The rapid transition toward renewable-dominated energy systems has transformed microgrids (MGs) into highly interconnected cyber-physical systems (CPSs), where advanced communication, intelligent control, and inverter-based resources enhance operational flexibility while introducing unprecedented security vulnerabilities. This paper presents a comprehensive review of cyber-physical security and resilience enhancement strategies for MGs by establishing a unified framework that integrates attack scenarios, detection methodologies, and defense mechanisms. The evolving threat landscape is systematically analyzed, including false data injection, denial-of-service, spoofing, replay, and coordinated hybrid attacks targeting communication infrastructures, control layers, and physical power dynamics. Furthermore, existing detection approaches are critically reviewed and categorized into model-based, statistical, artificial intelligence and machine learning-based, physics-informed, and digital twin-enabled frameworks. Particular attention is given to emerging techniques such as deep learning, reinforcement learning, graph neural networks, federated learning, and physics-informed neural networks, highlighting their potential and limitations under nonlinear and uncertain inverter-dominated operating conditions. Defense and resilience strategies are investigated through secure communication, adaptive control, redundancy, attack mitigation, self-healing operation, and recovery-oriented approaches. The review further emphasizes the importance of real-time validation platforms and digital twins for predictive risk assessment and autonomous cyber-physical resilience management. Finally, key research challenges are identified, including scalability, explainability of intelligent security models, realistic hardware-in-the-loop validation, standardized resilience metrics, and the development of integrated physics–AI frameworks for future secure and adaptive MGs.
This study addresses the gap in understanding sports consumers' behavior regarding omni-channel strategies, with a focus on the moderating roles of customer experience and brand trust. Drawing on the Stimulus-Organism-Response (S-O-R) framework, the Technology Acceptance Model (TAM), and commitment-trust theory, a quasi-experimental omnichannel retail setting was utilized with a sample of 166 sports product consumers. The results indicate that consumer outcomes differ significantly across strategies for repurchase intention, customer satisfaction, and brand loyalty (p < .001, partial eta(2) ranging from 0.454 to 0.521). Specifically, following the logical progression of the consumer journey, Webrooming emerged as the primary driver for repurchase intention (M = 4.94), whereas the Online-to-Offline configuration was most effective in enhancing customer satisfaction (M = 4.96) and brand loyalty (M = 4.96). Scheffe's post-hoc tests revealed several significant pairwise differences, confirming that channel advantages are outcome-specific: digital research excels in driving intent, while integrated physical touchpoints are essential for securing relational outcomes. Customer experience and brand trust both significantly moderated the effects of omni-channel strategies on all three outcomes. In both cases, differences between strategies were more pronounced among consumers with lower experience and trust.This study elucidates the differential impacts of omni-channel strategies on sports product consumers and highlights the importance of methodological rigor, specifically post-stratification weighting as a robustness check, in enhancing the generalizability of retailing research. The substantive conclusions remained unchanged when the same models were re-estimated on the raked, weighted sample, confirming the stability of the findings under post-stratification adjustment.
Social anxiety disorder is one of the most common anxiety disorders in adulthood. Cognitive-behavioral therapy (CBT) is an established effective intervention; however, previous meta-analyses have rarely focused specifically on adult populations. Therefore, the present study aimed to systematically synthesize existing evidence and estimate the overall effect size of CBT in reducing social anxiety symptoms in adults. This systematic review and meta-analysis was conducted through a comprehensive search of national databases from 2006 to 2024. Out of 567 identified studies, 14 met the inclusion criteria and were included in the final analysis. Results indicated that CBT has a large and statistically significant effect on reducing social anxiety symptoms in adults (Hedges’ g = −2.274). Despite moderate heterogeneity across studies, including randomized controlled and quasi-experimental designs, sensitivity analyses supported the robustness of the findings. The main contribution of this study is its exclusive focus on adult populations and the synthesis of previously fragmented evidence that has not been sufficiently addressed in the literature. Overall, CBT appears to be an effective intervention for social anxiety in adults; however, future research should further examine the role of demographic and treatment-related factors.
Deep eutectic solvents (DESs) are versatile and sustainable new green solvents that can be employed in the different application fields. DESs are created by combining a hydrogen bond donor and a hydrogen bond acceptor to form a stable liquid mixture with many unique physicochemical properties, including low volatility, tunable polarity, and excellent thermal stability. In this study, novel Glycerol-based deep eutectic solvents (DESs) were prepared by mixing different molar ratios of 1-propanol and 1-butanol as a hydrogen bond acceptor and glycerol as a hydrogen bond donor. The prepared new deep eutectic solvents were investigated by measuring their physical properties, such as freezing point, density, sound velocity, conductivity, and refractive index at different temperatures. The physical properties of new Glycerol-based DESs were decreased with increasing temperature and molar ratio due to increasing voids within deep eutectic mixtures. Furthermore, the new Glycerol-based DESs were applied for the first time as a liquid fuel.
Optimal safe trajectory tracking for nonlinear systems remains challenging, especially when safety constraints conflict with tracking objectives. The state-dependent Riccati equation (SDRE) provides efficient nonlinear optimal control but lacks a principled way to enforce general safety constraints. This paper proposes a suboptimal safety-critical tracking controller by embedding a general class of barrier states into the SDRE. This turns safety constraints into dynamic state variables and transforms the problem into an augmented optimal control task. For stable and marginally stable reference generators, tuning the weighting matrices enables effective tracking and guarantees safety when constraints do not conflict. In conflicting scenarios, the controller prioritizes safety over tracking accuracy. The paper establishes rigorous pointwise stabilizability and detectability conditions to ensure the solvability of the resulting SDRE. Simulations and experiments on a cable-driven parallel robot validate the method’s effectiveness and real-time feasibility, demonstrating strong performance in safety-critical applications.
