Search Results (320)

Background and Aim: Food hygiene is fundamental to public health, ensuring safe and nutritious food free from contaminants, and is vital for economic development and sustainability. The Hazard Analysis and Critical Control Points (HACCP) system is a crucial tool for managing risks in food production. Despite global recognition of food safety’s importance, significant disparities exist, especially in Southern Italy, where diverse food production, tourism, and economic factors pose challenges to enforcing hygiene standards. This study evaluates non-compliance with food hygiene regulations within a Local Health Authority (LHA) in Calabria, Southern Italy, to inform effective public health strategies. Materials and Methods Authorized by the Food Hygiene and Nutrition Service (FHNS) of the LHA, the study covers January 2022 to December 2024, analyzing 579 enterprises with 1469 production activities. Inspections followed EC Regulation No. 852/2004, verifying the correct application of procedures based on the Hazard Analysis and Critical Control Points (HACCP) principles, including the operator’s monitoring of Critical Control Points (CCPs), and adherence to Good Hygiene Practices (GHPs). Non-compliances were classified by severity, and corrective and punitive actions were applied. Data were analyzed annually and across the full period using descriptive statistics and chi-squared tests to assess trends. Results: Inspection coverage increased markedly from 29.8% of production activities in 2022 to 62.5% in 2023, sustaining 62.0% in early 2024, exceeding the growth of new activities. Inspections were mainly triggered by RASFF alerts (22.4%), routine controls (20.0%), and verification of previous prescriptions (14.3%). The most frequent corrective measures were long-term prescriptions (28.6%), violation reports (22.9%), and short-term prescriptions (20.0%). Enterprises averaged 4.61 production activities, highlighting operational complexity. Conclusions: This study provides a granular analysis of food hygiene non-compliance within a Local Health Authority (LHA) in Southern Italy, to inform effective public health strategies. While official control data may be publicly available in some contexts, our research offers a unique, in-depth view of inspection triggers, non-compliance patterns, and corrective measures, which is crucial for understanding specific regional challenges. The analysis reveals that the prevalence of long-term prescriptions and reliance on RASFF alerts indicate systemic challenges requiring sustained interventions. Full article

CBRN incidents are characterized by high uncertainty in terms of agent identity, dissemination methods, and situational context. This unpredictability complicates effective and timely response, especially in the initial phase before specialist services arrive, and lays the burden of applying protection and response measures on members of civil society participating in the incident. This paper proposes a structured classification framework for CBRN hazards to address this gap, integrating key characteristics from existing systems such as the GHS (Globally Harmonized System), WHO (World Health Organization) biosafety levels, and radiological exposure guidelines. The system emphasizes properties relevant for first responders and non-specialists, including observable effects, exposure routes, and hazard endpoints such as toxicity, virulence, and radiation dose. The goal is to enable rapid hazard recognition, improve communication, and support situational decision-making in public security scenarios. Full article

Food contamination poses a significant global public health challenge, necessitating the accurate detection of hazardous substances within complex food matrices. Magnetic core–shell nanomaterials have emerged as critical materials for trace contaminant analysis due to their efficient magnetic separation capabilities, excellent adsorption performance, and tunable surface functionalities. By encapsulating magnetic cores with functional shells, these nanomaterials combine rapid magnetic responsiveness with advantageous shell properties, including target-specific recognition, enhanced dispersibility, colloidal stability, and high surface area. This enables a comprehensive detection approach encompassing target adsorption, rapid separation, and signal amplification. Magnetic core–shell nanomaterials have been effectively integrated with techniques including magnetic solid-phase extraction (MSPE), fluorescence (FL) assays, and lateral flow immunoassays (LFIAs), demonstrating broad applicability in food safety monitoring and detection. This review outlines synthesis strategies for magnetic core–shell nanomaterials, highlights their applications for food contaminant detection, and discusses future challenges and prospects in the field of food safety analysis. Full article

Constructing a probabilistic assessment method is the primary task and key step in liquefaction research. This paper presents a systematic investigation into liquefaction potential evaluation methods. Through a comparative analysis of three conventional assessment methods, we identify critical limitations in existing approaches regarding accuracy and adaptability. A probabilistic ANN model was developed using field-collected standard penetration test (SPT) data from 311 liquefaction case histories. The model demonstrates superior performance with an overall accuracy of 86.17%, achieving 83.33% and 90.00% recognition rates for liquefied and non-liquefied cases, respectively. Key metrics, including precision (91.84%), recall (83.33%), and F1-score (87.38%), indicate robust discriminative capability. Comparative studies confirm the ANN model’s advantages over traditional methods in terms of prediction reliability and operational practicality. The research outcomes offer significant value for improving current liquefaction hazard assessment protocols in geotechnical engineering practice. Full article

Traffic accidents caused by black ice have become a serious public safety concern due to their high fatality rates and the limitations of conventional detection systems under low visibility. Millimeter-wave (mmWave) radar, capable of operating reliably in adverse weather and lighting conditions, offers a promising alternative for road surface monitoring. In this study, six representative road surface conditions—dry, wet, thin-ice, ice, snow, and sludge—were experimentally implemented on asphalt and concrete specimens using a temperature and humidity-controlled chamber. mmWave radar data were repeatedly collected to analyze the temporal variations in reflected signals. The acquired signals were transformed into range-based spectra using Range-Fast Fourier Transform (Range-FFT) and converted into statistical features and graphical representations. These features were used to train and evaluate classification models, including Extreme Gradient Boost (XGBoost), Light Gradient-Boosting Machine (LightGBM), Convolutional Neural Networks (CNN), and Vision Transformer (ViT). While machine learning models performed well under dry and wet conditions, their accuracy declined in hazardous states. Both CNN and ViT demonstrated superior performance across all conditions, with CNN showing consistent stability and ViT exhibiting competitive accuracy with enhanced global pattern-recognition capabilities. Comprehensive robustness evaluation under various noise and blur conditions revealed distinct characteristics of each model architecture. This study demonstrates the feasibility of mmWave radar for reliable road surface condition recognition and suggests potential for improvement through multimodal sensor fusion and time-series analysis. Full article

Railway service interruptions and electrical hazards often arise due to bird nests concealed within the intricate, highly symmetric overhead catenary networks of high-speed lines. These nests are difficult to pinpoint automatically, not only because they are diminutive and often merge visually with the surroundings but also due to occlusions and the persistent lack of substantial labeled datasets. To address this bottleneck, this work presents the High-Speed Railway Catenary Nest Dataset (HRC-Nest), merging 800 authentic images and 1000 synthetic samples to capture a spectrum of scenarios. Building on the symmetry of catenary structures—where nests appear as localized asymmetries—the Symmetry-Aware Railway Nest Detection Framework (RNDF) is proposed, an enhanced YOLOv12 system for accurate and robust nest detection in symmetric high-speed railway catenary environments. With the A2C2f_HRAMi design, the RNDF learns from multi-level features by unifying residual and hierarchical attention strategies. The SCSA component boosts the recognition in visually cluttered or obstructed settings further by jointly processing spatial and channel-wise signals. To sharpen the detection accuracy, particularly for subtle, hidden nests, the Focaler-GIoU loss guides bounding box optimization. Comparative studies show that the RNDF consistently outperforms recent detectors, surpassing the YOLOv12 baseline by 5.95% mAP@0.5 and 26.16% mAP@0.5:0.95, underscoring its suitability for symmetry-aware, real-world catenary anomaly monitoring. Full article

Falls from height are a critical safety concern in the construction industry, underscoring the need for effective identification of high-risk worker behaviors near hazardous edges for proactive accident prevention. This study aimed to address this challenge by developing an improved action recognition model. We propose a novel dynamic spatio-temporal graph convolutional network (CoG-STGCN) that incorporates a center of gravity (CoG)-aware mechanism. The method computes global and local CoG using anthropometric priors and extracts four key dynamic CoG features, which a Multi-Layer Perceptron (MLP) then uses to generate modulation weights that dynamically adjust the skeleton graph’s adjacency matrix, enhancing sensitivity to stability changes. On a self-constructed dataset of eight typical edge-related hazardous behaviors, CoG-STGCN achieved a Top-1 accuracy of 95.83% (baseline ST-GCN: 93.75%) and an average accuracy of 94.17% in fivefold cross-validation (baseline ST-GCN: 92.91%), with significant improvements in recognizing actions involving rapid CoG shifts. The CoG-STGCN provides a more effective and physically informed approach for intelligent unsafe behavior recognition and early warning in built environments. Full article

In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time–frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness. Full article

Considering the necessity of food safety testing, various biosensors have been developed based on biological elements (e.g., antibodies, aptamers), chemical elements (e.g., molecularly imprinted polymers), physical elements (e.g., nanopores) as recognition substances. According to the sensing patterns of signal transduction, the biosensors could be classified into optical and electrochemical biosensing, including fluorescence sensing, Raman sensing, colorimetric sensing, electrochemical sensing, etc. To enhance the sensing sensitivity, kinds of nanomaterials have been applied for signal amplification. With merits of high selectivity, sensitivity, and accuracy, the sensing strategies have been widely applied for food safety testing. This review highlights their signal output behavior, (e.g., fluorescence intensity shifts, Raman peak alterations, colorimetric changes, electrochemical current/voltage/impedance variations), nanostructure-mediated amplification mechanisms, and the fundamental recognition principles. Future efforts should prioritize multiplexed assay platforms, integration with microfluidics and smart devices, novel biorecognition elements, and sustainable manufacturing. Emerging synergies between biosensors and AI-driven data analytics promise intelligent monitoring systems for predictive food safety management, addressing challenges in food matrix compatibility and real-time hazard identification. Full article

With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems. Full article

Electric bicycles in elevators pose serious safety hazards. Fires in the confined space make escape difficult, and recent accidents involving e-bike fires have caused casualties and property damage. To prevent e-bikes from entering elevators and improve public safety, this design employs the Nezha development board as the upper computer for visual detection. It uses deep learning algorithms to recognize hazards like e-bikes. The lower computer orchestrates elevator controls, including voice alarms, door locking, and emergency halt. The system comprises two parts: the upper computer uses the YOLOv11 model for target detection, trained on a custom e-bike image dataset. The lower computer features an elevator control circuit for coordination. The workflow covers target detection algorithm application, dataset creation, and system validation. The experiments show that the YOLOv11 demonstrates superior e-bike detection performance, achieving 96.0% detection accuracy and 92.61% mAP@0.5, outperforming YOLOv3 by 6.77% and YOLOv8 by 15.91% in mAP, significantly outperforming YOLOv3 and YOLOv8. The system accurately identifies e-bikes and triggers safety measures with good practical effectiveness, substantially enhancing elevator safety. Full article

Industrial and urban activity has inevitably changed the water environment and caused significant impacts on water resources’ quality and quantity. The identification of related impacts is particularly important in the context of increasing water shortages due to climate change. Overlapping industrial impacts and drought occurrence have resulted in the long-lasting deterioration of surface water status. Therefore, the mitigation of negative impacts is crucial for relevant and sustainable water management in river basins. One of the most impactful branches of industry is underground coal mining, which requires dewatering deposits and excavations. Mine waters discharged into rivers have induced significant increases of salinity, while urban wastewaters have increased biogenic contamination in surface waters. Sustainable development goals require water protection, energy transition, and circularity; therefore, coal will be repurposed in favor of alternative sources of energy. The phasing out of coal and cessation of dewatering of mines would rapidly reduce mine waters’ impact on the environment. However, in heavily industrialized urban basins, the share of natural waters in river flows is exceptionally low—due to significant and long-lasting transformations, industrial and urban wastewaters are the main constitutive components in certain river hydrological regimes. The case study of Bytomka in the Upper Silesian Coal Basin, Southern Poland is a vivid example of a river basin significantly impacted by urban and industrial activity over a long-term period. The Bytomka River’s water status and the development of its watershed area is an example of complex and overlapping impacts, wherein sustainable water management requires proper recognition of prevailing factors such as mine water discharges, climate change and drought periods, wastewater impacts, and urbanization of the water basin area. The presented study reveals key findings showing that future coal mine closures would result in significant water resource shortages due to a reduction of mine water discharges, significant biogenic (N and P) pollution increases, and hazards of harmful algal blooms. Therefore, there is an urgent need to increase the retention potential of the watershed, use nature-based solutions, and mitigate negative impacts of the coal mining transition. The increase in treatment capability of industrial wastewater and sewage discharge would help to cope with the natural water vulnerability induced by the impacts of climate change. Full article

Natural caves have a great heritage and natural value, which has made them a tourist attraction that contributes positively to the diversification of tourist offerings in Spain. Volcanic caves are a particular type of natural cave, exclusive to the Canary Islands. The tourist management of these caves entails certain peculiarities that do not occur in other types of tourist establishments. The caves are exposed to certain natural hazards that are important to recognize, evaluate, and where appropriate, plan and adopt the necessary measures to guarantee the safety of visitors and workers. The main natural hazard is the structural stability of the cavity, which can affect workers and visitors. Volcanic caves present structural, lithological, and geomechanical singularities that require a specific methodology to study their stability. This study proposes a specific protocol for the early detection and management of instabilities in tourist volcanic caves, in order to help with the proper management of this ecotourism resource. To this end, tools are provided for the recognition, characterization, and geological and geomechanical analysis, classification of the types of instability in volcanic tubes, and geospatial techniques to control the structural stability. Full article

In recent years, numerous satellite-based systems have been developed to monitor and study volcanic activity from space. This progress reflects the growing demand for accurate and timely monitoring to reduce volcanic risk. Observing volcanoes from a satellite perspective provides key advantages, enabling continuous data acquisition and near-real-time assessment of volcanic activity. Multispectral sensors operating across various regions of the electromagnetic spectrum can detect thermal anomalies associated with lava flows, pyroclastic flows, ash plumes, and volcanic gases. Traditional hotspot detection techniques based on fixed thresholds often miss subtle anomalies on a global scale. In contrast, advanced machine learning algorithms offer a data-driven alternative. We designed and implemented the V-STAR application (Volcanic Satellite Thermal Anomalies Recognition) on Google Earth Engine (GEE) to leverage cloud computing for processing large geospatial datasets in real time. It employs supervised machine learning, specifically Random Forests, to adapt to evolving volcanic conditions. This enhances the accuracy and responsiveness of volcanic monitoring, offering valuable insights into potential eruptive behavior. Here, we present V-STAR as a robust and accessible tool that integrates satellite data and advanced analytics. Through its intuitive interface, V-STAR provides a comprehensive visualization of key volcanic features. The resulting analyses reveal hidden patterns in thermal data, contributing to improved disaster risk reduction strategies associated with volcanic hazards. Full article

Metal casting foundries present hazardous working conditions, making traditional training methods costly, time-consuming, and potentially unsafe. To address these challenges, this study presents a Virtual Reality (VR) training framework developed for the Tennessee Tech University (TTU) Foundry. The objective is to enhance introductory training and safety education by providing an immersive, interactive, and risk-free environment where trainees can familiarize themselves with safety protocols, equipment handling, process workflows, and machine arrangements before engaging with real-world operations. The VR foundry environment is designed using Unreal Engine, a freely available software tool, to create a high-fidelity, interactive simulation of metal casting processes. This system enables real-time user interaction, scenario-based training, and procedural guidance, ensuring an engaging and effective learning experience. Preliminary findings and prior research indicate that VR-based training enhances learning retention, improves hazard recognition, and reduces training time compared to traditional methods. While challenges such as haptic feedback limitations and initial setup costs exist, VR’s potential in engineering education and industrial training is substantial. This work-in-progress study highlights the transformative role of VR in foundry training, contributing to the development of a safer, more efficient, and scalable workforce in the metal casting industry. Full article