Search Results (285)

As an effective engineering countermeasure against frost heave damage in seasonally frozen regions, thermal insulation boards (TIBs) were employed in embankments. This study established a test section featuring a thermal insulation–waterproof geotextile embankment in Dingxi, Gansu Province. Temperature and water content at various positions and depths within both the thermal insulation embankment (TIE) and an ordinary embankment (OE) were monitored and compared to analyze the effectiveness of the TIB. Following the installation of the insulation layer, the temperature distribution within the embankment became more uniform. The TIB effectively impeded downward heat transfer (cold energy influx) during the winter and upward heat transfer (heat energy flux) during the warm season. However, the water content within the TIE was observed to be higher than that in the OE, with water accumulation notably occurring at the embankment toe. While the TIB successfully mitigated slope damage and superficial soil frost heave, the waterproof geotextile concurrently induced moisture accumulation at the embankment toe. Consequently, implementing complementary drainage measures is essential. In seasonally frozen areas characterized by dry weather and relatively high winter temperatures, the potential damage caused by concentrated rainfall events to embankments requires particular attention. Full article

Ozone (O₃) has re-emerged in periodontology for its antimicrobial, oxygenating, and immunomodulatory actions, yet its role in regeneration remains contentious. This narrative review synthesizes current evidence on adjunctive ozone use in periodontal therapy, delineates cellular constraints—especially in periodontal ligament fibroblasts (PDLFs)—and explores mitigation strategies using bioactive compounds and advanced delivery platforms. Two recent meta-analyses indicate that adjunctive ozone with scaling and root planing yields statistically significant reductions in probing depth and gingival inflammation, with no significant effects on bleeding on probing, plaque control, or clinical attachment level; interpretation is limited by heterogeneity of formulations, concentrations, and delivery methods. Mechanistically, ozone imposes a dose-dependent oxidative burden that depletes glutathione and inhibits glutathione peroxidase and superoxide dismutase, precipitating lipid peroxidation, mitochondrial dysfunction, ATP depletion, and PDLF apoptosis. Concurrent activation of NF-κB and upregulation of IL-6/TNF-α, together with matrix metalloproteinase-mediated extracellular matrix degradation and tissue dehydration (notably with gaseous applications), further impairs fibroblast migration, adhesion, and ECM remodeling, constraining regenerative potential. Emerging countermeasures include co-administration of polyphenols (epigallocatechin-3-gallate, resveratrol, curcumin, quercetin), coenzyme Q10, vitamin C, and hyaluronic acid to restore redox balance, stabilize mitochondria, down-modulate inflammatory cascades, and preserve ECM integrity. Nanocarrier-based platforms (nanoemulsions, polymeric nanoparticles, liposomes, hydrogels, bioadhesive films) offer controlled ozone release and co-delivery of protectants, potentially widening the therapeutic window while minimizing cytotoxicity. Overall, current evidence supports ozone as an experimental adjunct rather than a routine regenerative modality. Priority research needs include protocol standardization, dose–response definition, long-term safety, and rigorously powered randomized trials evaluating bioactive-ozone combinations and nanocarrier systems in clinically relevant periodontal endpoints. Full article

Cybersecurity management plays a key role in preserving the operational security of nuclear power plants (NPPs), ensuring service continuity and system resilience. The growing number of sophisticated cyber-attacks against NPPs requires cybersecurity experts to detect, analyze, and defend systems and data from cyber threats in near real time. However, managing a large numbers of attacks in a timely manner is impossible without the support of Artificial Intelligence (AI). This study recognizes the need for a structured and in-depth analysis of the literature in the context of NPPs, referring to the role of AI technology in supporting cyber risk assessment processes. For this reason, a systematic literature review (SLR) is adopted to address the following areas of analysis: (i) critical assets to be preserved from cyber-attacks through AI, (ii) security vulnerabilities and cyber threats managed using AI, (iii) cyber risks and business impacts that can be assessed by AI, and (iv) AI-based security countermeasures to mitigate cyber risks. The SLR procedure follows a macro-step approach that includes review planning, search execution and document selection, and document analysis and results reporting, with the aim of providing an overview of the key dimensions of AI-based cybersecurity in NPPs. The structured analysis of the literature allows for the creation of an original tabular outline of emerging evidence (in the fields of critical assets, security vulnerabilities and cyber threats, cyber risks and business impacts, and AI-based security countermeasures) that can help guide AI-based cybersecurity management in NPPs and future research directions. From an academic perspective, this study lays the foundation for understanding and consciously addressing cybersecurity challenges through the support of AI; from a practical perspective, it aims to assist managers, practitioners, and policymakers in making more informed decisions to improve the resilience of digital infrastructure. Full article

Cybersecurity is a key factor influencing the development of the smart grid paradigm. The integration of information and communication technologies into energy networks introduces new cybersecurity requirements, vulnerabilities, and threats. Typical countermeasures and security measures require optimization and customization for implementation in a distributed and heterogeneous smart grid environment. In this paper, we propose a holistic approach to smart grid cybersecurity by considering information security attributes at the level of requirements, threats, and countermeasures analysis. The results of the conducted review enabled us to develop a holistic cybersecurity framework for smart grids, while also analyzing the challenges and barriers related to security measures, as well as the possibilities for their mitigation. Full article

Covert channels enable hidden communication that poses significant security risks, particularly when smartphones are used as transmitters. This paper presents the first end-to-end implementation and evaluation of an electromagnetic (EM) covert channel on modern Samsung Galaxy S21, S22, and S23 smartphones (Samsung Electronics Co., Ltd., Suwon, Republic of Korea). We first demonstrate that a previously proposed method relying on zero-volume playback is no longer effective on these devices. Through a detailed analysis of EM emissions in the 0.1–2.5 MHz range, we discovered that consistent, volume-independent signals can be generated by exploiting the hardware’s recovery delay after silent audio playback. Based on these findings, we developed a complete system comprising a stealthy Android application for transmission, a time-based modulation scheme, and a demodulation technique designed around the characteristics of the generated signals to ensure reliable reception. The channel’s reliability and robustness were validated through evaluations of modulation time, probe distance, and message length. Experimental results show that the maximum error-free bit rate (bits per second, bps) reached 0.558 bps on Galaxy S21 and 0.772 bps on Galaxy S22 and Galaxy S23. Reliable communication was feasible up to 0.5 cm with a near-field probe, and a low alignment-aware bit error rate (BER) was maintained even for 100-byte messages. This work establishes a practical threat, and we conclude by proposing countermeasures to mitigate this vulnerability. Full article

Precise classification of landslide types is critical for targeted hazard mitigation, although the absence of type-specific classifications in many existing inventories limits their utility for effective risk management. This study develops a transferable machine learning approach to identify cliff-type landslides from unclassified records, with a focus on earthquake-prone regions. Using the Forest-based and Boosted Classification and Regression (FBCR) tools in ArcGIS Pro, a model was trained on 167 landslide points and 167 non-landslide points from Tokushima Prefecture, Japan. The model achieved high predictive performance, with 84% accuracy and sensitivity, an F1 score of 84%, and a Matthews correlation coefficient (MCC) of 0.68. The trained model was applied to the Kegalle District, Sri Lanka, and validated against a recently updated inventory specifying landslide types, resulting in an accuracy of 80.1%. It also enabled retrospective identification of cliff-type landslides in older inventories, providing valuable insights for early hazard assessment. Spatial analysis showed strong correspondence between predicted cliff-type zones and key conditioning factors, including specific elevation ranges, steep slopes, high soil thickness, and proximity to roads and buildings. This study integrates FBCR-based modelling with a cross-regional application framework for cliff-type landslide classification, offering a practical, transferable tool for refining inventories, guiding countermeasures, and improving preparedness in regions with similar geomorphological and seismic settings. Full article

Integration of sensing, communication, computing, and intelligence (ISCCI) represents a pivotal advancement in B5G and 6G technologies, offering transformative potential for the Internet of Vehicles (IoV). As IoV systems become increasingly integral to intelligent transportation and autonomous driving, these systems also face escalating security challenges across multiple layers, including physical, network, application, and system dimensions. (1) This paper comprehensively surveys these security issues, systematically analyzing the threats encountered at each layer and proposing targeted countermeasures to mitigate risks. (2) Furthermore, the paper explores future trends in IoV security, emphasizing the roles of 6G networks, blockchain technology, and digital twins in addressing emerging challenges. (3) Finally, based on a comprehensive review of current research and insights, this paper aims to serve as a foundational reference for advancing secure and sustainable IoV ecosystems. Full article

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. The presented study quantifies the prevalence of MW in naturalistic and simulated driving environments and shows its impact on driving behaviors. We document its negative effects on braking reaction times and lane-keeping consistency, and we assess recent advancements in objective detection methods, including EEG signatures, eye-tracking metrics, and physiological markers. We also identify key cognitive and contextual risk factors, including high perceived risk, route familiarity, and driver fatigue, which increase MW episodes. Also, we survey emergent countermeasures, such as haptic steering wheel alerts and adaptive cruise control perturbations, designed to sustain driver engagement. Despite these advancements, the MW research shows persistent challenges, including methodological heterogeneity that limits cross-study comparisons, a lack of real-world validation of detection algorithms, and a scarcity of long-term field trials of interventions. Our integrated synthesis, therefore, outlines a research agenda prioritizing harmonized measurement protocols, on-road algorithm deployment, and rigorous evaluation of countermeasures under naturalistic driving conditions. Full article

This paper presents an attack detection, response, and recommendation framework designed to protect the integrity and operational continuity of IoT-based critical infrastructure, specifically focusing on an energy use case. With the growing deployment of IoT-enabled smart meters in energy systems, ensuring data integrity is essential. The proposed framework monitors smart meter data in real time, identifying deviations that may indicate data tampering or device malfunctions. The system comprises two main components: an attack detection and prediction module based on machine learning (ML) models and a response and adaptation module that recommends countermeasures. The detection module employs a forecasting model using a long short-term memory (LSTM) architecture, followed by a dense layer to predict future readings. It also integrates a statistical thresholding technique based on Tukey’s fences to detect abnormal deviations. The system was evaluated on real smart meter data in a testbed environment. It achieved accurate forecasting (MAPE < 2% in most cases) and successfully flagged injected anomalies with a low false positive rate, an effective result given the lightweight, unsupervised, and real-time nature of the approach. These findings confirm the framework’s applicability in resource-constrained energy systems requiring real-time cyberattack detection and mitigation. Full article

Standalone tsunami defense structures have demonstrated limitations in mitigating wave energy during the 2011 Japan tsunami. In order to mitigate future tsunamis in Japan, multi-layered protective mechanisms have been suggested or implemented after the incident. These include heightening the destroyed or existing embankment with concrete or stones, protecting embankments with concrete blocks, compacting the landward soil, elevating the ground following the coastal embankment, and incorporating green belts. Despite extensive research on the mitigation effects of such multiple countermeasures, the optimal structural configuration remains uncertain. In this study, we evaluated the performance of a multiple mitigation system consisting of a landward forest (F) on an elevated mound (M) following a seaward embankment (E) under a range of supercritical flow conditions using a flume experiment. Several mound heights and lengths were selected to determine the optimum mound for installing the forest. The combination of E and F of 12 rows of trees on M with a minimum height of 1.8 cm (Case EMF_R12) created the greatest water cushion depth between E and M. When M was positioned without F, the water cushion between E and M was created by raising the height of the mound rather than its length. Conversely, a mound with a minimum height and length with a forest was found to be effective in creating the largest water cushion and maximum reduction of the flow energy. The highest energy reduction was between 45 and 70% in this experiment. These findings provide useful insights for developing multiple tsunami mitigation strategies that combine artificial and natural approaches. Full article

Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of detecting ransomware attacks by monitoring the usage of resources, triggered by anomalous behavior during an active attack. By analyzing the recorded parameters after recovery and logging any adverse effects, we were able to train the system for better detection patterns. The proposed solution allows for detection and intervention against the crypto and locker types of ransomware attacks. In previous work, the authors introduced a novel anti-ransomware tool for Windows platforms, known as R-Locker, which demonstrates high effectiveness and efficiency in countering ransomware attacks. The R-Locker solution employs “honeyfiles”, which serve as decoy files to attract ransomware activities. Upon the detection of any malicious attempts to access or alter these honeyfiles, R-Locker automatically activates countermeasures to thwart the ransomware infection and mitigate its impact. Building on our prior R-Locker framework this work introduces a multi-stage detection architecture with resource–behavioral hybrid analysis, achieving cross-platform efficacy against evolving ransomware families not addressed previously. Full article

Weather patterns substantially influence extreme weathers in Japan. Extreme high temperature events can cause serious health problems, including heat stroke. Therefore, understanding weather patterns, along with their impacts on human health, is critically important for developing effective public health measures. This study examines the impact of weather patterns on heat stroke risk, focusing on a two-tiered high-pressure system (DH: double high) consisting of a lower tropospheric western Pacific subtropical high (WPSH) and an overlapping upper tropospheric South Asian high (SAH), which is thought to cause high-temperature events in Japan. In this study, the self-organizing map technique was utilized to investigate the relationship between pressure patterns and the number of heat stroke patients in four populous cities. The study period covers July and August from 2008 to 2021. The results show that the average number of heat stroke patients in these cities is higher on DH days than on WPSH days in which SAH is absent. The probability of an extremely high daily number of heat stroke patients is more than twice as high on DH days compared to WPSH days. Notably, this result remains true even when WPSH and DH days are compared within the same air temperature range. This is attributable to the higher humidity and stronger solar radiation under DH conditions, which enhances the risk of heat stroke. Large-scale circulation anomalies similar to the Pacific–Japan teleconnection are found on DH days, suggesting that both high humidity and cloudless conditions are among the large-scale features controlled by this teleconnection. Early countermeasures to mitigate heat stroke risk, including advisories for outdoor activities, should be taken when DH-like weather patterns are predicted. Full article

Substantial progress has been made in the development of radiation countermeasures, resulting in the recent approval of several mitigators; however, there has yet to be an approved prophylactic radioprotectant. Research on countermeasure performance in mixed neutron and gamma radiation fields has also been scarce. Fibroblast-stimulating lipopeptide (FSL-1) is a novel synthetic agonist for toll-like receptor 2/6. In previous studies, the administration of FSL-1 before and after gamma radiation significantly improved survival outcomes for mice through the activation of the NF-κB pathway. In the current study, we tested FSL-1’s radioprotective abilities in a mixed radiation field that models one produced by a nuclear detonation in 11–14-week-old C57BL/6 male and female mice. We demonstrate that a single dose of 1.5 mg/kg of FSL-1 administered 12 h prior to 65% neutron 35% gamma mixed-field (MF) irradiation enhances survival, accelerates recovery of hematopoietic cell and stem cell populations, reduces inflammation, and protects innate immune function in mice. FSL-1’s ability to recover blood and protect immune functions is important in countering the high rate of incidence of sepsis caused by MF radiation’s damaging effects. These results demonstrate that FSL-1 is a promising prophylactic countermeasure where exposure to MF radiation is anticipated. Full article

The incorporation of Driver Monitoring Systems (DMSs) in vehicles is fundamental to enhancing road safety by continuously assessing driver behavior and identifying signs of fatigue or distraction. However, as these technologies evolve, they also present considerable cybersecurity challenges. This research undertakes an extensive Threat Analysis and Risk Assessment (TARA) of DMSs, adhering to the ISO/SAE 21434 standard, to methodically detect and assess potential security threats. A total of 115 threats were recognized and classified into 95 low-risk, 16 medium-risk, and 4 high-risk scenarios, underscoring key vulnerabilities in data transmission, sensor reliability, and communication frameworks. To mitigate these risks, we suggest a range of countermeasures, including enhanced encryption techniques, stringent authentication protocols, and reinforced access control mechanisms, designed to strengthen the security posture of DMSs in practical applications. This study introduces a structured framework for evaluating and addressing cybersecurity threats in alignment with industry regulations, facilitating the dependable and safeguarded implementation of DMSs in future vehicle architectures while contributing to ongoing progress in automotive cybersecurity. Full article

Artificial Intelligence (AI) security research is promising and highly valuable in the current decade. In particular, deep neural network (DNN) security is receiving increased attention. Although DNNs have recently emerged as a prominent tool for addressing complex challenges across various machine learning (ML) tasks and DNNs stand out as the most widely employed, as well as holding a significant share in both research and industry, DNNs exhibit vulnerabilities to adversarial attacks where slight but intentional perturbations can deceive DNNs models. Consequently, several studies have proposed that DNNs are exposed to new attacks. Given the increasing prevalence of these attacks, researchers need to explore countermeasures that mitigate the associated risks and enhance the reliability of adapting DNNs to various critical applications. As a result, DNNs have been protected against adversarial attacks using a variety of defense mechanisms. Our primary focus is DNN as a foundational technology across all ML tasks. In this work, we comprehensively survey and present the latest research on DNN security based on various ML tasks, highlighting the adversarial attacks that cause DNNs to fail and the defense strategies that protect the DNNs. We review, explore, and elucidate the operational mechanisms of prevailing adversarial attacks and defense mechanisms applicable to all ML tasks utilizing DNN. Our review presents a detailed taxonomy for attacker and defender problems, providing a comprehensive and robust review of most state-of-the-art attacks and defenses in recent years. Additionally, we thoroughly examine the most recent systematic review concerning the measures used to evaluate the success of attack or defense methods. Finally, we address current challenges and open issues in this field and future research directions. Full article