Search Results (2,188)

Water distribution infrastructures are increasingly incorporating the IoT in the form of sensing and computing power to improve control over the system and achieve greater adaptability to water demand. This evolution, from physical to cyber-physical systems, comes with an attack perimeter extended from physical infrastructure to cyberspace. Being able to detect this novel kind of attack is gaining traction in the scientific community. Machine learning detection algorithms, which are showing encouraging results in cybersecurity applications, are leveraging the increasing number of datasets published in the water distribution community for better attack detection. These datasets also begin to reflect this novel cyber-physical aspect in two ways, first by conducting cyberattacks against the testbed infrastructures during data acquisition, and secondly by including network traffic data along with the physical data captured during the experimentations. However, current machine learning models do not fully take into account this cyber-physical component, being only trained either on the physical or on the network data. This paper addresses this problem by providing a multi-layer approach to applying machine learning to cyber-physical systems, by combining physical and network traffic data and assessing their effects on the attack detection performance of machine learning algorithms, as well as the cross-impact with data enriched with graph metrics. Full article

Stress-related anxiety disorders and anxiety-like behavior in post-traumatic stress disorder (PTSD) are associated with altered neurocircuitry pathways, neurotransmitter systems, and the activities of monoamine and glucocorticoid-metabolizing enzymes. Resveratrol, a natural polyphenol, is recognized for its antioxidant, anti-inflammatory, and antipsychiatric properties. Previous studies suggest that resveratrol reduces anxiety-like behavior in animal PTSD models by downregulating key enzymes such as 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD-1) and monoamine oxidases (MAOs). However, the underlying mechanisms remain unclear. In this study, we explored the efficacy of resveratrol in treating stress-induced anxiety using a chronic predator stress model in rats. Resveratrol was administered intraperitoneally at 100 mg/kg following a 10-day stress exposure, and anxiety behavior was assessed with an elevated plus maze. Our results indicated that stress-related anxiety correlated with increased activities of brain MAO-A, MAO-B, and hepatic 11β-HSD-1, alongside elevated oxidative stress markers in the brain and liver. Resveratrol treatment improved anxiety behavior and decreased enzyme activities, oxidative stress, and hepatic damage. We demonstrate that resveratrol exerts antianxiogenic effects by modulating glucocorticoid and monoamine metabolism in the brain and liver. These findings suggest resveratrol’s potential as a therapeutic agent for anxiety disorders, warranting further clinical investigation. Full article

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance. Full article

Integrating Internet of Things (IoT) devices into port operations has brought substantial improvements in efficiency, automation, and connectivity. However, this technological advancement has also introduced new operational risks, particularly in terms of cybersecurity vulnerabilities and potential disruptions. The primary objective of this scientific article is to comprehensively analyze and identify the primary security threats and vulnerabilities that IoT devices face when deployed in port environments. This includes examining potential risks, such as unauthorized access, cyberattacks, malware, etc., that could disrupt critical port operations and compromise sensitive information. This research aims to assess the critical entities associated with IoT devices in port environments and develop a comprehensive risk-management framework tailored to these settings. It also aims to explore and propose strategic measures and best practices to mitigate these risks. For this research, a risk-management framework grounded in the principles of ORM, which includes risk avoidance, reduction, sharing, and retention strategies, was developed. The primary outcome of this research is the development of a comprehensive risk-management framework specifically tailored for IoT devices in port environments, utilizing Operational Risk-Management (ORM) methodology. This framework will systematically identify and categorize critical vulnerabilities and potential threats for IoT devices. By addressing these objectives, the article seeks to provide actionable insights and guidelines that can be adopted by port authorities and stakeholders to safeguard their IoT infrastructure and maintain operational stability in the face of emerging threats. Full article

Background/Objectives: The aim of this study was to evaluate the effects of lifelong team handball/football training on regional bone health and body composition in elderly women. Methods: Seventeen elderly women team handball/football players (65.9 ± 5.7 years) and twenty-one untrained age-matched women (controls) (67.7 ± 5.1 years) participated. Whole-body and regional dual-energy X-ray absorptiometry scans of arms, legs, and lower spine (L1–L4) were performed. Results: We observed 8% and 9% higher bone mineral density (BMD) and bone mineral content (BMC), respectively, at the whole-body level and in the legs and 11.5% higher BMC in the legs in team handball/football players compared to untrained age-matched controls (p < 0.05). Higher total and leg lean body mass (p < 0.05), along with lower total body fat percentage (p < 0.05) and higher T- and Z-scores, markers of fragility risk fracture (0.294 ± 1.461 vs. −0.538 ± 1.031; 1.447 ± 1.278 vs. 0.724 ± 0.823, respectively), were also found in team handball/football players compared to controls (p < 0.05). No significant differences in nutritional habits were observed between groups. Conclusions: Our study suggest that the beneficial effects of lifetime handball/football practice on bone preservation in elderly women occur independently from nutritional intake, which emphasize the potential role of team sports in osteoporosis prevention. Future studies should focus on the cofounding factors and causative mechanisms mediated by team sport practice in osteoporosis prevention. Full article

Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early diagnosis and treatment are crucial. While Alvarado’s clinical scoring system is not sufficient, ultrasound and computed tomography (CT) imaging are effective but have downsides such as operator-dependency and radiation exposure. This study proposes the use of machine learning methods and a locally collected reliable dataset to enhance the identification of acute appendicitis while detecting the differences between complicated and non-complicated appendicitis. Machine learning can help reduce diagnostic errors and improve treatment decisions. This study conducted four different experiments using various ML algorithms, including K-nearest neighbors (KNN), DT, bagging, and stacking. The experimental results showed that the stacking model had the highest training accuracy, test set accuracy, precision, and F1 score, which were 97.51%, 92.63%, 95.29%, and 92.04%, respectively. Feature importance and explainable AI (XAI) identified neutrophils, WBC_Count, Total_LOS, P_O_LOS, and Symptoms_Days as the principal features that significantly affected the performance of the model. Based on the outcomes and feedback from medical health professionals, the scheme is promising in terms of its effectiveness in diagnosing of acute appendicitis. Full article

The increasing sophistication of cyberattacks necessitates the development of advanced detection systems capable of accurately identifying and mitigating potential threats. This research addresses the critical challenge of cyberattack detection by employing a comprehensive approach that includes generating a realistic yet imbalanced dataset simulating various types of cyberattacks. Recognizing the inherent limitations posed by imbalanced data, we explored multiple data augmentation techniques to enhance the model’s learning effectiveness and ensure robust performance across different attack scenarios. Firstly, we constructed a detailed dataset reflecting real-world conditions of network intrusions by simulating a range of cyberattack types, ensuring it embodies the typical imbalances observed in genuine cybersecurity threats. Subsequently, we applied several data augmentation techniques, including SMOTE and ADASYN, to address the skew in class distribution, thereby providing a more balanced dataset for training supervised machine learning models. Our evaluation of these techniques across various models, such as Random Forests and Neural Networks, demonstrates significant improvements in detection capabilities. Moreover, the analysis also extends to the investigation of feature importance, providing critical insights into which attributes most significantly influence the predictive outcomes of the models. This not only enhances the interpretability of the models but also aids in refining feature engineering and selection processes to optimize performance. Full article

In recent years, interest in renewable energy and photovoltaic systems has increased significantly. The design and implementation of photovoltaic systems are various, and they are in continuous development due to the technologies used. Photovoltaic systems are becoming increasingly complex due to the constantly changing needs of people, who are using more and more intelligent functions such as remote control and monitoring, power/energy prediction, and detection of broken devices. Advanced remote supervision and control applications use artificial intelligence approaches and expose photovoltaic systems to cyber threats. This article presents a detailed examination of the applications of various remote-control, artificial intelligence, and cybersecurity techniques across a diverse range of solar energy sources. The discussion covers the latest technological innovations, research outcomes, and case studies in the photovoltaics field, as well as potential challenges and the possible solutions to these challenges. Full article

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts. Full article

This paper thoroughly assesses the ARM and RISC-V architectures in the context of high-performance computing (HPC). It includes an analysis of Docker and Kubernetes integration. Our study aims to evaluate and compare these systems’ performance, scalability, and practicality in a general context and then assess the impact they might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in working with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM’s preparedness and the potential of RISC-V while acknowledging the increased complexity and significant trade-offs involved at this point. Full article

Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model’s performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively. Full article

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models. Full article

The analysis of utilising unmanned aerial vehicles (UAVs) to form flying networks in obstacle conditions and various algorithms for obstacle avoidance is conducted. A planning scheme for deploying a flying LiFi network based on UAVs in a production facility with obstacles is developed and described. Such networks are necessary to ensure reliable data transmission from sensors or other sources of information located in dangerous or hard-to-reach places to the crisis centre. Based on the planning scheme, the following stages are described: (1) laying the LiFi signal propagation route in conditions of interference, (2) placement of the UAV at the specified points of the laid route for the deployment of the LiFi network, and (3) ensuring the reliability of the deployed LiFi network. Strategies for deploying UAVs from a stationary depot to form a flying LiFi network in a room with obstacles are considered, namely the strategy of the first point for the route, the strategy of radial movement, and the strategy of the middle point for the route. Methods for ensuring the uninterrupted functioning of the flying LiFi network with the required level of reliability within a given time are developed and discussed. To implement the planning stages for deploying the UAV flying LiFi network in a production facility with obstacles, the “Simulation Way” and “Reliability Level” software tools are developed and described. Examples of utilising the proposed software tools are given. Full article

Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The entire digitalized manufacturing systems operate through the Internet, and hence, cybersecurity threats have become a problem area for manufacturing companies. The impacts can be very serious because cyber-attacks can penetrate operations carried out in the physical infrastructure, causing explosions, crashes, collisions, and other incidents. This research is a thematic literature review of the deterrence to such attacks by protecting IoT devices by employing provenance blockchain and artificial intelligence. The literature review was conducted on four themes: cloud manufacturing design, cybersecurity risks to the IoT, provenance blockchains for IoT security, and artificial intelligence for IoT security. These four themes of the literature review were critically analyzed to visualize a framework in which provenance blockchain and artificial intelligence can be integrated to offer a more effective solution for protecting IoT devices used in cloud manufacturing from cybersecurity threats. The findings of this study can provide an informative framework. Full article

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats. Full article