Search Results (14,481)

As a novel type of ocean monitoring tool, unmanned sailboats exhibit significant application potential. In this study, a novel wing sail structure for offshore unmanned sailboats is proposed and its performance compared with that of the conventional NACA 0021 wing sail. The Reynolds-averaged Navier–Stokes (RANS) equations are employed for numerical analysis, and the aerodynamic performance is evaluated using ANSYS Fluent. The results indicate that the lift coefficient and lift-to-drag ratio of the HF-14-CE-01 wing sail are significantly superior to those of the NACA 0021 wing sail. Compared to the NACA 0021 wing sail, the HF-14-CE-01 wing sail has undergone structural optimization. The HF-14-CE-01 wing sail demonstrates improved wind direction efficiency, uniform force distribution, ease of adjustment, and extends the service life of the sail. Subsequent research examined the influence of aspect ratio on both the aerodynamic performance of the wing sail and the thrust generated by the unmanned sailboat, identifying an optimal aspect ratio of 4 for the HF-14-CE-01 wing sail. Analysis of the velocity and static pressure contour maps for the HF-14-CE-01 wing sail identified a critical angle of attack of 28°, providing a clear visual representation of its aerodynamic performance. Furthermore, compared with other rigid sail designs, the HF-14-CE-01 wing sail achieved a 30.9% increase in peak lift coefficient, indicating superior propulsion capability. Full article

Plants are constantly exposed to various environmental challenges, such as drought, flooding, heavy metal toxicity, and pathogen attacks. To cope with these stresses, they employ several adaptive strategies. This review highlights the potential of plant-derived smoke (PDS) solution as a natural biostimulant for improving plant health and resilience, contributing to both crop productivity and ecological restoration under abiotic and biotic stress conditions. Mitigating effects of PDS solution against various stresses were observed at morphological, physiological, and molecular levels in plants. PDS solution application involves strengthening the cell membrane by minimizing electrolyte leakage, which enhances cell membrane stability and stomatal conductance. The increased reactive-oxygen species were managed by the activation of the antioxidant system including ascorbate peroxidase, superoxide dismutase, and catalase to meet oxidative damage caused by challenging conditions imposed by flooding, drought, and heavy metal stress. PDS solution along with other by-products of fire, such as charred organic matter and ash, can enrich the soil by slightly increasing its pH and improving nutrient availability. Additionally, some studies indicated that PDS solution may influence phytohormonal pathways, particularly auxins and gibberellic acids, which can contribute to root development and enhance symbiotic interactions with soil microbes, including mycorrhizal fungi. These combined effects may support overall plant growth, though the extent of PDS contribution may vary depending on species and environmental conditions. This boost in plant growth contributes to protecting the plants against pathogens, which shows the role of PDS in enduring biotic stress. Collectively, PDS solution mitigates stress tolerance in plants via multifaceted changes, including the regulation of physico-chemical responses, enhancement of the antioxidant system, modulation of heavy metal speciation, and key adjustments of photosynthesis, respiration, cell membrane transport, and the antioxidant system at genomic/proteomic levels. This review focuses on the role of PDS solution in fortifying plants against environmental stresses. It is suggested that PDS solution, which already has been determined to be a biostimulant, has potential for the revival of plant growth and soil ecosystem under abiotic and biotic stresses. Full article

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article

Background/Objectives: The extension of euthanasia and physician-assisted suicide to individuals with mental disorders presents a profound ethical, clinical, and legal challenge. While increasingly accepted in some jurisdictions, their application in psychiatric contexts—particularly in cases of depression—raises concerns about diagnostic precision, therapeutic adequacy, and the validity of informed consent. This study examines two controversial Belgian cases to explore the complexities of euthanasia for psychological suffering. Methods: A qualitative case analysis was conducted through a qualitative analysis of publicly available media sources. The cases were examined through clinical, psychoanalytic, and medico-legal lenses to assess diagnostic clarity, treatment history, and ethical considerations. No access to official medical records was available. Case Presentation: The first case involved a young woman whose depressive symptoms were reportedly linked to trauma from a terrorist attack. The second concerned a middle-aged woman convicted of infanticide and later diagnosed with Major Depression. Discussion: In both cases, euthanasia was granted on the grounds of “irreversible psychological suffering.” However, the absence of detailed clinical documentation, potential unresolved trauma, and lack of psychodynamic assessment raised doubts about the robustness of the evaluations and the validity of informed consent. Conclusions: These findings highlight the need for a more rigorous, multidisciplinary, and ethically grounded approach to psychiatric euthanasia. This study underscores the importance of precise diagnostic criteria, comprehensive treatment histories, and deeper exploration of unconscious and existential motivations. Safeguarding clinical integrity and ethical standards is essential in end-of-life decisions involving mental illness. Full article

To meet the requirements of geometric nonlinear modeling and bending–torsion coupling analysis of long, flexible offshore blades, this paper develops a high-precision engineering simplified model based on the Absolute Nodal Coordinate Formulation (ANCF). The model considers nonlinear variations in linear density, stiffness, and aerodynamic center along the blade span and enables efficient computation of 3D nonlinear deformation using 1D beam elements. Material and structural function equations are established based on actual 2D airfoil sections, and the chord vector is obtained from leading and trailing edge coordinates to calculate the angle of attack and aerodynamic loads. Torsional stiffness data defined at the shear center is corrected to the mass center using the axis shift theorem, ensuring a unified principal axis model. The proposed model is employed to simulate the dynamic behavior of wind turbine blades under both shutdown and operating conditions, and the results are compared to those obtained from the commercial software Bladed. Under shutdown conditions, the blade tip deformation error in the y-direction remains within 5% when subjected only to gravity, and within 8% when wind loads are applied perpendicular to the rotor plane. Under operating conditions, although simplified aerodynamic calculations, structural nonlinearity, and material property deviations introduce greater discrepancies, the x-direction deformation error remains within 15% across different wind speeds. These results confirm that the model maintains reasonable accuracy in capturing blade deformation characteristics and can provide useful support for early-stage dynamic analysis. Full article

Unmanned Aerial Vehicles (UAVs) face collision risks due to Beyond Visual Line of Sight operations. Therefore, UAV Traffic Management (UTM) systems are used to manage and monitor UAV flight paths. However, centralized UTM systems are susceptible to various security attacks and are inefficient in managing flight data from different service providers. It further fails to provide low-latency communication required for UAV real-time operations. Thus, this paper proposes to integrate Drone-to-Drone (D2D) communication protocol into a secure public blockchain-based UTM system to enable direct communication between UAVs for efficient collision avoidance. The D2D protocol is designed using SHA256 hash function and bitwise XOR operations. A proof of concept has been built to verify that the UTM system is secure by enabling authorized service providers to view sensitive flight data only using legitimate secret keys. The security of the protocol has been analyzed and has been proven to be secure from key disclosure, adversary-in-the-middle, replay, and tracking attacks. Its performance has been evaluated and is proven to outperform existing studies by having the lowest computation cost of 0.01 ms and storage costs of 544–800 bits. Full article

Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments. Full article

There is currently no effective therapeutic capable of arresting or inducing regression of primary or metastatic brain cancers. This article presents both pre-clinical and clinical studies supportive that a new bioengineered technology could induce regression and/or elimination of primary and metastatic brain cancers through three disease-modifying mechanisms. Transcranial Radiofrequency Wave Treatment (TRFT) is non-thermal, non-invasive and self-administered in-home to safely provide radiofrequency waves to the entire human brain. Since TRFT has already been shown to stop and reverse the cognitive decline of Alzheimer’s Disease in small studies, evidence is provided that three key mechanisms of TRFT action, alone or in synergy, could effectively treat brain cancers: (1) enhancement of brain meningeal lymph flow to increase immune trafficking between the brain cancer and cervical lymph nodes, resulting in a robust immune attack on the brain cancer; (2) rebalancing of the immune system’s cytokines within the brain or brain cancer environment to decrease inflammation therein and thus make for an inhospitable environment for brain cancer growth; (3) direct anti-proliferation/antigrowth affects within the brain tumor microenvironment. Importantly, these mechanisms of TRFT action could be effective against both visualized brain tumors and those that are yet too small to be identified through brain imaging. The existing animal and human clinical evidence presented in this perspective article justifies TRFT to be clinically tested immediately against both primary and metastatic brain cancers as monotherapy or possibly in combination with immune checkpoint inhibitors. Full article

Advanced Persistent Threats (APTs) pose significant cybersecurity challenges due to their multi-stage complexity. Knowledge graphs (KGs) effectively model APT attack processes through node-link architectures; however, the scarcity of high-quality, annotated datasets limits research progress. The primary challenge lies in balancing annotation cost and quality, particularly due to the lack of quality assessment methods for graph annotation data. This study addresses these issues by extending existing APT ontology definitions and developing a dynamic, trustworthy annotation framework for APT knowledge graphs. The framework introduces a self-verification mechanism utilizing large language model (LLM) annotation consistency and establishes a comprehensive graph data metric system for problem localization in annotated data. This metric system, based on structural properties, logical consistency, and APT attack chain characteristics, comprehensively evaluates annotation quality across representation, syntax semantics, and topological structure. Experimental results show that this framework significantly reduces annotation costs while maintaining quality. Using this framework, we constructed LAPTKG, a reliable dataset containing over 10,000 entities and relations. Baseline evaluations show substantial improvements in entity and relation extraction performance after metric correction, validating the framework’s effectiveness in reliable APT knowledge graph dataset construction. Full article

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

Machine learning (ML) has greatly improved intrusion detection in enterprise networks. However, ML models remain vulnerable to adversarial attacks, where small input changes cause misclassification. This study evaluates the robustness of a Random Forest (RF), a standard neural network (NN), and a Transformer-based Network Intrusion Detection System (NIDS). It also introduces ADV_NN, an adversarially trained neural network designed to improve resilience. Model performance is tested using the UNSW-NB15 dataset under both clean and adversarial conditions. The attack types include Projected Gradient Descent (PGD), Fast Gradient Sign Method (FGSM), and Black-Box transfer attacks. The proposed ADV_NN achieves 86.04% accuracy on clean data. It maintains over 80% accuracy under PGD and FGSM attacks, and exceeds 85% under Black-Box attacks at $ϵ=0.15$. In contrast, the RF, NN, and Transformer-based models suffer significant degradation under adversarial perturbations. These results highlight the need to incorporate adversarial defenses into ML-based NIDS for secure deployment in real-world environments. Full article

Due to the rapid expansion of Internet of Things devices and their associated network, security has become a critical concern, necessitating the development of reliable security mechanisms. Anomaly-based NIDS leveraging machine learning and deep learning have emerged as key solutions in detecting abnormal network traffic patterns. However, one challenge that affects the detection rate of machine learning or deep learning-based anomaly NIDS is the class data imbalance present in the existing dataset. Datasets are crucial for the development and evaluation of anomaly-based NIDS for IoT systems. In this study, we introduce EmuIoT-VT, a dataset generated by creating virtual replicas of IoT devices implementing a novel emulation-based method, enabling realistic network traffic generation without relying on any external network emulators. The data was collected in an isolated offline environment to capture clean, uncontaminated network traffic. The EmuIoT-VT is balanced-by-design, containing 28,000 labeled records that are evenly distributed across devices, classes, and subclasses, and supports both binary and multiclass classification tasks. It includes 82 features extracted from raw PCAP data and includes attack categories such as DoS, brute force, reconnaissance, and exploitation. This article presents the novel method and creation of the EmuIoT-VT dataset, detailing data collection, balancing strategy, and details of the dataset structure, and proposes directions for future work. Full article

As Industrial Control Systems (ICSs) increasingly adopt cloud–edge collaborative architectures, they face escalating risks from complex cross-domain cyber threats. To address this challenge, we propose a novel threat assessment framework specifically designed for cloud–edge-integrated ICSs. Our approach systematically identifies and evaluates security risks across cyber and physical boundaries by building a structured dataset of ICS assets, attack entry points, techniques, and impacts. We introduce a unique set of evaluation indicators spanning four key dimensions—system modules, attack paths, attack methods, and potential impacts—providing a holistic view of cyber threats. Through simulation experiments on a representative ICS scenario inspired by real-world attacks, we demonstrate the framework’s effectiveness in detecting vulnerabilities and quantifying security posture improvements. Our results underscore the framework’s practical utility in guiding targeted defense strategies and its potential to advance research on cloud–edge ICS security. This work not only fills gaps in the existing methodologies but also provides new insights and tools applicable to sectors such as smart grids, intelligent manufacturing, and critical infrastructure protection. Full article

Software-Defined Networking (SDN) is a network architecture that decouples the control plane from the data plane, enabling centralized, programmable management of network traffic. SDN introduces centralized control and programmability to modern networks, improving flexibility while also exposing new security vulnerabilities across the application, control, and data planes. This paper provides a comprehensive overview of SDN security threats and defenses, covering recent developments in controller hardening, trust management, route optimization, and anomaly detection. Based on these findings, we present a comparative analysis of SDN controllers in terms of performance, scalability, and deployment complexity. This culminates in the introduction of the Cloud-to-Edge Layer Two (CELT)-Secure switch, a virtual OpenFlow-based data-plane security mechanism. CELT-Secure detects and blocks Internet Control Message Protocol flooding attacks in approximately two seconds and actively disconnects hosts engaging in Address Resolution Protocol-based man-in-the-middle attacks. In comparative testing, it achieved detection performance 10.82 times faster than related approaches. Full article

Phishing attacks are an increasingly common cybersecurity threat and are characterized by deceiving people into giving out their private credentials via emails, websites, and messages. An insight into students’ challenges in recognizing phishing threats can provide valuable information on how AI-based detection systems can be improved to enhance accuracy, reduce false positives, and build user trust in cybersecurity. This study focuses on students’ awareness of phishing attempts and evaluates AI-based phishing detection systems. Questionnaires were circulated amongst students, and responses were evaluated to uncover prevailing patterns and issues. The results indicate that most college students are knowledgeable about phishing methods, but many do not recognize the dangers of phishing. Because of this, AI-based detection systems have potential but also face issues relating to accuracy, false positives, and user faith. This research highlights the importance of bolstering cybersecurity education and ongoing enhancements to AI models to improve phishing detection. Future studies should include a more representative sample, evaluate AI detection systems in real-world settings, and assess longer-term changes in phishing-related awareness. By combining AI-driven solutions with education a safer digital world can created. Full article