Abstract: A non-contact fall detection system, which integrates 4D imaging radar sensors with arti-ficial intelligence (AI) technology, is proposed to monitor fall accidents among the elderly. Existing wearable devices may cause discomfort during use, and camera-based systems raise privacy concerns. The solution developed in this study addresses these issues by adopting 4D radar sensors. The radar sensors generate Point Cloud data to enable the system to analyze the positions and postures of the body. Using a CNN model, these pos-tures are classified into standing, sitting, and lying, while criteria based on changes in the speed and position distinguish between falls and slow-lying movements. The Point Cloud data were normalized and organized using zero padding and k-means clustering to en-hance the learning efficiency. The proposed model achieved 98.66% accuracy in posture classification and 95% in fall detection. The monitoring system provides real-time visual representations through a web-based dashboard and Unity-based 3D avatars, along with immediate alerts in case of a fall. In conclusion, this study demonstrates the effectiveness of real-time fall detection technology and highlights the need for further research on mul-ti-sensor integration and application in various indoor environments.

Abstract: This paper provides a comprehensive analysis of photovoltaic (PV) systems, their development and security issues in the past decade in Europe and Romania. It begins with the presentation of the PV systems development in the two regions, and proceeds with the critical risk evaluation of PV systems as essential components of the energy infrastructure of Romania. The article presents the authors' arguments in support of the proposal to include PV systems in the critical infrastructure category, reflecting their strategic importance to national energy resilience. This is achieved through a comprehensive assessment of the current levels of safety, security, cybersecurity, and physical protection of PV systems, highlighting potential vulnerabilities that may compromise operational continuity. The evaluation of cybersecurity risks leads to the conclusion that PV systems face increasing exposure to digital threats, reinforcing the urgent need for robust cyber defense mechanisms in this rapidly evolving sector.This study aims to create an entire set of guidelines for enhancing the security and resilience of PV systems as they increasingly form a critical component of sustainable energy infrastructure.

Abstract: As the attention to using hydrogen as a potential energy storage medium for power generation and mobility increases, hydrogen production, storage, and transportation safety should be considered. For instance, hydrogen's extreme physical and chemical properties and the wide range of flammable concentrations raise many concerns about the current safety measures in processing other flammable gases. Hydrogen cloud accumulation in case of leakage in confined spaces can lead to reaching the hydrogen lower flammability limit (LFL) within seconds if the hydrogen is not properly evacuated from space. At Jülich Research Centre, hydrogen mixed with natural gas is foreseen to be used as a fuel for the central heating system of the campus. In this work, the release, dispersion, formation, and spread of the hydrogen cloud in case of hydrogen leakage inside the central utility building of the campus are numerically simulated using the OpenFOAM-based containmentFOAM CFD codes. Additionally, different ventilation scenarios are simulated to investigate the behavior of the hydrogen cloud. The results show that locating exhaust openings close to the ceiling and the potential leakage source can be the most effective way to evacuate hydrogen from the building safely. Improper placement of the exhaust outlets can significantly decrease the effectiveness of the forced ventilation system, even lower than the effectiveness of natural ones. These results can be used as a guide to the locations of the ventilation outlets for closed spaces with hydrogen systems. Also, these simulations can be used in the future to study the effect of hydrogen sensor locations on the response of the ventilation system and, accordingly, on the hydrogen-combustible cloud volume inside the space.

Abstract: In operational testing contexts, testers face dual challenges of constrained timeframes and limited resources, both of which impede the generation of reliability test data. To address this issue, integrating data from similar systems with test data can effectively expand data sources. This study proposes a systematic approach wherein the mission of the system under test (SUT) is decomposed to identify candidate subsystems for data combination. A phylogenetic tree representation is constructed for subsystem analysis and subsequently mapped to a mixed integer programming (MIP) model, enabling efficient computation of similarity factors. A reliability assessment model that combines data from similar subsystems is established. The similarity factor is regarded as a covariate, and the regression relationship between it and the subsystem failure time distribution is established. Leveraging Bayesian theory, the joint posterior distribution of regression coefficients is derived. These coefficients are then sampled via the No-U-Turn Sampler (NUTS) algorithm to obtain reliability estimates. Numerical case studies demonstrate that the proposed method outperforms existing approaches, yielding more robust similarity factors and higher accuracy in reliability assessments.

Abstract: A adaptable, resilient, safe and secure power system is essential for ensuring energy and national security, having a direct impact on a state's economy, social stability, and well-being through the following requirements: Ensuring continuity of power supply (a robust power system guarantees uninterrupted access to electricity for citizens, institutions, and industries, reducing the risk of disruptions caused by technical deficiencies, cyberattacks, or geopolitical instability); Energy independence and reduction of external dependence (a state that produces sufficient electricity from its own sources is less vulnerable to international market fluctuations and external pressures, while diversifying energy sources—renewable, nuclear, hydrocarbons—reduces import dependence and economic vulnerability); Security of power infrastructure (protecting electricity networks from physical and cyberattacks is essential for the normal functioning of society, and developing modern infrastructure—smart grids, electricity storage—ensures the resilience of the energy system); Economic stability and national development (an efficient power system supports industry, agriculture, and services, contributing to economic growth, while lower energy costs enhance economic competitiveness and attract investments); Environmental protection and energy transition (adopting renewable sources and clean technologies reduces dependence on fossil fuels and minimizes environmental impact, while increasing energy efficiency and reducing carbon emissions are essential for long-term sustainability); Strategic and geopolitical role (countries with significant energy resources have greater influence on the international stage, and regional energy cooperation can strengthen diplomatic and economic relations). A secure and efficient energy system is the backbone of national security, guaranteeing economic stability, strategic independence, and population protection. Investments in modern infrastructure, clean technologies, and diversification of energy sources are crucial for the energy future of any nation. The authors of this study have identified all elements of instability and insecurity within Romania's Power System, with the aim of evaluating them and subsequently developing safety and security strategies.

Abstract: We use the data of three papers “Statistical Inference on the Shape Parameter of Inverse Generalized Weibull Distribution” (Zhuang et al.), “Sequential Confidence Intervals for Comparing Two Proportions with Applications in A/B Testing” (Hu et al.) and “On Designing of Bayesian Shewhart-Type Control Charts for Maxwell Distributed Processes with Application of Boring Machine” (Alshahrani et al.) to compare the above authors findings with ours. From the analysis we get different results: the cause is that they use the Probability Limits of the PI (Probability Interval) as they were the Confidence Limits (Control Limits of the Control Charts, CCs). The Control Limits in the Shewhart CCs are based on the Normal Distribution (Central Limit Theorem, CLT) and are not valid for non-normal distributed data: consequently, the decisions about the “In Control” (IC) and “Out Of Control” (OOC) states of the process are wrong. The Control Limits of the CCs are wrongly computed, due to unsound knowledge of the fundamental concept of Confidence Interval. Minitab and other software (e.g. JMP, SAS) use the “T Charts”, claimed to be a good method for dealing with “rare events”, but their computed Control Limits of the CCs are wrong. The same happens for the Confidence Limits of the parameters of the distribution involved in the papers (Weibull, Inverse Weibull, Gamma, Binomial, Maxwell). We will show that the Reliability Integral Theory (RIT) is able to solve these problems and the Sequential way of dealing with data.

Abstract: In order to study the characteristics of the thermal runaway process of a full-size prefabricated cabin energy storage system, a full-scale prefabricated cabin energy storage physical fire test platform was designed, using 100% SOC energy storage battery packs as the thermal runaway object, and conducts full-scale prefabricated cabin energy storage system physical fire experiments. The experiment analyzes the early change rules of parameters such as temperature, voltage, CO, and VOC after the energy storage system thermal runaway, and explores the technical methods to improve the fire protection of electrochemical energy storage systems. The results show that the time when the surface temperature of the runaway cell undergoes a sudden change is 37 seconds later than the time when the voltage undergoes a sudden change; the CO at the bottom and middle of the runaway cluster reaches the alarm threshold 25 seconds and 39 seconds earlier than that at the top of the cluster, respectively, and the peak concentration of CO at the bottom and middle of the cluster is more than three times that at the top of the cluster. The opening of the fan will cause the CO concentration on the left side of the thermal runaway cluster to be higher than that of the runaway cluster; before the battery thermal runaway, the VOC concentration at the middle and top of the runaway cluster is generally higher than that at the bottom of the cluster. After the thermal runaway occurs, the VOC concentration at the bottom of the thermal runaway cluster exceeds that at other positions of the runaway cluster and the adjacent cluster; the tVOC at the top, middle, and bottom of the thermal runaway cluster is 2296 seconds, 1681 seconds, and 1464 seconds earlier than the tCO, respectively, but the initial detection value of VOC fluctuates more than that of CO.

Abstract: The Hellenic Cadastre, which is expected to be fully operational by the end of 2025, represents a major modernisation step in Greece's technical and legal documentation of property rights as the successor to the country's land registries system. It will also constitute a land administration system, since it will encompass not only property rights but also restrictions and regulations in the context of RRR. A significant technical but also legal challenge inherent to this system pertains to the resolution of deviations between surfaces calculated prior to 1990 based on older geodetic reference systems, and recalculated today using the current GGRS87 reference system. Deviations that arise from geodetic transformations between older and modern projected reference systems and are compounded by limitations inherent in historical surveying techniques and in the geodetic infrastructure that was available at the time. To address this issue, we introduce the Local Area Distortion Factor (LADF), a novel metric factor designed to adjust and harmonise property areas across different geodetic systems. This real-world case study offers a practical illustration of the application of LADF, demonstrating its capacity to enhance the precision of cadastral records while preserving interpretability for both experts and non-experts. LADF can also be used to improve land adjustment processes during the implementation of urban plans, property valuation, taxation and notary acts that are in different reference systems.

Abstract: Pears are highly valued by consumers worldwide due to their unique taste and flavor profile, leading to their extensive cultivation and global consumption. Pesticides are vital in the prevention and management of pests and diseases in pear production; however, the intensive application of these agrochemicals has resulted in significant contamination issues, which adversely affect the quality and safety of pear products. As a result, the monitoring of pesticide residues in pears is essential to ensure the safety of the fruit and to safeguard the public health. This review paper attempts to provide readers with an overview of the occurrence and dissipation of pesticide residues in pears, as well as the analytical techniques employed for their detection. Furthermore, potential directions for future research are suggested, with the goal of contributing valuable insights to ongoing studies on pesticide residues in pears.

Abstract: This research incorporates artificial intelligence (AI) into asset integrity and process safety (AIPS) management, aiming to revolutionize conventional methods. It facilitates the automation of risk assessments, enhances predictive analytics, and supports the development of proactive measures to mitigate potential incidents. It explores the application of a generative pre-trained transformer (GPT) based large language models (LLM) to analyse and classify AIPS indicators from vast datasets to generate actionable recommendations to prevent future incidents. A comparative study between two onshore liquefied natural gas (LNG) plants; one utilizing AI-driven AIPS management and the other relying on manual data analysis is presented. The results indicate that AI-driven approaches significantly enhance the accuracy and speed of incident classifications, reducing data processing times. The test model effectively predicts potential future failures by analysing past incident patterns, enabling informed decision-making to prevent and mitigate future failures. The findings highlight the importance of adopting AI-driven AIPS management as a standard practice. It also emphasises the need for stronger collaboration between academia and industry in AI solutions to drive technological advancements for sustainability.

Abstract: The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv10 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables.

Abstract: This review aims to place open, laparoscopic, robotic and image-guided robotic surgical interventions in the context of complex medical surgeries, taking into account patient well-being, staff effort and task reliability. It deduces the specificities of each technique and subsequently focuses on image-guided interventions and their practice in staff training, preparation and implementation of a possible autonomous intervention. These complex interventions are intended to be minimally invasive (MI), precise and safe therapies. The accuracy of robotic positioning could be improved by reductions in complexity and un-certainty involved in the intervention procedure. These can be achieved by matching the real controlled procedure and its virtual replica. The contribution discusses considera-tions for staff training and/or planning of surgical interventions using real and virtual phantoms, and the use of augmented matched digital twins (DT) for real interventions. The different topics presented in the article, although explicit, are reinforced by examples from the literature to facilitate a deeper understanding. The results of this review highlight the importance of robotic imaging-assisted procedures involving MI, nonionizing and precise interventions. Moreover, DTs currently integrated in different health applications, combined with digital tools, could provide an effective solution for the management of such interventions.

Abstract: To address the growing security risks posed by unauthorized UAV activities, this paper proposes a real-time two-dimensional direction-finding (DF) system for UAV based on radio frequency (RF) signals. The system employs a six-element uniform circular array (UCA), synchronized HackRF One receivers, and a hybrid algorithm integrating the multiple signal classification (MUSIC) method with a novel weighted average algorithm (WAA). By optimizing the MUSIC spectrum search process, the WAA reduces computational complexity by over 99.9% (from 3240000 to 1200 spectral function calculations), enabling real-time azimuth and elevation angle estimation. Experimental results demonstrate an average azimuth error of 7.0° and an elevation error of 7.7°, for UAV hovering distances of 30-200 m and heights of 20-90 m. Real-time flight tracking further validates the system’s dynamic monitoring capability. The hardware platform, featuring omnidirectional coverage (0-360° azimuth, 0-90° elevation) and dual-band operation (2.4 GHz/5.8 GHz), offers scalability and cost-effectiveness for low-altitude security applications. Despite limitations in elevation sensitivity due to UCA geometry, this work establishes a practical foundation for UAV monitoring, emphasizing computational efficiency, real-time performance, and adaptability to dynamic environments.

Abstract:

In the present paper subject of the investigations is the reliability assessment of the single-stage reversible Hydropower Unit No. 3 (HU3) in the Bulgarian Pumped Hy-dro-Electric Storage (PHES) plant “Chaira”, which processes the waters of the “Belmeken” dam and “Chaira” dam. Preceding destruction of HU4 and its virtual simulation, analysis and the conclusions for the rehabilitation and safety provided the information for the possible processes in HU3. Detailed analysis of the consequences of prolonged use of HU3 was carried out. The Supervisory Control and Data Acquisition (SCADA) system records were studied. Fault Tree Analysis (FTA) is applied to determine the component relationships and subsystem failures that can lead to an undesired primary event. The functional structure of the system was depicted as a causal chain of failure effects. The probability of system failure was estimated based on the failure probabilities of the primary events. The effects of static loads, dynamic loads and low-cycle loads were investigated. Based on the experience and the investigations of the HU4 and its damages, as well as of the failures in the stay vanes of HU3 it is recommended to organize monitoring for water ingress into the drainage holes, which will allow detecting failures in a timely manner.

Abstract: In the oil and gas industry, drilling operations face complex and dynamic challenges that require real-time monitoring and proactive decision-making to ensure safety, operational efficiency, and environmental protection. Drilling fluid systems, which are essential for maintaining well stability, cooling equipment, and preventing blowouts, can be prone to risks such as pressure anomalies, viscosity changes, and contamination. This article explores the role of predictive analytics in mitigating these risks in real-time. By integrating advanced sensors and machine learning models, predictive analytics can detect anomalies, forecast potential failures, and suggest corrective actions before critical issues arise. The study highlights how real-time data collected from drilling fluid systems can be analyzed to predict equipment malfunctions, fluid imbalances, and hazardous events, ultimately reducing incidents and improving safety outcomes. The article also discusses the challenges of data integration, system accuracy, and operator training, while emphasizing the potential for predictive analytics to enhance decision-making and operational resilience in high-risk drilling environments. The findings suggest that by leveraging predictive analytics, drilling operations can achieve more reliable, cost-effective, and safer outcomes, paving the way for future advancements in risk management and fluid system optimization.

Abstract: The evolving complexity of drilling operations, particularly in challenging environments, has heightened the need for advanced technologies that ensure safety and operational efficiency. Next-generation drilling fluid monitoring, combined with predictive analytics, represents a groundbreaking approach to enhancing hazard prevention and risk mitigation. This article explores the integration of real-time drilling fluid monitoring systems with predictive analytics to anticipate and prevent operational hazards in challenging drilling environments. By analyzing key parameters such as pressure, flow rate, and fluid properties, predictive models can identify potential issues such as equipment failure, blowouts, and environmental violations before they occur. The study highlights the effectiveness of these technologies in real-time decision-making, reducing incidents, and improving overall drilling performance. Furthermore, the article discusses the challenges of implementing these advanced systems, including data quality, integration with existing infrastructure, and operator adaptation. The findings demonstrate that next-gen drilling fluid monitoring not only improves safety but also optimizes operational efficiency, providing a proactive approach to managing complex drilling operations. This approach has the potential to transform drilling safety protocols, providing a comprehensive, data-driven strategy for hazard prevention in the oil and gas industry.

Abstract: Microplastics (MPs), ubiquitous environmental pollutants, pose substantial threats to aquatic ecosystems and organisms, including the model species Daphnia magna. This study examined the effects of polyethylene (PE) and polystyrene (PS) MPs on D. magna, focusing on ingestion, epigenetic alterations, and transcriptional responses. Exposure experiments revealed a concentration-dependent accumulation of MPs, with PS particles showing higher ingestion rates due to their higher density and propensity for aggregation. Epigenetic analyses demonstrated that exposure to PE MPs significantly reduced global DNA methylation (5-mC), suggesting hypomethylation as a potential stress response. Conversely, DNA hydroxymethylation (5-hmC) displayed variability under PS exposure. Transcriptional analysis identified a marked downregulation of Vitellogenin 1 (v1) and upregulation of Ecdysone Receptor B (ecr-b), highlighting stress-related and adaptive molecular responses. These findings enhance our understanding of the molecular and epigenetic effects of MPs on aquatic organisms, offering critical insights for developing effective environmental management and conservation strategies in the face of escalating MP pollution.

Abstract:

In this paper, to reinforce the safety of boats, we develop a technology to improve the stability and quality of boat equipment manufacturing through vacuum infusion process fusion. Safe mold design and manufacture are performed to determine the resin flow rate and water surface flow of the boat, and the performance of vacuum maintenance work is guaranteed through the tensile and compressive strength of the manufactured hull and deck. When manufacturing a boat air mechanism (Aerostat), the adhesive strength between the materials of the equipment and the deformation of the joints are very important factors for safety. Process fusion to minimize a number of manual work processes due to the nature of the equipment manufacturing, minimiz-es deformation after manufacturing through an accurate manufacturing ratio. Accordingly, the mixing ratio of the resin and curing agent is accurately controlled as an optimal condition for drying and securing safety of the boat, and durability and quality are improved by analyzing working conditions such as resin flow and flowchart by time and converting optimal infor-mation into a databse. This enables efficient production process management with a small number of workers in multiple workplaces, which is expected to increase production efficiency and safety design.

Abstract: Ensuring electrical safety in underground mines is a fundamental priority given to the major risks associated with this unfriendly work environment. It involves a set of technical, organisational and educational measures to reduce the hazards for workers and minimise the risks of accidents and occupational diseases due to electrical causes. The old and precarious coal extraction methods, in conjunction with the obsolete infrastructure and electrical installations, with high accidentological danger, they can endanger the lives of underground workers every day of work. The precariousness of working conditions and working materials do not accord with safety at work, overlapping with the carelessness of decision-makers, make these underground mines a major factor of accidents and professional illnesses. In this paper, the authors identified, estimated, prioritized and evaluated the vulnerabilities within underground mines and developed actions and resources necessary to mitigate, stop and/or eliminate vulnerabilities and mitigation strategy, stopping and/or eliminating them in the context of increased electrical safety.

Abstract: In high-risk mining environments, the emotional state of individuals can play a critical role in shaping decision-making during emergencies. To investigate this phenomenon, we adopted an event-related potential (ERP) approach within a within-subject experimental framework. Participants were first exposed to a series of emotionally evocative cues designed to elicit distinct affective states, immediately followed by decision-making tasks that simulate coal mine emergency scenarios. Behavioral indices—such as reaction speed and task engagement—were recorded concurrently with ERP signals, with a specific focus on components including N1, P2, N2, P300, and LPP. Our analysis revealed that both the valence and intensity of emotional cues substantially modulated the decision-making process, as reflected by significant variations in response times and ERP measures. These findings offer fresh insights into the neural mechanisms through which emotional factors influence critical decisions in crisis situations, highlighting implications for the development of enhanced safety strategies in coal mining operations.