Search Results (8,364)

The purpose of this research is to examine the impact of industrial noise levels ranging from 74 to 76 dB—which fall below the legal limit of 80 dB—on complex physiological and psychological stress responses of workers. The study employs a multimodal approach, combining objective acoustic measurements according to the EN ISO 9612:2009 standard with the monitoring of physiological parameters, specifically galvanic skin response (GSR), blood pressure, and heart rate, complemented by subjective assessments through questionnaires. Key findings revealed that the C-weighted noise level L_CEX (r = 0.67) demonstrates a stronger correlation with stress response and heart rate (r = 0.66) than the standard A-weighted filter (L_AEX). Although noise explains only approximately 4% of heart rate variability (R² ≈ 0.04), providing indirect support for the multifactorial nature of stress, subjectively, 71% of workers expressed a need for noise reduction due to accompanying symptoms such as headaches and tinnitus. The highest level of cardiovascular load was consistently recorded at workstation SZ7. The results suggest that industrial noise may represent a contributing factor to psychosocial risk even at levels below regulatory limits. The results provide indirect support for the hypothesis that low-frequency noise (LFN) components play a role in psychosocial stress, suggesting the need for further investigation using detailed spectral analysis in the prevention of industrial psychosocial diseases. Full article

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. Full article

Oil leakage in oil-immersed power transformers poses a significant threat to grid reliability, potentially causing severe electrical accidents and environmental pollution if not detected in time. Detecting oil leakage outdoors, however, remains challenging due to the impact of weather conditions such as fog, humidity, and rain, which obscure the leakage signs and complicate real-time detection. To address these challenges, we propose a solution that integrates infrared thermal imaging with a CNN-SVM hybrid architecture. The core of this approach lies in shifting from traditional Softmax-cross-entropy-based empirical risk minimization (ERM) to maximum-margin-based structural risk minimization (SRM). A fully fine-tuned MobileNetV3 transforms low-contrast, boundary-softened infrared thermal images—often affected by fog and moisture—into a more discriminative high-dimensional feature space, where positive and negative samples become linearly separable. This is followed by replacing Softmax with a linear SVM and using hinge loss to enforce a margin constraint, which maximizes the classification margin and improves robustness to input perturbations. Experimental results show that our proposed method outperforms all compared models, achieving an accuracy of 0.990, significantly higher than ResNet50_BCE (0.908), EfficientNetB0 (0.925), YOLOv11n-CLS (0.930), and ViT (0.929). In terms of F1-Score (0.989) and AUC (0.995), MobileNetV3-SVM also demonstrates excellent performance, ensuring outstanding classification capability. Additionally, the model achieves an inference latency of only 6.3 ms, demonstrating excellent real-time inference performance, highlighting its potential for transformer oil monitoring applications. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. Full article

The corrosion behavior and mechanism of Q235 steel during a two-year exposure to the subtropical marine atmospheric environment on an offshore platform in the East China Sea were investigated in this study. Methods including corrosion weight loss measurement, macro/micro-morphological observation (using a digital camera, SEM, and 3D-CLSM), composition analysis (XRD and XPS), and electrochemical tests (EIS and Tafel polarization curves) were employed to systematically examine corrosion kinetics, rust layer evolution, and electrochemical performance. The results indicated that the corrosion rate of Q235 steel initially increased and subsequently decreased with prolonged exposure, with the atmospheric corrosivity reaching CX level as defined (according to the ISO 9223 standard). The corrosion products transitioned from an early-stage rust layer predominantly consisting of γ-FeOOH to a later-stage layer primarily composed of α-FeOOH and Fe₃O₄. XPS analyses revealed that both the α*/γ* ratio and the Fe(II)/Fe(III) ratio increased over time, demonstrating a progressive improvement in the protective properties of the rust layer. The polarization resistance of the rust layer gradually rose, while the corrosion current density declined significantly, further confirming the enhanced stability and protective performance of the rust layer following long-term exposure. Chloride ions accumulated at defects within the rust layer, inducing local acidification, which played a key role in promoting the initiation and propagation of pitting corrosion. This study elucidated the corrosion behavior and mechanism of Q235 steel in the marine atmospheric environment of the East China Sea. Despite the increase in exposure time from 6 to 24 months, during which the electrochemical stability of the rust layer enhanced over time, it failed to prevent the initiation and propagation of severe localized corrosion—an issue of critical importance for load-bearing structures. The findings provide important theoretical and data support for service-life assessment and corrosion protection design of offshore photovoltaic steel structures. Full article

To address the challenges of rapid water loss and insufficient long-term inhibition efficiency of conventional inhibitors in the high-temperature environments of deep goafs, a novel, environmentally friendly Deep Eutectic Inhibitor (DEI) was synthesized. This DEI utilizes citric acid (Ca) and proline (Pr) as the hydrogen bond donor and acceptor, respectively, with ascorbic acid (VC) and propyl gallate (PG) serving as antioxidants. A moisture retention evaluation model based on Fick’s law of diffusion was established to systematically investigate the liquid-domain stability of the DEI across a temperature range of 30 °C to 120 °C. The results demonstrate that the DEI exhibits superior moisture retention capabilities under high-temperature conditions, with the relative moisture retention peaking in the 80–110 °C range. Mechanistically, the formation of a robust hydrogen bond network effectively counteracts moisture evaporation driven by thermal kinetic energy. Furthermore, the DEI demonstrated significant inhibition effects on four coal samples with varying degrees of metamorphism. Tests on oxidative heat release characteristics revealed that DEI treatment delayed the initial oxidation temperature of the coal. Kinetic analysis further indicated that during the critical oxidation stage (200–300 °C), the apparent activation energy of the treated coal samples increased by 10.28–18.9 kJ/mol, effectively suppressing the spontaneous combustion process. This study contributes to the development of high-efficiency and eco-friendly fire prevention materials for coal mines. Full article

Laboratory asset maintenance in higher education institutions often suffers from inefficiencies due to incomplete data and reactive maintenance practices. We designed a radio frequency identification (RFID)-based information system that supports preventive maintenance and decision-making for laboratory asset management. Utilizing the rapid application development method, the system was developed through iterative prototyping and stakeholder engagement. The system integrates RFID-based asset identification with a web-based interface for real-time monitoring and log management. A decision-support module was also implemented, allowing stakeholders to prioritize maintenance tasks based on asset age, repair frequency, and usage patterns. Evaluation results of user acceptance testing showed an average score of 82%, indicating strong usability and relevance. The results demonstrate that integrating RFID with decision-support features significantly improve maintenance planning, reduce operational risk, and optimize resource allocation in academic laboratory environments. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

Network Intrusion Detection Systems (NIDS) play a crucial role in protecting network environments against cyberattacks. However, traditional NIDS rely heavily on predefined attack signatures, which limits their ability to detect zero-day attacks. Although machine learning-based intrusion detection techniques have been widely adopted in Network Intrusion Prevention Systems (NIPS), publicly available network traffic datasets often suffer from severe class imbalance, leading to biased learning and degraded detection performance. To address this issue, this study proposes data augmentation framework based on a 3D-GAN (Three-Discriminator Generative Adversarial Network). The proposed architecture integrates an autoencoder, a CNN (Convolutional Neural Network), and an LSTM (Long Short-Term Memory) network as parallel discriminators to capture the statistical, spatial, and temporal characteristics of network traffic. By jointly optimizing multiple discriminator losses, the framework enhances training stability and generates high-quality synthetic samples. Experiments were conducted on the CIC-UNSW-NB15 dataset using Random Forest-, XGBoost (eXtreme Gradient Boosting)-, and BiGRU (Bidirectional Gated Recurrent Unit)-based classifiers. Two augmented datasets were constructed to address class imbalance, containing approximately 100,000 and 350,000 samples, respectively. Among them, Dataset 2, augmented using the proposed 3D-GAN, demonstrated the most significant performance improvement. Compared to the original imbalanced dataset, the XGBoost classifier trained on Dataset 2 achieved approximately a 4% increase in both accuracy and F1-score, while reducing the false positive rate and false negative rate by approximately 3.5%. Furthermore, the optimal configuration attained an F1-score of 0.9816, indicating superior capability in modeling complex network traffic patterns. Overall, this study highlights the potential of GAN-based data augmentation for alleviating class imbalance and improving the robustness and generalization of intrusion detection systems. Full article

Secure cross-domain UAV authentication is challenging because identity verification alone is insufficient to guarantee safe operation. In many UAV applications, it is equally critical to verify that a UAV is currently located within an authorized geographic region. Existing approaches often expose precise GPS coordinates, rely on static identifiers that enable tracking, or fail to guarantee the freshness and authenticity of location evidence. These weaknesses allow replay, location spoofing, and trajectory inference attacks, especially in multi-domain environments. To address these limitations, we propose PrivLocAuth, a zero-knowledge-based cross-domain UAV authentication protocol that enforces geofence restrictions without revealing actual locations. In PrivLocAuth, UAVs encode their current coordinates into fresh Pedersen commitments, which are attested by the home Local Domain Server (LDS) using short-lived Schnorr signatures. Based on these attested commitments, UAVs generate Bulletproof range proofs to demonstrate compliance with cross-domain server-defined geofences. This design ensures that UAVs operate within authorized airspace while preserving strong location privacy. PrivLocAuth further incorporates a lightweight elliptic curve cryptography (ECC) and Schnorr signature-based credential framework that enables unlinkable authentication across-domains, preventing session correlation and identity tracking. Formal security analysis demonstrates resistance to impersonation, replay, geofence-bypass, and linkage attacks. Experimental evaluation shows low computational latency and minimal communication overhead, confirming the protocol’s suitability for resource-constrained UAV platforms operating in dynamic cross-domain environments. Full article

Real-time monitoring of wire bow is critical for ensuring wafer quality and preventing wire breakage in diamond multi-wire sawing (MWS). However, the deployment physical sensors in industrial MWS environments is hindered by severe sludge contamination, limited installation space, and high maintenance costs. To address these challenges, this paper proposes a novel data-driven soft sensor framework utilizing machine learning methods to predict wire bow based on readily accessible process data. A feature engineering pipeline, combining variance thresholding and correlation analysis, is established to identify key process variables. Subsequently, six representative ML algorithms are systematically evaluated, with eXtreme Gradient Boosting (XGBoost) optimized via two-stage hyperparameter optimization emerging as the superior model. Experimental results from an industrial MWS machine demonstrate that the proposed model achieves a coefficient of determination ($R^2$) of 0.992 and a mean absolute error (MAE) of 0.116 mm. Furthermore, the prediction is also extended to spatially distributed positions (head, middle, and tail) of the wire web. Finally, SHAP (SHapley Additive exPlanations) is utilized to elucidate the mechanical dependencies. This work provides a reliable and low-cost solution for wire bow monitoring during the MWS process. Full article

Neltuma juliflora (Sw.) Raf. (syn. = Prosopis juliflora (Sw.) DC.) is among the world’s most aggressive woody invaders, yet its ecological impacts remain poorly quantified in hyper-arid environments, where soils are calcareous and ecosystems recover slowly from disturbance. In this study, we tested two hypotheses: (1) the presence of N. juliflora changes native plant diversity, as well as soil and key physicochemical properties in hyper-arid Qatar, and (2) agricultural farms act as primary sources of N. juliflora invasion. Using a comparative observational design across 62 sites (45 invaded and 17 non-invaded), we applied a generalised additive model (GAM) and a generalised linear mixed model (GLMM) to quantify invasion drivers and the impact of invasion on perennial species diversity, respectively. Additionally, we used the Wilcoxon rank-sum test to compare the soil properties in the invaded and non-invaded sites. Our results indicate that N. juliflora is positively associated with farms, with the probability of occurrence declining by ca. 20% for each kilometre farther away from agricultural farms. This pattern suggests substantial propagule pressure from agricultural farms. Perennial species richness declined from 7.5 species at 0% N. juliflora cover to 4.8 species at full cover (36% reduction). Invaded sites were characterised by higher amounts of coarse sand (16%); reduced silt–clay fractions (5%); and elevated salinity indicators, including electrical conductivity (0.744 dS m⁻¹) and total dissolved solids (476 mg L⁻¹), while major N–P–K pools remained unchanged. These findings demonstrate measurable invasion-related changes in soil conditions and native perennial diversity in hyper-arid ecosystems and highlight the role of agricultural land use as a key driver of biological invasion. From a sustainability perspective, early detection, targeted control near agricultural and grazing zones, and integration of invasive species monitoring into land-use planning frameworks are essential to prevent further ecosystem degradation, protect biodiversity, and enhance the resilience of desert landscapes under increasing climate and land-use pressures. Full article

Oropouche virus (OROV) is an emerging arthropod-borne virus in the Americas that has evolved from a pathogen historically restricted to forest environments into an increasingly important regional and international public health concern. Despite decades of documented circulation, the true burden of OROV infection remains substantially underestimated, largely because of frequent misdiagnosis and the high proportion of asymptomatic or subclinical infections. This review synthesizes current evidence on the historical emergence, epidemiology, transmission dynamics, and clinical features of OROV, with a particular focus on populations at increased risk due to biological susceptibility, environmental exposure, and limited access to healthcare. Drawing on seroepidemiological data, we demonstrate that OROV transmission is far more widespread than routine surveillance suggests and examine how factors such as age, pregnancy, immune status, underlying health conditions, occupational exposure, and healthcare accessibility interact to influence disease risk and detection. Although multiple vaccine platforms have shown promise in preclinical studies, progress toward clinical development remains constrained by limited immunological evidence, shortcomings of available animal models, diagnostic uncertainty, and structural barriers in endemic regions. We propose that future OROV vaccine development prioritize population-specific needs rather than focusing solely on technological platforms, and that effective prevention will require integrating vaccination with strengthened surveillance, improved diagnostics, and equitable delivery systems. Full article

The Bayan Obo Mining District, recognized as the largest rare-earth resource base worldwide, has experienced significant surface instability due to intensive mining and large-scale dumping activities. To address the challenges posed by complex geological conditions and mining-induced disturbances, this study employs dual-ascending Sentinel-1A C-band Synthetic Aperture Radar (SAR) datasets (Path 11 and Path 113) and applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to retrieve time-series deformation along the line-of-sight (LOS) direction for each track. Through temporal normalization and spatial matching, paired LOS observations from the two tracks were established. Based on the SAR observation geometry and under the assumption that the north–south component is negligible, a LOS projection model was constructed and a geometric decomposition was performed to derive the east–west and vertical two-dimensional deformation fields. The results indicate that the study area is generally stable, while significant subsidence occurs in the northern pit and adjacent waste-dump zones, with local maximum rates approaching 50 mm/year, predominantly controlled by the vertical component. The two-dimensional deformation analysis reveals that vertical displacement dominates surface motion, whereas east–west movement shows smaller amplitudes but clear directional concentration. In particular, the east–west slopes exhibit slightly higher velocities, suggesting a lateral adjustment tendency along this direction, likely related to the overall east–west geometric configuration of the open-pit and waste-dump areas. Time-series observations further reveal that precipitation-related surface deformation occurs with an approximate two-month delay, reflecting the hydrological–mechanical coupling processes of rainfall infiltration, pore-water pressure propagation, and dump-material consolidation. Overall, this study reveals the multi-dimensional deformation characteristics and precipitation-driven stage-wise response of the mining area, demonstrating the effectiveness of the dual-ascending SBAS-InSAR for two-dimensional deformation monitoring in highly disturbed environments, and providing a scientific basis for surface stability assessment and geohazard prevention. Full article

Mineral oil hydrocarbons (MOH) are increasingly reported in agri-food systems, but their presence in dairy production is not yet fully characterized. This study investigated the occurrence of mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) in feed and milk collected from three dairy farms with different production conditions, with the aim of supporting a system-wide interpretation of contaminant presence. Samples were analyzed using confirmed chromatographic methods, and the results were descriptively evaluated to express variability both within and between farms. Both feed and milk samples showed heterogeneous contamination patterns, highlighting the influence of environmental exposure, technological activities and biological variations, rather than a direct linear transfer. To help the interpretation, a conceptual framework linking environment, feed, animals and milk was proposed. From a sustainability point of view, the results highlight the importance of preventive monitoring and farm-level management strategies to reduce contaminant pressure in dairy production. Overall, the study furnishes empirical evidence that contributes to a broader understanding of contaminant occurrence and supports integrated approaches for food safety and sustainable dairy production. Full article

As network slicing gains traction in cloud computing environments, efficient management and orchestration systems are required to realize the benefits of this technology. These systems must enable dynamic provisioning and resource optimization of virtualized services spanning multiple network slices. Nevertheless, the common resource overprovisioning practice implemented by service providers leads to the inefficient use of resources, limiting the ability of Mobile Network Operators (MNOs) to rent new network slices to more vertical customers. Hence, efficient resource allocation mechanisms are essential to achieve optimal network performance and cost-effectiveness. This paper proposes a predictive model for network slice resource optimization based on resource sharing between Virtualized Network Functions (VNFs). The model employs deep learning models based on Long Short-Term Memory (LSTM) and Transformers for CPU resource usage prediction and a reactive algorithm for resource sharing between VNFs. The model is powered by a telemetry system proposed as an extension of the 3GPP network slice management architectural framework. The extended architectural framework enhances the automation and optimization of the network slice lifecycle management. The model is validated through a practical use case, demonstrating the effectiveness of the resource sharing algorithm in preventing VNF overload and predicting resource usage accurately. The findings demonstrate that the sharing mechanism enhances resource optimization and ensures compliance with service level agreements, mitigating service degradation. This work contributes to the efficient management and utilization of network resources in 5G networks and provides a basis for further research in network slice resource optimization. Full article