Search Results (6,793)

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area. Full article

This paper presents the electrochemical behavior of stainless steel 304 (SS304), a material often utilized in the wine industry, in the presence of varying concentrations of ascorbic acid (AcAS), introduced in a neutral solution (Na₂SO₄ 0.25 M + 12% (v/v) EtOH). The experimental part of this paper included potentiodynamic polarization and chronoamperometry techniques to evaluate the influence of ascorbic acid on the corrosion processes in the test solutions. Electrochemical impedance spectroscopy (EIS) has been used to investigate the charge transfer at the interface and the formation of a protective film in the absence and presence of AcAS. The Tafel method was employed to determine the kinetic parameters of the corrosion process studied. Additionally, several models of adsorption isotherms were applied to describe the interactions between AcAS and the stainless steel surface, with the Freundlich and Dubinin–Radushkevich isotherms demonstrating the most robust correlation, based on the R² correlation coefficients. Quantum chemical calculations (DFT) were also performed to clarify the molecular mechanism via which AcAS functions as an eco-friendly corrosion inhibitor in winemaking-related environments. Full article

Federated learning, a decentralized machine learning framework, allows multiple participants to jointly train models while keeping their raw data local and unshared. Nevertheless, during the exchange of model updates, the communicated information can still introduce privacy vulnerabilities and potentially result in the exposure of user data. Over the past few years, differential privacy methods have been broadly incorporated into federated learning frameworks to strengthen the protection of sensitive data. Nevertheless, the noise required to satisfy differential privacy guarantees often causes significant degradation in model performance. Prior studies have typically employed a fixed noise-injection strategy following gradient clipping. Although such methods provide privacy protection, they overlook the varying importance of different gradient dimensions, resulting in noise being injected into unimportant or redundant parameters, thereby causing unnecessary performance loss. To address these limitations, we propose an adaptive differentially private federated learning scheme with importance-based gradient updates (AIGU-DPFL). Specifically, we focus on coordinates with high information content and introduce an adaptive noise injection mechanism, which perturbs gradient updates to satisfy differential privacy guarantees while dynamically controlling noise intensity, thereby achieving sparse and noise-effective gradient updates. AIGU-DPFL markedly enhances the training effectiveness of federated learning models. Comprehensive evaluations conducted on real-world datasets indicate that the proposed method achieves superior performance compared to existing differentially private federated learning techniques. Full article

Healthcare-associated infections (HAIs) remain a significant global challenge, affecting approximately 7% of patients in developed countries and over 10% in developing regions, according to the World Health Organization. Medical textiles, particularly hospital bed linens and pillowcases, play a critical role in the transmission of pathogenic microorganisms due to their porous structure and moisture-retaining properties, which support microbial survival and proliferation, including bacteria such as Staphylococcus aureus and Escherichia coli. Conventional disinfection methods, including laundering and thermal treatments, provide only temporary protection, leading to rapid recontamination during use. In recent years, various antimicrobial agents and functionalization techniques have been developed to impart long-lasting antiseptic properties to textile materials. However, these approaches differ significantly in terms of antimicrobial efficiency, durability, cost-effectiveness, and environmental impact, making the selection of optimal strategies challenging for practical healthcare applications. This review provides a comprehensive comparative analysis of antimicrobial agents used in healthcare textile functionalization, including metal-based nanoparticles, organic compounds, and bio-based materials. In addition, it evaluates key modification methods such as coating, padding, and in situ synthesis, with particular emphasis on their influence on antimicrobial performance, wash durability, and practical applicability. Furthermore, this review discusses major challenges associated with the use of antiseptic coatings, including toxicity, environmental concerns, and economic limitations. Based on the analysis, promising directions for the development of safer, cost-effective, and durable antimicrobial textile systems are highlighted, offering valuable insights for future research and real-world healthcare applications. Full article

Gradient Boosted Decision Trees (GBDTs) are popular for their strong predictive performance. However, in domains like finance and healthcare, data are often distributed across organizations, making collaborative model training challenging due to privacy concerns. Vertical federated learning (VFL) enables such collaboration when data are split by features, but many existing methods focus on protecting raw data while exposing sensitive model information, such as gradients and Hessians—especially to the label-owning party. Techniques like Homomorphic Encryption and Secret Sharing help, but often rely on trusted or privileged parties and may still leak intermediate statistics. To address this, we propose MPC-XGB , a privacy-preserving framework for training XGBoost under VFL with an honest-but-curious threat model. It uses secure three-party computation with Replicated Secret Sharing, distributing data across non-colluding servers and performing all computations on shares. This ensures that raw data, labels, and model statistics remain hidden, while supporting both secure training and prediction. Experiments show that MPC-XGB achieves strong performance (0.93 accuracy, 0.82 AUC), comparable to that of existing methods, with improved privacy guarantees. Full article

Face recognition technologies are increasingly deployed in transport infrastructures to improve efficiency and security, but they raise significant privacy and data protection concerns. This study reviews how privacy-preserving face recognition techniques can address these challenges in real-world settings. Using a systematic literature review approach, the paper analyses research across technical, operational, and governance perspectives. The findings show that while advanced methods such as encryption, federated learning, and de-identification can reduce data exposure, they are rarely implemented in operational systems, which tend to prioritize performance and scalability. At the same time, governance-focused studies emphasize issues such as proportionality, accountability, and fundamental rights, often without clear links to technical solutions. Overall, the review highlights a fragmented landscape and a gap between research and practice, underscoring the need for integrated approaches that align privacy-preserving techniques with practical deployment constraints and regulatory requirements. Full article

Biometric sensing systems enable accurate identity recognition using unique physiological traits. These systems can be unimodal (single trait) or multimodal (multiple traits, such as iris and fingerprint). Biometric templates, digital representations of these traits, enhance security over traditional methods but are vulnerable to attacks. Unlike passwords, compromised templates cannot be replaced, necessitating robust protection. Various security schemes exist, including cancellable biometrics, biometric cryptosystems, sensing technology, and biometrics in the encrypted domain. Cancellable biometrics apply transformations, such as biometric salting, to obscure the original data. Biometric cryptosystems integrate cryptographic techniques, including key generation and key binding, to enhance security. Biometrics in the encrypted domain, such as homomorphic encryption, ensures data remains encrypted during storage and computation. This survey focuses on the fuzzy vault method, a key-binding biometric cryptosystem. It analyses its applications, security performance, and associated challenges across different domains. By analysing advancements in fuzzy vault mechanisms, this paper provides insights into enhancing sensor-based biometric security. The study aims to serve as a reference for researchers exploring secure and efficient biometric authentication methods, ensuring robust protection against unauthorised access while maintaining the integrity and usability of biometric data in real-world applications. Full article

The safety of workers in industrial settings is ensured through the correct use of Personal Protective Equipment (PPE). The use of such equipment can be monitored using Deep Learning (DL). Federated Machine Learning (FML) is a technique that can be used in this context to preserve the privacy of sensitive information and provide explainability for the models adopted. Explainability techniques are an essential resource for interpreting the classification performed by the model. In this regard, this study aims to evaluate, through the adoption of specific similarity indices, the robustness and consistency of the explainability algorithms adopted to identify the areas of the images that are decisive for PPE classification. The dataset consists of 1600 real images representing work environments, in which staff are portrayed both with and without Personal Protective Equipment; specifically, there are workers wearing helmets, workers wearing reflective vests, workers wearing both devices and, finally, workers without any PPE. SSIM, VIF and SCC are the most relevant indices involved in the study. In the experimental phase, their mean values stand at 0.99, 0.96 and 0.96 for the intra-client study, and 0.96, 0.91 and 0.71 in the inter-client analysis. Full article

Using low-pressure cold spray technique, Fe-Al composite coatings with different Al contents were applied to the surface of 45 steel to improve its corrosion resistance in chloride-containing settings. The microstructure, mechanical characteristics, and electrochemical corrosion behavior of the coatings were thoroughly examined in relation to the Al content (2, 4, 6, and 8 wt.%). The findings show that the microhardness of the composite coating decreases monotonically (from 157.98 HV to 99.29 HV) as the Al content rises because of the increased proportion of the soft phase; in contrast, the porosity and corrosion current density show a pattern of first decreasing and then increasing. The coating porosity was reduced to a minimum (1.37%) when the Al concentration reached 6 wt.% because the soft Al particles experienced enough plastic flow to fill the holes in the hard Fe matrix. The 6Al composite coating demonstrated the best electrochemical protection performance in a 3.5 wt.% NaCl solution, with the lowest corrosion current density (2.237 × 10⁻⁴ A/cm²) and the strongest interfacial charge transfer resistance. The synergistic corrosion protection mechanism comprising significantly densified physical shielding and microgalvanic sacrificial anode protection by the active Al phase was clarified in this study. The ideal composition ratio for this system was determined to be 6 wt.% Al by carefully matching the coating’s mechanical load-bearing needs with long-term corrosion prevention goals. Full article

Medium-voltage (MV) networks are increasingly relying on grid-forming inverter-based resources (IBRs) due to the worldwide transition towards renewable energy sources. This transformation poses considerable challenges for traditional protection schemes that were initially developed for systems powered by inertia-based generation. Key challenges include the low and controlled contributions of fault current, two-way power flows, diminished system inertia, and swiftly changing transient behaviors. These elements weaken the effectiveness of standard protection methods such as overcurrent, distance, and differential protection schemes. A critical review of recent advancements in adaptive protection schemes, impedance-based techniques, virtual synchronous machines, and enhancements in inverter control is provided. However, despite these advancements, current solutions frequently lack validation in real-world scenarios, encounter difficulties in detecting high-impedance faults, and face scalability issues. There remains a demand for protection strategies that are resilient, coordinated, and specifically designed to address the distinct dynamics of MV systems dominated by grid-forming inverters. Full article

As China’s first World Heritage Mixed Property site, Mount Tai enjoys international renown, with its historic buildings serving both as the central carriers of its cultural heritage and as significant tourism resources. Existing studies have predominantly emphasized the form, scale, and construction techniques of individual buildings or architectural complexes, while less attention has been given to the overall spatial pattern shaped by the interplay of natural and social environments and to the mechanisms underlying its formation. Taking the administrative area of Tai’an City as the study extent, this research selects 451 officially protected historic buildings, classified by period and type, and employs GIS-based spatial analysis and statistical methods to examine their spatiotemporal distribution patterns and influencing factors. The results indicate the following. (1) The temporal distribution exhibits an И-shaped fluctuation pattern, with ancient architecture and ancient sites together accounting for nearly 60% of the total and constituting the core resource categories. This distribution curve is shaped jointly by preservation conditions, social stability, and heritage designation preferences. (2) The spatial distribution displays a pronounced clustering pattern, with the kernel density core shifting over forty kilometers from southwest to northeast, generating an evolutionary trajectory from Dawen River basin agglomeration to Mount Tai mountain belt agglomeration. (3) The overall pattern is associated with both natural and anthropogenic factors. During the early stages, natural conditions such as hydrology and topography provided foundational constraints, whereas in later periods, human factors, including fengshan ritual culture, religious activities, economic development, and institutional governance, exhibit increasingly apparent associations with the distribution pattern. Based on these findings, this study proposes a strategic spatial framework comprising one cultural pilgrimage ring and four thematic corridors, which translates the spatial analytical results into planning implications for the regional integration of historic building resources, and discusses differentiated conservation strategies, thereby providing an analytical foundation and a reference pathway for the dissemination of Mount Tai culture and the sustainable development of heritage tourism. Full article

The city of Veracruz preserves buildings mainly constructed during the 16th and 17th centuries, where carved madreporic coral was used as ashlar and as a component in mortars. These historic structures, now part of Mexico’s built heritage, show various degrees of deterioration caused by erosion and prolonged exposure to environmental elements. Restoration using original materials is currently nearly impossible due to ecological restrictions protecting coral reefs. In this context, and under the principles of the tailor-made technique, the present research revisits physico-mechanical and chemical studies conducted on the corals used in the construction of one of the most representative buildings in the city. The results were compared with those obtained from the formulation of experimental mortars using readily available materials—such as air lime, siliceous aggregates, and calcium carbonate—with the aim of reproducing the physical, mechanical, and chemical properties observed in the original corals. Laboratory tests allowed evaluation of their compatibility and performance, seeking to develop alternative materials that enable conservation interventions without compromising the integrity of the base material or the historic structures. The design of mortars is intended to be used in the restoration processes of buildings that are part of the built historical heritage. This is the starting point for understanding the characteristics of the mortar and its compatibility with the substrate, which could be used for repairing stone blocks and for preparing new mortars for masonry and plastering, since research on restoration mortars has largely overlooked this type of building with coral masonry due to its rarity. Therefore, this research is of particular interest. The mixtures formulated with calcareous sand were the most compatible with the reference coral material, while those made with silica sand exhibited properties superior to the corals, and marine sands showed very poor behavior, potentially compromising the integrity of the buildings. In physical–mechanical tests, formulations that include calcareous sand and silica sand (2 mm) demonstrated behavior closest to that of coral, consistent with chemical analysis results, where mortars formulated with calcareous sand registered the highest contents of CaO and portlandite. Mercury intrusion porosimetry indicated that the mortar formulated with silica sand (2 mm) has a porosity only 4.07% lower than that of the coral, while mortars formulated with calcareous sand and lime paste are between 11.17% and 16.87% lower. Therefore, one of the mixtures that stands out as the best option due to its similarity in physical–mechanical and chemical results is the composite that is not found at the extremes of the results obtained in the various tests carried out. The use of calcareous sand, as previously mentioned, enhances its behavior and affinity with the coral masonry, as demonstrated in the tests. Full article

This study investigates the spatial distribution of ambient dose equivalent rates (ADER) on Avala and Kosmaj mountains, two protected landscapes located within the territory of the City of Belgrade, Serbia. Both sites, characterized by rich biodiversity and cultural heritage, were analyzed to assess their radiological safety and suitability for outdoor recreation. In mid-October 2025, in situ measurements were conducted at 42 sampling points using the Radex RD1503+ GM counter. The recorded values ranged from 0.085 to 0.2 µSv/h, remaining below the recommended safety threshold of 0.2 µSv/h. To visualize the gamma dose spatial variability, all field data were georeferenced and processed in QGIS 3.28.10 using the Inverse Distance Weighting (IDW) interpolation method. Integration of GIS and Remote Sensing techniques enabled the correlation between gamma radiation patterns, land cover, and elevation gradients derived from digital elevation models (DEMs). The comprehensive GIS-based approach confirms that Avala and Kosmaj maintain low natural background radiation levels comparable to global averages for similar geomorphological settings, and therefore are safe and suitable for sports, tourism and recreation. The applied combination of field dosimetry, Remote Sensing, and geostatistical modeling provides a valuable framework for continuous environmental monitoring and sustainable landscape management in protected mountainous landscapes in Central Serbia. Full article

Background: Eosinophilic granulomatosis with polyangiitis (EGPA), formerly Churg–Strauss syndrome, is a rare necrotizing vasculitis characterized by asthma, eosinophilia, and systemic granulomatosis vasculitis. Perioperative risk is primarily driven by airway hyperreactivity, potential cardiac disease, chronic immunosuppressive therapy, and reported alterations in plasma cholinesterase activity. Evidence specifically addressing anesthetic management remains scarce and largely limited to case-based reports. Methods: A focused narrative review was conducted by searching MEDLINE (via PubMed), Scopus, and Embase from inception to January 2026 for publications reporting perioperative anesthetic management in patients with EGPA/Churg–Strauss syndrome. Case reports and case-based descriptions providing explicit anesthetic details were qualitatively synthesized. Results: Available evidence consists predominantly of isolated case reports across heterogeneous surgical settings, including ENT, abdominal, orthopedic, ambulatory, pediatric, and rare cardiac procedures. Recurring perioperative principles include optimization of bronchial disease and continuation of inhaled therapy; minimization of airway stimulation and avoidance of histamine-releasing drugs; selection of induction agents preserving hemodynamic stability in the presence of myocardial involvement; preference for non-depolarizing neuromuscular blockade with quantitative monitoring (and consideration for sugammadex when appropriate); individualized corticosteroid management and multimodal, opioid-sparing analgesia, often supported by regional techniques. Conclusions: In the absence of dedicated perioperative guidelines, anesthetic care for EGPA should be individualized based on clinical phenotype and organ involvement. A structured approach targeting airway protection, cardiovascular stability, safe neuromuscular management, and opioid-sparing analgesia may represent a pragmatic risk-mitigation framework. These considerations are illustrated by an institutional experience in mitral valve surgery. Full article

The disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and protect institutional stability. The main objective of this study was to develop a supervised machine learning model for estimating the risk of disqualification among registered professionals based on historical and contextual variables. An empirical, applied, and quantitative study was conducted by analyzing more than 5.7 million financial records corresponding to 27,964 registered professionals. Multiple supervised classification algorithms, including ensemble models such as CatBoost and XGBoost, were evaluated using stratified cross-validation and class-balancing techniques to address the substantial imbalance in the data. The results indicated that CatBoost performed best (F1-score = 57.96%; AUC = 0.72), whereas XGBoost showed greater stability across cross-validation folds. In conclusion, the model developed supports the timely identification of members at high-risk of disqualification, enabling the implementation of early warning systems and proactive institutional financial management strategies. Full article