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Because violations of information security (ISec) and privacy have become ubiquitous in both personal and work environments, academic attention to ISec and privacy has taken on paramount importance. Consequently, a key focus of ISec research has been discovering ways to motivate individuals to engage in more secure behaviors. Over time, the protection motivation theory (PMT) has become a leading theoretical foundation used in ISec research to help motivate individuals to change their security-related behaviors to protect themselves and their organizations. Our careful review of the foundation for PMT identified four opportunities for improving ISec PMT research. First, extant ISec studies do not use the full nomology of PMT constructs. Second, only one study uses fear-appeal manipulations, even though these are a core element of PMT. Third, virtually no ISec study models or measures fear. Fourth, whereas these studies have made excellent progress in predicting security intentions, none of them have addressed actual security behaviors. This article describes the theoretical foundation of these four opportunities for improvement. We tested the nomology of PMT, including manipulated fear appeals, in two different ISec contexts that model the modern theoretical treatment of PMT more closely than do extant ISec studies. The first data collection was a longitudinal study in the context of data backups. The second study was a short-term cross-sectional study in the context of anti-malware software. Our new model demonstrated better results and stronger fit than the existing models and confirms the efficacy of the four potential improvements we identified.

To make decisions, animals must evaluate candidate choices by accessing memories of relevant experiences. Yet little is known about which experiences are considered or ignored during deliberation, which ultimately governs choice. We propose a normative theory predicting which memories should be accessed at each moment to optimize future decisions. Using nonlocal ‘replay’ of spatial locations in hippocampus as a window into memory access, we simulate a spatial navigation task in which an agent accesses memories of locations sequentially, ordered by utility: how much extra reward would be earned due to better choices. This prioritization balances two desiderata: the need to evaluate imminent choices versus the gain from propagating newly encountered information to preceding locations. Our theory offers a simple explanation for numerous findings about place cells; unifies seemingly disparate proposed functions of replay including planning, learning, and consolidation; and posits a mechanism whose dysfunction may underlie pathologies like rumination and craving.

Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.

The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.

This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

An energy crisis has seen regular power blackouts throughout South America. Andersson Boscán reports from Ecuador where the situation is costing lives in both hospitals and homes

Directional Overcurrent Relay (DOCR) coordination has become an increasingly important problem in modern distribution networks, where growing system complexity and high fault-current variability demand fast and highly selective protection strategies. With the widespread integration of smart grids and renewable energy sources, determining relay settings accurately and in an optimized manner is critical to maintaining system reliability. This study aims to systematically evaluate the effectiveness of five advanced generation metaheuristic algorithms (Zebra Optimization Algorithm (ZOA), African Vultures Optimization Algorithm (AVOA), Transit Search Optimization (TSO), Nutcracker Optimization Algorithm (NOA), and Artificial Rabbits Optimization (ARO)) for minimizing the total primary relay operating time. In this context, the DOCR coordination problem is formulated as an optimization task in which the Time Multiplier Setting (TMS) and Pickup Setting (PS) are jointly tuned under inverse time–current characteristics. A standard and reproducible MATLAB/Simulink-based analysis framework is established, and the three-phase short-circuit fault scenario that produces the highest fault current is consistently applied to the IEEE 3-, 8-, and 15-bus test systems. To ensure the reliability of the findings, multiple repeated simulations are performed and the results are statistically validated. The numerical results show that ARO achieves the lowest total primary relay operating time in both the IEEE 3-bus (0.23 s) and 8-bus (2.86 s) systems compared with the other algorithms considered (ZOA, AVOA, TSO, and NOA), while NOA provides the best performance in the 15-bus system with a total operating time of 5.25 s, outperforming ARO, ZOA, AVOA, and TSO. When compared with classical and other heuristic methods reported in the literature for the same or similar test systems, the proposed advanced algorithms reduce the total primary relay operating time by approximately 60% to 97%, while still satisfying the selectivity requirements. Furthermore, the results indicate that algorithm performance is sensitive to network scale and topology: ARO is more effective in small and medium-sized systems, NOA performs better in larger systems, and TSO exhibits balanced behaviour across all test systems. Overall, the findings demonstrate that advanced metaheuristic algorithms provide effective, scalable, and reliable solutions for DOCR coordination, highlighting their high applicability potential in future protection schemes for smart-grid and renewable-integrated distribution networks.

There is no doubt that the transition towards renewable energies is generating many changes on different continents, some with greater impacts than others, but the development that has occurred is recognized and widely accepted. The progress has been significant but it is necessary to analyze the roadmaps that have been proposed so far at the island level so that decision makers have sufficient tools to commit the much-needed economic resources to transform their energy systems into 100% renewable ones. These approaches are not simple and the hard work of the authors who have disseminated their research is recognized. The roadmaps are planned based on the energy potential available in the territories and the future energy demand. Within countries, it is important to increase the economic resources to allocate to investments in environmentally friendly renewable energies. In this review of 100% renewable smart systems on islands, the situation of the American continent, its challenges and its long-term approaches in the different geographical areas facing 2050 are analyzed. This article shows that research into the design of 100% renewable energy systems in scientific articles is fairly new but has gained more and more attention in recent years. In total, 175 articles published since 2002 were identified and analyzed. Many of these articles have a predominant focus on the electricity sector. As a general result, it has been determined that although there has been significant progress towards an orderly energy transition, this has not been consistent with the international agreements signed since the Paris Summit, which is a real challenge in complying with the new commitment of the COP28 of Dubai in tripling the participation of renewables.