Search Results (1,046)

Background/Objectives: To compare the external load (EL) of elite youth soccer players during official international matches between age categories and playing positions. Methods: The sample consisted of 42 elite youth soccer players categorized by age categories, U-15, U-17 and U-20 and playing positions: central defender (CD); fullback (FB); midfielder (MF); wide attacker (WA) and striker (ST). The Vector X7 (Catapult Sports) device was used for collecting the following EL variables: total distance traveled (TD), player load (PL) and distance traveled per velocity band 0 to 7 km/h (D7); 7 to 13 km/h (D13); 13 to 19 km/h (D19); 19 to 23 km/h (D23) and >23 km/h (HSR). Linear mixed-effect models were applied to analyze the differences. Results: Large differences were found between positions (p < 0.01) in TD (η²p = 0.48), PL (η²p = 0.30), D19 (η²p = 0.44), D23 (η²p = 0.68) and HSR (η²p = 0.53). Large differences were found according to category between U-15 and U-17 in TD (p = 0.006 and η²p = 0.25) and D13 (p = 0.003 and η²p = 0.27). Large interaction effects were found in DT (p = 0.014 and η²p = 0.44) and D23 (p = 0.002 and η²p = 0.51). Conclusions: This study concludes that there are differences in EL in official matches in elite youth players between age categories and playing position. These differences can be applied in practice to design individualized training by playing position and to monitor EL during microcycles. Full article

The recent growing adversarial activity against critical systems, such as the power grid, has raised attention on the necessity of appropriate measures to manage the related risks. In this setting, our research focuses on developing tools for early detection of adversarial activities, taking into account the specificities of the energy sector. We developed a framework to design and deploy AI-based detection models, and since one cannot risk disrupting regular operation with on-site tests, we also included a testbed for evaluation and fine-tuning. In the test environment, adversarial activity that produces realistic artifacts can be injected and monitored, and evidence analyzed by the detection models. In this paper we concentrate on the emulation of attacks inside our framework: A tool called SecuriDN is used to define, through a graphical interface, the network in terms of devices, applications, and protection mechanisms. Using this information, SecuriDN produces sequences of attack steps (based on the MITRE ATT&CK project) that are interpreted and executed by software called Netsploit. A case study related to Distributed Energy Resources is presented in order to show the process stages, highlight the possibilities given by our framework, and discuss possible limitations and future improvements. Full article

A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely on subjective human labels. To improve the early detection of asthma worsening in children in homecare setting, we trained and evaluated a Deep Learning model based on spirometry-labelled lung sounds recordings to detect asthma exacerbation. A single-center prospective observational study was conducted between November 2020 and September 2022 at a tertiary pediatric pulmonology department. Electronic stethoscopes were used to record lung sounds before and after bronchodilator administration in outpatients. In the same session, children also underwent spirometry, which served as the reference standard for labelling the lung sound data. Model performance was assessed on an internal validation set using receiver operating characteristic (ROC) curves. A total of 16.8 h of lung sound recordings from 151 asthmatic pediatric outpatients were collected. The model showed promising discrimination performance, achieving an AUROC of 0.763 in the training set, but performance in the validation set was limited (AUROC = 0.398). This negative result demonstrates that acoustic features alone may not provide sufficient diagnostic information for the early detection of asthma attacks, especially in mostly asymptomatic outpatients typical of homecare settings. It also underlines the challenges introduced by differences in how digital stethoscopes process sounds and highlights the need to define the severity threshold at which acoustic monitoring becomes informative, and clinically relevant for home management. Full article

Cyber coercion, where legitimate users are forced to perform actions under duress, poses a serious insider threat to modern organizations, especially to critical infrastructure. Traditional security controls and monitoring tools struggle to distinguish coerced actions from normal user actions. In this paper, we propose a cyber coercion detection system that analyzes a user’s activity using an integrated large language model (LLM) to evaluate contextual cues from user commands or actions and current policies and procedures. If the LLM indicates coercion, behavioral methods, such as keystroke dynamics and mouse usage patterns, and physiological signals such as heart rate are analyzed to detect stress or anomalies indicative of duress. Experimental results show that the LLM-assisted multimodal approach shows potential in detecting coercive activity with and without detected coercive communication, where multimodal biometrics assist the confidence of the LLM in cases in which it does not detect coercive communication. The proposed system may add a critical detection capability against coercion-based cyber-attacks, providing early warning signals that could inform defensive responses before damage occurs. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

Submarine cable systems, serving as the critical backbone of global communications, face evolving resilience threats. This work proposes a novel sensing attack that utilizes ultra-narrow-linewidth lasers to surveil these infrastructures. First, the Narrowband Jamming Attack (NJA) is introduced as an evolution of conventional physical-layer jamming. NJA is divided into three categories according to the spectral position, and the non-overlapping class represents the proposed sensing attack. Its operational principles and the key parameters determining its efficacy are analyzed, along with its deployment strategy in submarine cable systems. Finally, the sensing capability is validated via OptiSystem simulations. Results demonstrate successful localization of vibrations within the 50–200 Hz range on a 1 km fiber, achieving a spatial resolution of 1 m, and confirm the influence of vibration parameters on sensing performance. This work reveals that the proposed sensing attack has the potential to covertly monitor environmental data, thereby posing a threat to information security in submarine cable systems. Full article

This study addresses the problem of anomaly detection in water treatment systems by proposing a hybrid VAE–LSTM model with a combined loss function that integrates reconstruction and prediction errors. Following the signal flow of wastewater treatment systems, data acquisition, transmission, and cyberattack scenarios were simulated, and a dual-dimensional learning framework of “feature space—temporal space” was designed: the VAE learns latent data distributions and computes reconstruction errors, while the LSTM models temporal dependencies and computes prediction errors. Anomaly decisions are made through feature extraction and weighted scoring. Experimental comparisons show that the proposed fusion model achieves an accuracy of approximately 0.99 and an F1-Score of about 0.75, significantly outperforming single models such as Isolation Forest and One-Class SVM. It can accurately identify attack anomalies in devices such as the LIT101 sensor and MV101 actuator, e.g., water tank overflow and state transitions, with reconstruction errors primarily beneath 0.08 ensuring detection reliability. In terms of time efficiency, Isolation Forest is suitable for real-time preliminary screening, while VAE-LSTM adapts to high-precision detection scenarios with an “offline training (423 s) + online detection (1.39 s)” mode. This model provides a practical solution for intelligent monitoring of industrial water treatment systems. Future research will focus on model lightweighting, enhanced data generalization, and integration with edge computing to improve system applicability and robustness. The proposed approach breaks through the limitations of traditional single models, demonstrating superior performance in detection accuracy and scenario adaptability. It offers technical support for improving the operational efficiency and security of water treatment systems and serves as a paradigm reference for anomaly detection in similar industrial systems. Full article

Diabrotica virgifera virgifera Le Conte, 1868 (Coleoptera: Chrysomelidae), known as the western corn rootworm, is one of the most important alien insect pests affecting maize crops globally. It causes significant economic losses by feeding on the roots, which affects plant stability and nutrient absorption, as well as by attacking essential aerial organs (leaves, silk, pollen). Since its accidental introduction into Europe, the species has expanded its range across maize-growing regions, raising concerns about future distribution under climate change. This study aimed to estimate the risk of pest establishment across Europe over three future time frames (2034, 2054, 2074) based on geographic coordinates, climate data, and maize distribution. Spatial simulations were performed in QGIS using national centroid datasets, risk classification criteria, and temperature anomaly maps derived from Copernicus and ECA&D databases for 1992–2024. The results indicate consistently high risk in southern and southeastern regions, with projected expansion toward central and western areas by 2074. Risk zones showed clear spatial aggregation and directional spread correlated with warming trends and maize availability. The pest’s high reproductive potential, thermal tolerance, and capacity for human-assisted dispersal further support these predictions. The model emphasizes the need for expanded surveillance in at-risk zones and targeted policies in areas where D. v. virgifera has not yet established. Future work should refine spatial predictions using field validation, genetic monitoring, and dispersal modeling. The results contribute to anticipatory pest management planning and can support sustainable maize production across changing agroclimatic zones in Europe. Full article

This review is devoted to a comprehensive analysis of modern forms of information warfare in the context of digitalization and global interconnectedness. The work considers fundamental theoretical foundations—cognitive distortions, mass communication models, network theories and concepts of cultural code. The key tools of information influence are described in detail, including disinformation, the use of botnets, deepfakes, memetic strategies and manipulations in the media space. Particular attention is paid to methods of identifying and neutralizing information threats using artificial intelligence and digital signal processing, including partial digital convolutions, Fourier–Galois transforms, residue number systems and calculations in finite algebraic structures. The ethical and legal aspects of countering information attacks are analyzed, and geopolitical examples are given, demonstrating the peculiarities of applying various strategies. The review is based on a systematic analysis of 592 publications selected from the international databases Scopus, Web of Science and Google Scholar, covering research from fundamental works to modern publications of recent years (2015–2025). It is also based on regulatory legal acts, which ensures a high degree of relevance and representativeness. The results of the review can be used in the development of technologies for monitoring, detecting and filtering information attacks, as well as in the formation of national cybersecurity strategies. Full article

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations. Full article

Wireless sensor networks (WSNs) play a major role in various applications, but the main challenge is to maintain security and balanced energy efficiency. Classical routing protocols struggle to achieve both energy efficiency and security because they are more vulnerable to security risks and resource limitations. This paper introduces QSEER, a novel approach that uses quantum technologies to overcome these limitations. QSEER employs quantum-inspired optimization algorithms that leverage superposition and entanglement principles to efficiently explore multiple routing possibilities, thereby identifying energy-efficient paths and reducing redundant transmissions. The proposed protocol enhances the security of data transmission against eavesdropping and tampering by using the principles of quantum mechanics, thus mitigating potential security vulnerabilities. Through extensive simulations, we demonstrated the effectiveness of QSEER in achieving both security and energy efficiency objectives, which achieves 15.1% lower energy consumption compared to state-of-the-art protocols while maintaining 99.8% data integrity under various attack scenarios, extending network lifetime by an average of 42%. These results position QSEER as a significant advancement for next-generation WSN deployments in critical applications such as environmental monitoring, smart infrastructure, and healthcare systems. Full article

Submerged floating tunnel (SFT) may be subjected to sudden explosive loads such as internal vehicle explosions, terrorist attacks, and external explosions during operation. Based on the Arbitrary Lagrange–Euler (ALE) method, the locally truncated SFT model and fluid–structure interaction model of internal air and external water are established. Spherical explosives are used to simulate the destructive impact of internal explosions at different positions of the road inside the SFT and key positions at the bottom of the road. The results show that the peak accelerations at the monitoring points caused by the explosions of vehicles on the road rapidly decay within a range of three times the radius of the SFT, and circularly distributed damage appears on the explosion-facing side of the road surface. Longitudinal extensional damage occurs at the junction of the road surface and the SFT wall as well as the bottom supporting wall. Longitudinal cracks appear on the SFT wall. The peak accelerations at the monitoring points of the internal road caused by the concealed bomb at the bottom of the SFT rapidly decay within a range of twice the radius of the SFT, and the damage to the SFT is mainly concentrated on the road surface and the supporting wall. The most dangerous direction of external underwater explosion is determined to be directly below the SFT. When the scaled distance of the explosion is less than 0.543 m/kg^1/3, the accelerations at the monitoring points of the internal road show a single-peak trend with rapid rise and decay, and circumferential through-cracks appear on the SFT wall. The supporting wall connecting the SFT wall and the internal road transmits stress to the road, causing extensive damage. Full article

High-temperature corrosion is a frequently observed phenomenon in waste incineration facilities. Municipal solid waste presents substantial corrosion potential attributed to elevated chlorine content and significant inhomogeneity in calorific value and chemical composition, rendering stable plant operation and corrosion control challenging. Conventional countermeasures, such as cladding or reduced steam parameters, lack temporal resolution and incur substantial costs or reduced efficiency. For this study, a waste incineration plant was equipped with an online corrosion monitoring system featuring ten sensors distributed across three vertical boiler passes. The system employs an electrochemical measurement principle to enable the detection of corrosion with temporal resolution. The recorded data reveals decreasing corrosion attack and increasingly stable deposits along the flue gas path. Combined with the temperature measurements, the sensor data proves the effectiveness of the shower cleaning in the third pass and confirms successful removal of the deposits. Statistical analysis shows a correlation between CO content and sensor data, while other parameters (e.g., steam flow, flue gas temperatures) exhibit no conclusive correlations, emphasizing the system’s added value. Chemical analysis of the electrodes and deposits reveal significant indications of chlorine and sulfur, suggesting chlorine-catalyzed active oxidation as the predominant corrosion mechanism. Full article

The growing reliance on satellite-based infrastructures for communication, navigation, defense, and environmental monitoring has magnified the urgency of securing Space Information Networks (SINs) against cyber threats. This paper presents a comprehensive review of the vulnerabilities, threat vectors, and advanced countermeasures impacting SINs. Key vulnerabilities, including system complexity, use of Commercial Off-the-Shelf (COTS) components, lack of standardized security frameworks, and emerging quantum threats, are critically analyzed. This paper classifies cyber threats into active and passive categories, highlighting real-world case studies such as Denial-of-Service attacks, message modification, eavesdropping, and satellite transponder hijacking. A detailed survey of countermeasures follows, focusing on AI-driven intrusion detection, federated learning approaches, deep learning techniques, random routing algorithms, and quantum-resistant encryption. This study emphasizes the pressing need for integrated, resilient, and proactive security architectures tailored to the unique constraints of space systems. It concludes by identifying research gaps and recommending future directions to enhance the resilience of SINs against evolving cyber threats in an increasingly contested space environment. Full article

The rapid development of an increasing volume of web apps and the improper testing of the resulting code invariably provide more attack surfaces to potentially exploit. This leads to higher chances of facing cybersecurity breaches that can negatively impact both the users and providers of web services. Moreover, current data leaks resulting from breaches are most probably the fuel of future breaches and social engineering attacks. Given the context, a better analysis and understanding of web attacks are of the utmost priority. Our study provides practical insights into developing, implementing, deploying, and actively monitoring a web application-agnostic honeypot with the objective of improving the odds of defending against web attacks. Full article