Search Results (1,789)

Low-altitude unmanned aerial vehicle (UAV)-based traffic object detection is challenged by substantial scale variations from aerial perspectives, the extremely small pixel proportions of distant traffic participants, complex road background interference, unstable illumination, and severe occlusion in dense traffic scenes. To address these problems, this paper proposes ACP2-YOLO, an improved YOLO11-based detection framework for low-altitude UAV traffic scenarios, with the goal of enhancing the detection of vehicles, pedestrians, and non-motorized traffic participants. The proposed framework introduces two key improvements. First, a lightweight hybrid ACmix module that integrates convolution and self-attention is embedded into the network, enabling the model to jointly capture local detailed features and global contextual dependencies and thereby strengthen feature representation under complex backgrounds. Second, a P2 small-object detection layer is added to the original three-scale detection structure of YOLO11 to construct a four-scale P2–P5 feature pyramid. By allowing shallow high-resolution features to directly participate in object prediction, this design effectively reduces spatial information loss caused by deep downsampling and improves small-object perception. Experiments on the VisDrone2019 dataset show that the improved model achieves 53.1% Precision, 41.1% Recall, 42.9% mAP@50, and 26.3% mAP@50–95, outperforming the baseline YOLO11 by 4.2, 4.2, 5.0, and 3.6 percentage points, respectively. Comparisons with mainstream YOLO-series detectors further demonstrate its superior overall accuracy, small-object detection capability, and adaptability to complex scenes, indicating its potential for UAV-based traffic monitoring, road safety inspection, and intelligent transportation perception. Full article

Semantic segmentation of 3D point clouds remains difficult when LiDAR or depth-camera data are sampled unevenly. This paper presents DANet, a 3D semantic segmentation framework built on joint density- and semantics-adaptive convolution. Its core operator, Density-Adaptive Radius Convolution (DAR-Conv), predicts point-wise neighborhood radii before feature aggregation by combining density-driven initialization with semantics-aware modulation. In this way, dense regions can use compact receptive fields, whereas sparse or semantically complex regions can draw on broader contextual support. DANet also includes a Gated Adaptive Cross-Layer Fusion (GACF) module, which aligns encoder–decoder features and performs gated fusion with residual refinement. Experiments on S3DIS and NPM3D show that DANet obtains the highest reported mean accuracy (mAcc) among the compared methods on S3DIS, and high mean Intersection over Union (mIoU) and overall accuracy (OA) on NPM3D, supporting the usefulness of density- and semantics-aware receptive-field adaptation. Full article

Against the background of China’s large-scale, fast-paced, and uniformly constructed urban renewal, Lingnan arcade buildings are confronted with unprecedented conservation challenges, a contextual feature that endows this research with unique academic value in the international arena. Drawn from the authors’ practical participation in a government-led arcade conservation project in Zhao’an, three pragmatic research gaps unaddressed by existing theories are identified. Through a systematic comparative analysis of conservation and sustainable renewal models, as well as specific adaptive reuse cases of arcade buildings in four representative Lingnan cities, Macao, Hong Kong, Guangzhou and Shantou, three targeted recommendations corresponding to the aforementioned dilemmas are proposed to inform future practices: 1. Arcade conservation practices should not be restricted to superficial facade restoration. Instead, a well-defined intervention framework shall be established prior to the implementation of any conservation works. 2. Sufficient emphasis shall be placed on the material and structural authenticity throughout the design and construction phase. 3. A sustainable long-term operation mechanism shall be formulated post restoration to facilitate the revival of the inherent vitality of arcade buildings. Full article

Liquid desiccant dehumidification, an energy-efficient technology for air humidity control, has gained significant attention in recent years. In this study, the air temperature and humidity prediction models of liquid desiccant cooling and dehumidification (LDCD) system are built based on the proposed ITSO-TCN-BiGRU-SA. In the proposed model, the TCN is employed to obtain local features within sequence and improve the learning ability of temporal dependencies; BiGRU strengthens the model through global and bidirectional contextual relationships; self-attention mechanism assigns different weights to each time step. The ITSO algorithm, which combines the nonlinear adaptive weights and Levy flight strategy, is proposed to find the optimal hyperparameters of network. Accordingly, the model prediction accuracy is improved. Through comprehensive comparative analysis with other models under a series of experiment results, the superior performance of the developed models was systematically validated. Furthermore, based on the model predictions and experimental results, a comprehensive analysis was performed to systematically investigate the impact of system inlet parameters on cooling and dehumidification capacity and efficiency, which can provide valuable guidance for system control and operation. Full article

This paper examines how the emergence of over-parameterized artificial intelligence models and the phenomenon of double descent challenge the classical assumption that simpler models generalize better. Traditional predictive analytics relied on parsimonious models grounded in the bias-variance trade-off, where increasing complexity was expected to produce overfitting. However, recent advances in deep learning demonstrate that highly over-parameterized models can achieve superior generalization after surpassing the interpolation threshold. This paradigm shift has enabled systems such as AlphaFold, Aurora, Delphi-2M, and recommenders to model complex, high-dimensional relationships through contextual attention rather than global feature selection. The paper argues that higher education analytics remains largely reductionist, relying on limited variables such as GPA, demographics, and course completion rates to identify “at-risk” students. While interpretable, these approaches often fail to capture the dynamic and multidimensional nature of student success. In response, this study proposes a transition toward over-parameterized personalization, where students’ academic and behavioral histories are modeled as longitudinal high-dimensional sequences. Drawing parallels to commercial recommendation systems such as Amazon, Netflix, and YouTube, the paper explores how higher education can move from generalized early-warning systems toward adaptive “n-of-1” interventions. Importantly, the paper is conceptual rather than empirical: it develops a research agenda and a set of testable propositions, and it identifies the evaluation designs—temporally valid prediction protocols and causal intervention studies—by which the promise of over-parameterized personalization in higher education should be assessed before any claim of superiority can be made. Full article

Low-light color image enhancement remains a challenging task for vision-based decision systems, which must simultaneously address illumination correction, noise suppression, contrast recovery, and color preservation from a single degraded observation. This study proposes ResGASP-GAN, a GAN-based low-light image enhancement framework built around a residual-output generator that integrates batch-size-independent normalization, multi-scale contextual aggregation, and channel-wise feature recalibration within a conditional adversarial setting. Group Normalization is integrated into the proposed generator to reduce dependence on batch statistics during small-batch training, while a Squeeze-and-Excitation (SE) module adaptively enables channel-wise feature recalibration and helps preserve structural and chromatic information. The proposed generator uses reflection-padded convolutions to reduce boundary artifacts, and it features a multi-scale bottleneck composed of dilated residual blocks and Atrous Spatial Pyramid Pooling to capture spatially varying illumination patterns. The model is optimized using a compound objective that combines an adversarial term with an ${\ell }_1$ reconstruction loss, balancing perceptual realism with pixel-level fidelity. Experimental evaluation employed the LOL-v1, LOL-v2-Real, and LOL-v2-Synthetic datasets using reference-based metrics: PSNR, SSIM, and LPIPS. No-reference perceptual metrics were also used, including NIQE and BRISQUE. The results indicate that the proposed method achieves competitive structural similarity and visual image quality on LOL-v2-Real, competitive reconstruction performance on LOL-v1, and good generalization on LOL-v2-Synthetic, with the second-best metrics of PSNR = 22.30 dB, SSIM = 0.9154, and LPIPS = 0.1022 among the reported methods on this last dataset. In contrast, using no-reference metrics, this study achieved very good results, with the lowest BRISQUE of 10.4540 and a competitive NIQE of 4.4396, providing high-quality visual–perceptual reconstruction. Overall, the proposed architecture provides a competitive GAN-based alternative for low-light image enhancement, combining residual connections, multi-scale contextual modeling, and channel-wise feature refinement. This architecture provides the lowest inference time over all discussed models, which is highly required in real-time outdoor applications such as robot navigation and mapping. Full article

Accurate semantic segmentation of railway point clouds is essential for intelligent railway inspection and infrastructure management. However, railway scenes contain elongated structures, locally similar objects, and severe class imbalance, while the discriminative value of LiDAR intensity is still insufficiently exploited. To address these issues, this study proposes RailDLA-Net, an intensity-aware deep local aggregation framework for railway point cloud semantic segmentation. The network adopts an encoder–decoder architecture and integrates a railway structure-guided residual local encoding module to capture local geometric variations, a hierarchical structural context aggregation module to model multi-level contextual dependencies, and an intensity-aware adaptive attention mechanism to enhance the discrimination of geometrically similar categories. In addition, a geometry–semantics collaborative objective is introduced to jointly optimize final predictions, intermediate semantic representations, and local spatial structures. Experiments were conducted on Rail3D and WHU-Railway3D datasets to evaluate RailDLA-Net under different railway scene conditions. The proposed method achieved 96.89% OA, 91.92% mAcc, and 88.44% mIoU on Rail3D, and 92.36% OA, 91.84% mAcc, and 83.63% mIoU on WHU-Railway3D. Compared with the baseline methods, RailDLA-Net showed competitive overall performance and improved the balance across categories, especially in more complex and imbalanced railway scenes. Ablation studies further demonstrated the effects of key configuration settings and the loss design. Together with the evaluations on two railway point cloud datasets, these results support the effectiveness of RailDLA-Net as an integrated railway-oriented framework for fine grained point cloud semantic segmentation. Full article

The growing availability of large-scale online data has created new opportunities for analysing public discourse on climate change, although the reconstruction of structured social representations within digital environments remains methodologically challenging. This study proposes a computational framework for adapting the Hierarchical Evocation Method (HEM), grounded in Social Representation Theory, to large-scale social media discourse. Using a continuously updated Reddit dataset on climate change, the approach combines theory-informed lexical anchoring with data-driven semantic expansion to construct a renewable energy subcorpus comprising 91,817 comments published between 2018 and 2026. Representational structures are reconstructed through user-level lexical diffusion, positional salience, rhetorical foregrounding, and co-occurrence analysis, enabling the identification of central, peripheral, and contrastive components within online discourse. The results reveal a relatively stabilised representational core centred on climate transition, fossil dependency, renewable infrastructures, and socio-economic transformation, while peripheral zones display greater contextual variability and evaluative fragmentation. Longitudinal analyses further suggest a progressive consolidation of renewable energy discourse despite high user turnover and sustained growth in participation. The framework additionally highlights the relevance of affective and interactional dimensions, particularly through the widespread use of ironic and sceptical emoji configurations. Methodologically, the study provides a transparent and reproducible computational pipeline that extends classical evocation-based approaches to large-scale, dynamic corpora. More broadly, the findings contribute to sustainability communication research by showing how renewable energy is collectively framed and negotiated within English Reddit-based digital discussions. Full article

Robust multimodal sentiment analysis requires models that can integrate linguistic, acoustic, and visual cues while avoiding over-reliance on noisy nonverbal signals. This study proposes UCMR-Net, a text-anchored residual fusion framework for continuous multimodal sentiment intensity prediction. The model uses contextual textual representations as the primary semantic backbone and introduces acoustic and visual representations as adaptive residual correction signals. Instead of treating the learned positive residual coefficient as a direct estimate of aleatoric or epistemic uncertainty, the proposed framework interprets it as a residual reliability score for regulating nonverbal contribution. Under a unified five-seed evaluation protocol on CMU-MOSI, UCMR-Net achieves MAE = 0.699 ± 0.009, RMSE = 0.997 ± 0.011, Pearson correlation = 0.802 ± 0.006, Acc-2 = 85.82 ± 0.62%, and F1 = 85.60 ± 0.62% (mean ± SD). Controlled ablation results show that text anchoring is the dominant contributor to regression improvement, while residual fusion, adaptive residual weighting, counterfactual distillation, and multi-task supervision provide secondary stabilization effects. Unified missing-modality evaluation further indicates that performance remains relatively stable when audio or visual streams are removed, but degrades substantially when text is unavailable, confirming that the model is text-anchored rather than modality-symmetric. Calibration analysis shows that raw UCMR-Net only modestly improves calibration-related metrics, whereas post-hoc temperature scaling reduces ECE from 0.121 to 0.064 and NLL from 2.337 to 2.286. Additional CMU-MOSEI validation suggests that the proposed fusion strategy generalizes beyond CMU-MOSI, although cross-dataset transfer remains more challenging. Overall, UCMR-Net provides an effective and empirically validated framework for complete-modality sentiment intensity prediction, with moderate robustness under nonverbal missing or corrupted conditions. Full article

3D point cloud understanding plays a vital role in remote sensing, robotic perception and intelligent scene analysis. However, real-world point cloud data are often affected by sensing noise, incomplete geometry, occlusion, and irregular sampling, posing significant challenges to reliable geometric representation learning and long-range contextual modeling. Existing methods typically rely on fixed neighborhood aggregation or computationally expensive global interaction mechanisms, leaving considerable room for improvement in terms of robustness and efficiency under complex sensing conditions. To address these challenges, we propose GeoSeqNet, a geometry-aware contextual learning framework for robust 3D point cloud analysis. Specifically, an Enhanced Local Operator (ELO) is introduced to strengthen local geometric representation, while a Geometric Encoding Module (GEM) is employed to preserve spatial geometric information during long-range feature interactions. In addition, an Adaptive Gate Fusion (AGF) module is designed to effectively integrate Gate-Scaled LSTM and GRU branches, enabling efficient long-range contextual modeling. By jointly exploiting local geometric cues and long-range contextual information, GeoSeqNet achieves robust feature learning with low computational overhead. Extensive experiments on ModelNet40, ScanObjectNN, and ShapeNetPart demonstrate the effectiveness of GeoSeqNet. The proposed method achieves competitive performance while maintaining a favorable efficiency–accuracy trade-off and exhibits strong robustness in complex real-world scenarios. These results indicate that GeoSeqNet provides an effective and reliable solution for point cloud understanding in challenging sensing environments. Full article

Cyberattacks via email remain a major menace to people, companies, and critical infrastructures, and effective spam and phishing detection is a social concern. Nevertheless, the current methods, such as NetSpam and SMART, tend to have issues with non-homogenous data streams, lack of contextual knowledge, poor generalization, and inability to adapt to changing attack patterns. The existing techniques are not strong in terms of multimodal fusion and cannot effectively transfer trust or update risk scores in dynamic conditions. To overcome these shortcomings, this paper presents a Zone-Aware Multimodal Graph-Based Transformer that combines text, image, video, and metadata streams in a smooth manner to detect threats in emails. The three key novelties of the proposed framework include AAGFusion to contrastively align multimodal features and hierarchically fuse them using transformers; MAGNN-SASO to classify zones, compute similarity across zones, and optimize bio-inspired optimization; and Q-BayesTrustNet-X to propagate trust, risk score, Bayesian calibration, and continual learning, and provide interpretable feedback by using LRP-based explainability. The experimental findings prove that the proposed system has a high level of performance, with the accuracy, precision, and specificity reaching 98.57, 97.51, and 99.48, respectively, which proves its efficiency in high-fidelity and real-world spam and phishing detection. Full article

This paper focuses on the development of 3D digital resources and a learning platform for the Engineering Drawing course, exploring construction pathways and implementation methodologies while further investigating the application potential of artificial intelligence techniques in model construction and instructional processes. The 3D digital resources encompass basic geometric elements, complex structures, and engineering entities, supporting interactive operations such as rotation, scaling, and sectioning. These resources effectively overcome the inherent limitations of traditional engineering drawing laboratories, including high costs, fragility, delayed updates, limited coverage, and inadequate adaptability to individual student differences. The integration of AI technologies significantly enhances the efficiency of digital resource development and effectively stimulates student engagement in learning. At the pedagogical practice level, this paper proposes two AI-based conceptual teaching frameworks, namely the Adversarial Learning Model and the Challenge-based Learning Model. The learning platform incorporates knowledge graphs and process-based assessment mechanisms, achieving an organic integration of personalized and contextualized learning. A three-year longitudinal teaching performance analysis reveals a marked improvement in overall student grades, with a notable increase in high-grade proportions and a decrease in failure rates. Questionnaire survey results further confirm that students’ spatial imagination, comprehension, and practical application abilities have been strengthened, with minimal adverse effects. Moreover, in extracurricular activities, students participating in graphic design competitions have achieved outstanding performance. Comprehensive findings indicate that the synergistic application of digital resources, online learning platforms, and advanced AI technologies show a positive correlation with improved teaching effectiveness and provide robust support for the cultivation of innovative engineering talents. Full article

Sustainable tourism in oasis landscapes requires governance approaches that go beyond generic destination-growth models and standardized sustainability checklists. In living cultural landscapes such as Al-Ahsa Oasis, tourism development is inseparable from water systems, palm-grove agriculture, rural livelihoods, heritage values, climate stress, visitor behavior, and institutional accountability. This paper develops a governance framework for sustainable tourism in Al-Ahsa Oasis, Saudi Arabia, a UNESCO World Heritage cultural landscape. Methodologically, it is a conceptual framework-development study based on structured critical review and contextual synthesis. It critically interprets international sustainable tourism, heritage management, visitor management, and sustainable investment frameworks in relation to recent Al-Ahsa-specific evidence on rural tourism, resident satisfaction, farm-tourist behavior, rural lodges, land-cover change, and World Heritage governance. The study proposes a six-dimensional framework structured around cultural landscape stewardship, water-sensitive ecological governance, agricultural landscape continuity and rural accommodation regulation, community benefit and local enterprise, climate-responsive visitor management and interpretation, and monitoring and institutional accountability. These dimensions are treated as interdependent governance fields rather than separate checklist categories. The framework is further operationalized through an indicator and governance matrix linking suggested indicators, data sources, governance mechanisms, and responsible actors. The paper contributes by reframing sustainable oasis tourism as an adaptive governance problem rather than a technical exercise in applying predefined criteria. It provides an evidence-informed basis for planning, monitoring, stakeholder deliberation, policy refinement, and future empirical testing in Al-Ahsa and comparable arid cultural landscapes. Full article

Mining-area remote sensing scene classification plays an important role in mineral resource monitoring and ecological environment assessment. However, high-accuracy deep learning models usually exhibit complex architectures, large parameter sizes, and high computational costs, which limit their deployment on resource-constrained edge devices. To address this issue, this paper proposes a knowledge distillation and channel-aware non-local attention network (KDANet) for lightweight mining-area remote sensing scene classification. Specifically, a pretrained multi-layer Transformer-based high-performance network (HTransNet) is adopted as the teacher model, while ShuffleNetV2 is used as the lightweight student model. The proposed channel-aware non-local attention module (CANLAM) adaptively enhances high-level features during the distillation process, enabling the student model to better capture critical channel-wise and spatial contextual information. Furthermore, a multi-perspective distillation strategy is constructed by jointly optimizing contrastive distillation loss, relational distillation loss, and feature distillation loss, guiding the student model to approximate the teacher model from both feature representation and output distribution perspectives. Experimental results on the previously constructed CUG_MA mining scene dataset demonstrate that, with a reduction of approximately 52% in parameters and 62% in computational cost, KDANet achieves an overall accuracy (OA) of 69.48%. The lightweight student model maintains competitive classification performance while significantly improving computational efficiency and outperforming several mainstream distillation approaches. Full article

Image–Text retrieval requires accurate semantic alignment between visual content and natural language descriptions. Most existing methods primarily rely on region features, which are typically object-centric and may overlook important contextual information. In contrast, grid features provide denser spatial coverage and richer local details, but often lack explicit semantic structure. To better exploit their complementarity, we propose a novel Region–Grid Interaction and Calibration Network (RGICN) for Image–Text retrieval. Specifically, we first design a Global-Guided Feature Interaction Module to promote information exchange between region and grid features under the guidance of global visual semantics, allowing object-level semantics and contextual cues to complement each other. We then introduce a Text-Guided Feature Calibration Module, which leverages auxiliary image descriptions to calibrate visual features by suppressing redundant and text-irrelevant content. Finally, an Adaptive Gating Fusion Module is developed to dynamically integrate multiple visual representations according to the input image, yielding a more comprehensive and discriminative visual embedding. Extensive experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of RGICN and its competitive performance against recent state-of-the-art methods. Full article