Search Results (215)

In modern e-learning environments, ensuring both student identity verification and concentration monitoring during online examinations has become increasingly important. This paper introduces a robust sequential framework that integrates Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and fuzzy logic to achieve reliable face recognition and interpretable attentiveness assessment. Images from the Extended Yale B (cropped) dataset are preprocessed through grayscale normalization and resizing, while GANs generate synthetic variations in pose, illumination, and occlusion to enrich the training set and improve generalization. The CNN extracts discriminative facial features for identity recognition, and a fuzzy inference system transforms the CNN’s confidence scores into human-interpretable concentration levels. To stabilize learning and prevent overfitting, the model incorporates dropout regularization, batch normalization, and extensive data augmentation. Comprehensive evaluations using confusion matrices, ROC–AUC, and precision–recall analyses demonstrate an accuracy of 98.42%. The proposed framework offers a scalable and interpretable solution for secure and reliable online exam proctoring. Full article

The extensive mining area and multitude of working sites in Maping Phosphate Mine result in a complex ventilation system. This complexity manifests as uneven airflow distribution at working faces, posing considerable challenges for efficient ventilation management. An intelligent ventilation management system based on the Python PyQt5 library was developed for Maping Phosphate Mine to improve ventilation efficiency, lower dust concentration at the working face, and enhance safety by addressing uneven air volume distribution. The implementation of an integrated system, comprising a 3D ventilation network model, remote control capabilities, and smart algorithms, has successfully realized zonal planning and on-demand ventilation in the mine’s underground workings. To adapt to the fluctuating air demand at the tunneling face, a remote intelligent control scheme for louvered dampers was implemented. This dynamic demand-based strategy achieves precise distribution of air volume throughout the ventilation network. The research results demonstrate that the system effectively addresses the uneven distribution of air volume, thereby improving the overall ventilation environment and reducing the risk of ventilation-related accidents. The system serves dual purposes: it provides an intelligent ventilation control mechanism and integrates seamlessly with the key subsystems for underground safety production. This synergy is instrumental in advancing the mine’s digitalization and intelligent transformation initiatives. Field test results indicate that the system achieved a 30% reduction in energy consumption and a 70% decrease in dust concentration at the working face, respectively. Full article

Local Owner-Operated Retail Outlets (LOOROs) in India faced unprecedented disruption during the COVID-19 pandemic, with digital transformation emerging as both a challenge and an opportunity. The growing dominance of larger online and offline competitors, who swiftly adopted digital payments, posed a threat to traditional business models of these small neighborhood retailers. This study employs the Stimulus–Organism–Response (S-O-R) framework to examine the antecedents shaping LOORO owners’ attitudes toward digital payment practices and how these attitudes influence their intention and actual adoption. A survey of 175 LOOROs in Navi Mumbai was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that resource availability and customer care significantly influenced adoption, whereas competitor and customer pressure had little effect. Overall, LOORO owners demonstrated a positive outlook toward integrating digital payment systems, indicating their adaptive capacity to navigate the digital shift accelerated by the COVID-19 pandemic. Full article

Facial expression recognition (FER) technology has progressively matured over time. However, existing FER methods are primarily optimized for frontal face images, and their recognition accuracy significantly degrades when processing profile or large-angle rotated facial images. Consequently, this limitation hinders the practical deployment of FER systems. To mitigate the interference caused by large pose variations and improve recognition accuracy, we propose a FER method based on profile-to-frontal transformation and multimodal learning. Specifically, we first leverage the visual understanding and generation capabilities of Qwen-Image-Edit that transform profile images to frontal viewpoints, preserving key expression features while standardizing facial poses. Second, we introduce the CLIP model to enhance the semantic representation capability of expression features through vision–language joint learning. The qualitative and quantitative experiments on the RAF (89.39%), EXPW (67.17%), and AffectNet-7 (62.66%) datasets demonstrate that our method outperforms the existing approaches. Full article

The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly deep learning, has catalyzed a paradigm shift. This review provides a systematic, comprehensive, and forward-looking analysis of the radar signal deinterleaving landscape, critically bridging foundational techniques with the cognitive frontiers. Previous reviews often focused on specific technical branches or predated the deep learning revolution. In contrast, our work offers a holistic synthesis. It explicitly links the evolution of algorithms to the persistent challenges of the CME. We first establish a unified mathematical framework and systematically evaluate classical approaches, such as PRI-based search and clustering algorithms, elucidating their contributions and inherent limitations. The core of our review then pivots to the deep learning-driven era, meticulously dissecting the application paradigms, innovations, and performance of mainstream architectures, including Recurrent Neural Networks (RNNs), Transformers, Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs). Furthermore, we venture into emerging frontiers, exploring the transformative potential of self-supervised learning, meta-learning, multi-station fusion, and the integration of Large Language Models (LLMs) for enhanced semantic reasoning. A critical assessment of the current dataset landscape is also provided, highlighting the crucial need for standardized benchmarks. Finally, this paper culminates in a comprehensive comparative analysis, identifying key open challenges such as open-set recognition, model interpretability, and real-time deployment. We conclude by offering in-depth insights and a roadmap for future research, aimed at steering the field towards end-to-end intelligent and autonomous deinterleaving systems. This review is intended to serve as a definitive reference and insightful guide for researchers, catalyzing future innovation in intelligent radar signal processing. Full article

Facial expression recognition (FER) is crucial for affective computing and human–computer interaction; however, it is still difficult to achieve under various conditions in the real world, such as lighting, occlusion, and pose. This work presents a lightweight hybrid network, SE-Hybrid + Face-ViT, which merges convolutional and transformer architectures through multi-level feature fusion and adaptive channel attention. The network includes a convolutional stream to capture the fine-grained texture of the image and a retrained Face-ViT branch to provide the high-level semantic context. Squeeze-and-Excitation (SE) modules adjust the channel responses at different levels, thus allowing the network to focus on the emotion-salient cues and suppress the redundant features. The proposed architecture, trained and tested on the large-scale AffectNet benchmark, achieved 70.45% accuracy and 68.11% macro-F1, thereby outperforming the latest state-of-the-art models such as TBEM-Transformer, FT-CSAT, and HFE-Net by around 2–3%. Grad-CAM-based visualization of the model confirmed accurate attention to the most significant facial areas, resulting in better recognition of subtle expressions such as fear and contempt. The findings indicate that SE-Hybrid + Face-ViT is a computationally efficient yet highly discriminative FER strategy that successfully addresses the issue of how to preserve details while globally reasoning with contextual information locally. Full article

In contemporary education, learners face the challenge of navigating an overwhelming abundance of information. Traditional search methods, often limited to keyword matching, fail to capture the nuanced meaning and relationships within educational materials. Our multimodal approach combines Sentence Transformer for text and Inception V3 for images to generate vector embeddings for textbooks which are stored in an Elasticsearch database. Learners’ queries again are converted to vector embeddings which are matched through cosine similarity with stored embeddings, resulting in retrieval of relevant material which is ranked and then synthesized using large language model (LLM) APIs. The approach retrieves answers based on semantic search rather than keywords. The system also integrates GenAI capabilities separately, specifically leveraging LLM APIs, to generate context-aware answers to user-posed questions at varying levels of complexity, e.g., beginner, intermediate, and advanced. Through comprehensive evaluation, we demonstrate the system’s ability to retrieve coherent answers across multiple sources, offering significant advancements in cross-text and cross-modal retrieval tasks. This work also contributes to the international discourse on ethical GenAI integration in curricula and fosters a collaborative human–AI learning ecosystem. Full article

The increasing challenges posed by climate change demand holistic approaches to mitigate ecosystem degradation. In Mediterranean-type regions—biodiversity hotspots facing intensified droughts, fires, and biological invasions—such strategies are particularly relevant. Among invasive species, Acacia longifolia produces substantial woody and leafy biomass when removed, offering an opportunity for reuse as soil-improving material after adequate processing. This study aimed to evaluate the potential of invasive A. longifolia Green-waste compost (Gwc) as a soil amendment to promote soil recovery and native plant establishment after fire. A field experiment was carried out in a Mediterranean ecosystem using Arbutus unedo, Pinus pinea, and Quercus suber planted in control and soils treated with Gwc. Rhizospheric soils were sampled one year after plantation, in Spring and Autumn, to assess physicochemical parameters and microbial community composition (using composite samples) through Next-Generation Sequencing. Our study showed that Gwc-treated soils exhibited higher moisture content and nutrient availability, which translated into improved plant growth and increased microbial richness and diversity when compared with control soils. Together, these results demonstrate that A. longifolia Gwc enhances soil quality, supports increased plant fitness, and promotes a more diverse microbiome, ultimately contributing to faster ecosystem recovery. Transforming invasive biomass into a valuable resource could offer a sustainable, win–win solution for ecological rehabilitation in fire-affected Mediterranean environments, enhancing soil and ecosystem functioning. Full article

Facial expression recognition (FER) is a key task in affective computing and human–computer interaction, aiming to decode facial muscle movements into emotional categories. Although deep learning-based FER has achieved remarkable progress, robust recognition under uncontrolled conditions (e.g., illumination change, pose variation, occlusion, and cultural diversity) remains challenging. Traditional Convolutional Neural Networks (CNNs) are effective at local feature extraction but limited in modeling global dependencies, while Vision Transformers (ViT) provide global context modeling yet often neglect fine-grained texture and frequency cues that are critical for subtle expression discrimination. Moreover, existing approaches usually focus on single-domain representations and lack adaptive strategies to integrate heterogeneous cues across spatial, semantic, and spectral domains, leading to limited cross-domain generalization. To address these limitations, this study proposes a unified Multi-Domain Feature Enhancement and Fusion (MDFEFT) framework that combines a ViT-based global encoder with three complementary branches—channel, spatial, and frequency—for comprehensive feature learning. Taking into account the approximately bilateral symmetry of human faces and the asymmetric distortions introduced by pose, occlusion, and illumination, the proposed MDFEFT framework is designed to learn symmetry-aware and asymmetry-robust representations for facial expression recognition across diverse domains. An adaptive Cross-Domain Feature Enhancement and Fusion (CDFEF) module is further introduced to align and integrate heterogeneous features, achieving domain-consistent and illumination-robust expression understanding. The experimental results show that the proposed method consistently outperforms existing CNN-, Transformer-, and ensemble-based models. The proposed model achieves accuracies of 0.997, 0.796, and 0.776 on KDEF, FER2013, and RAF-DB, respectively. Compared with the strongest baselines, it further improves accuracy by 0.3%, 2.2%, and 1.9%, while also providing higher F1-scores and better robustness in cross-domain testing. These results confirm the effectiveness and strong generalization ability of the proposed framework for real-world facial expression recognition. Full article

Many cities worldwide face decline due to mineral-resource exhaustion, with mining-induced subsidence and land degradation posing urgent land use challenges. At the same time, carbon neutrality has become a global agenda, promoting ecological restoration, emissions reduction, and green transformation in resource-exhausted cities. However, empirical evidence on how carbon neutrality strategies drive land use transition remains scarce. Taking Xuzhou, China, as a case study, we integrate the GeoSOS–FLUS land use simulation model with a Markov chain model to project land use patterns in 2030 under three scenarios: natural development (ND), land recovery (LR), and carbon neutrality (CN). Using emission factors and a land use carbon inventory, we quantify spatial distributions and temporal shifts in carbon emission and sequestration. Results show that LR’s rigid recovery policies restrict broader transitions, while the CN scenario effectively reshapes land use by enhancing the competitiveness of low-carbon types such as forests and new-energy land. Under CN, built-up land expansion is curbed, forests and new-energy land are maximized, and emissions fall by 4.95% from 2020. Carbon neutrality offers opportunities for industrial renewal and ecological restoration in resource-exhausted cities, steering transformations toward approaches that balance ecological function and carbon benefits. Long-term monitoring is required to evaluate policy sustainability and effectiveness. Full article

The growth of aquaculture poses significant challenges for disease management, impacting economic sustainability and global food security. Traditional diagnostics are slow and require expertise, while current deep learning models, including CNNs and Transformers, face a trade-off between capturing global symptom context and maintaining computational efficiency. This paper introduces FishMambaNet, a novel framework that integrates selective state space models (SSMs) with convolutional networks for accurate and efficient fish disease diagnosis. FishMambaNet features two core components: the Fish Disease Detection State Space block (FSBlock), which models long-range symptom dependencies via SSMs while preserving local details with gated convolutions, and the Multi-Scale Convolutional Attention (MSCA) mechanism, which enriches multi-scale feature representation with low computational cost. Experiments demonstrate state-of-the-art performance, with FishMambaNet achieving a mean Average Precision at 50% Intersection over Union (mAP@50) of 86.7% using only 4.3 M parameters and 10.7 GFLOPs, significantly surpassing models like YOLOv8-m and RT-DETR. This work establishes a new paradigm for lightweight, powerful disease detection in aquaculture, offering a practical solution for real-time deployment in resource-constrained environments. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

In the context of complex scenarios, identifying the posture of individuals is a critical technology in the fields of intelligent surveillance and autonomous driving. However, existing methods face challenges in effectively balancing real-time performance, occlusion, and recognition accuracy. To address this issue, we propose a lightweight hybrid model, referred to as YOLO-SwinTransformer, in this study. This model utilizes YOLOv8’s CSP Darknet as the primary network to achieve efficient multi-scale feature extraction. It integrates the Path Aggregation Network aggregation (PANet) and HRNet with high-resolution multi-scale feature extraction, enhancing cross-level semantic information interaction. The primary innovation of this model is the design of a modified Swin Transformer posture identification module, incorporating the Spatial Locality-Aware Module (SLAM) to enhance local feature extraction, achieving a combined modeling of space attention and time-series continuity. This effectively addresses the challenges posed by occlusion and video distortion in identifying posture. Additionally, we have extended the CIoU Loss and weighted mean square error loss functions to improve posture identification strategies, enhancing the precision of key points. Ultimately, extensive experimentation with both the COCO dataset and the self-built realistic road dataset demonstrated that the YOLO-SwinTransformer model achieved a state-of-the-art Average Precision (AP) of 84.9% on the COCO dataset, representing a significant 12.8% enhancement over the YOLOv8 baseline (72.1% AP). More importantly, on our challenging self-built real-world road dataset, the model achieved 82.3% AP (a 13.7% improvement over the baseline’s 68.6% AP), proving its superior robustness in complex occlusion and low-light scenarios. The model’s size is 27.3 M, and its lightweight design enables 39–41 FPS of real-time processing on edge devices, providing a feasible solution for intelligent monitoring and autonomous driving applications with high precision and efficiency. Full article

Personal protective equipment (PPE) like single-use face masks is discarded after a single use and poses a significant danger to the environment, resulting in plastic pollution. Most of the face masks are made from synthetic polymers and are non-biodegradable to the environment; hence, concerns are being raised about polymers’ environmental impact. Most of the previous studies so far focus on polypropylene (PP) disposable masks and limited data related to environmental abiotic degradation behavior. There is a lack of studies aiming to understand the degradation behavior of different masks and the influence of physical-chemical factors. In this paper, we report on the environmental abiotic degradation of cloth, surgical and respirator filter facepiece 1 (FFP1) masks by accelerated artificial weathering. Furthermore, physical-chemical properties of masks were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC) and Thermo Gravimetric Analysis (TGA). The cloth and FFP1 masks are made from polyethylene terephthalate (PET) and surgical masks were made from polypropylene (PP). Masks were exposed to an accelerated weathering test, which simulates the effects of natural sunlight and reproduces the damage caused by weathering elements such as sunlight, rain and dew. Masks were exposed to Ultraviolet radiation (UV) for 120, 240 and 360 h followed by condensation at 50 °C for 4 h. The FTIR results show that PET cloth and FFP1 PET masks are not degrading with the 360 h maximum exposure duration, which is equivalent to ±180 days. The FTIR scan of the PP surgical mask after 120 h of exposure time shows that it was degraded and broken down into fragments. For the PET cloth mask, a 58% reduction in crystallinity and heat of enthalpy was observed after 120 h of exposure. UV exposure causes a chain scission reaction, breaking down the ester bond in the case of the PET cloth mask. In the case of the PET FFP1 mask exposed to UV for 120, 240 and 360 h, a drastic reduction in crystallinity was observed as compared to the neat (original) PET FFP1 mask. Neat PET cloth and FFP1 masks have higher onset and maximum degradation temperatures as compared to the 120, 240 and 360 h UV exposed masks. Neat PET cloth and FFP1 masks have better resistance to thermal degradation. Full article

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations $({\sigma }_r,{\sigma }_a,{\sigma }_c)$ are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows. Full article