Search Results (1,072)

As hybrid work arrangements become more established in organisations, the need for effective design and implementation strategies has grown significantly. Evidence indicates that employee wellbeing and engagement in hybrid work environments are declining and this presents a critical challenge for human resource management (HRM) professionals. This presents HRM professionals with a critical imperative of improving wellbeing, while maintaining engagement and productivity at work. This aligns closely with the United Nations’ 17 Sustainable Development Goals, particularly those that promote wellbeing and decent work. Through a systematic synthesis of 78 studies, this research investigates the key determinants of employee engagement and wellbeing in hybrid work contexts. The conceptual framework for this study is grounded in existing theoretical perspectives from the Job Demands–Resources model, Saks Frameworks and wellbeing perspective presented by Guest. The analysis identifies five critical factors that influence engagement and wellbeing outcomes in hybrid work, accompanied by evidence-based propositions for practice. These recommendations encompass: establishing well-equipped workspaces with appropriate flexibility in both location and time; developing organisational culture and leadership through enhanced communication and collaboration mechanisms; strategically allocating jobs and tasks whilst fostering effective networks and collaboration tools and implementing targeted training interventions to mitigate technostress and burnout associated with digital workloads. We advocate for future research to develop comprehensive models, frameworks and wellbeing interventions to guide HRM professionals in addressing these challenges at both the local and global levels. Full article

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

Recognizing the limitations of traditional vehicle routing models in urban environments, this work presents the Time-Dependent Truck-Drone Green Vehicle Routing Problem with Pickup and Delivery (TDTDGVRPPD) to simultaneously optimize environmental impact and operational efficiency. We first develop a truck fuel consumption and carbon emission model that accounts for the effects of time-varying speeds and real-time loads during delivery. A nonlinear energy consumption model is then proposed for drones, considering payload weight. Based on these models, a mathematical formulation is developed to minimize the total operational cost, including truck and drone usage costs, truck fuel and carbon emission costs, drone energy consumption costs, truck–drone coordination time costs, and time-window violation penalties. The model also incorporates truck no-entry zones, time-varying speeds, and customers’ simultaneous pickup and delivery demands. An Improved Whale Optimization Algorithm (IWOA) hybridized with Variable Neighborhood Search (VNS) is developed to solve the problem. Simulation results show that the proposed model and algorithm effectively optimize truck departure times to avoid traffic congestion, reduce truck–drone coordination time, and lower total logistics costs and energy consumption, thereby contributing to energy conservation and emission reduction in logistics operations. Full article

Following the PRISMA-ScR framework for scoping reviews, we systematically searched five databases (Scopus, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science) using a Boolean query combining real-time processing, 3D reconstruction, and deformation modelling terms. From 86 records identified, 56 peer-reviewed publications (2009–2025) were retained after two-stage screening and organized into a unified taxonomy covering sensing modalities (RGB-D, LiDAR, tactile), reconstruction pipelines (volumetric fusion, NRSfM, neural radiance fields), and deformation models (FEM, PBD, mass-spring, GNN-based surrogates, differentiable simulators). Of the 56 included works, 60% were published between 2022 and 2025, confirming the field’s rapid growth. Neural and implicit representations account for 20% of contributions, FEM-based methods for 16%, and hybrid or application-specific pipelines for 21%. Four systemic gaps emerge: the absence of a unified physics-aware benchmark; unresolved speed–accuracy trade-offs (PBD achieves >30 FPS on desktop GPUs for 10³–10⁴ vertex meshes but lacks mapping to physical material constants (Young’s modulus, Poisson’s ratio), limiting material fidelity; full-order FEM ensures physically consistent stress–strain behavior but runs at only 1–10 FPS without order reduction; reduced-order FEM recovers interactive rates for low-frequency deformation modes); fragile handling of occlusions and multi-object contact; and limited end-to-end integration of sensing and simulation. The findings support the presentation of a research roadmap centered on model order reduction, differentiable physics, multimodal sensing fusion, and standardized evaluation protocols, with implications for robust digital twins of deformable environments. Full article

With the aim of achieving outstanding thermal control and corrosion resistance properties, a white MAO thermal control coating sealed by a silicon–zirconium hybrid sol–gel layer was prepared in this work. The corrosion behavior of the coating was evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) in 3.5 wt.% NaCl solution. Microstructural and compositional characterizations were conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive spectroscopy (EDS). Results indicated that the sol–gel/MAO composite coating significantly outperformed the single-layer MAO coating in corrosion resistance, primarily due to effective sealing of micro-pores and cracks by the sol–gel layer, which prevented the penetration of corrosive agents. The post-immersion morphological observations were in good agreement with the EIS results. After immersion, the corrosion current density of the composite coating only increased from 10^−6.4 to 10^−5.1 A/cm², while the corrosion potential decreased from −1.25 V to −1.35 V. The post-immersion morphological observations were consistent with EIS results. Meanwhile, the composite coating can effectively mitigate the thermal control performance degradation caused by corrosion. Compared with the MAO coating, the absolute increase in solar absorptance of the sol–gel/MAO coating is reduced by 60%. After 168 h of accelerated corrosion tests in a simulated marine environment, the solar absorptance (α_S) of the sol–gel/MAO coating increased by only 0.05. This study demonstrates that the combination of MAO and sol–gel treatment provides a promising strategy for the development of lightweight, corrosion-resistant magnesium alloys for aerospace applications. Full article

Traditional job shop scheduling research primarily focuses on regular performance measures such as makespan. However, in a Just-in-Time (JIT) production environment, the objective shifts toward minimizing non-regular measures, specifically the weighted sum of earliness and tardiness (E/T) penalties, as excessive earliness leads to increased work-in-process inventory costs. Addressing the JIT scheduling problem in Assembly Job-shop Scheduling Problem (AJSP) is challenging, as traditional genetic algorithms (GAs) often suffer from premature convergence due to the randomness of initial populations. This paper proposes an Improved Genetic Algorithm (IGA) based on a multi-rule collaborative initialization mechanism. The algorithm explicitly incorporates assembly tree structure constraints during the encoding phase. For population initialization, a hybrid strategy is designed by integrating forward scheduling, backward scheduling, and forward-scheduling-based delay adjustment rules to ensure both the quality and diversity of the initial solutions. Simulation experiments and ablation studies demonstrate that the proposed IGA consistently achieves lower total weighted costs across various problem scales compared to standard algorithms. The results validate that the collaborative initialization strategy effectively balances global exploration and local exploitation, providing a robust solution for AJSP under JIT constraints. Full article

Due to climate change, corrosive conditions, and hydrogen-rich environments, steel energy pipelines inevitably develop a variety of defects. These deficiencies compromise pipeline safety and reliability, and neglecting them may result in pipeline leaks, fractures, and even potentially catastrophic explosions. Although a considerable body of literature reviews the effects of metal-loss defects like corrosion and cracks on pipeline safety and reliability, the impact of geometric deformation, like dents, lacks a comprehensive review. This work employs a hybrid systematic literature review (SLR) and bibliometric analysis (BA) to investigate the current research status of pipeline dent assessment. Four questions are answered: (1) What are the publication distribution characteristics, active journals, production organizations, and production authors related to research on pipeline dents? (2) What criteria have been employed for evaluating the pipeline dent? (3) From what perspective has the integrity of dented pipelines been assessed, and what research approaches have been used? (4) What are the future challenges and prospects of pipeline dent studies? The findings demonstrate that depth-, strain-, and damage-based evaluation criteria are widely employed to assess pipeline dents, each with merits and limitations. Despite the simplicity and ease of use of depth- and strain-based criteria, they are prone to underestimation flaws. In contrast, damage-based criteria, which consider multiple factors, are limited by their complexity and high computational resource requirements. The reliability of dented pipelines is predicted with remaining strength, fatigue life, and failure pressure using theoretical modeling, experimental testing, numerical simulation, or a combination of these methods. Future dent studies should involve refining numerical models, full-scale testing under varied loading conditions, and integrating advanced sensing techniques for real-time inspection. Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively. Full article

Cyber–Physical Systems (CPSs) are increasingly vulnerable to Distributed Denial-of-Service (DDoS) attacks, which can disrupt critical operations and compromise system safety. Although deep learning (DL) techniques are widely adopted for cyberattack detection, conventional DL-based classifiers often struggle to handle the uncertainty and ambiguity inherent in network traffic data. To address this limitation, this paper proposes an AI-driven hybrid framework, termed LSTM–Fuzzy–CPS, for adaptive DDoS detection in CPS environments. Unlike prior LSTM–Fuzzy approaches that are primarily restricted to SDN settings, the proposed framework is adapted for CPS environments and introduces continuous risk scoring, reduced false positives for safety-critical operation, and proportional mitigation mechanisms. The framework consists of a detection module and a conceptual mitigation module. The detection module, named LSTM–Fuzzy–Detector, integrates an LSTM network with a Mamdani-type fuzzy inference system that maps LSTM outputs into a continuous risk score using triangular membership functions (Low, Medium, High) and centroid defuzzification. The mitigation module is designed as a rule-based conceptual framework that translates risk levels into adaptive response actions; however, its experimental implementation is left for future work. The proposed detector is evaluated on the CICIoT2023 dataset and achieves an accuracy of 99.83% with a false-positive rate of 0.12%, demonstrating strong robustness against complex and evolving attack patterns. These results indicate that the proposed framework provides an effective, interpretable, and scalable solution for intelligent threat detection in CPS environments. Full article

In recent years, reinforcement learning (RL) has been applied to frontier-based exploration to enhance a robot’s decision-making policy and improve exploration performance. In this work, we address this scenario with the aim of pushing forward the finding of the optimal frontier selection policy in unknown, unstructured environments, with RL deployed for a minimal sensing drone setup. We propose a novel policy architecture, featuring an attention module that uses the global map features captured by a convolutional neural network together with local frontier features in the form of scalar values, trained end-to-end with a scoring network using the Proximal Policy Optimization algorithm over a 2D randomized unstructured environment. Our approach demonstrates improved exploration efficiency in the evaluated scenarios, as it surpasses purely heuristic-based frontier selection strategies used as baselines for other RL methods, achieving shorter paths than the Nearest Frontier, the Hybrid Approach, and the TARE local horizon, as well as one-shot sim-to-real policy deployment. Full article

The grocerant represents an emerging hybrid retail–foodservice format integrating grocery shopping, prepared meals, and in-store dining. Although practically significant, the academic literature remains limited and dispersed. This PRISMA-informed semi-systematic review synthesizes 16 studies—including direct grocerant research and adjacent work on retail innovation, prepared foods, and digital food retail—to clarify the current state of knowledge. The review followed structured database searches, citation tracking, title/abstract screening, and full-text eligibility assessment. Three main perspectives emerged. First, consumer-focused studies emphasize customer experience, food healthiness, multidimensional perceived value (functional, hedonic, social, and financial), brand prestige, in-store dining behavior, and loyalty. Second, strategic research positions grocerants within retail format innovation and competitive convergence between grocery and restaurant sectors. Third, operational perspectives link grocerants to prepared-food systems, retail food environments, and omnichannel transformation. Major gaps include limited operational and comparative research, geographic concentration, and weak digital integration. The review suggests that grocerants function as evolving systems where convenience, experience, branding, and digital transformation converge. Full article

Accurate identification of visually similar targets in Unmanned Aerial Vehicle (UAV) imagery is hindered by significant inter-class ambiguity and viewpoint variability. While hierarchical deep learning mitigates these challenges, existing architectures relieve manual design, introducing subjectivity and limiting cross-domain scalability. In this work, we propose an objective, data-driven method for the automated synthesis of hierarchical classification structures. Our approach uses a hybrid inter-class proximity metric that integrates geometric distances between latent-feature-space centroids with empirical misclassification probabilities. Using a hierarchical agglomerative clustering algorithm optimized via an inconsistency coefficient, we synthesize a coarse-to-fine cascade that deploys YOLOv11 for feature extraction and FT-Transformers for specialized identification. Experimental validation on the VisDrone2019 and UAV123 datasets demonstrates that the automatically generated hierarchy achieves a peak F1-score of 94.9%, outperforming the monolithic YOLOv11 model by 0.8% and matching human-designed cascades. Sensitivity analysis indicates an optimal hybrid weight range of 0.4–0.6. The findings confirm that our automated synthesis provides high adaptability and reliability for real-time edge AI deployments, ensuring robust performance in dynamic monitoring environments without requiring manual redesign. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

Flexible oxide electronics require dielectric layers that combine low-temperature processability, low leakage current, high capacitance density, and mechanical reliability. In this work, we prepared ZrAlO_x-PVP hybrid dielectric films through a low-temperature self-combustion solution process at 180 °C and systematically investigated the effect of PVP doping (0–2 wt%). The results show that PVP promotes the formation of M-O-C related bonding environments, suggesting the construction of an organic–inorganic crosslinked structure. Moderate PVP incorporation effectively suppresses leakage pathways, whereas excessive PVP induces polymer aggregation and trap-assisted conduction. Among all samples, the film on flexible PI (polyimide) with a PVP doping concentration of 0.5 wt% exhibits the best overall performance, with a leakage current as low as 1.89 × 10⁻⁸ A/cm² at 1 MV/cm, a dielectric constant of 8.88. After static bending at a radius of 20 mm, the film maintains stable dielectric behavior, indicating improved stress tolerance. Flexible IGZO TFT fabricated with the optimized dielectric shows a mobility of 11.84 cm² V⁻¹ s⁻¹, a threshold voltage of 0.48 V, and a subthreshold swing of 0.24 V dec⁻¹ before bending. This work demonstrates that moderate PVP crosslinking provides an effective balance between defect suppression and stress relaxation, offering a practical interface-engineering strategy for low-temperature flexible high-k dielectrics. Full article