Search Results (5,516)

In complex urban environments, Global Navigation Satellite System (GNSS) signals are easily affected by building blockage and multipath effects, which may degrade positioning quality or even cause GNSS denial. As a result, conventional integrated navigation systems suffer from accumulated errors due to insufficient global constraints. To address this problem, a multi-source integrated positioning method incorporating semantic information is proposed. Fixed traffic lights are selected as semantic landmarks, and an object detection network is used to extract the center pixel coordinates and detection confidence of the landmarks. Then, by combining depth information, camera pose, and the prior global coordinates of fixed semantic landmarks, a semantic target inversion model is established to transform two-dimensional image information into three-dimensional position estimates in the world coordinate system. Semantic factors are further constructed and incorporated into backend factor graph optimization. To determine the weighting of semantic factors, the influences of pixel localization error, depth estimation error, camera pose error, and prior coordinate error of fixed semantic landmarks on semantic observations are analyzed, and a noise covariance model for semantic factors is established. Finally, an unmanned ground vehicle experimental platform is built to validate and analyze the proposed factor graph algorithm. The experimental results show that, under GNSS-degraded conditions, the algorithm with semantic factors can provide supplementary global constraints for the system and effectively suppress accumulated positioning errors. In Experiment 1, compared with the algorithm without semantic factors, the maximum absolute trajectory error is reduced by 46.26%. To further verify the applicability of the proposed method in more complex scenarios, Experiment 2 is conducted on a longer route with multiple semantic landmarks and a more severe GNSS-degraded interval. The results show that the proposed method reduces the maximum APE from 6.5432 m to 3.4778 m, corresponding to a reduction of approximately 46.85%. These results demonstrate that the proposed semantic factor can improve the robustness of multi-source fusion localization in GNSS-degraded environments. Full article

The increasing incidence of traffic accidents involving elderly pedestrians has highlighted the necessity for effective strategies to improve visibility in low-light environments. Conventional safety garments based on retroreflective materials or optical fibers exhibit limitations, including passive operation and low luminance. In this study, a textile-based organic light-emitting diode (OLED) safety garment with automatic light-sensing functionality is proposed to overcome these limitations. The OLED devices were fabricated on an ultrathin polyethylene terephthalate (PET) substrate and transferred onto a textile substrate to maintain flexibility and wearability. A light-emitting module incorporating a LilyPad Arduino and ambient light sensor was implemented to enable automatic illumination under low-light conditions. The fabricated textile-based OLED exhibited a luminance of 550 cd/m² at 4.5 V and maintained stable performance after transfer, with a T₅₀ lifetime of 485 h. Thermal analysis showed a minimal temperature increase of 2.9 °C after 5 h of operation, remaining below body temperature. Moreover, mechanical testing confirmed over 95% luminance retention after 2,000 bending cycles. The fabricated OLED-based luminous safety garment exhibited lightweight wearability with a total weight of 140 g and improved visibility at observation distances of up to 50 m under low-light conditions. These results indicate that the proposed OLED-based luminous safety garment can offer a viable solution for enhancing the safety of elderly pedestrians. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

Rapid urbanization has intensified artificial light at night (ALAN) and reduced access to natural dark sky environments. Dark sky parks provide a potential spatial approach for nighttime environmental protection, ecological conservation, and astronomical recreation. This study develops a constraint-based suitability assessment framework for urban dark sky park site selection and applies it to Wuhan, China. Multi-source geospatial data were integrated into a 1 km × 1 km evaluation grid. The AHP–Delphi method was used to determine indicator weights, while land cover constraints were introduced to exclude artificial surfaces from candidate evaluation areas. Weighted overlay analysis, sensitivity analysis, continuous patch screening, and dark sky quality verification were then conducted. The results show that (1) artificial light visibility (ALV) and cloudless days (CVD) are the most important indicators, with weights of 0.328 and 0.250, respectively; (2) 29.38% of the evaluation units are classified as most suitable or more suitable; (3) the spatial pattern of highly suitable areas remain relatively stable, with Jaccard overlap rates of 73.65% and 87.09% under alternative weighting scenarios; and (4) continuous patch screening identifies Caidian and Yangda as priority candidate areas. Further verification using the Bortle Scale, a nine-level classification of night darkness, shows that the Caidian patch reached Bortle class 4 and National Astronomical Observatories (NAOC) dark sky class 1, indicating stronger practical feasibility for dark sky park development. The proposed framework provides a methodological reference for integrating dark sky protection, land use feasibility, and urban planning in metropolitan regions. Full article

Autonomous driving systems face critical perception failures in dense fog, where conventional RGB cameras suffer from severe degradation due to atmospheric scattering and reduced visibility. This paper presents an adaptive multi-modal fusion framework that synergistically integrates gated imaging with 3D LiDAR point clouds to achieve robust obstacle detection under visibility conditions as low as 50 m. Unlike standard cameras that passively capture scattered ambient light, gated cameras employ time-synchronized active illumination to physically filter backscattered photons, preserving structural features even in low-visibility scenarios. We propose a novel Adaptive Feature-Weighting Network (AFW-Net) that dynamically adjusts sensor modality contributions based on real-time environmental degradation assessment. The framework incorporates three key innovations: (1) a cross-modal feature extraction module that exploits the complementary physical properties of gated imaging and LiDAR, (2) an attention-based adaptive fusion mechanism that quantifies per-modality reliability through uncertainty estimation, and (3) a degradation-aware training strategy using weather-specific augmentation. Extensive experiments on the Princeton Automated Driving Dataset demonstrate that our approach maintains detection average precision (AP) above 82% under dense fog conditions (50 m visibility), representing a 23.7% improvement over state-of-the-art RGB-LiDAR fusion methods that exhibit substantial performance degradation to 58.4% AP. Ablation studies validate the necessity of each component, and cross-dataset evaluation confirms the generalization capability of the proposed framework. The adaptive weighting mechanism proves particularly effective, dynamically rebalancing modality contributions across the gated imaging and LiDAR branches while maintaining LiDAR geometric constraints. This work establishes a robust perception paradigm for safety-critical autonomous systems operating in low-visibility environmental conditions. Full article

This study was conducted to address the challenges of detecting citrus fruits in complex orchard environments characterized by overlap, occlusion, and variable lighting conditions. To tackle these issues, an improved detection model named YOLO-MGP was developed based on the YOLOv8n architecture. Four key enhancements were introduced to the core components of the detection framework. First, the primary backbone network was replaced with MobileNetV3, which substantially reduced computational requirements while preserving the capability for multi-scale feature extraction. Second, a C2f-GLU module was incorporated into the neck network. By leveraging Gated Linear Units, this module strengthens the feature selection and fusion processes. Third, an additional P2 detection layer was added to improve the detection of small targets. This modification was complemented by the integration of a Coordinate Attention mechanism, which refines the distribution of feature weights across spatial and channel dimensions. Finally, the CIoU loss was replaced by PIoU to enhance the accuracy of bounding box regression, particularly for occluded and overlapping targets. Experimental results demonstrate that the YOLO-MGP model achieved a precision of 94.2%, a recall of 89.7%, and a $mA{P}_{50}$ of 95.7% on our custom citrus dataset. By substantially reducing the number of parameters while maintaining competitive detection performance, the proposed method offers a practical and lightweight solution for fruit detection in automated harvesting systems. Full article

Among the ten local breeds of the Veneto region in Italy, Pepoi (PP) and Ermellinata di Rovigo (ER) hens start laying eggs earlier than the others. The egg laying rate (27–34 weeks of age) is higher (p < 0.01) in PP than in ER hens. Egg quality (at 33 weeks of age, 120 eggs/breed) was studied in fresh 1 day-old eggs and in preserved 21 day-old eggs according to breed and storage temperature (12 and 21 °C). Fresh ER eggs showed higher (p < 0.01) egg weights, yolk pH, Haugh units and yolk indices and lower (p < 0.01) eggshell lightness and thickness, surface area-to-volume ratios, and albumen pH than PP eggs, but the yolk-to-albumen ratio was similar between the breeds. After 21 days of storage, the egg traits showed the same trend for significant differences between breeds, with the exception of albumen pH and Haugh units, which were similar. Eggs stored at 21 °C showed lower (p < 0.01) Haugh units and yolk index values and higher (p < 0.01) albumen and yolk pH, albumen yellowness, and weight loss than eggs stored at 12 °C. Stored PP and ER eggs also differed in terms of observed changes in Haugh units, yolk pH and yolk index values with storage temperature: ER eggs showed higher (p < 0.01) yolk index values than PP eggs at both storage temperatures. Eleven weeks after the onset of laying, significant differences were observed in several traits of fresh and stored eggs from the studied breeds, particularly regarding the strength of the vitelline membrane. These preliminary results contribute to the characterization of the storage suitability of eggs from local breeds and to future crossbreeding programmes for enhancing chicken biodiversity. Full article

This study evaluated the effects of dietary coenzyme Q10 (CoQ10) supplementation on growth performance, slaughter performance, meat quality, antioxidant capacity, serum profiles, and intestinal morphology in Minxinan black rabbits. A total of 250 rabbits were allocated to five dietary treatments containing 0, 30, 60, 120, or 240 mg/kg CoQ10 for 14 weeks after a 1-week adaptation period. Results indicated that supplementation with 60 mg/kg CoQ10 resulted in the highest final body weight (2.83 kg) and average daily gain (29.54 g/day), with a significantly reduced feed-to-gain ratio and mortality rate compared to the control group. Regarding slaughter performance, the 60 mg/kg group significantly reduced the abdominal fat rate. In terms of meat quality, the 60 and 120 mg/kg groups showed significantly reduced drip loss and shear force, while meat lightness (L*) increased in all supplemented groups. Cooking loss was significantly reduced in the 60 mg/kg group. Antioxidant capacity in cardiac muscle and longissimus thoracis et lumborum (LTL) muscle was enhanced, particularly at 60 mg/kg, with significantly elevated activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and total antioxidant capacity (T-AOC), alongside reduced malondialdehyde (MDA) content. Furthermore, the 60 mg/kg group increased LTL muscle polyunsaturated fatty acid (PUFA) content, elevated serum levels of triiodothyronine (T3), growth hormone (GH), and insulin-like growth factor-1 (IGF-1), enhanced immunoglobulin concentrations, and improved intestinal morphology. In conclusion, dietary supplementation with 60 mg/kg CoQ10 improved growth performance, carcass leanness, PUFA content, and antioxidant status in broiler rabbits. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

Light is a critical environmental cue regulating development and quality in edible fungi, yet the effects of dynamic light regimes (for example, transitions from white to blue light) remain poorly understood. We systematically investigated how white-light pretreatment duration (0, 4, 8, or 12 h) and two blue-light regimes—B6 (6 h blue followed by white until harvest) and Bc (continuous blue until harvest)—affect fruiting-body development, yield, color, textural properties, and nutritional quality of Pleurotus pulmonarius. The experiment was conducted at a single commercial production facility in Zhejiang Province, China, using the commercial strain P. pulmonarius (cultivar ‘Jinxiu’). Two-way ANOVA revealed significant interactions between white-light pretreatment and blue-light regime for cap a* value (red-green), cap width, cap hardness and chewiness, stipe hardness, number of fruiting bodies, and several nutrient components. All dynamic light regimes reduced cap L* value (lightness) and b* value (yellow-blue); continuous blue (Bc) produced a darker cap. Yield responses to blue-light duration depended on pretreatment: without white pretreatment, Bc outperformed B6, whereas with 4–12 h white pretreatment B6 produced higher yields. Relative to the control (CK), all dynamic regimes significantly increased total free amino acids and essential amino acids. Except for W4B6 and W12B6, all other treatments significantly increased crude protein; total soluble sugar, crude fat, and crude fiber decreased in most treatments compared to CK. These results indicate that an optimized transition from white to blue light can synergistically improve the color, nutritional quality and yield of P. pulmonarius. The W8Bc regime (8 h white pretreatment followed by continuous blue until harvest) produced the highest cap chewiness (21.65 N·mm) and free amino acid content (3110.44 μg·g⁻¹), the darkest cap color, and the top comprehensive score in the entropy-weighted TOPSIS evaluation, despite ranking second in yield and high-quality rate. Under the conditions tested (single cultivar ‘Jinxiu’ at one production base), we recommend the W8Bc light regime as suitable for industrial cultivation of Pleurotus pulmonarius. However, it should be noted that these findings cannot be generalized to the entire species without further validation across multiple strains and multiple locations. Full article

With the increasing adoption of unattended power distribution substations, accurate foreign object detection has become critical to ensure safe system operation. This study proposes a detection model tailored for substation monitoring, targeting hazards such as fire, water accumulation, and small animal intrusion, while accounting for varying on-site illumination conditions. First, an adaptive illumination normalization module is introduced to accommodate diverse lighting conditions, thereby enhancing its capability to capture foreign objects under complex illumination environments. Second, a multi-scale feature extraction and attention-based refinement structure is developed to effectively capture foreign objects with diverse sizes and textures, aligning with the specific detection requirements of substation scenarios. Third, a task-oriented loss function is constructed by incorporating illumination-adaptive weighting into the objectness component, thereby enhancing robustness under uneven illumination conditions. Experimental results demonstrate that the proposed method outperforms representative detection approaches, validating its effectiveness for foreign object detection in substation monitoring applications. Full article

The purpose of side-channel leakage detection is to determine whether or not there is side-channel leakage in the target cryptographic chip. The application of grouping (i.e., dividing the collected power traces into groups based on a property of the intermediate value, such as the Hamming weight of a byte or the bit value of an S-box output) in side-channel leakage detection is a research hotspot. The bit-level grouping mode and the byte-value grouping mode are proposed by previous scholars. However, the bit-level grouping mode does not match the byte operation architecture of cryptographic chips, resulting in an overly fine detection granularity and a high computational complexity. Although the byte-value grouping mode takes into account the byte operation architecture of cryptographic chips, it will cause unequal sizes of traces contained in two groups, reducing the test efficiency. In light of this, we propose the Interval-Optimized Hamming-Weight-Oriented Grouping (IHOG) Mode. IHOG groups data according to the Hamming weight (HW) of byte, dividing them into two groups with Hamming weights of {0, 1, 2, 3} and {5, 6, 7, 8}. In this way, it solves the problem of overly fine detection granularity and high computational complexity caused by bit-level grouping, and it also addresses the issue of unequal sample sizes and low test efficiency caused by the byte-value grouping mode. This paper verifies the effectiveness of the proposed IHOG method using four datasets, namely DPA v4, AES HD, Custom Dataset 1, and Custom Dataset 2. The results show that, compared with three existing grouping schemes such as HW value, bit value, and byte value, the IHOG scheme proposed in this paper increases the accuracy of leakage detection by 37.2%, 18.5%, and 146.3% respectively at the selected leakage points. Full article

Honeybees are indispensable pollinators for agricultural ecosystems, and a colony’s stability and reproductive capacity depend critically on the presence of a healthy queen. Acoustic monitoring has emerged as a promising non-invasive, lighting-independent approach for long-term colony observation. However, existing studies have largely been confined to single-apiary datasets or merged datasets from multiple similar apiaries for model training. Moreover, model evaluation has relied primarily on overall performance metrics, with insufficient attention to cross-region generalization and the detection of queen loss, a rare but critical condition. This study systematically investigates three complementary strategies: noise-augmented data diversification, lightweight convolutional neural network (CNN) architecture optimization via comprehensive ablation experiments, and transfer learning with fine-tuning to bridge the domain gap between source and target apiaries. Under cross-apiary evaluation, the proposed approach achieves an accuracy of 92.79%, a negative-class F1-score of 0.7900, and a negative-class recall of 0.7834 when only limited target-domain training samples are available. With full target-domain training data, the same strategy further attains an accuracy of 95.05%, a negative-class F1-score of 0.8596, and a negative-class recall of 0.8733. t-distributed Stochastic Neighbor Embedding (t-SNE) visualization demonstrates that noise augmentation effectively expands sample diversity in the feature space, while Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps confirm the successful transfer of source-domain acoustic features to the target domain. This work provides a practical approach for deploying acoustic-based queen status monitoring across diverse apiaries with minimal local data collection. Full article

This study presents a structure-aware inference framework for tooth numbering and lesion localization in visible light oral images. Tooth numbering is often compromised by class imbalance and structural inconsistency caused by the uneven distribution of tooth types, motivating the integration of anatomical priors into the inference process. The framework first partitions the dental arch into quadrants using a deep learning-based detection module to establish spatial organization. Based on this, an Anchor-Teeth-Guided Inference (ATGI) strategy reconstructs globally consistent tooth numbering by leveraging dental arch continuity, bilateral symmetry, and confidence-guided anchor selection, thereby improving the recognition of underrepresented tooth classes. Visually suspicious lesion regions are independently detected and spatially associated with numbered teeth, enabling joint structural and lesion-aware analysis. Evaluated on a multi-source dataset, the method achieves a weighted F1-score of 0.813 for 32-class tooth numbering, outperforming end-to-end baselines while improving spatial consistency. Lesion localization yields F1-scores of 0.850 for caries-related regions and 0.789 for gingivitis-related regions. These results demonstrate that incorporating anatomical constraints enhances numbering robustness and improves rare-class recognition in visible light dental image analysis, showing potential for screening-oriented oral assessment and teledentistry applications. Full article