Search Results (10,068)

The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence. Full article

Widespread casualties and property damage due to structural failures following devastating earthquakes have once again highlighted the critical significance of evaluating the seismic performance of existing buildings. In this context, a fundamental part of modern pre-disaster management is to evaluate the potential seismic risks of existing structures and implementing the necessary precautions. This study focuses on determining regional risk priorities using a rapid assessment methodology applied to a sample of reinforced-concrete (RC) structures in the Centre of Bitlis city, situated in the high-seismic-risk Lake Van Basin. Risk prioritization was made among the buildings based on the Turkish Rapid Assessment technique revised in 2019 for 100 different RC buildings with one to seven stories. The negative parameters utilized in this method were analyzed both in relation to the 6 February 2023, Kahramanmaraş earthquakes and the assessed building stock. Additionally, the study provides a comprehensive review of the existing building inventory across the region and offers recommendations for potential precautions to mitigate earthquake risks. Full article

Immunogenic cell death (ICD) is a specialized form of cell death that triggers antitumor immune responses. In tumors, ICD promotes the release of tumor-associated and tumor-specific antigens, thereby reshaping the immune microenvironment, restoring antitumor immunity, and facilitating tumor eradication. However, the regulatory mechanisms of ICD and its immunological effects vary across tumor types, and a comprehensive understanding remains limited. We systematically analyzed the expression of 34 ICD-related regulatory genes across 33 tumor types. Differential expression at the RNA, copy number variation (CNV), and DNA methylation levels was assessed in relation to clinical features. Associations between patient survival and RNA expression, CNVs, single-nucleotide variations (SNVs), and methylation were evaluated. Patients were stratified into immunological subtypes and further divided into high- and low-risk groups based on optimal prognostic models built using a machine learning framework. We explored the relationships between ICD-related genes and immune cell infiltration, stemness, heterogeneity, immune scores, immune checkpoint and regulatory genes, and subtype-specific expression patterns. Moreover, we examined the influence of immunotherapy and anticancer immune responses, applied three machine learning algorithms to identify prognostic biomarkers, and performed drug prediction and molecular docking analyses to nominate therapeutic targets. ICD-related genes were predominantly overexpressed in ESCA, GBM, KIRC, LGG, PAAD, and STAD. RNA expression of most ICD-related genes was associated with poor prognosis, while DNA methylation of these genes showed significant survival correlations in LGG and UVM. Prognostic models were successfully established for 18 cancer types, revealing intrinsic immune regulatory mechanisms of ICD-related genes. Machine learning identified several key prognostic biomarkers across cancers, among which NT5E emerged as a predictive biomarker in head and neck squamous cell carcinoma (HNSC), mediating tumor–immune interactions through multiple ligand–receptor pairs. This study provides a comprehensive view of ICD-related genes across cancers, identifies NT5E as a potential biomarker in HNSC, and highlights novel targets for predicting immunotherapy response and improving clinical outcomes in cancer patients. Full article

Nowadays, Flutter with the Dart programming language has become widely popular in mobile developments, allowing developers to build multi-platform applications using one codebase. An increasing number of companies are adopting these technologies to create scalable and maintainable mobile applications. Despite this increasing relevance, university curricula often lack structured resources for Flutter/Dart, limiting opportunities for students to learn it in academic environments. To address this gap, we previously developed the Flutter Programming Learning Assistance System (FPLAS), which supports self-learning through interactive problems focused on code comprehension through code-based exercises and visual interfaces. However, it was observed that many students completed the exercises without fully understanding even basic concepts, if they already had some knowledge of object-oriented programming (OOP). As a result, they may not be able to design and implement Flutter/Dart codes independently, highlighting a mismatch between the system’s outcomes and intended learning goals. In this paper, we propose a guided self-study approach of integrating documentation, code, visual output, and exercise in FPLAS. Two existing problem types, namely, Grammar Understanding Problems (GUP) and Element Fill-in-Blank Problems (EFP), are combined together with documentation, code, and output into a new format called Integrated Introductory Problems (INTs). For evaluations, we generated 16 INT instances and conducted two rounds of evaluations. The first round with 23 master students in Okayama University, Japan, showed high correct answer rates but low usability ratings. After revising the documentation and the system design, the second round with 25 fourth-year undergraduate students in the same university demonstrated high usability and consistent performances, which confirms the effectiveness of the proposal. Full article

Background/Objectives: Wilson’s disease (WD) is an autosomal recessive disorder characterized by pathological copper accumulation, primarily in the liver and brain. Severe hepatic involvement can be effectively treated with liver transplantation (LT). Geographic variation in ATP7B mutations suggests the presence of regional patterns that may impact disease presentation and management. This study aims to investigate the genetic basis of WD in patients from a major LT center in Iran. Methods: A retrospective analysis was conducted on clinical, biochemical, and pathological data from patients suspected of WD who underwent evaluation for LT between May 2020 and June 2025 at Shiraz University of Medical Sciences. Genetic testing was carried out on 20 patients at the Shiraz Transplant Research Center (STRC). Direct mutation analysis of ATP7B was performed for all patients, and the results correlated with clinical and demographic information. Results: In total, 20 WD patients who underwent liver transplantation (15 males, 5 females) carried 25 pathogenic or likely pathogenic ATP7B variants, 21 of which were previously unreported. Fifteen patients were homozygous, and five were compound-heterozygous; all heterozygous combinations occurred in the offspring of second-degree consanguineous unions. Recurrent changes included p.L549V, p.V872E, and p.P992S/L, while two nonsense variants (p.E1293X, p.R1319X) predicted truncated proteins. Variants were distributed across copper-binding, transmembrane, phosphorylation, and ATP-binding domains, and in silico AlphaMissense scores indicate damaging effects for most novel substitutions. Post-LT follow-up showed biochemical normalization in the majority of recipients, with five deaths recorded during the study period. Conclusions: This single-center Iranian study reveals a highly heterogeneous ATP7B mutational landscape with a large proportion of novel population-specific variants and underscores the benefit of comprehensive gene sequencing for timely WD diagnosis and family counseling, particularly in regions with prevalent consanguinity. Full article

This research is to assess the potential use of phosphate sludge from the Gafsa (Tunisia) phosphate laundries as an alternative raw material for the manufacture of ecological refractory bricks. Feasibility was evaluated through comprehensive physico-chemical and mineralogical characterizations of the raw materials using X-ray diffraction (XRD), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FTIR), and thermal analysis (TGA-DTA). Bricks were formulated by substituting phosphate sludge with clay and diatomite, then activated with potassium silicate solution to produce geopolymeric materials. Specific formulations exhibited mechanical performance ranging from 7 MPa to 26 MPa, highlighting the importance of composition and minimal water absorption values of approximately 17.8% and 7.7%. The thermal conductivity of the bricks was found to be dependent on the proportions of diatomite and clay, reflecting their insulating potential. XRD analysis indicated the formation of an amorphous aluminosilicate matrix, while FTIR spectra confirmed the development of new chemical bonds characteristic of geopolymerization. Thermal analysis revealed good stability of the materials, with mass losses mainly related to dehydration and dehydroxylation processes. Environmental assessments showed that most samples are inert or non-hazardous, though attention is required for those with elevated chromium content. Overall, these findings highlight the viability of incorporating phosphate sludge into fired brick production, offering a sustainable solution for waste valorization in accordance with the circular economy. Full article

Background/Objectives: Astaxanthin, a naturally occurring carotenoid renowned for its potent antioxidant properties, has been proposed as a dietary supplement for weight management due to its potential effects on adipose tissue and skeletal muscle metabolism, as well as its anti-inflammatory properties. This meta-analysis systematically evaluated the impact of astaxanthin supplementation on body mass index (BMI) and body weight in adult populations. Methods: Comprehensive searches of reputable databases were conducted, adhering to the PRISMA guidelines, with statistical analyses performed using Jamovi. Results: The study incorporated data from nine clinical trials. Pooled results indicated no significant reduction in the context of BMI (−0.2162; 95% CI: −0.4697 to 0.0374) and a non-significant decrease in body weight (0.0230; 95% CI: −0.4534 to 0.4994) relative to control groups. The heterogeneity observed across studies was 30.1251% (p = 0.1593) for BMI and 73.3885% (p = 0.0002) for body weight management. The dose–response analysis showed no statistically significant association between astaxanthin dosage and outcomes related to BMI and body weight management. Additionally, statistical assessment of funnel plot asymmetry indicated no evidence of publication bias. Conclusions: The findings indicate that astaxanthin does not provide benefits in BMI regulation nor in weight control management, highlighting the need for additional large-scale and long-term clinical trials. This study contributes to the growing body of evidence on the role of nutraceuticals in metabolic health, providing a foundation for future clinical recommendations. Full article

This study presents a comprehensive assessment of lighting conditions in the Intensive Care Units (ICUs) of two major hospitals in the Democratic Republic of Congo (DRC): Hospital du Cinquantenaire in Kinshasa and Jason Sendwe Hospital in Lubumbashi. A mixed-methods approach was employed, integrating continuous illuminance monitoring with structured staff surveys to evaluate visual comfort in accordance with the EN 12464-1 standard for indoor workplaces. Objective measurements revealed that more than 52.2% of the evaluated ICU workspaces failed to meet the recommended minimum illuminance level of 300 lux. Subjective responses from healthcare professionals indicated that poor lighting significantly reduced job satisfaction by 40%, lowered self-rated task performance by 30%, decreased visual comfort scores from 4.1 to 2.6 (on a 1–5 scale), and increased the prevalence of well-being symptoms (eye fatigue, headaches) by 25–35%. Frequent complaints included eye strain, glare, and discomfort with posture, with these issues often exacerbated during the rainy season due to reduced natural daylight. The study highlights critical deficiencies in current lighting infrastructure and emphasizes the need for urgent improvements in clinical environments. Moreover, inconsistent energy supply to these healthcare settings also impacts the assurance of visual comfort. To address these shortcomings, the study recommends transitioning to energy-efficient LED lighting, enhancing access to natural light, incorporating circadian rhythm-based lighting systems, enabling individual lighting control at workstations, and ensuring a consistent power supply via the integration of solar inverters to the grid supply. These interventions are essential not only for improving healthcare staff performance and safety but also for supporting better patient outcomes. The findings offer actionable insights for hospital administrators and policymakers in the DRC and similar low-resource settings seeking to enhance environmental quality in critical care facilities. Full article

Introduction: 3D-printing is an emerging technology in the field of prosthetics, offering advantages such as cost-effectiveness, ease of customization, and improved accessibility. While previous reviews have focused on limited aspects, the aim of this systematic review is to provide a comprehensive evaluation of the clinical outcomes of 3D-printed prostheses for both upper and lower limbs. Methods: A search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across six databases (PubMed, Web of Science, EBSCO, Scopus, Cochrane Library, and Sage). Studies on 3D-printed prostheses in human rehabilitation that focused on the clinical outcomes of the device were included, while studies lacking clinical data, 3D printing details, or focusing on traditional manufacturing methods were excluded. Finally, the risk of bias was assessed using the modified Downs & Black Checklist. Results: A total of 1420 studies were identified, with 11 meeting the inclusion criteria. The included studies assessed different 3D-printed prosthetic types and upper and lower limb prostheses. The main clinical outcomes analyzed were functional performance, design and material integrity, and overall effectiveness of 3D-printed prostheses. Studies on upper limb prostheses reported improved dexterity, range of motion (ROM), and user satisfaction, despite some durability limitations. Lower limb prostheses showed enhancements in comfort, gait parameters, and customization, particularly in amphibious and partial foot designs. Conclusions: 3D-printed prostheses show potential to improve functional performance, patient satisfaction, fit, and implementation feasibility compared to conventional methods. However, limitations such as small sample sizes, variability in assessment tools, and limited high-quality evidence highlight the need for further research to support broader clinical adoption. Full article

In the process of large-scale water conservancy and hydropower station construction in the southwest region of China, obtaining the deep overburden pressuremeter modulus E_m is of great significance for the calculation of foundation bearing capacity and dam foundation settlement. However, due to the complex nature of the soil properties in deep overburden layers, conducting deep-hole pressuremeter tests is challenging, time-consuming, and costly. In order to efficiently and accurately obtain the pressuremeter modulus of deep overburden, this paper takes the deep overburden in the river valley where a large hydropower station dam is located in the southwest region as the research object. It proposes a method based on data-driven prediction of the pressuremeter modulus and combines it with the physical mechanism to carry out the reliability analysis of the prediction results. By constructing a database of soil physical and mechanical parameters, including the pressuremeter modulus, the prediction performance of Random Forest (RF), Support Vector Regression (SVR), and BP Neural Network on the pressure modulus was evaluated. The Particle Swarm Optimization (PSO) was utilized for hyperparameter optimization to enhance the reliability of prediction results. The results indicate that the RF and PSO-RF models exhibit a comprehensive advantage for accurately predicting the pressuremeter modulus. The prediction results of the model for new data have a strong correlation with the results calculated by the Menard formula, which demonstrates the reliability of the model. Therefore, establishing the relationship between the conventional physical and mechanical parameters of deep overburden and the pressuremeter modulus, and predicting the pressuremeter modulus based on data-driven methods, has significant engineering value for obtaining the pressuremeter modulus of deep overburden efficiently, economically, and reliably. It also holds significant importance for the extended application of machine learning in the field of soil parameter prediction. Full article

Structured quantitative data, such as survey responses in human resource management research, are often analysed using machine learning methods, including logistic regression. Although these methods provide accurate statistical predictions, their results are frequently abstract and difficult for non-specialists to comprehend. This limits their usefulness in practice, particularly in contexts where eXplainable Artificial Intelligence (XAI) is essential. This study proposes a domain-independent approach for the autonomous classification and interpretation of quantitative data using visual processing. This method transforms individual responses based on rating scales into visual representations, which are subsequently processed by Convolutional Neural Networks (CNNs). In combination with Class Activation Maps (CAMs), image-based CNN models enable not only accurate and reproducible classification but also visual interpretability of the underlying decision-making process. Our evaluation found that CNN models with bar chart coding achieved an accuracy of between 93.05% and 93.16%, comparable to the 93.19% achieved by logistic regression. Compared with conventional numerical approaches, exemplified by logistic regression in this study, the approach achieves comparable classification accuracy while providing additional comprehensibility and transparency through graphical representations. Robustness is demonstrated by consistent results across different visualisations generated from the same underlying data. By converting abstract numerical information into visual explanations, this approach addresses a core challenge: bridging the gap between model performance and human understanding. Its transparency, domain-agnostic design, and straightforward interpretability make it particularly suitable for XAI-driven applications across diverse disciplines that use quantitative response data. Full article

Cancer classification using high-dimensional genomic data presents significant challenges in feature selection, particularly when dealing with datasets containing tens of thousands of features. This study presents a new application of the Simultaneous Perturbation Stochastic Approximation (SPSA) method for feature selection on large-scale cancer datasets, representing the first investigation of the SPSA-based feature selection technique applied to cancer datasets of this magnitude. Our research extends beyond traditional SPSA applications, which have historically been limited to smaller datasets, by evaluating its effectiveness on datasets containing 35,924 to 44,894 features. Building upon established feature-ranking methodologies, we introduce a comprehensive evaluation framework that examines the impact of varying proportions of top-ranked features (5%, 10%, and 15%) on classification performance. This systematic approach enables the identification of optimal feature subsets most relevant to cancer detection across different selection thresholds. The key contributions of this work include the following: (1) the first application of SPSA-based feature selection to large-scale cancer datasets exceeding 35,000 features, (2) an evaluation methodology examining multiple feature proportion thresholds to optimize classification performance, (3) comprehensive experimental validation through comparison with ten state-of-the-art feature selection and classification methods, and (4) statistical significance testing to quantify the improvements achieved by the SPSA approach over benchmark methods. Our experimental evaluation demonstrates the effectiveness of the feature selection and ranking-based SPSA method in handling high-dimensional cancer data, providing insights into optimal feature selection strategies for genomic classification tasks. Full article

Introduction: Direct pulp capping (DPC) aims to preserve the vitality of the dental pulp by placing a protective biocompatible material over the exposed pulp tissue to facilitate healing. There are several calcium-silicate materials that have been designed to promote mineralization and the regulation of inflammation. These have strong potential for the repair and regeneration of dental pulp. Among them, Biodentine (BD) and EndoSequence RRM Putty (ES) have been found to promote in vitro and in vivo mineralization while minimizing some of the limitations of the first-generation calcium-silicate-based materials. Theracal-LC (TLC), a light-cured, resin-modified calcium-silicate material, is a newer product with potential to improve the clinical outcomes of DPC, but existing studies have reported conflicting findings regarding its biocompatibility and ability to support pulpal healing in direct contact with the pulp. A comprehensive assessment of the biocompatibility and pulpal protection provided by these three capping materials has not yet been performed. Aim: We aimed to quantify the inflammatory response, dentin bridge formation, and material adaptation following DPC using three calcium-silicate materials: ES, BD, and TLC. Materials and Methods: DPC was performed on the maxillary first molar of C57BL/6 female mice. Maxilla were collected and processed at 1 and 21 days post-DPC. The early inflammatory response was measured 24 h post-procedure using confocal imaging of anti-Lys6G6C, which indicates the extent of neutrophil and monocyte infiltration. Reparative mineralized bridge formation was assessed at 21 days post-procedure using high-resolution micro-computed tomography (micro-CT) and histology. Lastly, the homogeneity of the capping materials was evaluated by quantifying voids in calcium-silicate restorations using micro-CT. Results: DPC using TLC induced less infiltration of Lys6G6C⁺ cells at 24 h than BD or ES. BD promoted higher volumes of tertiary dentin than TLC, but TLC and ES showed no significant differences in volume. No differences were observed in material adaptation and void spaces among the three capping materials. Conclusions: All three materials under investigation supported pulp healing and maintained marginal integrity. However, TLC induced a lower inflammatory response on day 1 and induced similar levels of tertiary dentin to ES. These observations challenge the common perception that resin-based capping materials are not suitable for direct pulp capping. Our findings underscore the need to balance biological responses with physical properties when selecting pulp capping materials to improve long-term clinical success. Full article

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article