste (MSW) are global problems, particularly in low- to middle-income states like Pakistan. These economic systems will need to tackle problems regarding municipal solid waste disposal to accomplish a sustainable future in waste management. Still, the determination of MSW procedures is frequently influenced by unstable, vague, and inadequately stated criteria. To deal with this issue, we designed an interactive model that uses intuitionistic fuzzy hypersoft sets (IFHSSs) to find the optimal thermochemical processing system for MSW. The main objective of this research is to define interactional operational laws for intuitionistic fuzzy hypersoft numbers and to use these laws to build interaction aggregation operators (AOs) and ordered AOs along with their basic characteristics. Based on developed operators, a novel Evaluation Based on the Distance from the Average Solution (EDAS) technique is proposed to integrate multiple attribute group decision making (MAGDM) issues. The suggested strategy is used to analyze five thermochemical treatment techniques for MSW, using a case study focusing on Pakistan’s particular MSW administration problems to choose the most economical technique. Therefore, the new structure is assessed with established methodologies to illustrate its stability. The comparison of results proves that the implications of the stated approach will be more effective and capable than the existing approaches. Full article

Universities, as hubs for educating future leaders and decision-makers, hold a crucial role in advancing sustainable development. However, the challenge of effectively integrating sustainability into university practices and student behavior remains significant. The Ecological Footprint, subjective well-being, and academic performance are three critical dimensions that, when evaluated together, offer a comprehensive view of sustainability in the educational context. This study aims to apply a university sustainability assessment model called ’Sunshine’ to university students in a diverse sample of five different countries. Additionally, the study provides a critical analysis of the relationships among the indicators of Ecological Footprint, Happiness, and Academic Performance. This application seeks to test the robustness of the model and explore lifestyle differences among students, providing valuable insights for decision-making in the context of university sustainability. Data were collected through specific questionnaires administered to a representative sample of students, and analyses were conducted using descriptive and inferential statistical techniques. The results show that Brazilian, American, and Peruvian students exhibit an unsustainable lifestyle, requiring more than one planet to support their consumption habits. However, they are considered happy and perform well academically. These students were classified as environmentally distracted, highlighting a disconnect between their environmental awareness and practices. Chinese students showed a high ecological footprint, contrasting with the Italian group, which had an ecological footprint below one planet. However, both groups presented similar results, with low happiness indices and high academic performance. On the other hand, the group of Mexican students was the most sustainable, achieving acceptable levels in all three sustainability indicators. The analyses revealed that academic performance is related to happiness in some groups but not happiness in Ecological Footprint. This study significantly contributes by testing and validating the model in a multicultural and diverse sample, offering insights that can guide institutional policies to promote sustainability in the university environment. Full article

Vaccine hesitancy is a significant global issue and is recognized by the World Health Organization (WHO) as one of the most pressing threats to public health. Defined as the delay in acceptance or refusal of vaccines despite their availability, vaccine hesitancy undermines decades of progress in preventing vaccine-preventable diseases. The issue is complex, influenced by misinformation, distrust in healthcare systems, cultural beliefs, and access barriers. These challenges require innovative and empathetic solutions to increase vaccine acceptance. Addressing this growing epidemic requires a multifaceted approach, which involves broader strategies and policymaking and in addition, effective communication tools for clinicians. Motivational Interviewing (MI), a patient-centered communication technique, offers an effective strategy to address pediatric vaccine hesitancy by fostering trust, understanding, and informed decision-making. This review aims to explore the problem of pediatric vaccine hesitancy in the United States, examine its underlying factors, and highlight evidence-based strategies, including Motivational Interviewing, to address this growing concern in clinical and public health settings. It offers practical guidance for healthcare providers and pediatricians to tackle this growing problem effectively and emphasizes the need for a combined effort of communication, community outreach, education, and systemic policy to overcome vaccine hesitancy. Full article

Road traffic accidents (RTAs) are a significant public health and safety concern, resulting in numerous injuries and fatalities. The growing number of cases referred to traffic accident rooms in courts has underscored the necessity for an automated solution to determine victim indemnifications, particularly given the limited number of specialized judges and the complexity of cases involving multiple victims. This paper introduces RTACompensator, an artificial intelligence (AI)-driven decision support system designed to automate indemnification calculations for road accident victims. The system comprises two main components: a calculation module that determines initial compensation based on factors such as age, salary, and medical assessments, and a machine learning (ML) model that assigns liability based on police accident reports. The model uses Arabic bidirectional encoder representations from transformer (AraBERT) embeddings to generate contextual vectors from the report, which are then processed by extreme gradient boosting (XGBoost) to determine responsibility. The model was trained on a purpose-built Arabic corpus derived from real-world legal judgments. To expand the dataset, two data augmentation techniques were employed: multilingual bidirectional encoder representations from transformers (BERT) and Gemini, developed by Google DeepMind. Experimental results demonstrate the model’s effectiveness, achieving accuracy scores of 97% for the BERT-augmented corpus and 97.3% for the Gemini-augmented corpus. These results underscore the system’s potential to improve decision-making in road accident indemnifications. Additionally, the constructed corpus provides a valuable resource for further research in this domain, laying the groundwork for future advancements in automating and refining the indemnification process. Full article

This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our results in diverse global electricity systems. By integrating EGT with advanced methodologies such as DRL, this study develops a comprehensive framework that addresses both the dynamic nature of electricity markets and the strategic adaptability of market participants. This hybrid approach allows for the simulation of complex market scenarios, capturing the nuanced decision-making processes of enterprises under varying conditions of uncertainty and competition. The review systematically evaluates the effectiveness and cost-efficiency of various control policies implemented within electricity markets, including pricing mechanisms, capacity incentives, renewable integration incentives, and regulatory measures aimed at enhancing market competition and transparency. Our analysis underscores the potential of EGT to significantly enhance market resilience, enabling electricity markets to better withstand shocks such as sudden demand fluctuations, supply disruptions, and regulatory changes. Moreover, the integration of EGT with DRL facilitates the promotion of sustainable energy integration by modeling the strategic adoption of renewable energy technologies and optimizing resource allocation. This leads to improved overall market performance, characterized by increased efficiency, reduced costs, and greater sustainability. The findings contribute to the development of robust regulatory frameworks that support competitive and efficient electricity markets in an evolving energy landscape. By leveraging the dynamic and adaptive capabilities of EGT and DRL, policymakers can design regulations that not only address current market challenges but also anticipate and adapt to future developments. This proactive approach is essential for fostering a resilient energy infrastructure capable of accommodating rapid advancements in renewable technologies and shifting consumer demands. Additionally, the review identifies key areas for future research, including the exploration of multi-agent reinforcement learning techniques and the need for empirical studies to validate the theoretical models and simulations discussed. This study provides a comprehensive roadmap for optimizing electricity markets through strategic and policy-driven interventions, bridging the gap between theoretical game-theoretic models and practical market applications. Full article

Developing efficient strategies for VOC emission abatement is an urgent task for protection of the environment and human health. Complete catalytic oxidation exhibits advantages, making it an effective, environmentally friendly, and economically profitable approach for VOC elimination. Pd-based catalysts are known as highly active for hydrocarbon catalytic oxidation. The nature of carrier materials is of particular importance because it may affect activity by changing physicochemical properties of the palladium species. In this work, Al₂O₃, CeO₂, CeO₂-Al₂O₃, and Y-doped CeO₂-Al₂O₃ were used as carriers of palladium catalysts. Methane and benzene were selected as representatives of two types of hydrocarbons. A decisive step in complete methane oxidation is the first C–H bond breaking, while the extraordinary stability of the six-membered ring structure is a challenge in benzene oxidation. The support effect was explored by textural measurements using XRF, XRD, XPS, EPR, and TPR techniques. Three ceria-containing samples showed superior CH₄ oxidation performance, achieving 90% methane conversion at about 300 °C and complete oxidation at 320 °C. Evidence for presence of Pd²⁺ species in all samples regarded as most active was provided by XP-derived analysis. Pd/Y-Ce/Al catalysts exhibited very high activity in benzene oxidation by reaching 100% conversion at 180 °C. The contributions of higher Pd and Ce³⁺ surface concentrations, the presence of O₂⁻-adsorbed superoxo species, and Pd⁰ ↔ PdO redox transfer were considered. The potential of a simple, environmentally friendly, and less energy demanding mechanochemical preparation procedure of mixed oxides was demonstrated. Full article

Introduction: Nosocomial lower respiratory tract infections (nLRTIs), including hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP), remain significant challenges due to high mortality, morbidity, and healthcare costs. Implementing accurate and timely diagnostic strategies is pivotal for guiding optimized antimicrobial therapy and addressing the growing threat of antimicrobial resistance. Areas Covered: This review examines emerging microbiological diagnostic methods for nLRTIs. Although widely utilized, traditional culture-based techniques are hindered by prolonged processing times, limiting their clinical utility in timely decision-making. Advanced molecular tools, such as real-time PCR and multiplex PCR, allow rapid pathogen identification but are constrained by predefined panels. Metagenomic next-generation sequencing (mNGS) provides comprehensive pathogen detection and resistance profiling yet faces cost, complexity, and interpretation challenges. Non-invasive methods, including exhaled breath analysis using electronic nose (e-nose) technology, gene expression profiling, and biomarker detection, hold promise for rapid and bedside diagnostics but require further validation to establish clinical applicability. Expert Opinion: Integrating molecular, metagenomic, biomarker-associated, and traditional diagnostics is essential for overcoming limitations. Continued technological refinements and cost reductions will enable broader clinical implementation. These innovations promise to enhance diagnostic accuracy, facilitate targeted therapy, and improve patient outcomes while contributing to global efforts to mitigate antimicrobial resistance. Full article

Background and Objectives: Epithelial ovarian–tubal–peritoneal cancer (EOC) is the most common type of ovarian cancer. Optimal cytoreductive surgery is the most important prognostic factor in its management. When complete cytoreduction is anticipated to be challenging, neoadjuvant systemic chemotherapy (NACT) becomes an alternative. Imaging modalities are utilized in the decision-making process for primary treatment. The purpose of this study is to evaluate the diagnostic performance and accuracy of preoperative MRI, CT, and 18F-FDG PET/CT in detecting the extent of EOC. Materials and Methods: Between 2017 and 2018, 24 patients with primary (with or without neoadjuvant chemotherapy) or recurrent EOC diagnosed at the Department of Gynecologic Oncology, Istanbul University, Istanbul Faculty of Medicine, were enrolled in this study. These 24 women underwent preoperative imaging modalities within 7 days prior to surgery. The results were compared with histopathological findings, considered the gold standard. Results: We evaluated 24 anatomic regions most commonly involved in EOC. The sensitivity of MRI, CT, and PET/CT in detecting ≥ 0.5 cm implants was 95%, 84%, and 86%, respectively. However, when including implants < 0.5 cm, sensitivity decreased significantly to 40%, 38%, and 42%, respectively. The calculated area under the curve (AUC) for tumors, including those < 0.5 cm, was evaluated as weak for all three modalities (MRI: 0.689, CT: 0.678, PET/CT: 0.691), with PET/CT detecting the largest area. For detecting tumors ≥ 0.5 cm, the AUCs were 0.974, 0.921, and 0.923 for MRI, CT, and PET/CT, respectively. The largest AUC was calculated with MRI, and the AUCs for all three methods were evaluated as excellent. Accuracy was comparable among all three imaging modalities, and no statistically significant differences were found (p < 0.05). Conclusions: While imaging modalities are valuable tools for evaluating abdominal spread in epithelial ovarian cancer (EOC), they have demonstrated limited success in detecting miliary disease. The risk of false negatives for miliary tumors on PET/CT may be mitigated by combining it with other imaging modalities such as MRI or CT. Further investigations are necessary to identify more accurate imaging techniques for this challenging clinical scenario. Full article

Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. Consequently, there is a dramatic need for effective Android malware detection methods. This paper suggests a novel fuzzy rank-based fusion approach for Android malware detection (ANDFRF). The suggested ANDFRF primarily consists of two steps: in the first step, five machine learning algorithms, comprising K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGbooost (XGB) and Light Gradient Boosting Machine (LightGBM), were utilized as base classifiers for the initial identification of Android Apps either as goodware or malware apps. Second, the fuzzy rank-based fusion approach was employed to adaptively integrate the classification results obtained from the base machine learning algorithms. By leveraging rankings instead of explicit class labels, the proposed ANDFRF method reduces the impact of anomalies and noisy predictions, leading to more accurate ensemble outcomes. Furthermore, the rankings reflect the relative importance or acceptance of each class across multiple classifiers, providing deeper insights into the ensemble’s decision-making process. The proposed framework was validated on two publicly accessible datasets, CICAndMal2020 and DREBIN, with a 5-fold cross-validation technique. The proposed ensemble framework achieves a classification accuracy of 95.51% and an AUC of 95.40% on the DREBIN dataset. On the CICAndMal2020 LBC dataset, it attains an accuracy of 95.31% and an AUC of 95.30%. Experimental results demonstrate that the proposed scheme is both efficient and effective for Android malware detection. Full article