Search Results (37,376)

Visible near-infrared (VNIR) spectroscopy offers a cost-effective solution to quantify the spatiotemporal dynamics of soil organic carbon (SOC), especially in the context of rapid advances in spectra-based local modeling approaches using large-scale soil spectral libraries. And yet, direct temporal transferability of VNIR spectroscopic modeling (applying historical models to new spectral data) and its capability to monitor temporal changes in SOC remain underexplored. To address this gap, this study uses the LUCAS Soil dataset (2009 and 2015) from France to evaluate the effectiveness of localized spectral models in detecting SOC changes. Two local learning algorithms, memory-based learning (MBL) and GLOBAL-LOCAL algorithms, were adapted to integrate spectral and soil property similarities during local training set selection, while also incorporating LUCAS 2009 soil measurements (clay, silt, sand, CEC) as covariates. These adapted local learning algorithms were then compared against global partial least squares regression (PLSR). The results demonstrated that localized models substantially outperformed global PLSR, with MBL achieving the highest accuracy for croplands, grasslands, and woodlands (R² = 0.72–0.79, RMSE = 4.73–20.92 g/kg). Incorporating soil properties during the local learning procedure reduced spectral heterogeneity, leading to improved SOC prediction accuracy. This improvement was particularly pronounced after excluding organic soils from grasslands and woodlands, as evidenced by 13.3–21.1% decreases in the RMSE. Critically, for SOC monitoring, spectrally predicted SOC successfully identified over 70% of samples experiencing significant SOC changes (>10% loss or gain), effectively capturing the spatial patterns of SOC changes. This study demonstrated the potential of localized spectral modeling as a cost-effective tool for monitoring SOC dynamics, enabling efficient and large-scale assessments critical for sustainable soil management. Full article

Computer-Aided Design (CAD) software has long served as a foundation for planning and modeling in Architecture, Engineering, and Construction (AEC). In recent years, the introduction of Augmented Reality (AR) and Virtual Reality (VR) has significantly reshaped the CAD landscape, offering novel interaction paradigms that bridge the gap between digital prototypes and real-world spatial understanding. These technologies have enabled users to engage with 3D architectural content in more immersive and intuitive ways, facilitating improved decision making and communication throughout design workflows. As digital design services grow more complex and span multiple media platforms—from desktop-based modeling to immersive AR/VR environments—evaluating usability and User Experience (UX) becomes increasingly challenging. This paper presents a longitudinal usability and UX study of a multiplatform house design pipeline (i.e., structured workflow for creating, adapting, and delivering house designs so they can be used seamlessly across multiple platforms) comprising a web-based application for initial house creation, a mobile AR tool for contextual exterior visualization, and VR applications that allow full-scale interior exploration and configuration. Together, these components form a unified yet heterogeneous service experience across different devices and modalities. We describe the iterative design and development of this system over three distinct phases (lasting two years), each followed by user studies which evaluated UX and usability and targeted different participant profiles and design maturity levels. The paper outlines our approach to cross-platform UX evaluation, including methods such as the Think-Aloud Protocol (TAP), standardized usability metrics, and structured interviews. The results from the studies provide insight into user preferences, interaction patterns, and system coherence across platforms. From both participant and evaluator perspectives, the iterative methodology contributed to improvements in system usability and a clearer mental model of the design process. The main research question we address is how iterative design and development affects the UX of the heterogeneous service. Our findings highlight important considerations for future research and practice in the design of integrated, multiplatform XR services for AEC, with potential relevance to other domains. Full article

Self-supervised monocular depth estimation from oblique UAV videos is crucial for enabling autonomous navigation and large-scale mapping. However, existing self-supervised monocular depth estimation methods face key challenges in UAV oblique video scenarios: depth discontinuity from geometric distortion under complex viewing angles, and spatial ambiguity in weakly textured regions. These challenges highlight the need for models that combine global reasoning with geometric awareness. Accordingly, we propose RMTDepth, a self-supervised monocular depth estimation framework for UAV imagery. RMTDepth integrates an enhanced Retentive Vision Transformer (RMT) backbone, introducing explicit spatial priors via a Manhattan distance-driven spatial decay matrix for efficient long-range geometric modeling, and embeds a neural window fully-connected CRF (NeW CRFs) module in the decoder to refine depth edges by optimizing pairwise relationships within local windows. To mitigate noise in COLMAP-generated depth for real-world UAV datasets, we constructed a high-fidelity UE4/AirSim simulation environment, which generated a large-scale precise depth dataset (UAV SIM Dataset) to validate robustness. Comprehensive experiments against seven state-of-the-art methods across UAVID Germany, UAVID China, and UAV SIM datasets demonstrate that our model achieves SOTA performance in most scenarios. Full article

The study aimed to evaluate the effect of betanin—a bioactive, natural pigment found in beetroot and prickly pear—on cognitive function, motor performance, and neurotransmission in a mouse model of Parkinson’s disease (PD). Aged mice with PD-like symptoms induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) were pretreated with betanin (50 or 100 mg/kg b.w./day) via drinking water. Behavioural tests assessed motor skills, anxiety-related behaviour, and spatial memory. Biochemical analyses of central nervous system structures were conducted using high-performance liquid chromatography (HPLC) to determine neurotransmitter levels and metabolites. Betanin improved motor and cognitive functions in MPTP-treated mice. While learning ability remained unchanged, the 50 mg/kg dose alleviated spatial memory deficits. Biochemically, betanin moderately limited dopamine depletion and significantly influenced dopamine metabolism and serotonin levels. These findings suggest that betanin, as a functional food component, may exert neuroprotective effects and support cognitive and motor function in neurodegenerative conditions such as PD. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

Purpose: To investigate regional changes in tear film quality associated with orthokeratology (Ortho-K) lens wear using high-resolution spatial mapping and to evaluate the potential of artificial intelligence (AI) models in anticipating these changes. Methods: This study analysed tear film quality in 92 Ortho-K wearers divided into three groups based on lens wear duration (10–29 days, 30–90 days, and ≥91 days). Placido-based topographer measurement was used to generate regional tear film maps before and after treatment. A custom MATLAB pipeline enabled regional comparisons and statistical mapping. A feedforward neural network was trained to forecast local tear film quality using spatial data. Results: Single-value global mean metrics showed minimal changes in tear film quality across groups. However, regional mean mapping revealed significant mid-peripheral and peripheral deterioration over time, particularly in nasal and temporal corneal zones. These changes were often overlooked by global averaging and remained invisible through tear film breakup time (TBUT) measurements. The AI model predicted spatial tear quality with high accuracy (R ≥ 0.9 in testing), capturing nuanced regional variations. Conclusions: The regional analysis uncovers subtle, clinically relevant tear film disruptions caused by Ortho-K lens wear, particularly in peripheral areas. These insights challenge the adequacy of traditional single-value global mean assessments. The AI model demonstrates the potential for non-invasive, predictive evaluation of tear stability, supporting more personalised and effective Ortho-K care. Full article

Foreign object debris (FOD) in civil aviation environments poses severe risks to flight safety. Conventional detection primarily relies on manual visual inspection, which is inefficient, susceptible to fatigue-related errors, and carries a high risk of missed detections. Therefore, there is an urgent need to develop an efficient and convenient intelligent method for detecting aircraft FOD. This study proposes a detection model based on a Siamese network architecture integrated with a spatial transformation module. The proposed model identifies FOD by comparing the registered features of evidence-retention images with their corresponding normally distributed features. A dedicated aircraft FOD dataset was constructed for evaluation, and extensive experiments were conducted. The results indicate that the proposed model achieves an average improvement of 0.1365 in image-level AUC (Area Under the Curve) and 0.0834 in pixel-level AUC compared to the Patch Distribution Modeling (PaDiM) method. Additionally, the effects of the spatial transformation module and training dataset on detection performance were systematically investigated, confirming the robustness of the model and providing guidance for parameter selection in practical deployment. Overall, this research introduces a novel and effective approach for intelligent aircraft FOD detection, offering both methodological innovation and practical applicability. Full article

Urban traffic noise has become an increasingly significant environmental and public health issue, with many cities—particularly those experiencing rapid urban growth, such as Kuwait—recording levels that often exceed recommended limits. In this study, we present a detailed, data-driven approach for assessing and predicting equivalent continuous noise levels (LAeq) in residential neighborhoods. The analysis draws on measurements taken at 12 carefully chosen sites covering different road types and urban settings, resulting in 21,720 matched observations. A range of predictors was considered, including road classification, traffic composition, meteorological variables, spatial context, and time of day. Four predictive models—Linear Regression, Support Vector Machine (SVM), Gaussian Process Regression, and Bagged Trees—were evaluated through 5-fold cross-validation. Among these, the Bagged Trees model achieved the strongest performance (R² = 0.91, RMSE = 2.13 dB(A)). To better understand how the model made its predictions, we used SHAP (SHapley Additive Explanations) analysis, which showed that road classification, location, heavy vehicle volume, and time of day had the greatest influence on noise levels. The results identify the main determinants of traffic noise in Kuwait’s urban areas and emphasize the role of targeted design and planning in its mitigation. Full article

The spatial configuration of cultivated land is crucial for modern agricultural production; therefore, research on cultivated land aggregation and spatial connectivity holds significant importance for enhancing agricultural production efficiency and ensuring food security. This study selected Daqing City, China, as the research area and constructed a three-level nested framework of “patch–local–regional” scales. The aggregation degree was calculated through landscape pattern indices and the MSPA model, and connectivity was evaluated using the Omniscape algorithm based on circuit theory to explore the spatiotemporal evolution patterns of cultivated land configuration and analyze their spatial correlations, proposing classified optimization strategies. The results indicate the following: (1) the spatiotemporal distribution characteristics of cultivated land aggregation in Daqing City exhibit a spatial pattern of “high in the north and south, low in the middle,” with an overall declining trend from 2000 to 2020; (2) high-connectivity areas are primarily distributed in Lindian County in the north and Zhaozhou and Zhaoyuan Counties in the south, while low-connectivity areas are concentrated in the central urban area and surrounding regions; (3) the aggregation degree and connectivity demonstrate positive spatial correlation, with the Global Moran’s index increasing from 0.358 in 2000 to 0.413 in 2020; and (4) based on the aggregation degree and connectivity characteristics, the study area can be classified into four types: scattered imbalance–isolated dysfunction, regular imbalance–connected dysfunction, scattered improvement–connected optimization, and regular improvement–connected optimization. This study provides new research perspectives for cultivated land protection. The proposed multi-scale aggregation–connectivity research method and classification system offer important reference value for the efficient utilization and management optimization of cultivated land. Full article

Short-term passenger flow prediction provides critical data-driven support for optimizing resource allocation, guiding passenger mobility, and enhancing risk response capabilities in urban rail transit systems. To further improve prediction accuracy, this study proposes a hybrid SMA-VMD-Informer-BiLSTM prediction model. Addressing the challenge of error propagation caused by non-stationary components (e.g., noise and abrupt fluctuations) in conventional passenger flow signals, the Variational Mode Decomposition (VMD) method is introduced to decompose raw flow data into multiple intrinsic mode functions (IMFs). A Slime Mould Algorithm (SMA)-based optimization mechanism is designed to adaptively tune VMD parameters, effectively mitigating mode redundancy and information loss. Furthermore, to circumvent error accumulation inherent in serial modeling frameworks, a parallel prediction architecture is developed: the Informer branch captures long-term dependencies through its ProbSparse self-attention mechanism, while the Bidirectional Long Short-Term Memory (BiLSTM) network extracts localized short-term temporal patterns. The outputs of both branches are fused via a fully connected layer, balancing global trend adherence and local fluctuation characterization. Experimental validation using historical entry flow data from Weihouzhuang Station on Xi’an Metro demonstrated the superior performance of the SMA-VMD-Informer-BiLSTM model. Compared to benchmark models (CNN-BiLSTM, CNN-BiGRU, Transformer-LSTM, ARIMA-LSTM), the proposed model achieved reductions of 7.14–53.33% in $f_{mse}$, 3.81–31.14% in $f_{rmse}$, and 8.87–38.08% in $f_{mae}$, alongside a 4.11–5.48% improvement in $R^2$. Cross-station validation across multiple Xi’an Metro hubs further confirmed robust spatial generalizability, with prediction errors bounded within $f_{mse}$: 0.0009–0.01, $f_{rmse}$: 0.0303–0.1, $f_{mae}$: 0.0196–0.0697, and $R^2$: 0.9011–0.9971. Furthermore, the model demonstrated favorable predictive performance when applied to forecasting passenger inflows at multiple stations in Nanjing and Zhengzhou, showcasing its excellent spatial transferability. By integrating multi-level, multi-scale data processing and adaptive feature extraction mechanisms, the proposed model significantly mitigates error accumulation observed in traditional approaches. These findings collectively indicate its potential as a scientific foundation for refined operational decision-making in urban rail transit management, thereby significantly promoting the sustainable development and long-term stable operation of urban rail transit systems. Full article

This paper presents a dual-structured convolutional neural network (CNN) for image classification, which integrates two parallel branches: CNN-A with spatial attention and CNN-B with channel attention. The spatial attention module in CNN-A dynamically emphasizes discriminative regions by aggregating channel-wise information, while the channel attention mechanism in CNN-B adaptively recalibrates feature channel importance. The extracted features from both branches are fused through concatenation, enhancing the model’s representational capacity by capturing complementary spatial and channel-wise dependencies. Extensive experiments on a 12-class image dataset demonstrate the superiority of the proposed model over state-of-the-art methods, achieving 98.06% accuracy, 96.00% precision, and 98.01% F1-score. Despite a marginally longer training time, the model exhibits robust convergence and generalization, as evidenced by stable loss curves and high per-class recognition rates (>90%). The results validate the efficacy of dual attention mechanisms in improving feature discrimination for complex image classification tasks. Full article

Accurate real-time detection of blueberry maturity is vital for automated harvesting. However, existing methods often fail under occlusion, variable lighting, and dense fruit distribution, leading to reduced accuracy and efficiency. To address these challenges, we designed a lightweight deep learning framework that integrates improved feature extraction, attention-based fusion, and progressive transfer learning to enhance robustness and adaptability To overcome these challenges, we propose BMDNet-YOLO, a lightweight model based on an enhanced YOLOv8n. The backbone incorporates a FasterPW module with parallel convolution and point-wise weighting to improve feature extraction efficiency and robustness. A coordinate attention (CA) mechanism in the neck enhances spatial-channel feature selection, while adaptive weighted concatenation ensures efficient multi-scale fusion. The detection head employs a heterogeneous lightweight structure combining group and depthwise separable convolutions to minimize parameter redundancy and boost inference speed. Additionally, a three-stage transfer learning framework (source-domain pretraining, cross-domain adaptation, and target-domain fine-tuning) improves generalization. Experiments on 8,250 field-collected and augmented images show BMDNet-YOLO achieves 95.6% mAP@0.5, 98.27% precision, and 94.36% recall, surpassing existing baselines. This work offers a robust solution for deploying automated blueberry harvesting systems. Full article

Spatial reasoning ability plays a critical role in predicting academic outcomes, particularly in STEM (science, technology, engineering, and mathematics) education. According to the Cattell–Horn–Carroll (CHC) theory of human intelligence, spatial reasoning is a general ability including various specific attributes. However, most spatial assessments focus on testing one specific spatial attribute or a limited set (e.g., visualization, rotation, etc.), rather than general spatial ability. To address this limitation, we created a mixed spatial test that includes mental rotation, object assembly, and isometric perception subtests to evaluate both general spatial ability and specific attributes. To understand the complex relationship between general spatial ability and mastery of specific attributes, we used a higher-order linear logistic model (HO-LLM), which is designed to simultaneously estimate high-order ability and sub-attributes. Additionally, this study compares four spatial ability classification frameworks using each to construct Q-matrices that define the relationships between test items and spatial reasoning attributes within the HO-LLM framework. Our findings indicate that HO-LLMs improve model fit and show distinct patterns of attribute mastery, highlighting which spatial attributes contribute most to general spatial ability. The results suggest that higher-order LLMs can offer a deeper and more interpretable assessment of spatial ability and support tailored training by identifying areas of strength and weakness in individual learners. Full article

Maize, one of the world’s three major food crops, plays a vital role in global food security. Analyzing the spatiotemporal patterns of maize cultivation in Hunan Province and their influencing factors contributes to enhancing planting quality and efficiency, optimizing production patterns, and supporting provincial food security initiatives. Utilizing maize cultivation data from Hunan Province (2001–2023), this study employed the standard deviation ellipse, center of gravity shift model, and principal component analysis to examine production patterns and their drivers. Key findings include the following: (1) The maize planting area exhibited an overall increasing trend from 2001 to 2023, with a spatial convergence from the northwest towards the east. Cultivation hot spots were identified in Shaoyang, Loudi, and Changde. Maize cultivation was predominantly concentrated in areas with gentle slopes (0–3°) and gradually shifted eastward towards similar terrain. (2) The provincial maize production center of gravity followed a “Z”-shaped trajectory, moving eastward and southward with Loudi City as its core. While the spatial distribution pattern shifted from “northwest–southeast” to “west–east”, the core concentration area maintained its “northwest–southeast” orientation. Concurrently, the fragmentation of cultivated land within the maize planting landscape increased. (3) Maize planting hot spots expanded from the northwest towards the central and eastern regions, extending southward. Cold spot areas shifted from the central region towards the northeast. By the study’s end, the central region had emerged as the core maize planting area. (4) Agricultural production conditions and policy factors were identified as the main drivers of spatiotemporal changes in maize acreage within Hunan Province. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article