Search Results (6,323)

While the literature acknowledges the importance of emotion management for effective leadership, no leadership theory embeds the management of contextual emotions that involuntarily spread among multiple workplace stakeholders (i.e., holistic emotional contagion) and are jointly intertwined with leaders’ actions. The present research aimed to: (1) include emotional contagion into leadership theorizing and assess the cross-country validity of the accompanying measure (Leader Awareness of Holistic Contagion Scale; LAHCS), and (2) examine the LAHCS’ convergent, discriminant and nomological/criterion validity. Data (Study 1) from 1454 Italian employees supported the LAHCS construct and convergent validity with multiple leadership scales and discriminant validity against group-member-prototypicality. Data (Study 2) from the U.S. (N = 400) and Italy (N = 186) supported measurement invariance. SEM model results suggest that leaders’ awareness of holistic contagion and their orientation to manage contagion are associated with higher followers’ commitment and leadership satisfaction. Interestingly, the leader’s engagement in active exploration of contagion exchanges and their awareness of the leader–follower emotional distance is associated with followers’ higher burnout, lower commitment and leadership dissatisfaction. In conclusion, our cross-country findings support the LAHCS validity and reveal that leaders who are aware of workplace emotional traffic are appreciated. Notably, if they attempt to actively explore this traffic or are aware of followers’ emotional distance, then the situation becomes likely intrusive and uncomfortable, resulting in followers’ dissatisfaction, poor commitment and distress. For scholars and practitioners alike, our findings provide a leadership conceptual framework, including emotional contagion as a springboard to the understanding of some apparently inconvenient truths about emotions and leadership. Full article

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s². Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s². Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure. Full article

Software-defined networking (SDN) represents a paradigm shift in network management, but its centralized control plane introduces new and severe security vulnerabilities. Conventional intrusion detection systems, including signature- and rule-based methods, lack adaptability and interpretability in the face of evolving threats. This paper proposes a multi-hybrid deep learning fusion ensemble (MHDLFE) to enhance intrusion detection in SDN environments. The framework integrates Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models via feature fusion and a meta-classifier, thereby improving both detection performance and robustness. To address the critical need for transparency in security systems, the proposed approach incorporates Explainable AI techniques, specifically Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing interpretable insights into model decisions. The proposed model achieves strong performance on the NSL-KDD and CIC-IDS2017 datasets, attaining near-perfect binary classification scores of 97.91% and 93.30%, and multiclass accuracies of 98.61% and 97.91%, respectively. These results demonstrate that the proposed framework delivers an effective and trustworthy SDN intrusion detection system by combining deep learning, ensemble fusion, and explainable AI to support accurate, transparent, and reliable cybersecurity decision-making. Full article

The ubiquity of resource-constrained Internet-of Things (IoT) nodes creates an urgent demand for network intrusion detection systems (NIDSs) optimized for edge devices with limited computing power. In this paper, we propose a new NIDS system based on Mamba. NIDS-Mamba uses a dynamic sparse attention and a lightweight state space to jointly learn from short-term anomaly and long-term attack patterns. We use standardized NF-UNSW-NB15 and NF-CSE-CIC-IDS2018 datasets to verify the effectiveness of this NIDS-Mamba model. We find that this NIDS-Mamba model is very effective in dealing with extreme class imbalance problems. In the NF-CSE-CIC-IDS2018 dataset, the model achieves 98.32% accuracy, 96.98% F1-score, and an AUC of 0.9996. Most notably, the model is very robust in handling extreme class imbalance problems in the NF-UNSW-NB15 dataset. It achieves 97.03% G-Mean, 0.7915 MCC, and 0.9983 AUC, far exceeding other baseline models. Compared to Transformer-based baselines, NIDS-Mamba achieves nearly an order-of-magnitude improvement in throughput while maintaining a parameter footprint compatible with edge deployment constraints. The proposed architecture effectively mitigates the quadratic complexity and memory wall inherent in standard Transformers, ensuring compatibility with Limited RAM and strict energy constraints. The proposed model achieves a compact design with 1.12 million parameters and a peak inference memory of 5.4 MB, ensuring its feasibility for edge-based IoT nodes. These properties make NIDS-Mamba a strong candidate for deployment on IoT gateways and edge sensor nodes in smart home, industrial IoT, and critical infrastructure scenarios. Full article

Microscopic pore structure strongly controls hydrocarbon storage and flow in carbonate reservoirs, but objective and continuous pore-type classification remains difficult because carbonate pore systems are multiscale, heterogeneous, and commonly interpreted using experience-based criteria. This study develops a reproducible workflow that integrates 912 mercury-intrusion capillary pressure (MICP) datasets from 34 wells with 474 paired thin-section and core-photograph observations from the S oilfield. Principal component analysis (PCA) reduces eight pore-structure parameters to three interpretable components that describe pore-throat scale, distribution uniformity, and connectivity/displacement behavior, retaining 87.63% of the total variance. K-means clustering identifies four pore types for dolomite and four for limestone, with k = 4 selected using the elbow criterion, silhouette coefficient, centroid interpretability, and petrographic consistency. Modified injection-to-final-state analysis (MIFA) is used as an internal MICP-based consistency check rather than as a fully independent validation; paired micro-observations provide cross-scale validation with 81.22% agreement. Lithology-constrained GR, SP, and AC response windows are then used for intra-field upscaling to uncored intervals, and field-scale back-checking shows 87% agreement with existing geological interpretations. The workflow reduces interpreter subjectivity, provides physically interpretable pore-type criteria, and is applicable to carbonate reservoirs with comparable MICP, petrographic, and logging constraints. Full article

Coastal zones are sensitive areas where marine and terrestrial systems interact. Seawater intrusion, a typical coastal geological hazard, poses a serious threat to groundwater resources. This study takes the northeastern coastal zone of Laizhou Bay, a representative area affected by seawater intrusion in China and relying on groundwater for agricultural irrigation, as the research area and integrates hydrochemical analysis, irrigation hazards assessment, and a hybrid-weighted Irrigation Water Quality Index (IRWQI) to reveal seasonal changes in groundwater irrigation suitability. Results show that (1) groundwater hydrochemical facies exhibits a shift from HCO₃-Ca type in the rainy season to Cl-Ca·Mg type in the dry season, with TDS and Cl⁻ increasing coastward. The Huangshui River estuary displays a striking seasonal reversal: minimally affected during the rainy season, it becomes moderately severely intruded in the dry season, owing to the contrast between the perennial Huangshui River and adjacent ephemeral streams. (2) Salinity hazard (EC, PS) is the most immediate seawater intrusion consequence, with dry-season PS expanding inland and rendering estuarine groundwater unsuitable for irrigation. Although sodium and magnesium hazards remain below critical thresholds, strong Cl⁻–Na⁺ and Cl⁻–Mg²⁺ correlations in the dry season signal emerging risks. Bicarbonate hazard declines via conservative mixing with Ca·Mg-rich seawater, masking other hazards. Permeability hazard exhibits moderate seasonal deterioration. (3) Spatially, the IRWQI values are systematically lower during the dry season, with contiguous severe-restriction zones emerging along the Huangshui, Yongwen, and Jiehe River estuaries. These findings indicate that under reduced recharge, seawater intrusion dominates groundwater irrigation quality, triggering a seasonal tipping point. The study provides a scientific basis for adaptive coastal groundwater management. Full article

Tensile fracture in asphalt involves complex mechanical responses and component migration. This study employs molecular dynamics (MD) simulations with the COPMASS II force field to investigate water intrusion at the asphalt–aggregate interface and subsequent tensile cracking at the nanoscale. To evaluate moisture damage, a ternary interface model was constructed using a specific distribution of water molecules at a target density. Results indicate that thickness significantly enhances moisture resistance; specifically, the asphalt film in the thinnest model (AS1) was penetrated by water molecules, leading to localized interfacial failure. Further uniaxial tensile simulations at a loading rate of 0.01 Å/psreveal that as film thickness increases (AS1 to AS4), the peak stress rises from 103.2 to 113.8 MPa, and the fracture energy increases from 136 to 747 kcal/mol. Based on the density redistribution of SARA fractions, component migration is divided into three stages: structural relaxation, resin-driven de-peptization, and polar component re-aggregation. Finally, the Asphaltene Index (I_A) is proposed as a predictive indicator, showing that cracks consistently initiate in regions with minimum I_A values. These findings provide quantitative insights into the molecular mechanisms underlying asphalt durability. Full article

Reservoir pore structure is intimately linked to seepage characteristics; thus, determining its spatial variations is essential for formulating precise development schemes and remaining oil recovery strategies. Although the second member of the Shahejie Formation (Es₂) reservoir in the central-eastern Jizhong Depression generally possesses favorable macroscopic physical properties, discrepancies exist in dynamic development performance and remaining oil distribution across different regions. To clarify the influence of pore structure on seepage behavior, this study investigates the Es₂ reservoir in the Wen’an and Wuqiang areas of the Jizhong Depression, Bohai Bay Basin, utilizing integrated analytical methods including casting thin sections, scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP), relative permeability tests, and microscopic visualized percolation experiments. The results demonstrate that the Wen’an area is dominated by primary intergranular pores with a bimodal throat distribution. Despite a high areal porosity (21.6%), its fine throats (3.87 μm) and severe heterogeneity (sorting coefficient: 16.20) lead to poor connectivity (mercury withdrawal efficiency: 11.29%), resulting in a finger-like water drive, a narrow two-phase co-seepage zone (30.48%), and a lower ultimate displacement efficiency (50.64%). In contrast, the Wuqiang area features dissolved-intergranular pores with a unimodal throat distribution. Benefiting from larger throats (7.75 μm) and lower heterogeneity (sorting coefficient: 4.32), it exhibits superior connectivity (mercury withdrawal efficiency: 31.57%), uniform displacement, a wider co-seepage zone (40.72%), and a higher ultimate efficiency (59.34%). Given the lower waterflooding efficiency in the Wen’an area, subsequent gas displacement experiments following waterflooding demonstrated an overall recovery increment of 25.83%. Based on the disparities in pore structures and seepage characteristics between the two areas, it is recommended that the Wuqiang area should continue utilizing conventional waterflooding, while the Wen’an area should consider gas displacement after waterflooding. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Manufacturing polishing tasks involve repetitive movements and sustained postures that increase exposure to work-related musculoskeletal disorders (WRMSDs). This study presents an intersectoral validation of the ergonomic assessment methodology applied to industrial metal polishing operators that considered sociodemographic, anthropometric, and health variables. This study surveyed 41 workers using the Nordic Musculoskeletal Questionnaire and assessed a subsample of 27 workers using the REBA method through AI-based computer vision. Symptom prevalence was highest in the neck (82.9%), shoulders (70.8%), lower back (68.3%), and wrists/hands (65.9%). Using a computer-vision AI-based tool to analyse posture, the REBA method identified moderate (70.3%), high (26.0%) and very high (3.7%) WRMSD risks for the upper arms, neck, and trunk, respectively, with women showing greater susceptibility. Spearman correlation analysis revealed significant associations between age, gender, health perception, and musculoskeletal risks. The findings confirm the ergonomic assessment method’s applicability and reliability for ergonomic risk assessment in industrial polishing tasks, emphasising the need for targeted interventions adapted to gender and age profiles to mitigate occupational hazards. The results support a non-intrusive assessment approach suitable for industrial deployment and for prioritising targeted, worker-stratified ergonomic interventions. Full article

Coastal groundwater in monsoon-dominated regions faces compounding threats from seasonal hydrological extremes and seawater intrusion (SWI), yet the molecular-scale response of dissolved organic matter (DOM) remains poorly understood. We conducted a two-season investigation in Mannar District, Sri Lanka, integrating hydrochemistry, fluorescence spectroscopy, and Fourier-transform ion cyclotron resonance mass spectrometry to characterize DOM dynamics across shallow and deep groundwater. Dry-season chloride averaged 302 mg/L (shallow—5 to 12 m) and 505 mg/L (tube wells—20 to 30 m), then declined by 60–80% during monsoon recharge. Despite this freshening, DOM dynamics were decoupled from salinity: shallow wells showed dry-season DOC peaks (6.64 mg/L) driven by soil concentration, while tube wells exhibited wet-season enrichment (5.02 mg/L). Shallow aquifers maintained consistently high humification indices (around 0.70) and aromatic-rich DOM, indicating sustained buffering by soil-derived inputs. In contrast, wet-season recharge in tube wells appeared to stimulate microbial processing, as indicated by elevated protein-like fluorescence (C2: 26% to 36%) and a higher contribution of nitrogen-bearing formulas (CHONs: 31.4% to 37.1%). Tube wells also accumulated reduced, energy-rich DOM with correspondingly high molecular lability indices. Paradoxically, correlation networks suggested that these saturated aliphatic and halogenated structures persist due to kinetic protection under low oxygen, high-salinity conditions. These findings indicate that aquifer structure and redox conditions control DOM biogeochemistry in coastal groundwater systems. At the molecular level, DOM dynamics are influenced by aquifer depth and seasonal recharge, leading to a decoupling between salinity and organic matter transformation. Full article

Machine learning helps intrusion detection systems learn new assaults quickly. These systems train on a dataset with several threats and may identify odd behavior. This research detects intrusion using Random Forest, KNN, and Gaussian Naive Bayes. We run the model on a comprehensive dataset. Dynamics Feature Selector (DFS) improves performance. This technique eliminates unnecessary inputs and improves predictions using statistical analysis and feature significance. DFS effectiveness is tested using the NSL-KDD dataset. The recommended hybrid approach, Gaussian NB, Random Forest, and KNN are compared in meta-learning. Getting excellent accuracy with fewer characteristics is the aim. In order to demonstrate how the model may function in actual cybersecurity scenarios, the final test makes use of common performance metrics such as accuracy, precision, recall, and F1-score. The proposed method outperforms previously reported results with around 96.09% accuracy, 93.21% precision, 92.53% recall, 92.79% F1-score, and 93.65% average performance. Full article

Host-based intrusion detection systems (HIDSs) can address the limitations of network-based detection by analyzing system calls and other low-level events. Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set of attack behaviors. To address this gap, this study builds a TextRank-based preprocessing pipeline on the LID-DS 2021 dataset and compares five end-to-end pipelines: Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network(CNN) + LSTM, LSTM, Bidirectional LSTM (BiLSTM), and CNN + Bidirectional Gated Recurrent Unit (BiGRU). Of the 15 scenarios in the dataset, six multi-stage attacks were excluded, and three representative scenarios were selected based on attack-category coverage and suitability for single-chunk host-level detection. Within these three selected scenarios and same-scenario file-level splits, the deep learning pipelines achieved F1-scores of 0.90–0.94, whereas RF ranged from 0.55 to 0.63. Among the evaluated pipelines, CNN + BiGRU produced the strongest overall results. These findings indicate that, under this constrained evaluation setting, sequential deep learning pipelines can be effective for scenario-specific system-call-based HIDS; however, broader generalization to unseen attacks or to the full LID-DS 2021 scenario set remains unverified. Full article

Background/Objectives: This numerical (finite element analysis/FEA) study aimed to analyze the internal stress distribution patterns caused by a 4 N orthodontic force during intrusion, extrusion, rotation, tipping, and translation, using four common failure criteria, in intact periodontium. Additionally, based on these stress patterns, the study sought to establish correlations between these failure criteria to determine the most appropriate one—brittle-like or ductile-like. The orthodontically induced internal resorption was also assessed, along with the influence of orthodontic movements on the topography of the resorptive processes. Methods: A total of 180 numerical simulations on nine 3D anatomically accurate models containing the second lower premolar (manually reconstructed, CBCT-based) were performed. The brittle-like Maximum Principal, Minimum Principal, and ductile-like Von Mises and Tresca criteria were employed for the numerical analyses. Results: Translation and rotation more frequently cause internal pulp chamber resorption (vestibular, occlusal, lingual–mesial walls). In rotation, the stress was directly caused by the force applied to the bracket, while in translation, the origin of the stress was from the lingual third cervical area. Intrusion and extrusion movements are most likely to cause resorption in the root canal’s cervical and middle thirds (vestibular and proximal walls) due to high stresses induced by movement at the external cervical vestibular region. Tipping seems to be least prone to internal resorption. Conclusions: A 4 N orthodontic force can induce internal resorption in the pulp chamber and in the middle and cervical thirds of the root canals. The ductile-like failure criteria appear to provide a more accurate assessment of internal orthodontically induced resorption than the brittle-like criteria. Full article

Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems (IDS), particularly for analyzing complex and high-dimensional network traffic. This paper presents a systematic survey of QML techniques applied to network intrusion detection. The survey reviews peer-reviewed studies published up to January 2026 that employ quantum, hybrid quantum–classical, and quantum-inspired learning models for IDS. The selected studies are analyzed with respect to the algorithms used, intrusion detection datasets, and evaluation metrics reported. The analysis shows that most current approaches rely on simulated quantum environments and legacy datasets, while evaluation practices remain inconsistent across studies. These findings highlight the early developmental stage of QML-based IDS and the need for standardized evaluation protocols and more realistic experimental settings. Finally, open challenges and future research directions are identified to support the development of reliable, scalable, and practically deployable QML-based intrusion detection systems. Full article