Search Results (175)

Unmanned Aerial Vehicle Ad Hoc Networks (UAVANETs) are characterized by highly dynamic topology changes and unstable link conditions, which necessitate deep collaboration between transport-layer congestion control and network-layer routing decisions to ensure service quality. However, the existing layered architecture of Software-Defined Networking (SDN) results in a significant separation between routing information and congestion control mechanisms, rendering traditional protocols ineffective in handling severe performance fluctuations caused by highly dynamic route switching. The significant disconnect between network-layer route planning and transport-layer congestion control strategies in Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs) leads to degraded transmission performance of BBR (Bottleneck Bandwidth and Round-trip propagation time) under high-dynamic route switching scenarios. As such, this paper proposes an in-band network telemetry (INT)-based cross-layer optimization scheme for BBR, named SDN-BBR. Firstly, a lightweight real-time route switching detection mechanism based on INT is designed. Secondly, a QoS inequality model before and after path switching is established, deriving the critical bandwidth of the new path and integrating it into the BBR algorithm to accelerate convergence and avoid congestion. Finally, the BBR state machine is redesigned to achieve cross-layer information fusion and coordinated control, thereby optimizing transmission performance. Experimental results show that the proposed scheme reduces convergence time by 69.8% and increases throughput by 73.9% in low-bandwidth to high-bandwidth switching scenarios; decreases packet loss rate by 86.8% and reduces delay by 8.3% in high-bandwidth to low-bandwidth switching scenarios; and improves throughput by 12.3%, lowers packet loss rate by 21%, and reduces delay by 7.9% in multi-traffic flow concurrent scenarios. The scheme significantly enhances the transmission performance of BBR in highly dynamic routing environments of SD-UAVANET. Full article

Digital substations (DSs) play a critical role in modern Energy and Power Electrical Systems (EPESs), enabling intelligent control, monitoring, and automation. With increased reliance on communication and sensing technologies, DSs are vulnerable to cyberattacks such as False Data Injection (FDI). An adversary may falsify transformer temperature readings, misleading protection mechanisms and resulting in incorrect disconnection actions. These false disconnections may disrupt power delivery, cause economic losses, and reduce equipment lifespan. To address these challenges, we propose a reinforcement learning-based approach for cyber protection of smart grids against false temperature data injection attacks. Specifically, this work designs a Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG) deep reinforcement learning algorithm that learns to detect normal patterns and responds to suspicious thermal patterns by dynamically adjusting disconnection decisions. The agents process sequential state features to differentiate between legitimate overload conditions and sudden anomalies caused by FDI attacks. We implement the proposed approach on the IEEE 30-bus distribution network using the Pandapower simulator. The experimental results indicate that the LSTM-DDPG controller outperforms conventional DDPG and DQN baselines, achieving a recall of 0.897, F1 of 0.945, precision of 1.00 and accuracy of 0.981 with a confidence interval of 95%. In addition, grid stability reaches up to 0.9815, 1.0, 1.0, 0.9926 with respect to the voltage stability score, transformer stability value, disconnection stability, and stability index, respectively. The proposed method led to fewer false disconnections, providing improved robustness against sensor manipulations. Full article

Deepfake technology poses significant threats to information authenticity and social trust, necessitating effective detection methods. However, existing detection approaches predominantly rely on high-complexity network architectures that, while accurate in controlled environments, suffer from prohibitive computational costs that hinder deployment in resource-constrained scenarios such as social media platforms. To address this efficiency-accuracy dilemma, we propose a lightweight face forgery detection method that systematically learns multi-scale forgery traces. Our approach features a four-stage lightweight architecture that hierarchically extracts features from local textures to global semantics, mimicking the human visual system. Within each stage, a multi-scale dynamic perception mechanism divides feature channels into parallel groups equipped with lightweight attention modules to capture forgery cues spanning pixel-level anomalies, local structures, regional patterns, and semantic inconsistencies. Furthermore, rather than relying on conventional feature fusion that risks suppressing subtle artifacts, we introduce a novel Context-Guided Dynamic Convolution. This mechanism uses mid-level spatial anomalies as active anchors to dynamically modulate high-level semantic filters, with the goal of mitigating the disconnect between semantic content and forgery evidence. Our model achieves strong performance, yielding an AUC of 91.98% on FaceForensics++ and 93.50% on DeepFake Detection Challenge, outperforming current state-of-the-art lightweight methods. Furthermore, compared to heavy Vision Transformers, our model achieves a superior performance-efficiency trade-off, requiring only 3.06 M parameters and 1.36 G FLOPs, making it highly suitable for real-time, resource-constrained deployment. Full article

Building Information Modeling (BIM) has changed the landscape of the architectural, engineering, and construction (AEC) industry in recent decades. However, BIM is not well researched in most developing countries; in particular, few studies have addressed its adoption in Malawi. A non-probability, purposive sampling approach was adopted. A total of 143 questionnaires were completed. This research reveals that, while construction experts are aware of BIM, the level of uptake remains quite low. Architects in Malawi are the most knowledgeable, followed by land surveyors and then engineers. This research shows that most experts in Malawi are at level 1 of BIM usage, which is the first stage of BIM adoption and is characterized by the use of 3D models and output representation. Furthermore, the study results have shown that the Malawian AEC sector is currently succeeding at the modelling stage of maturity but is stalled by lack of collaborative frameworks, such as Integrated Project Delivery (IPD). Therefore, unless the industry shifts toward a unified Common Data Environment (CDE), advanced capabilities like clash detection will remain underutilized and disconnected from broader project success metrics. Statistical analysis has shown that the correlation analysis demonstrates a strong link (r = 0.75) between Integrated Project Delivery (IPD) and high BIM maturity, whereas traditional Design-Bid-Build methods show a critical misalignment with digital workflows. The study identifies high software costs and a lack of national standards as the primary barriers to adoption. Therefore, there is a need for robust sensitization to the benefits of BIM and training to improve its uptake in the context of Malawi’s construction industry. In order to advance Malawi’s BIM maturity, the research recommends a strategic shift toward integrated procurement models, the establishment of national BIM mandates, and the modernization of technical education to bridge the existing knowledge gap. Full article

Healthcare fragmentation—characterized by poor coordination among independently operating organizations—systematically degrades care quality while escalating costs. While healthcare delivery inherently operates as a System of Systems (SoS), existing approaches lack semantic rigor to bridge governance principles with implementable architectures, and digital engineering paradigms remain disconnected from formal representations of regulatory constraints and organizational interdependencies. This paper presents a comprehensive Web Ontology Language (OWL 2 DL)-based ontology integrating structural, behavioral, and regulatory dimensions of healthcare SoS into a unified, computationally tractable framework. Developed following the Methontology engineering methodology and validated using the HermiT reasoner, the ontology formalizes constituent system categories through functional decomposition, establishes an interaction taxonomy distinguishing intra-category coordination from inter-category integration, and introduces the Covenant class as a novel governance mechanism. The covenant embeds legal frameworks (HIPAA, GDPR), interoperability protocols (FHIR, HL7), and technical standards (SNOMED, LOINC, ICD-11, ISO) as first-class ontological entities with explicit relationships to interaction properties. Governance enforcement is operationalized through a layered validation architecture comprising SWRL rules for deductive compliance checking, SHACL shapes for structural constraint validation, and OWL equivalentClass axioms for automated conflict detection. The ontology is further validated through four operational scenarios that demonstrate automated consent validation, standards compliance verification, protocol interoperability checking, and temporal compliance with conflict detection, alongside extended SPARQL queries that reveal constituent system landscapes, standards coverage, interaction networks, and topological properties through node degree calculation, hub identification, and network density analysis. The ontology enables pre-implementation governance assessments, evidence-based policy simulation, digital twin implementations with continuous compliance monitoring, and resilience planning through network analysis, transforming governance from reactive compliance checking to proactive coordination engineering. Full article

Digital twin (DT) technologies are increasingly applied in manufacturing to support monitoring, optimization, and predictive maintenance; however, most implementations remain operationally focused and disconnected from system-level decision-making and lifecycle engineering. This limitation is particularly critical in manufacturing environments that exhibit System-of-Systems (SoS) characteristics, where performance emerges from the interactions among autonomous, interdependent subsystems. This study proposes an integrated systems engineering framework in which the digital twin functions as a system-level integrator rather than a standalone simulation tool. The framework embeds Quality Function Deployment (QFD), Analytic Hierarchy Process (AHP), Reliability and Safety analysis (RAMST), and Statistical Process Control (SPC) within a unified digital twin architecture, enabling explicit traceability from stakeholder requirements to design decisions, operational control, and lifecycle performance. The framework is demonstrated through a robotic fastening system operating under high variability, multi-vendor integration, and reliability constraints. A high-fidelity digital twin was developed in MATLAB Simscape and synchronized with operational data via virtual sensors and SPC-based monitoring. Results from a 35-month simulation study (n = 1050 operations) show a 30% reduction in system downtime and a 15% improvement in fastening quality (torque and angle compliance), supported by 95% confidence intervals, alongside enhanced fault detection and preventive maintenance capabilities. The findings demonstrate that integrating decision-making, monitoring, and learning within a single DT environment supports resilient, adaptive manufacturing systems aligned with Industry 4.0–5.0 objectives. Full article

Detecting and disconnecting faults is of utmost importance in power systems to prevent damage, outages and limit the impact on the surrounding grid. However, there are faults that may not be detected by protective functions and therefore do not interrupt the operation. Such faults, which have not been considered during the design of an HVDC system despite causing negative operational impacts, are referred to as abnormal failure modes in this paper. Data from three cases of abnormal failure modes in point-to-point HVDC systems are presented. The first case regards a prolonged subsequential failure of a DC filter capacitor for an LCC-HVDC link. The second case presents a measurement disturbance resulting in power oscillations from a VSC-HVDC link. The third case shares details of an overload scenario of a grounding impedance due to DC voltage unbalance from asymmetric corona discharges. This study shares details from these failures and suggests recommendations based on the presented abnormal failure modes in HVDC applications, including multi-terminal HVDC systems. Full article

This article presents an online additive fault-detection system for the speed sensor of a 200 W shunt-type direct current (DC) motor, integrated into a power module controlled by an Insulated Gate Bipolar Transistor (IGBT). The system is designed to trigger an alarm signal when an additive fault occurs by comparing the Kalman Filter (KF) residual against a predefined detection threshold. Three specific fault types in the speed sensor were analyzed: offset, disconnection, and sinusoidal noise. Experimental results demonstrate effective fault detection across a speed range of 80 to 690 rpm under no-load conditions. However, when a constant torque of 0.5 Nm is applied, both the detection threshold and the subset of reliably identifiable faults must be adjusted. The main contribution of this study is the development of a customized real-time fault detection framework and the characterization of residual variations caused by unmodeled load disturbances in actual hardware. This approach improves the monitoring and fault-diagnosis capabilities of sensor systems in DC motors by quantifying the stochastic behavior of residuals under different operating constraints. Full article

Current Unmanned Aerial Vehicle (UAV) swarm designs prioritize physical reliability over network security, leaving systems vulnerable to increasingly sophisticated cyber threats in complex environments. Existing defense methods are mostly limited to peripheral network security technologies, such as encryption, authentication, and firewalls. Consequently, they lack deep integration at the formation architecture level. This separation results in a disconnect between system reliability design and security protection mechanisms, making it difficult to effectively deal with high-level security threats such as internal backdoor vulnerabilities. To this end, this paper proposes an endogenous security architecture for UAV swarm based on dynamic heterogeneous redundancy (DHR) and cooperative supervision. Firstly, a theoretical model of DHR system for UAV swarm was constructed, and discrete nodes are abstracted as dynamic heterogeneous resource pools. Through the formal definition of the heterogeneous executor space, redundancy adjudication mechanism, and dynamic scheduling method, we demonstrate how this architecture suppresses common mode failures by introducing internal and external uncertainties, thereby realizing the coordination and unification of safety and security. Secondly, a distributed security control strategy based on cooperative supervision is proposed, which uses cross-validation between neighbors to replace the centralized adjudication of traditional DHR, solves the problem of anomaly detection in a decentralized environment, and combines reactive cleaning and periodic disturbance scheduling to give the system the ability to self-heal against unknown threats. Simulations in various attack scenarios demonstrate the proposed method’s superiority over traditional architectures. Especially in the simulated dormant multi-mode Advanced Persistent Threat (APT) scenario, the system can still maintain availability of more than 81%, which effectively verifies the key role of the coordination mechanism of heterogeneity, redundancy and dynamics in enhancing the safety and security of UAV swarms. Full article

Antimicrobial resistance (AMR) poses a critical global public health threat, with the food chain serving as a significant transmission route connecting animals, environment, and humans. This review adopts a One Health perspective to analyze the key drivers of AMR dissemination across animal agriculture, aquaculture and food processing. We evaluate detection methodologies, contrasting the regulatory gold standard of culture-based phenotypic testing with rapid molecular advancements, including Whole Genome Sequencing (WGS), metagenomics, and emerging CRISPR-Cas diagnostics. While molecular tools offer unprecedented speed and resolution, challenges such as matrix interference, the viable but non-culturable (VBNC) state, and the genotype-phenotype disconnect remain. Finally, integrated mitigation strategies are also described, ranging from on-farm antimicrobial stewardship and innovative biofilm control to consumer hygiene practices. It is essential to bridge the technical and regulatory gaps in AMR surveillance in order to develop effective interventions and ensure a safer food system. Full article

Chain breakage in dual-chain scraper conveyors poses significant risks to the safe and efficient operation of coal mines. To address the challenges of harsh underground environments and the lack of effective synchronization monitoring, this paper presents the design and implementation of an intelligent monitoring system for conveyor integrity. The system integrates non-contact Hall-effect sensors with a custom-designed intrinsically safe data acquisition unit. A systematic algorithmic framework is designed, comprising an adaptive threshold and plateau seeking (ATPS) module and an adaptive clustering-based identification (ACCI) module, to enable high-accuracy automatic identification of chain elements. Furthermore, a novel synchronization evaluation design based on event correlation and statistical features is introduced to quantify inter-chain timing deviations. This leads to the construction of a Chain Synchronization Index (CSI) for desynchronization anomaly detection. Field experiments conducted under representative operating conditions, including normal operation and controlled single-chain disconnection scenarios, demonstrate that the proposed design achieves a chain element recognition accuracy of 98.2%. Under normal conditions, the CSI remains consistently high, while breakage faults are sensitively detected. The proposed system provides a practical engineering solution for synchronization-aware condition monitoring and anomaly warning of scraper conveyor chains in underground coal mines. Full article

Sub-Saharan Africa’s cereal yields remain ~60% below global benchmarks, while unmanned aerial vehicle (UAV) adoption in smallholder systems averages below 2–3% across major economies, revealing a performance–adoption disconnect that requires systematic investigation. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 synthesis of 127 sources quantifies the performance of UAV sensors and identifies mechanisms that constrain their adoption across regional agricultural systems. Random-effects meta-analysis synthesized evidence from 81 quantitative studies, yielding 101 total observations. Pooled detection accuracy was estimated from 49 studies contributing 52 observations (mean 90.2%, 95% confidence interval (CI): 89.8–92.6%). Yield prediction performance was assessed from 32 studies contributing 49 observations (pooled coefficient of determination (R²) = 0.841, 95% CI: 0.827–0.855), validating technical feasibility. Cost-effectiveness analysis reveals significant performance–price differentiation: red-green-blue (RGB) sensors achieve 89.4% accuracy at United States Dollar (USD) 16.50 per percentage point versus hyperspectral systems at 93.7% accuracy but at USD 132.17 per point, resulting in a 25.6 times cost differential. Yield prediction models demonstrate robust performance (R² = 0.81; cereal crops R² = 0.82). Barrier analysis identifies economic constraints as the primary limiter, with capital requirements reaching 0.8–3.1 times the annual smallholder income. Infrastructure deficits impose secondary constraints, particularly in rural electrification, below 50%. Case study synthesis reveals that coordinated interventions addressing multiple barriers simultaneously—cooperative ownership, off-grid infrastructure, and streamlined regulation—achieve substantially higher adoption than isolated approaches. Engineering economics positions RGB platforms for individual deployment and multispectral systems for cooperative scales (20–50 farmers), establishing feasible deployment pathways for tens of million regional smallholder operations. Full article

Micro-expressions (MEs) are involuntary facial movements that reveal genuine emotions, holding significant value in fields like deception detection and psychological diagnosis. However, micro-expression recognition (MER) is fundamentally challenged by the entanglement of subtle emotional motions with identity-specific features. Traditional methods, such as those based on Robust Principal Component Analysis (RPCA), attempt to separate identity and motion components through fixed preprocessing and coarse decomposition. However, these methods can inadvertently remove subtle emotional cues and are disconnected from subsequent module training, limiting the discriminative power of features. Inspired by the Bruce–Young model of facial cognition, which suggests that facial identity and expression are processed via independent neural routes, we recognize the need for a more dynamic, learnable disentanglement paradigm for MER. We propose LFD-TCMEN, a novel network that introduces an end-to-end learnable feature disentanglement framework. The network is synergistically optimized by a multi-task objective unifying orthogonality, reconstruction, consistency, cycle, identity, and classification losses. Specifically, the Disentangle Representation Learning (DRL) module adaptively isolates pure motion patterns from subject-specific appearance, overcoming the limitations of static preprocessing, while the Temporal-Complemented Motion Enhancement (TCME) module integrates purified motion representations—highlighting subtle facial muscle activations—with optical flow dynamics to comprehensively model the spatiotemporal evolution of MEs. Extensive experiments on CAS(ME)³ and DFME benchmarks demonstrate that our method achieves state-of-the-art cross-subject performance, validating the efficacy of the proposed learnable disentanglement and synergistic optimization. Full article

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and their correlation with perceived organizational readiness. Utilizing a quantitative survey of 53 specialized practitioners (N = 53), we assessed maturity across the six dimensions of the Bank of Thailand’s Cyber Resilience Assessment regulatory framework: Governance, Identification, Protection, Detection, Response, and Third-Party Risk Management. While descriptive statistics indicate high overall maturity ($\overline{\mathrm{x}}$ = 4.19, S.D. = 0.37), multiple regression analysis uncovers a critical “Protection Paradox”. Specifically, the “Protection” dimension exhibits a statistically significant negative impact on readiness (β = −0.432, p = 0.01), suggesting that over-engineered technical controls induce operational friction. In contrast, “Identification” emerged as the primary positive driver of readiness (β = 0.627, p < 0.01), highlighting visibility as a superior strategic lever. Furthermore, a structural disconnect was identified between strategic “Governance” and “Third-Party Risk Management” (r = 0.46), highlighting a “Silo Effect” where board-level policy fails to effectively mitigate supply chain risks. These findings suggest that financial institutions must pivot from volume-based compliance to risk-optimized integration to bridge these strategic and operational gaps. Full article