Search Results (62)

Narcissistic Leadership has been associated with negative organizational and individual outcomes, including employee intention to leave. However, the mechanism by which this leadership influences this intention to leave still needs to be further elucidated. This study investigates the mediating role of psychological capital (PsyCap) (comprising hope, self-efficacy, resilience, and optimism) in the relationship between the narcissistic leadership and the intention to leave. A non-probabilistic sample of 266 Portuguese employees from various organizational sectors, aged 18 to 53 (M = 29.13; SD = 7.53), predominantly women (62%), completed a self-administered online questionnaire. Results, calculated through the estimation of OLS regressions-based models, indicated that narcissistic leadership was positively related to turnover intention (Hypothesis 1) and that PsyCap significantly mediated that association (Hypothesis 2), particularly self-efficacy showed to be negatively associated with turnover intention, and optimism positively predicted the intention to leave the organization. Overall, the findings point to the key role of narcissistic leadership and psychological capital as antecedents of turnover intention, highlighting the opposite mediating effects of self-efficacy and optimism in the association between narcissistic leadership and turnover intention. The study’s findings are discussed, as well as their theoretical and practical implications. Full article

Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This work systematically reviews 111 publications from the last eight years on self-adaptive cloud design and operations patterns, classifying them by objectives, control scope, decision-making approach, automation level, and validation methods. Our analysis reveals that performance optimization dominates research goals, followed by cost reduction and security enhancement, with availability and reliability underexplored. Reactive feedback loops prevail, while proactive approaches—often leveraging machine learning—are increasingly applied to predictive resource provisioning and application management. Resource-oriented adaptation strategies are common, but direct application-level reconfiguration remains scarce, representing a promising research gap. We further catalog tools, platforms, and more than 30 publicly accessible datasets used in validation, and that dataset usage is fragmented without a de facto standard. Finally, we map the research findings on a generic application and system-level design for self-adaptive applications, including a proposal for a federated learning approach for SaaS application Agents. This blueprint aims to guide future work toward more intelligent, context-aware cloud automation. Full article

This study presents an integrated condition-monitoring approach for industrial processes. The proposed approach conveniently combines a computational intelligence-based mechanism to guarantee the resilience of the proposed scheme against unknown anomalies and a machine learning model with optimized parameters capable of unified detection and pinpointing of faults and cyberattacks in industrial plants. During the offline phase, process data are labeled, normalized, and used to train the machine learning model with hyperparameter tuned by using an optimization tool. In the online phase, the system performs real-time monitoring enhanced with a novelty mechanism to detect anomalous conditions not present in the training data, which are flagged for expert analysis and incorporated into the system through incremental learning. The implementation of the proposed strategy uses computational intelligence tools consisting of a multilayer perceptron neural network, local outlier factor, and differential evolution. The proposed framework was validated using the two-tank process benchmark, demonstrating superior detection accuracy of 99% and robustness compared to other machine learning algorithms. These results highlight the potential of combining fault diagnosis and cybersecurity in a unified architecture, thereby contributing to resilient and intelligent systems in the context of Industry 4.0/5.0. Full article

Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article

The proliferation of fake profiles on social media presents critical cybersecurity and misinformation challenges, necessitating robust and scalable detection mechanisms. Such profiles weaken consumer trust, reduce user engagement, and ultimately harm brand reputation and platform credibility. As adversarial tactics and synthetic identity generation evolve, traditional rule-based and machine learning approaches struggle to detect evolving and deceptive behavioral patterns embedded in dynamic user-generated content. This study aims to develop an AI-driven, multi-modal deep learning-based detection system for identifying fake profiles that fuses textual, visual, and social network features to enhance detection accuracy. It also seeks to ensure scalability, adversarial robustness, and real-time threat detection capabilities suitable for practical deployment in industrial cybersecurity environments. To achieve these objectives, the current study proposes an integrated AI system that combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) for deep semantic textual analysis, ConvNeXt for high-resolution profile image verification, and Heterogeneous Graph Attention Networks (Hetero-GAT) for modeling complex social interactions. The extracted features from all three modalities are fused through an attention-based late fusion strategy, enhancing interpretability, robustness, and cross-modal learning. Experimental evaluations on large-scale social media datasets demonstrate that the proposed RoBERTa-ConvNeXt-HeteroGAT model significantly outperforms baseline models, including Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). Performance achieves 98.9% accuracy, 98.4% precision, and a 98.6% F1-score, with a per-profile speed of 15.7 milliseconds, enabling real-time applicability. Moreover, the model proves to be resilient against various types of attacks on text, images, and network activity. This study advances the application of AI in cybersecurity by introducing a highly interpretable, multi-modal detection system that strengthens digital trust, supports identity verification, and enhances the security of social media platforms. This alignment of technical robustness with brand trust highlights the system’s value not only in cybersecurity but also in sustaining platform credibility and consumer confidence. This system provides practical value to a wide range of stakeholders, including platform providers, AI researchers, cybersecurity professionals, and public sector regulators, by enabling real-time detection, improving operational efficiency, and safeguarding online ecosystems. Full article

With the rapid proliferation of social media platforms and user-generated content, audio data is frequently shared, remixed, and redistributed online. This raises urgent needs for copyright protection and traceability to safeguard the integrity and ownership of such content. Resilience to desynchronization attacks remains a significant challenge in audio watermarking. Most existing techniques face a trade-off between embedding capacity, robustness, and imperceptibility, making it difficult to meet all three requirements effectively in real-world applications. To address this issue, we propose an improved patchwork-based audio watermarking algorithm. Each audio frame is divided into two non-overlapping segments, from which mid-frequency energy features are extracted and modulated for watermark embedding. A linearly decreasing buffer compensation mechanism balances imperceptibility and robustness. Additionally, an optimization algorithm is incorporated to enhance watermark transparency while maintaining resistance to desynchronization attacks. During watermark extraction, each bit of the watermark is recovered by analyzing the intra-frame energy relationships. Furthermore, we provide a theoretical analysis demonstrating that the proposed method is robust against various types of attack. Extensive experimental results demonstrate that the proposed scheme ensures high audio quality, strong robustness against desynchronization attacks, and a higher embedding capacity than existing methods. Full article

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Despite initial concerns about the severe negative impact of COVID-19 on individuals with a history of mental health problems and suicide attempts, its effects remain unclear. This study examined the pandemic’s impact on individuals with and without lived experience of suicide attempts. An online nationwide sample of 1351 adults from the United States completed questionnaires from 26 May to 25 June 2021. A history of suicide attempt(s) (n = 159; 12%) was associated with significantly higher odds of utilizing mental health services, hospitalization for psychiatric reasons, and contacting hotlines. This history predicted worse outcomes in functioning, optimism, despair, and impairment. Notably, 57.6% of these individuals believed surviving a suicide attempt made them more resilient, while 21.9% expressed uncertainty about its impact on their resilience. In sum, participants with a history of suicide attempt(s) reported more depressive symptoms, worse daily functioning, more despair, less optimism, and greater service utilization during the pandemic, yet many also cited increased resilience due to their suicide history. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article

With the increasing integration of renewable energy sources, distributed shared energy storage (DSES) systems play a critical role in enhancing power system flexibility, operational resilience, and energy sustainability. However, conventional scheduling methods often suffer from excessive communication burdens, limited scalability, and poor real-time responsiveness, especially when handling fast-changing frequency regulation signals and fluctuating renewable energy outputs. To address these challenges, this paper proposes a consensus-driven distributed online convex optimization method that enables a decentralized scheduling of energy storage units by leveraging the consensus algorithm for local decision-making while maintaining global consistency. Additionally, an adaptive event-triggered mechanism is designed to dynamically adjust the communication frequency based on system state variations, reducing redundant information exchange and ensuring convergence and stability in a fully distributed environment. Simulation results on the IEEE 14-bus test system show that the strategy reduces the communication load by 33–60% and improves the convergence speed by over 40% compared to baseline methods. It also demonstrates a strong adaptability to storage unit disconnection and reconnection. By enabling a fast and efficient response to grid services such as frequency regulation and renewable energy balancing, the proposed approach contributes to the development of intelligent and sustainable power systems. Full article

This review paper aims to present a comprehensive overview of the evaluation, simulation, and control of heating, ventilation, and air conditioning (HVAC) systems in nuclear power plants (NPPs), specifically highlighting their importance in maintaining operational safety, thermal performance, and energy efficiency. The study’s authors summarize recent developments in HVAC technologies, such as passive cooling systems, data-driven energy management frameworks, and intelligent control strategies, to cope with the specific challenges of NPPs. Various passive cooling systems, including heat pipes, thermosyphons, and loop heat pipes, have proven themselves by their ability to remove residual heat from spent fuel pools and reactors power plants with high efficiency. Through experimental studies, they have shown their ability to eliminate operational vulnerability to accidents or guarantee any desired long-term cooling. Intelligent sensor networks allow a more data-driven approach to HVAC control, enabling online energy management frameworks and advanced intelligent control systems. These exhibit considerable promise for optimizing HVAC performance, decreasing energy consumption, and improving operational flexibility in multi-zone systems. Such capabilities are ideal for addressing the dynamic and safety-critical nature of NPPs. They are first enabled by the use of these technologies for real-time monitoring, predictive maintenance, and adaptive control. When applied with advanced HVAC control systems, passive cooling techniques provide an exciting route to improve safety and energy efficiency. An overview of the key findings is that robust thermal management solutions combined with intelligent control and intelligent adaptation are essential when addressing the rapidly evolving demands of nuclear energy systems. This work highlights the priorities in the next generation of nuclear power plants, which should actively pursue seamless integration of out-of-system technologies into existing NPP infrastructures, enabling scalable, cost-effective, and resilient solutions. Full article

This paper presents a novel deep learning model, RLANet, based on the ResNet-LSTM-Multihead Attention module, designed for processing and classifying one-dimensional spectral data. The model incorporates ResNet, LSTM, and attention mechanisms, omitting the traditional fully connected layer to significantly reduce the parameter count while maintaining global spectral feature extraction. This design enables RLANet to be lightweight and computationally efficient, making it suitable for real-time applications, especially in resource-constrained environments. Furthermore, this study introduces the Kepler Optimization Algorithm (KOA) for hyperparameter tuning in deep learning, demonstrating its superiority over the traditional Bayesian optimization (BO) in achieving optimal hyperparameter configurations for complex models. Experimental results indicate that the RLANet model successfully achieves accurate identification of three types of engine oil products and their mixtures, with classification accuracy approaching one. Compared to conventional deep learning models, it features a significantly reduced parameter count of only 0.09 M, enabling the deployment of compact devices for rapid on-site classification of oil spill types. Furthermore, relative to traditional machine learning models, RLANet demonstrates a lower sensitivity to preprocessing methods, with the standard deviation of classification accuracy maintained within approximately 0.001, thereby underscoring its excellent end-to-end analytical capabilities. Moreover, even under a strong noise interference at a signal-to-noise ratio of 15 dB, its classification performance declines by only 19% relative to the baseline, attesting to its robust resilience. These results highlight the model’s potential for practical deployment in end-to-end online spectral analysis, particularly in resource-constrained hardware environments. Full article

Prolonged exposure to public health crises in the post-pandemic era poses significant threats to global mental health. To address this, we developed a conceptual model to analyse the spatiotemporal distribution of public emotions, using Weibo data from the 2022 Beijing bar outbreak (9 June–18 August). The model integrates lexicon-based emotion analysis, spatial autocorrelation tests, and content analysis to provide a comprehensive understanding of emotional responses across stages and regions. The findings reveal a multi-peak emotional cycle spanning emergency, contagion, and resolution stages, with significant emotional clustering in emergency zones, surrounding areas, and regions visited by infected individuals. Through coding, we identified 24 main-categories and 90 sub-categories, distilled into nine core themes that illustrate the interplay between influencing factors, public emotions, and online behaviours. Positive public emotions (e.g., hopefulness, gratitude, optimism) were linked to pandemic improvements and policy implementation, driving behaviours such as supporting prevention measures and resisting misinformation. Negative emotions (e.g., anger, anxiety, sadness) stemmed from severe outbreaks, insufficient controls, and restrictions on freedoms, leading to criticism and calls for accountability. This study bridges big data analytics with behavioural science, offering critical insights into evolving public emotions and behaviours. By highlighting spatiotemporal patterns and emotional dynamics, it provides actionable guidance for governments and health organizations to design targeted interventions, foster resilience, and better manage future social crises with precision and empathy. Full article

As online health and wellness programs become more ubiquitous post-pandemic, there is a need to better understand how people with physical disabilities respond to different types of program offerings. Online health promotion programs have become popular in the disability community, and programs offer a range of activities across various wellness domains (e.g., exercise, nutrition). This study examined factors predicting adherence to three different types of online health promotion programs tailored for people with physical disabilities. A survey was developed to examine factors associated with high, moderate, and low adherence to three different types of health promotion programs. Participants who completed an online wellness program were sent a survey that asked questions related to adherence to a range of wellness activities. The three programs included the MENTOR (Mindfulness, Exercise, and Nutrition to Optimize Resilience), GROWTH (Growing Resilience Out of Wellness and Thoughtful Habits), and SOSE (State of Slim Everybody) programs, all of which focus on different self-care strategies. MENTOR focused on educating participants about mindfulness, exercise, and nutrition; GROWTH aimed to deliver mental and emotional health techniques, while SOSE’s purpose was to teach participants how to implement healthy weight loss, weight management, and daily exercise practices. Results indicated that participant perceptions of program delivery, specifically programs being disability friendly, virtual environment enjoyment, having positive instructor relationships, adaptable content, the instructor’s knowledge about disability, the instructor’s use of appropriate language, and program satisfaction, affected the likelihood of high adherence among people with physical disabilities enrolled in the health and wellness programs. Full article