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Coal serves as the primary energy source for China, with production anticipated to reach 4.76 billion tons in 2024. However, the mining process generates a significant amount of gangue, with approximately 800 million tons produced in 2023 alone. Currently, China faces substantial gangue stockpiles, characterized by a low comprehensive utilization rate that fails to meet the country’s ecological and environmental protection requirements. The environmental challenges posed by the treatment and disposal of gangue are becoming increasingly severe. This review employs bibliometric analysis and theoretical perspectives to examine the latest advancements in gangue utilization, specifically focusing on the application of computational chemistry to elucidate the structural features and interaction mechanisms of coal gangue, and to collate how these insights have been leveraged in the literature to inform its potential utilization routes. The aim is to promote the effective resource utilization of this material, and key topics discussed include evaluating the risks of spontaneous combustion associated with gangue, understanding the mechanisms governing heavy metal migration, and modifying coal byproducts to enhance both economic viability and environmental sustainability. The case studies presented in this article offer valuable insights into the gangue conversion process, contributing to the development of more efficient and eco-friendly methods. By proposing a theoretical framework, this review will support ongoing initiatives aimed at the sustainable management and utilization of coal gangue, emphasizing the critical need for continued research and development in this vital area. This review uniquely combines bibliometric analysis with computational chemistry to identify new trends and gaps in coal waste utilization, providing a roadmap for future research. Full article

Machine learning techniques, particularly decision trees, have been extensively utilized in Network-based Intrusion Detection Systems (NIDSs) due to their transparent, rule-based structures that enable straightforward interpretation. However, this transparency presents privacy risks, as decision trees may inadvertently expose sensitive information such as network configurations or IP addresses. In our previous work, we introduced a sensitive pruning-based decision tree to mitigate these risks within a limited dataset and basic pruning framework. In this extended study, three privacy-preserving pruning strategies are proposed: standard sensitive pruning, which conceals specific sensitive attribute values; optimistic sensitive pruning, which further simplifies the decision tree when the sensitive splits are minimal; and pessimistic sensitive pruning, which aggressively removes entire subtrees to maximize privacy protection. The methods are implemented using the J48 (Weka C4.5 package) decision tree algorithm and are rigorously validated across three full-scale NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. To ensure a realistic evaluation of time-dependent intrusion patterns, a rolling-origin resampling scheme is employed in place of conventional cross-validation. Additionally, IP address truncation and port bilateral classification are incorporated to further enhance privacy preservation. Experimental results demonstrate that the proposed pruning strategies effectively reduce the exposure of sensitive information, significantly simplify decision tree structures, and incur only minimal reductions in classification accuracy. These findings reaffirm that privacy protection can be successfully integrated into decision tree models without severely compromising detection performance. To further support the proposed pruning strategies, this study also includes a comprehensive review of decision tree post-pruning techniques. Full article

Mangroves play a crucial role in ecosystems, and the accurate classification and real-time monitoring of mangrove species are essential for their protection and restoration. To improve the segmentation performance of mangrove UAV remote sensing images, this study performs species segmentation after the super-resolution (SR) reconstruction of images. Therefore, we propose SwinNET, an SR reconstruction network. We design a convolutional enhanced channel attention (CEA) module within a network to enhance feature reconstruction through channel attention. Additionally, the Neighborhood Attention Transformer (NAT) is introduced to help the model better focus on domain features, aiming to improve the reconstruction of leaf details. These two attention mechanisms are symmetrically integrated within the network to jointly capture complementary information from spatial and channel dimensions. The experimental results demonstrate that SwinNET not only achieves superior performance in SR tasks but also significantly enhances the segmentation accuracy of mangrove species. Full article

Background/Objectives: Clinically significant portal hypertension (CSPH) in cirrhotic patients impacts mortality rates and quality of life. CSPH increases the risk of systemic decompensation and could predispose to the deterioration of portal hypertension (PH)–gastric complications, such as portal hypertensive gastropathy (PHG) and portal hypertensive polyps (PHPs). In the management of CSPH with high-risk varices, endoscopic band ligation (EBL) is effective in preventing variceal bleeding. However, this procedure has several drawbacks, ranging from its inability to treat PH to the potential development of significant PH–gastric complications. The aim of our study is to evaluate endoscopic changes in PHG, PHPs, and gastric varices before and after the obliteration of esophageal varices, highlighting the potential risks of EBL. Methods: We retrospectively evaluated forty-four patients who underwent EBL for esophageal varices in emergency and elective settings, according to Baveno VII guidelines. We assessed the presence and severity of PHG, the status of gastric varices, and the number of PHPs before and after the eradication of esophageal varices. We used Fisher’s exact test and t-tests to compare the endoscopic and clinical-laboratory data statistically. A p-value < 0.05 was considered statistically significant. Results: This study found that after the eradication of varices, there was a significant worsening of PHG in 28 patients (63%) compared to before the procedure (p < 0.05). The condition remained stable in 14 patients (31%). However, it is worth noting that 90% of the patients exhibited severe PHG at baseline. Additionally, the absence of ascites and the non-administration of beta blockers at baseline were independent risk factors for worsening PHG (p < 0.05). Along with the deterioration of PHG, three patients (7%) developed gastric varices, all classified as type 1 gastroesophageal varices, and in two patients (4.5%), PHPs were formed. In particular, out of these two cases, the number of PHPs increased from one to two compared to the baseline. Conclusions: Our study underscores the association of EBL with a general worsening of PH–gastric complications and the protective effect of beta blockers in this context. Despite these promising results, future studies are needed to assess whether the worsening of PH–gastric complications is sustained over time and whether it is associated with a deterioration in clinical outcomes in patients with cirrhosis. Such evidence could help guide a more informed therapeutic decision between EBL and beta blockers. Full article

With the globalized deployment of cross-border vehicle location services and the trajectory data, which contain user identity information and geographically sensitive features, the variability in privacy regulations in different jurisdictions can further exacerbate the technical and compliance challenges of data privacy protection. Traditional static differential privacy mechanisms struggle to accommodate spatiotemporal heterogeneity in dynamic scenarios because of the use of a fixed privacy budget parameter, leading to wasted privacy budgets or insufficient protection of sensitive regions. This study proposes a reinforcement-learning-based dynamic noise optimization method (DNO-RL) that dynamically adjusts the Laplacian noise scale by real-time sensing of vehicle density, region sensitivity, and the remaining privacy budget via a deep Q-network (DQN), with the aim of providing context-adaptive differential privacy protection for cross-border vehicle location services. Simulation experiments of cross-border scenarios based on the T-Drive dataset showed that DNO-RL reduced the average localization error by 28.3% and saved 17.9% of the privacy budget compared with the local differential privacy under the same privacy budget. This study provides a new paradigm for the dynamic privacy–utility balancing of cross-border vehicular networking services. Full article

Effective management of fecal pollutants in rural sanitation is crucial for environmental health and public safety, especially in developing regions. In this study, temporal and regional variations in nutrient elements, heavy metals, pathogenic microorganisms (PMs), and antibiotic resistance genes (ARGs) of fecal samples from rural toilets in China were investigated. The moisture contents of the fecal samples average 92.7%, decreasing seasonally from 97.4% in summer to 90.6% in winter. The samples’ pH values range from 6.5 to 7.5, with a slight decrease in winter (6.8), while their electrical conductivity varies from 128.1 to 2150 μs/cm, influenced by regional diets. Chromium (9.0–49.7 mg/kg) and copper (31.9–784.4 mg/kg) levels vary regionally, with higher concentrations in Anhui and Guangxi Provinces due to dietary and industrial factors. Zinc contents range from 108.5 to 1648.9 mg/kg, with higher levels in autumn and winter, resulting from agricultural practices and Zn-containing fungicides, posing potential health and phytotoxicity risks. Seasonal and regional variations in PMs and ARGs were observed. Guangxi Province shows the high PM diversity in summer samples, while Jiangsu Province exhibits the high ARGs types in autumn samples. These findings highlight the need for improved waste management and sanitation solutions in rural areas to mitigate environmental risks and protect public health. Continued research in these regions is essential to inform effective sanitation strategies. Full article

A stroke is a critical medical condition and one of the leading causes of death among humans. Segmentation of the lesions of the brain in which the blood flow is impeded because of blood coagulation plays a vital role in drug prescription and medical diagnosis. Computed tomography (CT) scans play a crucial role in detecting abnormal tissue. There are several methods for segmenting medical images that utilize the main images without considering the patient’s privacy information. In this paper, a deep network is proposed that utilizes compressive sensing and ensemble learning to protect patient privacy and segment the dataset efficiently. The compressed version of the input CT images from the ISLES challenge 2018 dataset is applied to the ensemble part of the proposed network, which consists of two multi-resolution modified U-shaped networks. The evaluation metrics of accuracy, specificity, and dice coefficient are 92.43%, 91.3%, and 91.83%, respectively. The comparison to the state-of-the-art methods confirms the efficiency of the proposed compressive sensing-based ensemble net (CS-Ensemble Net). The compressive sensing part provides information privacy, and the parallel ensemble learning produces better results. Full article

In the modern era of smart grids, integrating advanced Geographic Information Systems (GISs) with protection coordination functionalities is pivotal for enhancing the reliability and efficiency of distribution networks. This paper presents an implementation of a web-based distribution network GIS platform that seamlessly integrates distribution system feeder GIS monitoring with the system model file layout, fault current analysis, and coordination simulation functions. The system can provide scalable and accessible solutions for power utilities, ensuring that protective devices operate in a coordinated manner to minimize outage impacts and improve service restoration times. The proposed GIS platform has demonstrated significant improvements in fault management and relay coordination through extensive simulation and field testing. This research advances the capabilities of distribution network management and sets a foundation for future enhancements in smart grid technology. Full article

Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article

The continuous advancement of fire protection technologies has necessitated the development of comprehensive safety standards, leading to an increasingly diversified and specialized regulatory landscape. This has made it difficult for fire protection professionals to quickly and accurately locate the required fire safety standard information. In addition, the lack of effective integration and knowledge organization concerning fire safety standard entities has led to the severe fragmentation of fire safety standard information and the absence of a comprehensive “one map”. To address this challenge, we introduce FT-FLAT, an innovative CNN–Transformer fusion architecture designed specifically for fire safety standard entity extraction. Unlike traditional methods that rely on rules or single-modality deep learning, our approach integrates TextCNN for local feature extraction and combines it with the Flat-Lattice Transformer for global dependency modeling. The key innovations include the following. (1) Relative Position Embedding (RPE) dynamically encodes the positional relationships between spans in fire safety texts, addressing the limitations of absolute positional encoding in hierarchical structures. (2) The Multi-Branch Prediction Head (MBPH) aggregates the outputs of TextCNN and the Transformer using Einstein summation, enhancing the feature learning capabilities and improving the robustness for domain-specific terminology. (3) Experiments conducted on the newly annotated Fire Safety Standard Entity Recognition Dataset (FSSERD) demonstrate state-of-the-art performance (94.24% accuracy, 83.20% precision). This work provides a scalable solution for constructing fire safety knowledge graphs and supports intelligent information retrieval in emergency situations. Full article

Tuberculosis (TB) remains a leading cause of death in Bangladesh, disproportionately affecting low socio-economic status (SES) populations. This review, guided by the WHO Social Determinants of Health framework and Rockefeller-Lancet Planetary Health Report, examined how social, economic, and ecological factors link SES to TB burden. The first literature search identified 28 articles focused on SES-TB relationships in Bangladesh. A second search through snowballing and conceptual mapping yielded 55 more papers of diverse source types and disciplines. Low-SES groups face elevated TB risk due to smoking, biomass fuel use, malnutrition, limited education, stigma, financial barriers, and hazardous housing or workplaces. These factors delay care-seeking, worsen outcomes, and fuel transmission, especially among women. High-SES groups more often face comorbidities like diabetes, which increase TB risk. Broader contextual drivers include urbanisation, weak labour protections, cultural norms, and poor governance. Recommendations include housing and labour reform, gender parity in education, and integrating private providers into TB programmes. These align with the WHO End TB Strategy, UN SDGs and Planetary Health Quadruple Aims, which expand the traditional Triple Aim for health system design by integrating environmental sustainability alongside improved patient outcomes, population health, and cost efficiency. Future research should explore trust in frontline workers, reasons for consulting informal carers, links between makeshift housing and TB, and integrating ecological determinants into existing frameworks. Full article

(1) The beach areas of Galveston, Texas, USA are heavily used for recreational activities and often experience elevated fecal indicator bacteria levels, representing a potential threat to ecosystem services, human health, and tourism-based economies that rely on suitable water quality. (2) During the span of 15 months (March 2022–May 2023), water samples that exceeded the U.S. Environmental Protection Agency-accepted alternative Beach Action Value (BAV) for enterococci of 104 MPN/100 mL were analyzed via microbial source tracking (MST) through quantitative polymerase chain reaction (qPCR) assays. The Bacteroides HF183 and DogBact as well as the Catellicoccus LeeSeaGull markers were used to detect human, dog, and gull fecal sources, respectively. The qPCR MST data were then utilized in a quantitative microbial risk assessment (QMRA) to assess human health risks. Additionally, samples collected in July and August 2022 were sequenced for 16S rRNA and matched with fecal sources through the Bayesian SourceTracker2 program. (3) Overall, 26% of the 110 samples with enterococci exceedances were positive for at least one of the MST markers. Gull was revealed to be the primary source of identified fecal contamination through qPCR and SourceTracker2. Human contamination was detected at very low levels (<1%), whereas dog contamination was found to co-occur with human contamination through qPCR. QMRA identified Campylobacter from canine sources as being the primary driver for human health risks for contact recreation for both adults and children. (4) These MST results coupled with QMRA provide important insight into water quality in Galveston that can inform future water quality and beach management decisions that prioritize public health risks. Full article

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disorder characterized by the destruction of insulin-producing pancreatic beta cells, resulting in lifelong insulin dependence. While genetic susceptibility—particularly human leukocyte antigen (HLA) class II alleles—is a major risk factor, accumulating evidence implicates viral infections as potential environmental triggers in disease onset and progression. This narrative review synthesizes current findings on the role of viral pathogens in T1DM pathogenesis. Enteroviruses, especially Coxsackie B strains, are the most extensively studied and show strong epidemiological and mechanistic associations with beta-cell autoimmunity. Large prospective studies—including Diabetes Virus Detection (DiViD), The environmental determinans of diabetes in the young (TEDDY), Miljøfaktorer i utvikling av type 1 diabetes (MIDIA), and Diabetes Autoimmunity Study in the Young (DAISY)—consistently demonstrate correlations between enteroviral presence and the initiation or acceleration of islet autoimmunity. Other viruses—such as mumps, rubella, rotavirus, influenza A (H1N1), and SARS-CoV-2—have been investigated for their potential involvement through direct cytotoxic effects, immune activation, or molecular mimicry. Interestingly, certain viruses like varicella-zoster virus (VZV) and cytomegalovirus (CMV) may exert modulatory or even protective influences on disease progression. Proposed mechanisms include direct beta-cell infection, molecular mimicry, bystander immune activation, and dysregulation of innate and adaptive immunity. Although definitive causality remains unconfirmed, the complex interplay between genetic predisposition, immune responses, and viral exposure underscores the need for further mechanistic research. Elucidating these pathways may inform future strategies for targeted prevention, early detection, and vaccine or antiviral development in at-risk populations. Full article

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

The anodic behavior of zinc electrodes is important for energy storage, corrosion protection, electrochemical processing, and other practical applications. This study investigates the anodic galvanostatic polarization of zinc foil and bulk electrodes in aqueous oxalic acid solutions, revealing significant differences in their electrochemical behavior, particularly in induction period durations. The induction period’s duration depended on electrolyte concentration, current density, and temperature. Notably, the temperature dependence of the kinetics exhibited contrasting trends: the induction period for foil electrodes increased with temperature, while that of bulk electrodes decreased. Chemical analysis and polishing treatment comparisons showed no significant differences between the foil and bulk electrodes. However, Scanning Electron Microscopy (SEM) observations of samples anodized at different temperatures, combined with Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) analysis of dissolved electrode material, provided insights into the distinct anodic behaviors. X-ray Diffraction (XRD) studies further confirmed these findings, revealing a crystallographic orientation dependence of the anodic behavior. These results provide detailed information about the electrochemical properties of zinc electrodes, with implications for optimizing their performance in various applications. Full article