Search Results (291)

With increasing exposure to natural hazards and anthropogenic risks, the robustness of transportation networks must be enhanced to ensure national security and long-term sustainability. However, robustness-optimization research has mainly focused on single-layer networks, while the systematic exploration of multilayer networks that better reflect real-world transportation system characteristics remains insufficient. This study establishes a multilayer integrated transportation network for China, encompassing road, railway, and waterway systems, based on complex network theory. The robustness of single-layer, integrated networks and the integrated transportation networks of the seven major regions is evaluated under various attack strategies. The results indicate that the integrated network exhibits superior robustness to single-layer networks, with the road sub-network proving pivotal for maintaining structural stability. Under the same edge addition ratio, the robustness improvement achieved by the low-importance node enhancement strategy is, on average, about 80% higher than that of the high-importance node strategy, with the effect becoming more significant as the edge addition ratio increases. These findings provide theoretical support for the vulnerability identification and structural optimization of transportation networks, offering practical guidance for constructing efficient, safe, and sustainable transportation systems. Full article

Accelerations and decelerations are critical components of soccer performance, reflecting mechanical load and injury risk, with understanding positional and temporal variations essential for optimizing training prescription. This study analyzed acceleration and deceleration demands in professional soccer players across playing positions and training microcycle phases. Twenty-five professional soccer players (26.6 ± 4.50 years) from a Spanish Second Division team were monitored using 18 Hz GPS STATSports (Newry, UK) devices during 16 training sessions and 4 official matches over four weeks. Accelerations and decelerations were categorized into six intensity zones (Z1–Z6, 0.5–1 to 5–10 m/s²), with players grouped by position: central defenders (CD), full-backs (FB), central midfielders (CM), attacking midfielders (AM) and forwards (FW). Match day (MD) significantly affected all variables (F > 4.75; p < 0.001, ω_p² = 0.13–0.42), with accelerations showing higher values at MD-2 for Z1, MD for Z2, MD-4 and MD for Z3–Z4, consistently reaching lowest values at MD-1. Decelerations peaked at MD across Z2–Z6, with MD-1 showing minimal preparation values. Positionally, FB exceeded other positions in low-intensity accelerations and decelerations (Z1–Z2), while CM dominated high-intensity decelerations (Z4–Z6). Total accelerations differed significantly by position (FB: 579 ± 163 vs. AM: 494 ± 184 events, p < 0.05). Training acceleration loads adequately replicate match demands, but deceleration preparation remains insufficient, representing a potential injury risk. Position-specific protocols should emphasize deceleration conditioning, particularly for CM and FB. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous patient monitoring, early diagnosis, and personalized treatments. However, the het-erogeneity of IoMT devices and the lack of standardized protocols introduce serious security vulnerabilities. To address these challenges, we propose a hybrid BiLSTM-DNN intrusion detection system, named HBiLD-IDS, that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Deep Neural Networks (DNNs), leveraging both temporal dependencies in network traffic and hierarchical feature extraction. The model is trained and evaluated on the CICIoMT2024 dataset, which accurately reflects the diversity of devices and attack vectors encountered in connected healthcare environments. The dataset undergoes rigorous preprocessing, including data cleaning, feature selection through correlation analysis and recursive elimination, and feature normalization. Compared to existing IDS models, our approach significantly enhances detection accuracy and generalization capacity in the face of complex and evolving attack patterns. Experimental results show that the proposed IDS model achieves a classification accuracy of 98.81% across 19 attack types confirming its robustness and scalability. This approach represents a promising solution for strengthening the security posture of IoMT networks against emerging cyber threats. Full article

According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations. Full article

Precipitation-hardenable stainless steels (PHSS) are widely used in various applications in the aeronautical industry such in as landing gear supports, actuators, and fasteners, among others. This research aims to study the pitting corrosion behavior of passivated martensitic precipitation-hardening stainless steel, which underwent passivation for 120 min at 25 °C and 50 °C in citric and nitric acid baths before being immersed in solutions containing 1 wt.% sulfuric acid (H₂SO₄) and 5 wt.% sodium chloride (NaCl). Electrochemical characterization was realized employing electrochemical noise (EN), while microstructural analysis employed scanning electron microscopy (SEM). The result indicates that EN reflects localized pitting corrosion mechanisms. Samples exposed to H₂SO₄ revealed activation–passivation behavior, whereas those immersed in NaCl exhibited pseudo-passivation, indicative of an unstable oxide film. Current densities in both solutions ranged from 10⁻³ to 10⁻⁵ mA/cm², confirming susceptibility to localized pitting corrosion in all test conditions. The susceptibility to localized attack is associated with the generation of secondary oxides on the surface. Full article

The Internet of Things (IoT) is rapidly advancing toward increased autonomy; however, the inherent dynamism, environmental uncertainty, device heterogeneity, and diverse data modalities pose serious challenges to its reliability and security. This paper proposes a novel framework for embedding security awareness into IoT systems—where security awareness refers to the system’s ability to detect uncertain changes and understand their impact on its security posture. While machine learning and deep learning (ML/DL) models integrated with explainable AI (XAI) methods offer capabilities for threat detection, they often lack contextual interpretation linked to system security. To bridge this gap, our framework maps XAI-generated explanations to a system’s structured security profile, enabling the identification of components affected by detected anomalies or threats. Additionally, we introduce a procedural method to compute an Importance Factor (IF) for each component, reflecting its operational criticality. This framework generates actionable insights by highlighting contextual changes, impacted components, and their respective IFs. We validate the framework using a smart irrigation IoT testbed, demonstrating its capability to enhance security awareness by tracking evolving conditions and providing real-time insights into potential Distributed Denial of Service (DDoS) attacks. Full article

Reliable aerodynamic performance estimation is essential for both preliminary design and optimization in various aeronautical applications. In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. The model is trained on an extensive database of NACA four-digit airfoils, covering angles of attack ranging from −5° to 14° and ten Reynolds numbers increasing in steps of 500,000 from 500,000 up to 5,000,000. As a novel contribution, this work investigates the impact of random seed initialization on model accuracy and reproducibility and introduces a seed-based ensemble strategy to enhance generalization. The best-performing single-seed model tested (seed 0) achieves a mean absolute percentage error (MAPE) of 1.1% with an ${\mathrm{R}}^2$ of 0.9998 for the lift coefficient prediction and 0.57% with an ${\mathrm{R}}^2$ of 0.9954 for the drag coefficient prediction. In comparison, the best ensemble model tested (seeds 610, 987, and 75025) achieves a lift coefficient MAPE of 1.43%, corresponding to ${\mathrm{R}}^2$ 0.9999, and a drag coefficient MAPE of 1.19%, corresponding to ${\mathrm{R}}^2$ = 0.9968. All the tested seed dependencies in this paper (ten single seeds and five ensembles) demonstrate an overall ${\mathrm{R}}^2$ greater than 0.97, which reflects the model architecture’s strong foundation. The novelty of this study lies in the demonstration that the same machine learning model, trained on identical data and architecture, can exhibit up to 250% variation in prediction error solely due to differences in random seed selection. This finding highlights the often-overlooked impact of seed initialization on model performance and highlights the necessity of treating seed choice as an active design parameter in ML aerodynamic predictions. Full article

Cyber threat intelligence (CTI) has become critical in enhancing cybersecurity measures across various sectors. This systematic review aims to synthesize the current literature on the effectiveness of CTI strategies in mitigating cyber attacks, identify the most effective tools and methodologies for threat detection and prevention, and highlight the limitations of current approaches. An extensive search of academic databases was conducted following the PRISMA guidelines, including 43 relevant studies. This number reflects a rigorous selection process based on defined inclusion, exclusion, and quality criteria and is consistent with the scope of similar systematic reviews in the field of cyber threat intelligence. This review concludes that while CTI significantly improves the ability to predict and prevent cyber threats, challenges such as data standardization, privacy concerns, and trust between organizations persist. It also underscores the necessity of continuously improving CTI practices by leveraging the integration of advanced technologies and creating enhanced collaboration frameworks. These advancements are essential for developing a robust and adaptive cybersecurity posture capable of responding to an evolving threat landscape, ultimately contributing to a more secure digital environment for all sectors. Overall, the review provides practical reflections on the current state of CTI and suggests future research directions to strengthen and improve CTI’s effectiveness. Full article

Background/Objectives: Immature platelet fraction (IPF) and reticulated platelets (RPs) are biomarkers reflecting the youngest and most metabolically active platelets in circulation. RPs, a subset of immature platelets, contain residual RNA and have been associated with increased thrombotic potential. Elevated IPF levels indicate enhanced platelet production, commonly observed during elevated platelet turnover, such as in autoimmune reactions, consumption, and thrombotic events. This systematic review aims to evaluate the potential role of IPF and RPs in the context of cerebrovascular events, specifically ischemic and hemorrhagic stroke, as well as transient ischemic attacks (TIAs), and to assess their clinical utility in stroke monitoring and management. Methods: A comprehensive literature search was conducted in PubMed, Scopus, Cochrane Library, and Web of Science for studies published between 2000 and 2024, which focused on IPF and RPs in human subjects with cerebrovascular events. Results: Six studies met the inclusion criteria. Findings suggest that elevated levels of IPF and RP are associated with the acute and chronic phases of ischemic stroke and TIA and may reflect increased platelet turnover and thrombotic activity. Some evidence supports their role in predicting stroke severity, recurrence, and underlying etiology, although results are not yet consistent across all studies. Conclusions: IPF and RPs are emerging biomarkers with potential applications in acute ischemic stroke and risk stratification. While current evidence is promising, further research is needed to standardize measurement techniques and validate their routine use in clinical practice. Full article

This paper addresses the design of nonfragile state estimators for memristor-based fractional-order neural networks that are subject to stochastic cyber-attacks and hybrid time delays. To mitigate the issue of limited bandwidth during signal transmission, quantitative processing is introduced to reduce network burden and prevent signal blocking. In real network environments, the outputs may be compromised by cyber-attacks, which can disrupt data transmission systems. To better reflect the actual conditions of fractional-order neural networks, a Bernoulli variable is utilized to describe the statistical properties. Additionally, novel conditions are presented to ensure the stochastic asymptotic stability of the augmented error system through a new fractional-order free-matrix-based integral inequality. Finally, the effectiveness of the proposed estimation methods is demonstrated through two numerical simulations. Full article

This study investigated the impact of sublethal concentrations of dinotefuran on the predatory behavior and detoxification enzyme activity of Harmonia axyridis, aiming to establish a theoretical foundation for the conservation and utilization of natural enemies and the effective management of wheat aphids. This study treated wheat aphids with sublethal concentrations (LC₂₀ and LC₃₀) of dinotefuran via the leaf dipping method and subsequently used them as prey for the fourth-instar larvae of H. axyridis. The predation amount, instantaneous attack rate, handling time, daily maximum predation amount, and detoxification enzyme activity of H. axyridis were statistically analyzed. The results indicated that the predation of H. axyridis on wheat aphids conformed to the Holling II disc equation. Moreover, in comparison to the control group, the handling time of H. axyridis on wheat aphids was extended, and at the same time, the instantaneous attack rate, maximum daily predation amount, and predation efficiency were all diminished. After the ingestion of LC₂₀- and LC₃₀-dinotefuran-treated aphids, the carboxylesterase levels in H. axyridis were not significantly different from the control, with levels 0.97-fold and 0.94-fold that of the control, respectively. Glutathione-S-transferase (GST) demonstrated an induction impact compared to the control, reaching 1.96- and 1.47-fold higher than the control, respectively. The activity of mixed-functional oxidase (MFO) demonstrated an induction effect compared to the control, measuring 1.98- and 3.04-fold higher than that of the control, respectively. Consequently, the predation function and detoxification enzyme activity of H. axyridis were influenced when consuming wheat aphids treated with sublethal concentrations of dinotefuran, with significant variations across different concentrations, potentially reflecting the survival strategy of insects under dinotefuran stress. Full article

With the rapid development of the Internet, malicious URL detection has emerged as a critical challenge in the field of cyberspace security. Traditional machine-learning techniques and subsequent deep-learning frameworks have shown limitations in handling the complex malicious URL data generated by contemporary phishing attacks. This paper proposes a novel detection framework, HSSLC-CharGRU (Hybrid Spatial–Sequential Attention Logically constrained neural network CharGRU), which balances high efficiency and accuracy while enhancing the generalization capability of detection frameworks. The core of HSSLC-CharGRU is the Gated Recurrent Unit (Gated Recurrent Unit, GRU), integrated with the HSSA (Hybrid Spatial–Sequential Attention, HSSA) module. The HSSLC-CharGRU framework proposed in this paper integrates symmetry concepts into its design. The HSSA module extracts URL sequence features across scales, reflecting multi-scale invariance. The interaction between the GRU and HSSA modules provides functional complementarity and symmetry, enhancing model robustness. In addition, the LCNN module incorporates logical rules and prior constraints to regulate the pattern-learning process during feature extraction, reducing the model’s sensitivity to noise and anomalous patterns. This enhances the structural symmetry of the feature space. Such logical constraints further improve the model’s generalization capability across diverse data distributions and strengthen its stability in handling complex URL patterns. These symmetries boost the model’s generalization across datasets and its adaptability and robustness in complex URL patterns. In the experimental part, HSSLC-CharGRU shows excellent detection accuracy compared with the current character-level malicious URL detection models. Full article

As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time. Full article

With the rapid advancement in artificial intelligence, autonomous driving has emerged as a prominent research frontier. Autonomous vehicles rely on high-precision high-definition map data, necessitating timely map updates by map companies to accurately reflect road conditions. This paper proposes an efficient and fair map-data-sharing mechanism for vehicular networks. To encourage vehicles to share data, we introduce a reputation unit to resolve the cold-start issue for new vehicles, effectively distinguishing legitimate new vehicles from malicious attackers. Considering both the budget constraints of map companies and heterogeneous data collection capabilities of vehicles, we design a fair incentive mechanism based on the proposed reputation unit and a reverse auction algorithm, achieving an optimal balance between data quality and procurement costs. Furthermore, the scheme has been developed to facilitate mutual authentication between vehicles and Roadside Unit(RSU), thereby ensuring the security of shared data. In order to address the issue of redundant authentication in overlapping RSU coverage areas, we construct a Merkle hash tree structure using a set of anonymous certificates, enabling single-round identity verification to enhance authentication efficiency. A security analysis demonstrates the robustness of the scheme, while performance evaluations and the experimental results validate its effectiveness and practicality. Full article

The results of this study, which involved treating cotton fabrics with three fluorescent hyperbranched polymers modified with 1,8-naphthalamide (P1), acridine (P2), and dansyl (P3) groups, could have applications in the development of antimicrobial textiles with self-disinfecting ability. The polymers, dissolved in DMF/water solution, were deposited on the cotton fabric using the exhaustion method. The fabrics were thoroughly analyzed by reflection spectra, CIEL*a*b* coordinates, and color difference (∆E). The release of the polymers from the cotton surface was studied in a phosphate buffer with pH = 7.4 and an acetate buffer with pH = 4.5 at 37 °C for 10 h. It is shown that at pH = 7.4, the release of the three polymers occurs slowly (about 4–5%). In contrast, in an acidic medium, due to protonation of the tertiary amino group of 1,8-naphthalimide, P1 passes significantly more readily into the aqueous solution (35%). The possibility of singlet oxygen (¹O₂) generation by the polymers and the cotton fabrics treated with them under sunlight irradiation was followed using an iodometric method. The microbiological activity was investigated against Gram-positive Bacillus cereus and Gram-negative Pseudomonas aeruginosa as model bacterial strains in the dark and after irradiation with sunlight. The antimicrobial activity of the polymers increased after light irradiation, as ¹O₂ attacks and destroys the bacterial cell membrane. Scanning electron microscopy showed that a stable bacterial biofilm had formed on the untreated cotton surface, but treatment with hyperbranched polymers prevented its formation. However, many bacteria were still observed on the fiber surface when the microbial test was performed in the dark, whereas only a few single bacteria were noticed after the illumination. A virucidal effect against respiratory viruses HRSV-2 and AAdV-5 was observed only after irradiation with sunlight. Full article