Search Results (650)

Denial-of-Service (DoS) attacks remain one of the most dangerous threats in modern Internet environments. They aim to overwhelm networks, servers, or online services with massive volumes of traffic, and maintaining service availability is a core pillar of cybersecurity. More importantly, DoS attack techniques continue to evolve. However, traditional intrusion detection systems (IDS) trained on fixed attack categories struggle to identify previously unknown DoS attack types and cannot dynamically incorporate newly emerging classes. To address this challenge, this study proposes a stage-wise network intrusion detection framework that integrates unknown attack detection, attack discovery, and class-incremental learning into a unified pipeline. The framework consists of three stages. First, an autoencoder-based anomaly detection approach is used to separate potential unknown DoS attack samples from known classes. Second, a clustering-and-merging strategy is applied to the detected unknown DoS samples to discover emerging attack clusters with similar structural characteristics. Third, the classifier architecture is expanded for each newly discovered cluster through a class-incremental learning mechanism, enabling the continual incorporation of new attack classes while maintaining stable detection performance on previously learned classes. Experimental results on the DoS category of the NSL-KDD dataset demonstrate that the proposed stage-wise framework can effectively isolate samples of unknown DoS attacks, accurately aggregate emerging attack clusters, and incrementally integrate newly discovered attack classes without significantly degrading recognition performance on previously learned classes. These results confirm the capability of the proposed framework to handle progressively emerging unknown DoS attacks. Full article

Fog computing extends cloud services to the network edge to support low-latency IoT applications. However, since fog environments are distributed and resource-constrained, intrusion detection systems must be adapted to defend against cyberattacks while keeping computation and communication overhead minimal. This systematic review presents research on intrusion detection systems (IDSs) for fog computing and synthesizes advances and research gaps. The study was guided by the “Preferred-Reporting-Items for-Systematic-Reviews-and-Meta-Analyses” (PRISMA) framework. Scopus and Web of Science were searched in the title field using TITLE/TI = (“intrusion detection” AND “fog computing”) for 2021–2025. The inclusion criteria were (i) 2021–2025 publications, (ii) journal or conference papers, (iii) English language, and (iv) open access availability; duplicates were removed programmatically using a DOI-first key with a title, year, and author alternative. The search identified 8560 records, of which 4905 were unique and included for qualitative grouping and bibliometric synthesis. Metadata (year, venue, authors, affiliations, keywords, and citations) were extracted and analyzed in Python to compute trends and collaboration. Intrusion detection systems in fog networks were categorized into traditional/signature-based, machine learning, deep learning, and hybrid/ensemble. Hybrid and DL approaches reported accuracy ranging from 95 to 99% on benchmark datasets (such as NSL-KDD, UNSW-NB15, CIC-IDS2017, KDD99, BoT-IoT). Notable bottlenecks included computational load relative to real-time latency on resource-constrained nodes, elevated false-positive rates for anomaly detection under concept drift, limited generalization to unseen attacks, privacy risks from centralizing data, and limited real-world validation. Bibliometric analyses highlighted the field’s concentration in fast-turnaround, open-access journals such as IEEE Access and Sensors, as well as a small number of highly collaborative author clusters, alongside dominant terms such as “learning,” “federated,” “ensemble,” “lightweight,” and “explainability.” Emerging directions include federated and distributed training to preserve privacy, as well as online/continual learning adaptation. Future work should consist of real-world evaluation of fog networks, ultra-lightweight yet adaptive hybrid IDS, self-learning, and secure cooperative frameworks. These insights help researchers select appropriate IDS models for fog networks. Full article

High-precision cooperative control of dual-arm manipulators faces significant challenges arising from complex dynamic coupling, parametric uncertainties, and external disturbances. Furthermore, in networked control scenarios, communication bandwidth and computational resources are inevitably constrained. To address these issues, this paper proposes a novel composite control framework that integrates adaptive dynamic programming (ADP) with active disturbance rejection control (ADRC) under a static event-triggering mechanism (SETM). First, to handle model uncertainties and external perturbations, a smooth nonlinear extended state observer (ESO) based on continuous fractional-power functions is developed. This observer guarantees finite-time convergence of the disturbance estimation without inducing the high-frequency chattering inherent in conventional sliding-mode observers. Second, leveraging the disturbance-compensated dynamics, a radial basis function (RBF) neural network-based ADP controller is designed to learn the optimal control policy online, thereby minimizing a quadratic performance index without requiring accurate model knowledge. Third, to improve resource utilization, a static event-triggering strategy is introduced to schedule control updates based on the system state and tracking error. Extensive simulation studies on a 3-DoF dual-arm system demonstrate that the proposed scheme achieves superior trajectory tracking accuracy and disturbance robustness while significantly reducing the communication frequency compared to time-triggered approaches. Full article

The adoption of Internet of Things (IoT) technologies has significantly enhanced precision farming by enabling continuous environmental monitoring and data-driven agricultural management. However, the increasing reliance on distributed sensor networks introduces critical challenges, including sensor faults, data anomalies, and cyber-physical security threats, which can undermine system reliability and decision accuracy. This study proposes an IoT-driven anomaly detection framework for smart agriculture that integrates a Convolutional Neural Network (CNN) optimized using a hybrid Particle Swarm Optimization and Genetic Algorithm (PSO–GA). The CNN learns complex spatio-temporal patterns from multivariate sensor data, while the PSO–GA strategy automatically tunes CNN hyperparameters to improve detection accuracy and model stability. To enhance adaptability under dynamic agricultural conditions, the proposed framework incorporates an online learning mechanism that incrementally updates the CNN model using newly arriving sensor data, enabling continuous adaptation to environmental changes and concept drift without full model retraining. Experiments conducted on a publicly available smart agriculture dataset demonstrate that the proposed CNN–PSO–GA framework achieves an accuracy of 74%, precision of 74%, recall of 100%, and an F1-score of 85%, outperforming baseline methods such as One-Class Support Vector Machine and Isolation Forest, particularly in reducing missed anomaly events. The results confirm the robustness, adaptability, and reliability of the proposed approach. Overall, the framework provides a practical and scalable solution for enhancing security, resilience, and operational effectiveness in precision farming systems. Full article

Researchers typically treat platform loyalty in online travel agency (OTA) settings as a static outcome of satisfaction, even though repeated platform use unfolds over time. However, consumers update evaluative judgments through learning and memory as they move from pre-consumption expectations to post-consumption experiences, gradually stabilizing evaluations rather than continuously revising them. To address this gap, we use a two-wave time-lagged survey capturing pre- and post-consumption evaluations to examine when and how satisfaction-based platform loyalty strengthens in OTA-mediated hotel choice. The results show that the relationship between satisfaction and platform loyalty intentions intensifies after consumption. Satisfaction increasingly functions as a decision-guiding cognitive signal. This strengthening reflects experience-driven reweighting of hotel choice attributes. Consumers reweight existing criteria through experience rather than introducing new ones. Notably, the importance of core attributes, especially room quality and online reviews, increases as experience accumulates. Satisfaction and platform loyalty intentions also display significant carryover effects, indicating that prior evaluations shape subsequent judgments through memory-based continuity. By showing that evaluative judgments stabilize through selective reinforcement of existing criteria, this study explains how satisfaction transforms from an outcome judgment to a cognitive anchor for future decisions and underscores the value of longitudinal approaches for understanding early-stage experience-based decision dynamics. Full article

The proliferation of digital health education content (DHEC) offers a transformative opportunity for medical training worldwide. While students in high-income countries routinely integrate these tools, their use and impact in low-resource settings such as Mozambique remain poorly understood. Exploring this topic offers interesting possibilities at the intersection of global health equity, digital literacy, and pedagogical innovation. This study assessed how Mozambican medical students engage with online health videos, examining the types of content they search for, preferred platforms, perceived benefits, and attitudes toward integrating these materials into medical training. A quantitative cross-sectional survey was administered to 151 second-year medical students at the Catholic University of Mozambique and Alberto Chipande University. A structured online questionnaire, comprising multiple-choice, Likert-scale, and open-ended questions, was used. Data were analyzed using descriptive statistics, cross-tabulation, chi-square test, and Cramer’s V effect size. All students (100%) reported searching for online health videos. They primarily do so via YouTube (92.1%) and use mobile phones (98.7%). Students mainly searched topics related to basic biomedical sciences (60%). They reported that video enhances their learning (86.8%), academic work (11.3%), and other skills (1.9%). Mean scores for utility (4.06), self-reported knowledge gain (4.05), and interest in continuing use (4.30) reflected positive perceptions. Furthermore, an overwhelming majority (91.4%) supported the institutional production of educational videos, whereas 8.6% disagreed, citing videos as a tool that diverts students’ focus from reading and a preference for traditional classes. No statistically significant gender-based differences were observed in usefulness, learning levels, or core interest in continuing to search for online videos (p > 0.05). Online health videos are widely used and positively perceived by Mozambican medical students as a supplementary learning tool. The findings highlight the need for institutions to create curriculum-aligned video libraries and strengthen students’ digital literacy, an affordable strategy for enhancing medical education in low-resource contexts. Full article

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static heuristics, which lack adaptability to dynamic conditions, or on metaheuristic optimizers, which often incur high computational overhead and centralized coordination. This paper proposes LITO, a lemur-inspired task offloading algorithm for edge, fog, and cloud continuum systems that models the infrastructure as a social system in which computing nodes assume distinct roles that mirror lemur social hierarchies. Building on an abstracted model of lemur group behavior, LITO incorporates two key lemur-inspired mechanisms: an energy-aware task assignment mechanism based on sun basking, a thermoregulation behavior in which lemurs seek favorable warm spots, mapped here to selecting energetically efficient execution nodes, and a cooperative scheduling policy based on huddling, group clustering under stress, mapped here to sharing load among overloaded nodes. These mechanisms are combined with a continual supervised policy-learning layer with contextual bandit feedback that refines offloading decisions from online feedback. The resulting multi-objective formulation jointly minimizes energy consumption and deadline violations while maximizing resource utilization and throughput under high-load conditions in the edge and fog segment of the continuum. Simulations under diverse workload regimes and task complexities show that LITO outperforms representative multi-objective offloading baselines in terms of energy consumption, resource utilization, latency, Service Level Agreement (SLA) violations, and throughput in congested scenarios. Full article

Digital harassment is an increasing challenge in higher education, with implications for students’ psychological well-being, perceived safety, and engagement in digital learning. As artificial intelligence (AI) increasingly mediates communication, visibility, and interaction across educational platforms, students’ exposure to online harm is shaped not only by individual behaviour but also by algorithmically structured interaction environments. Understanding these conditions is essential for protecting student well-being and supporting sustainable participation in AI-enhanced learning. This study examines university students’ exposure to digital harassment in AI-mediated learning environments using an expanded Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Survey data were collected from 2185 students, including Saudi nationals and international students enrolled in Saudi Arabian universities, representing Saudi Arabia and 32 other developing and emerging countries (33 countries in total). The model analyses associations among technological literacy, cybersecurity awareness, social media engagement intensity, digital identity visibility, AI-mediated interactions, and cultural norms, while also accounting for disciplinary and cultural context differences. The results indicate that AI-mediated interactions are most strongly associated with exposure to digital harassment. Higher social media engagement, more restrictive cultural norms, and greater visibility of digital identity are associated with increased exposure, whereas technological literacy and cybersecurity awareness are associated with lower reported exposure. Furthermore, greater exposure to digital harassment is linked to poorer mental health outcomes and reduced continuity in e-learning participation. Overall, the findings suggest that digital harassment in AI-driven educational settings is a structural sociotechnical issue associated with greater embeddedness in algorithmically mediated learning environments, rather than an isolated behavioural issue. The study highlights the need for responsible AI governance, enhanced digital literacy education, and culturally responsive institutional policies to support inclusive and sustainable higher education. Full article

Background: Democratic participation depends on three foundational social mechanisms: communication, interpersonal relationships, and socialization. While these mechanisms are well-understood in physical civic settings, their operation in digital environments remains unclear. For the purposes of this review, “fragility” is defined as a structural property of participatory systems, referring primarily to the conditional and variable alignment of these three mechanisms—an alignment that physical environments tend to support by default but that digital environments reproduce only under specific conditions. Methods: This study conducted a targeted high-impact review of twenty-two highly cited Scopus publications (2004–2025) to assess whether communication, interpersonal relationships, and socialization continue to function as core, but not individually sufficient, conditions for democratic engagement online. The review synthesizes findings across three research questions examining each mechanism, using narrative thematic analysis to identify dominant patterns within citation-established scholarship. Results: Across the reviewed corpus, participation strengthens when communication is informationally rich and heterogeneous, when relationships foster trust and bridging social capital, and when socialization environments support civic learning and identity formation. Weak informational content, homogeneous networks, and reduced socialization produce thinner or unstable democratic outcomes. The findings reveal that the three mechanisms operate interdependently: their democratic effects depend on simultaneous alignment rather than individual presence. Conclusions: Digital environments can support meaningful participation only when platform architecture reinforces these core social mechanisms. Strengthening informational diversity, relational openness, and digital socialization is essential for robust platform-mediated democratic engagement. Synthesizing these findings, the study proposes a Conditional Model of Digital Democratic Participation, which argues that digital fragility arises not from the medium itself but when the qualitative conditions required to validate the core social mechanisms fail to align. The Conditional Model differs from existing frameworks by treating communication, relationships, and socialization as interdependent mechanisms whose democratic effects are conditional on their simultaneous presence. Digital participation is not weak—it is conditional. Full article

Network fault diagnosis has evolved in response to the needs of modern networks, transitioning from traditional methods, such as passive and active monitoring, to advanced learning techniques. While conventional methods often introduce invasive traffic and control overhead, newer approaches face challenges such as increased internal processes and the need for extensive knowledge of network behavior. Learning-based methods offer an advantage by not requiring a complete network model, allowing the use of statistical and Machine Learning techniques to process historical data. However, existing learning methods face limitations, such as the need for extensive data samples and extended retraining periods, which can leave systems vulnerable to failures, particularly in dynamic environments. This work addresses these issues by proposing an incremental learning approach for continuous fault diagnosis in IP-based networks. The approach utilizes online learning to process symptoms in real-time, adapting to network changes while managing data imbalance through drift detection and rebalancing strategies, such as ADWIN and SMOTE. We evaluated the performance of this method using 25 incremental algorithms on the SOFI dataset. The results, assessed using metrics such as recall, G-mean, kappa, and MCC, demonstrated high performance over time, indicating the potential for resilient, adaptive fault detection processes in dynamic network environments. Additionally, a non-invasive process can be ensured through peripheral observation of failure symptoms, provided that data collection does not increase network traffic, overhead control, or internal network processes. Full article

Background/Objectives: The aim of this study was to synthesise qualitative evidence from family members’ experiences of long-term home care for older adults provided by live-in migrant caregivers. Methods: We conducted a systematic literature review with meta-synthesis using four online databases. The search included articles published between January 2016 and December 2025 on the CINAHL, PubMed, SCOPUS and WOS databases. Thematic synthesis of qualitative data was conducted. Results: Eleven papers from six different countries fulfilled the criteria and were included in the thematic synthesis. Four main themes were identified: 1. Not an easy decision. 2. A stranger at the heart of family life. 3. Two worlds that meet and need each other. 4. Improving the integration of migrant caregivers into family life. Hiring migrant caregivers to provide long-term home care for older adults can ease the burden on family caregivers, but it is an additional source of stress and worry. Conclusions: The family members of older adults call for greater financial and institutional support, as well as the involvement of social and health services in the training and education of families and migrant caregivers. Negotiation skills and the ability to reach consensus between older adults (OAs), family members and resident migrant caregivers are key to improving cohabitation and care for OAs. The primary goal is the well-being of the OAs, which involves overcoming cultural prejudices, learning together in response to the new situation, improving caregivers’ training, and ensuring continuity of care. Full article

Achieving safe and efficient sequential decision-making in dynamic and uncertain environments is a core challenge in intelligent manufacturing and robotic systems. During operation, systems are often subject to coupled multi-source uncertainties—such as stochastic disturbances, model mismatch, and environmental shifts—rendering traditional approaches based on deterministic models or post hoc safety verification incapable of simultaneously ensuring performance and safety. In particular, the non-differentiability of constraint satisfaction probabilities in chance-constrained decision-making severely impedes its integration with data-driven learning paradigms. To address these challenges, this paper proposes DeepChance-OPT (Deep Chance Optimization), an end-to-end differentiable disturbance-rejection decision framework tailored for dynamic grasping tasks in precision assembly. The framework first encodes historical observations and control sequences into a low-dimensional latent representation to extract key dynamic features relevant to decision-making. Subsequently, it models the temporal propagation of uncertainty in this latent space to predict the probability distribution of future states. Furthermore, via a differentiable chance-constrained mechanism, the risk of constraint violation is transformed into a continuous and differentiable penalty term, which is jointly optimized with the task performance objective to achieve synergistic improvement in both safety and efficiency. The entire framework is trained and executed under a unified end-to-end architecture, enabling closed-loop online sequential decision-making. Experiments on a precision silicon carbide wafer grasping task demonstrate that DeepChance-OPT achieves real-time performance (average decision latency < 4 ms) while reducing the constraint violation rate to 2.3%, significantly outperforming both traditional optimization and purely data-driven baselines. Under composite uncertainty scenarios—including parameter perturbations, measurement noise, and external disturbances—the success rate remains stably above 87.5%, fully validating the effectiveness of the proposed framework for robust, safe, and efficient decision-making in complex dynamic environments. This work provides a new paradigm for intelligent disturbance-rejection decision-making in high-precision manufacturing, offering both theoretical rigor and engineering practicality. Full article

The Four-Leg Three-Phase Voltage Source Inverter (4LVSI) is a versatile solution for integrating renewable energy sources (RESs) into distribution networks, as it compensates unbalanced voltages and currents while providing a path for zero-sequence components. Accurate current control is essential to ensure power quality and reliable operation under these conditions. Conventional controllers such as proportional–integral, resonant, or feedback-linearization methods achieve acceptable tracking under static dc-link conditions, but their performance degrades when dc-link voltage dynamics arise due to renewable-source fluctuations. This paper proposes a data-driven model reference neural control (MRNC) strategy for a four-leg inverter connected to RESs, explicitly accounting for dc-link voltage variations. The proposed controller reformulates the classical Model Reference Adaptive Control (MRAC) as a lightweight single-layer neural network whose adaptive weights are updated online using the Recursive Least Squares (RLS) algorithm. In this framework, the dc-link variations are not modeled explicitly but are implicitly learned through the data-driven adaptation process, as their influence is captured in the neural network regressors formed from real-time input–output measurements. This allows the controller to continuously identify the inverter dynamics and compensate the effect of dc-link fluctuations without requiring additional observers or prior modeling. The proposed approach is validated through detailed time-domain simulations and real-time Hardware-in-the-Loop (HIL) experiments implemented at a 10 kHz switching frequency. The results indicated that the RLS-based MRNC controller achieved the lowest steady-state current error, reducing it by approximately 1.85% and 1% compared to the Proportional-Resonant (PR) and One-Step-Ahead (OSAC) controllers, respectively. Moreover, under dc-link voltage variations, the proposed controller significantly reduced the current overshoot, achieving decreases of 5.9 A and 6.36 A relative to the PR controller. Full article

To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven control approach based on improved deep reinforcement learning (RL). Aiming to reduce the tin entrainment rate in smelting slag and CO emissions in exhaust gas, we construct a data-driven environment model with an 8-dimensional state space (including furnace temperature, pressure, gas composition, etc.) and an 8-dimensional action space (including lance parameters such as material flow, oxygen content, backpressure, etc.). We innovatively design a Dual-Action Discriminative Deep Deterministic Policy Gradient (DADDPG) algorithm. This method employs an online Actor network to simultaneously generate deterministic and exploratory random actions, with the Critic network selecting high-value actions for execution, consistently enhancing policy exploration efficiency. Combined with a composite reward function (integrating real-time Sn/CO content, their variations, and continuous penalty mechanisms for safety constraints), the approach achieves multi-objective dynamic optimization. Experiments based on real tin smelting production line data validate the environment model, with results demonstrating that the tin content in slag is reduced to between 3.5% and 4%, and CO content in exhaust gas is decreased to between 2000 and 2700 ppm. Full article

Accurate prediction of marine visibility is critical for ensuring safe and efficient maritime operations, particularly in dynamic and data-sparse ocean environments. Although visibility reduction is a natural and unavoidable atmospheric phenomenon, improved short-term prediction can substantially enhance navigational safety and operational planning. While deep learning methods have demonstrated strong performance in land-based visibility prediction, their effectiveness in marine environments remains constrained by the lack of fixed observation stations, rapidly changing meteorological conditions, and pronounced spatiotemporal variability. This paper introduces SeADL, a self-adaptive deep learning framework for real-time marine visibility forecasting using multi-source time-series data from onboard sensors and drone-borne atmospheric measurements. SeADL incorporates a continuous online learning mechanism that updates model parameters in real time, enabling robust adaptation to both short-term weather fluctuations and long-term environmental trends. Case studies, including a realistic storm simulation, demonstrate that SeADL achieves high prediction accuracy and maintains robust performance under diverse and extreme conditions. These results highlight the potential of combining self-adaptive deep learning with real-time sensor streams to enhance marine situational awareness and improve operational safety in dynamic ocean environments. Full article