Search Results (3,400)

Electroencephalography (EEG) provides insights into the neural mechanisms underlying attention, response inhibition, and distraction in cognitive tasks. This dataset was collected to examine neural activity in young drivers and non-drivers performing Go/No-Go tasks under visual and auditory distraction conditions. A total of 40 university students (20 drivers, 20 non-drivers; balanced by sex) completed eight experimental blocks combining visual or auditory stimuli with realistic distractions, such as text message notifications and phone call simulations. EEG was recorded using a 16-channel BrainAccess MIDI system at 250 Hz. Experiments 1, 3, 5, and 7 served as transitional blocks without participant responses and were excluded from behavioral and event-related potential analyses; however, their EEG recordings and event markers are included for baseline or exploratory analyses. The dataset comprises raw EEG files, event markers for Go/No-Go stimuli and distractions, and metadata on participant demographics and mobile phone usage. This resource enables studies of attentional control, inhibitory processes, and distraction-related neural dynamics, supporting research in cognitive neuroscience, brain–computer interfaces, and transportation safety. Full article

Rice, a dietary staple for over half of the global population, is highly susceptible to bacterial and fungal diseases such as bacterial blight, brown spot, and leaf smut, which can severely reduce yields. Traditional manual detection is labor-intensive and often results in delayed intervention and excessive chemical use. Although deep learning models like convolutional neural networks (CNNs) achieve high accuracy, their computational demands hinder deployment in resource-limited agricultural settings. We propose MCP-X, an ultra-compact CNN with only 0.21 million parameters for real-time, on-device rice disease classification. MCP-X integrates a shallow encoder, multi-branch expert routing, a bi-level recurrent simulation encoder–decoder (BRSE), an efficient channel attention (ECA) module, and a lightweight classifier. Trained from scratch, MCP-X achieves 98.93% accuracy on PlantVillage and 96.59% on the Rice Disease Detection Dataset, without external pretraining. Mechanistically, expert routing diversifies feature branches, ECA enhances channel-wise signal relevance, and BRSE captures lesion-scale and texture cues—yielding complementary, stage-wise gains confirmed through ablation studies. Despite slightly higher FLOPs than MobileNetV2, MCP-X prioritizes a minimal memory footprint (~1.01 MB) and deployability over raw speed, running at 53.83 FPS (2.42 GFLOPs) on an RTX A5000. It achieves 16.7×, 287×, 420×, and 659× fewer parameters than MobileNetV2, ResNet152V2, ViT-Base, and VGG-16, respectively. When integrated into a multi-resolution ensemble, MCP-X attains 99.85% accuracy, demonstrating exceptional robustness across controlled and field datasets while maintaining efficiency for real-world agricultural applications. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

Memory forensics is an essential cybersecurity tool that comprehensively examines volatile memory to detect the malicious activity of fileless malware that can bypass disk analysis. Image-based detection techniques provide a promising solution by visualizing memory data into images to be used and analyzed by image processing tools and machine learning methods. However, the effectiveness of image-based data for detection and classification requires high computational efforts. This paper investigates the efficacy of texture-based methods in detecting and classifying memory-resident or fileless malware using different image resolutions, identifying the best feature descriptors, classifiers, and resolutions that accurately classify malware into specific families and differentiate them from benign software. Moreover, this paper uses both local and global descriptors, where local descriptors include Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) and global descriptors include Discrete Wavelet Transform (DWT), GIST, and Gray Level Co-occurrence Matrix (GLCM). The results indicate that as image resolution increases, most feature descriptors yield more discriminative features but require higher computational efforts in terms of time and processing resources. To address this challenge, this paper proposes a novel approach that integrates Local Interpretable Model-agnostic Explanations (LIME) with deep learning models to automatically identify and crop the most important regions of memory images. The LIME’s ROI was extracted based on ResNet50 and MobileNet models’ predictions separately, the images were resized to 128 × 128, and the sampling process was performed dynamically to speed up LIME computation. The ROIs of the images are cropped to new images with sizes of (100 × 100) in two stages: the coarse stage and the fine stage. The two generated LIME-based cropped images using ResNet50 and MobileNet are fed to the lightweight neural network to evaluate the effectiveness of the LIME-based identified regions. The results demonstrate that the LIME-based MobileNet model’s prediction improves the efficiency of the model by preserving important features with a classification accuracy of 85% on multi-class classification. Full article

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

Background: The use of smartphone applications by patients can be utilized to transform peri-operative care. With the ever-evolving landscape, an updated systematic review is needed in this field. Objective: This study aims to summarize the smartphone-based applications used by patients as discussed in the academic literature in the setting of peri-operative patient care. Methods: Seven databases were searched to identify articles discussing the use of smartphone applications by patients peri-operatively. Articles were included if they examined the use of smartphone-based applications in the setting of the peri-operative period and examined the application’s usability and effectiveness. Each paper was appraised using CASP checklists and analyzed using the thematic synthesis method. Results: Overall, 18 articles were selected for this study from 8204 articles initially obtained. The themes that emerged from the analysis include the following benefits of smartphone applications in peri-operative patient care: (1) patient education and instruction, (2) clear communication, (3) decreasing complications and the use of healthcare resources, (4) post-operative monitoring and pain control, (5) improved patient support, satisfaction, and safety. Other themes also emerged such as requirements of a practical smartphone application, what to include in smartphone application assessments, limitations of smartphone application studies, and future directions of smartphone applications regarding patient peri-operative care. Conclusions: The landscape of mobile applications is exponentially growing and their use in the peri-operative period is imminent for the future. Their use can improve communication between surgical care professionals, enhance patient care in the peri-operative period, and strengthen medical education. Further studies, validation tools, and improvements will be required to implement their use and demonstrate outcomes that can guide recommendations surrounding their use. Full article

This study explores the potential connections between the digital economy and water conservation technologies in the context of China’s water resource consumption from 2008 to 2021. The research employs a state-of-the-art M-MQR technique, including the PCA index, and yields several significant findings. Empirical results reveal that digital technologies play a crucial role in reducing water consumption: Mobile technology decreases water use by −0.00001 to −0.00002 across quantiles, while internet access cuts consumption by −0.0000306 at lower quantiles and −0.0000167 at higher quantiles. The digital economy index shows an overall reduction in water consumption of −0.0537 at lower quantiles and −0.0292 at higher quantiles. Water conservation technologies, such as sprinkler irrigation, also contribute significantly, with reductions of −0.005 at the 10th quantile. Furthermore, water-saving investments show a positive effect on reducing water consumption, with reductions of −0.0105 at the 95th quantile. The study emphasizes that digitalization moderates the impact of water-saving technologies, reducing consumption by −0.0124 to −0.0118 at lower quantiles and −0.00812 to −0.00761 at middle quantiles. These results highlight the potential of digital infrastructure and water-saving investments to improve water use efficiency and address China’s water resource challenges. This study proposes that digital water supply and distribution system devices can help develop smart water infrastructure, reduce waste, and improve efficiency. Full article

Circular economy learning fosters competencies in sustainable resource management and environmental protection, which have been recognized by the OECD (Organization for Economic Cooperation and Development) to be essential for cross-curricular training and higher education. However, implementing gamification techniques through mobile applications remains challenging, as their effectiveness depends on students’ willingness to adopt them. This study evaluated acceptance of a gamified mobile application for circular economy learning among university students in Peru, analyzing the relationships between the constructs of the Technology Acceptance Model (TAM). A quantitative correlational case study involving 76 students was conducted. The results showed a moderate-to-high acceptance rate of 73.69%, with significant correlations identified between the TAM constructs. This study contributes to closing gaps in empirical evidence on the acceptance of technology for sustainability education in diverse contexts. Future studies should integrate generative artificial intelligence into gamified apps to deliver personalized feedback and employ learning analytics tools for progress tracking, supporting global efforts toward SGD 4 (Quality Education) and SDG 12 (Responsible Production and Consumption) for the transition to circular economies. Full article

The rapid advancement of 5G/6G networks and the Internet of Things has rendered mobile traffic patterns increasingly complex and dynamic, posing significant challenges to achieving precise cell-level traffic forecasting. Traditional deep learning models, such as LSTM and CNN, rely heavily on substantial datasets. When confronted with new base stations or scenarios with sparse data, they often exhibit insufficient generalisation capabilities due to overfitting and poor adaptability to heterogeneous traffic patterns. To overcome these limitations, this paper proposes a meta-learning framework—GMM-MCM-NF. This framework employs a Gaussian mixture model as a probabilistic meta-learner to capture the latent structure of traffic tasks in the frequency domain. It further introduces a multi-component synthesis mechanism for robust weight initialisation and a negative feedback mechanism for dynamic model correction, thereby significantly enhancing model performance in scenarios with small samples and non-stationary conditions. Extensive experiments on the Telecom Italia Milan dataset demonstrate that GMM-MCM-NF outperforms traditional methods and meta-learning baseline models in prediction accuracy, convergence speed, and generalisation capability. This framework exhibits substantial potential in practical applications such as energy-efficient base station management and resilient resource allocation, contributing to the advancement of mobile networks towards more sustainable and scalable operations. Full article

This scoping review examines how workplace language proficiency and corporate language policies function as dimensions of human capital, shaping employee mobility and organisational outcomes in multilingual contexts. Drawing on 12 empirical studies (2010–2025), supplemented by one influential review work used for context, the review integrates two analytical lenses: (1) language ceilings and walls, which capture invisible barriers to vertical and horizontal mobility, and (2) the Language Needs Analysis (LANA) framework, which categorises language demands at the individual, organisational, and operational levels. Findings indicate that language proficiency and inclusive language policies act as strategic resources that enhance employability, cross-border collaboration, and knowledge transfer. Conversely, rigid monolingual policies often reproduce inequalities and limit career progression. The review highlights the role of language-sensitive HRM in developing sustainable talent pipelines, advancing diversity and inclusion, and strengthening workforce resilience. Methodologically, this study applies PRISMA-ScR guidelines to ensure transparency and rigour, while offering a framework for future research at the intersection of human capital theory, language policy, and global HRM. By reframing communicative competence as career capital, the review underscores the need to integrate language training and policy design into broader human capital development strategies. Full article

Efficient spectrum allocation and telecom traffic steering in densified heterogeneous maritime communication networks remains a critical challenge due to user mobility, dynamic interference, and congestion across terrestrial, aerial, and sea-based transmitters. This paper introduces the Moving Colorable Graph (MCG) framework, a dynamic graph-theoretical representation of interferences that extends conventional graph coloring to capture the spatiotemporal evolution of heterogeneous wireless links under varying channel and traffic conditions. The formulated spectrum allocation problem is inherently non-convex, as it involves discrete frequency assignments, mobility-induced dependencies, and interference coupling among multiple transmitters and users, thus requiring suboptimal yet computationally efficient solvers. The proposed approach models resource assignment as a time-dependent coloring problem, targeting to optimally support users’ diverse demands in dense port-area networks. Considering a realistic port-area network with coastal, sea and Unmanned Aerial Vehicle (UAV) radio coverage, we design and evaluate three MCG-based algorithm variants that dynamically update frequency assignments, highlighting their adaptability to fluctuating demands and heterogeneous coverage domains. Simulation results demonstrate that the selective reuse-enabled MCG scheme significantly decreases network outage and improves user demand satisfaction, compared with static, greedy and heuristic baselines. Overall, the MCG framework may act as a flexible scheme for mobility-aware and congestion-resilient resource management in densified and heterogeneous maritime networks. Full article

The rational utilization of cultivated land resources is central to ensuring both ecological and food security in the Yangtze River Economic Belt (YREB), holding strategic significance for regional sustainable development. Using panel data from 2010 to 2023 for 130 cities in the YREB, this study examines a spatial correlation network (SCN) for non-grain land use (NGLU) and its driving forces via a modified gravity model, social network analysis (SNA), and quadratic assignment procedure regression. The results show the following: (1) The risk of NGLU continues to increase, with the spatial pattern evolving from a “single-peak right deviation” pattern to a “multi-peak coexistence” pattern featuring three-level polarization and gradient transmission, primarily driven by economic potential disparities. (2) The SCN has increased in density, but its pathways are relatively singular. Node functions exhibit significant differentiation, with high-degree nodes forming “control poles”, high-intermediate nodes dominating cross-regional risk transmission, and low-proximity nodes experiencing “protective marginalization”. Node centrality distribution is highly connected with the regional development gradient. (3) The formation of the spatial network is jointly driven by multiple factors. Geographical proximity, economic potential differences, comparative benefit differences, non-agricultural employment differences, and factor mobility all positively contribute to the spillover effect. Conversely, implementing cultivated land protection policies and the regional imbalance in local industrial development path dependence significantly inhibit the non-grain trend. This study further reveals that a synergistic governance system characterized by “axial management, node classification, and edge support” should be recommended to prevent the gradient risk transmission induced by economic disparities, providing a scientific basis for achieving sustainable use of regional cultivated land resources and coordinated governance of food security. Full article

Rural regions around the world continue to struggle with population decline, underutilized infrastructure, and economic stagnation. While tourism is often promoted as a tool for revitalization, conventional approaches tend to prioritize new construction and external ownership, raising concerns about environmental degradation, cultural dilution, and community exclusion. This study adopts a circular economy perspective to explore how adaptive reuse—repurposing abandoned buildings—can support sustainable rural tourism. Focusing on two rural cases in South Korea, the study examines the role of tourism cooperatives in transforming underused facilities into guesthouses, retail shops, visitor centers, and community hubs. Using a qualitative comparative case study approach combining interviews, observations, and content analysis, this study identified how cooperatives mobilize local resources, preserve cultural and natural assets, and reinvest tourism revenues into community-led initiatives. Findings reveal that cooperative-led adaptive reuse enhances local empowerment, cultural preservation, and economic sustainability. This study concludes that embedding circular economy principles within rural tourism fosters resilience and community-driven revitalization and that tourism cooperatives serve as an effective governance structure for implementing circular economy practices. Full article

Unmanned Aerial Vehicle-assisted Edge Computing (UAV-EC) leverages UAVs as aerial edge servers to provide computation resources to user equipment (UE) in dynamically changing environments. A critical challenge in UAV-EC lies in making real-time adaptive offloading decisions that determine whether and how UE should offload tasks to UAVs. This problem is typically formulated as Mixed-Integer Nonlinear Programming (MINLP). However, most existing offloading methods sacrifice strategy timeliness, leading to significant performance degradation in UAV-EC systems, especially under varying wireless channel quality and unpredictable UAV mobility. In this paper, we propose a novel framework that enhances offloading strategy timeliness in such dynamic settings. Specifically, we jointly optimize offloading decisions, transmit power of UEs, and computation resource allocation, to maximize system utility encompassing both latency reduction and energy conservation. To tackle this combinational optimization problem and obtain real-time strategy, we design a Quality of Experience (QoE)-aware Online Offloading (QO2) algorithm which could optimally adapt offloading decisions and resources allocations to time-varying wireless channel conditions. Instead of directly solving MIP via traditional methods, QO2 algorithm utilizes a deep neural network to learn binary offloading decisions from experience, greatly improving strategy timeliness. This learning-based operation inherently enhances the robustness of QO2 algorithm. To further strengthen robustness, we design a Priority-Based Proportional Sampling (PPS) strategy that leverages historical optimization patterns. Extensive simulation results demonstrate that QO2 outperforms state-of-the-art baselines in solution quality, consistently achieving near-optimal solutions. More importantly, it exhibits strong adaptability to dynamic network conditions. These characteristics make QO2 a promising solution for dynamic UAV-EC systems. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article