Search Results (5,506)

Exploring the spatio-temporal evolution patterns of rapeseed production at the county level in Sichuan Province, China, and analyzing the influence of natural conditions and socioeconomic development based on regional spatial characteristics, can help guide the rational distribution of crop production and provide a reference for the high-quality and sustainable development of the local rapeseed industry. Based on panel data from 2001 to 2023, this study employs GIS spatial analysis to examine the spatio-temporal evolution of rapeseed production in Sichuan and applies a Geodetector model to identify factors influencing its spatial and temporal variations. The results reveal that rapeseed production in Sichuan is concentrated in three main production areas: the northeastern Sichuan region, the middle Sichuan hilly region, and the Chengdu Plain. The dynamic evolution exhibits a composite pattern characterized by the stability and expansion of core areas, alongside breakthroughs and growth in peripheral regions, with increased production observed across 134 counties. The spatial center of rapeseed production shows short-range fluctuations and distinct regional anchoring, oscillating among Santai County, Shehong City, and Daying County, tracing a “Z”-shaped trajectory. Over the 23-year period, the global Moran’s I index ranged from 0.464 to 0.558, indicating a significant spatial clustering trend in rapeseed output among adjacent counties. Local spatial autocorrelation patterns were predominantly H-H, L-L, and L-H clusters. Factor detection identifies labor force availability, fertilizer application intensity, and effective irrigated area as the most influential factors. Interaction detection results consistently exhibit a two-factor enhancement effect. To enhance the rapeseed industry’s performance and efficiency, it is recommended to stabilize production capacity in the three core production areas, leverage central regions to strengthen radiation to the surrounding counties, optimize resource allocation based on clustering patterns, and focus on improving key factors such as labor and irrigation, as well as their synergistic effects. Full article

As a critical indicator of polar ecosystem dynamics, sea ice monitoring plays a pivotal role in climate change. However, as global warming accelerates the melting of sea ice, the complexity in the Arctic poses growing challenges for achieving high-precision sea ice classification. To address this issue, this study begins with the creation of a multi-source sea ice dataset based on GaoFen-3 fully polarimetric SAR data and Landsat optical imagery. In addition, the study proposes a Global–Local enhanced Deformable Convolution Network (GLDCN), which effectively captures long-range semantic dependencies and fine-grained local features of sea ice. To further enhance feature integration, an Adaptive Channel Attention Module (ACAM) is designed to achieve adaptive weighted fusion of heterogeneous SAR and optical features, substantially improving the model’s discriminative ability in complex conditions. Experimental results show that the proposed method outperforms several mainstream models on multiple evaluation metrics. The multi-source data fusion strategy significantly reduces misclassification among confusable categories, validating the importance of multimodal fusion in sea ice classification. Full article

The purpose of this study is to investigate the buckling behavior and failure mechanism of the boom of large-scale elevating jet fire trucks, so as to provide support for its safety design and service life improvement. In terms of research methods, a combination of double-version control tests and refined finite element simulations was adopted to carry out a systematic study. The research results show that the boom base plate exhibits typical sinusoidal wave buckling deformation when the load coefficient is between 0.45 and 0.5, and the wavelength is highly consistent with the theoretical prediction; under the critical load, the strain amplitude shows a significant nonlinear jump, which confirms the buckling mechanism of the coupling between geometric nonlinearity and material plasticity; under the ultimate load, the structure undergoes local buckling failure, the failure location is in good agreement with the simulation prediction, and the test results are highly consistent with the simulation results within the engineering allowable range, which verifies the reliability and applicability of the model. The research conclusion is the establishment of evaluation criteria for buckling failure of box-type knuckle arms: visible buckling waves appear, and the strain exceeds 40%. Based on this conclusion, optimizing the width-thickness ratio of the plate, strengthening the web constraint and improving the manufacturing process can effectively enhance the anti-buckling performance of the thin-walled box structure. Full article

Rice is one of the world’s most important staple crops, and outbreaks of insect pests pose a persistent threat to yield stability and food security in major rice-growing regions. Reliable field-scale rice pest detection remains challenging due to limited datasets, heterogeneous imaging conditions, and inconsistent annotations. To address these limitations, we construct RicePest-30, a field-oriented dataset comprising 8848 images and 62,227 annotated instances covering 30 major rice pest species. Images were collected using standardized square-framing protocols to preserve spatial context and visual consistency under diverse illumination and background conditions. Based on RicePest-30, YOLOv11 was adopted as the primary detection framework and optimized through a systematic hyperparameter tuning process. The learning rate was selected via grid search within the range of 0.001–0.01, yielding an optimal value of 0.002. Training was conducted for up to 300 epochs with an early-stopping strategy to prevent overfitting. For fair comparison, YOLOv5s, YOLOv8s, Faster R-CNN, and RetinaNet were trained for the same number of epochs under unified settings, using the Adam optimizer with a learning rate of 0.001. Model performance was evaluated using Precision, Recall, AP@50, mAP@50:95, and counting error metrics. The experimental results indicate that YOLOv11 provides the most balanced performance across precision, localization accuracy, and counting stability. However, all models exhibit degraded performance in small-object scenarios, dense pest distributions, and visually similar categories. Error analyses further reveal that class imbalance and field-scene variability are the primary factors limiting detection robustness. Overall, this study contributes a high-quality, uniformly annotated rice pest dataset and a systematic benchmark of mainstream detection models under realistic field conditions. The findings highlight critical challenges in fine-grained pest recognition and provide a solid foundation for future research on small-object enhancement, adaptive data augmentation, and robust deployment of intelligent pest monitoring systems. Full article

Background/Objectives: Hydroxyapatite (HAp)-based implants and HAp–titanium (HApTi) composites are widely used in orthopedic and dental applications, but their long-term success is limited by peri-implant bone loss. Local delivery of osteoactive molecules from implant surfaces may enhance osseointegration and reduce periprosthetic osteolysis. This study combined in silico modeling and experimental assays to compare calcium fructoborate (CaFb), sodium alendronate, and calcium alendronate as functionalization agents for HAp and HApTi implants. Methods: Molecular docking (AutoDock 4.2.6) and 100 ns molecular dynamics (MD) simulations (AMBER14 force field, SPC water model) were performed to characterize ligand–substrate interactions and to calculate binding free energies (ΔG_binding) and root mean square deviation (RMSD) values for ligand–HAp/HApTi complexes. HAp and HApTi discs obtained by powder metallurgy were subsequently functionalized by surface adsorption with CaFb or alendronate salts. The amount of adsorbed ligand was determined gravimetrically, and in vitro release profiles were quantified by HPTLC–MS for CaFb and by HPLC after FMOC derivatization for alendronates. Results: CaFb–HAp and CaFb–HApTi complexes showed the lowest binding free energies (−1.31 and −1.63 kcal/mol, respectively), indicating spontaneous and stable interactions. For HAp-based complexes, the mean ligand RMSD values over 100 ns were 0.27 ± 0.17 nm for sodium alendronate, 0.72 ± 0.28 nm for calcium alendronate (range 0.35–1.10 nm), and 0.21 ± 0.19 nm for CaFb (range 0.15–0.40 nm). For HApTi-based complexes, the corresponding RMSD values were 0.30 ± 0.15 nm for sodium alendronate, 0.72 ± 0.38 nm for calcium alendronate and 0.26 ± 0.14 nm for CaFb. These distributions indicate that CaFb and sodium alendronate maintain relatively stable binding poses, whereas calcium alendronate shows larger conformational fluctuations, consistent with its less favorable binding energies. Experimentally, CaFb exhibited the greatest chemisorbed amount and percentage on both HAp and HApTi, followed by sodium and calcium alendronate. HApTi supported higher loadings than HAp for all ligands. Release studies demonstrated a pronounced burst and rapid plateau for both alendronate salts, whereas CaFb displayed a slower initial release followed by a prolonged, quasi-linear liberation over 14 days. Conclusions: The convergence between in silico and adsorption–release data highlights CaFb as the most promising candidate among the tested ligands for long-term functionalization of HAp and HApTi surfaces. Its stronger and more stable binding, higher loading capacity and more sustained release profile suggest that CaFb-coated HApTi implants may provide a favorable basis for future in vitro and in vivo studies aimed at improving osseointegration and mitigating periprosthetic osteolysis, although direct evidence for osteolysis prevention was not obtained in the present work. Full article

Modern impact monitoring systems combine multiple inputs with machine learning (ML) models for impact detection, localization, and event assessment. Their accuracy relies on large, event-representative datasets, used for algorithmic development and ML model training. High-fidelity numerical models can provide augmented datasets by overcoming the cost and time limitations of experimental methods. This research presents an end-to-end numerical methodology for impact detection based on simulation (training) and experimental (testing) data. Initially, a finite element model (FEM) of our experimental setup utilizing piezoelectric transducer (PZT) sensors mounted on a thermoplastic plate is created. From the experimental impact signals, a few consistent cases are identified for feature extraction. A design of experiments explores the range of each parameter, and through surrogate optimization, the material and piezoelectric properties of the setup are determined. Subsequently, a virtual dataset, involving multiple impact cases, is created to train the ML models performing impact detection. Testing with experimental data shows results consistent with literature studies that used only experimental data for both training and testing. This work provides a systematic methodology for representative dataset generation and impact monitoring through ML, while addressing accurate FEM parameter identification from a few experimental tries. Full article

This study assesses the suitability of UAV aerial imagery-based photogrammetry for reconstructing underwater riverbed topography and its application in two-dimensional (2D) hydrodynamic modelling, with a particular focus on comparing RGB, multispectral, and fused RGB–multispectral imagery. Four Lithuanian rivers—Verknė, Šušvė, Jūra, and Mūša—were selected to represent a wide range of hydromorphological and hydraulic conditions, including variations in bed texture, vegetation cover, and channel complexity. High-resolution digital elevation models (DEMs) were generated from field-based surveys and UAV imagery processed using Structure-from-Motion photogrammetry. Two-dimensional hydrodynamic models were created and calibrated in HEC-RAS 6.5 using measurement-based DEMs and subsequently applied using photogrammetry-derived DEMs to isolate the influence of terrain input on model performance. The results showed that UAV-derived DEMs systematically overestimate riverbed elevation, particularly in deeper or vegetated sections, resulting in underestimated water depths. RGB imagery provided greater spatial detail but was more susceptible to local anomalies, whereas multispectral imagery produced smoother surfaces with a stronger positive elevation bias. The fusion of RGB and multispectral imagery consistently reduced spatial noise and improved hydrodynamic simulation performance across all river types. Despite moderate vertical deviations of 0.10–0.25 m, relative flow patterns and velocity distributions were reproduced with acceptable accuracy. The findings demonstrate that combined spectral UAV aerial imagery in photogrammetry is a robust and cost-effective alternative for hydrodynamic modelling in shallow lowland rivers, particularly where relative hydraulic characteristics are of primary interest. Full article

Deep learning-based sign language recognition plays a pivotal role in facilitating communication for the deaf community. Current approaches, while effective, often introduce redundant information and incur excessive computational overhead through global feature interactions. To address these limitations, this paper introduces a Deformable Correlation Network (DCA) designed for efficient temporal modeling in continuous sign language recognition. The DCA integrates a Deformable Correlation (DC) module that leverages spatio-temporal driven offsets to adjust the sampling range adaptively, thereby minimizing interference. Additionally, a multi-scale local sampling strategy, guided by motion prior, enhances temporal modeling capability while reducing computational costs. Furthermore, an attention-based Correlation Matrix Filter (CMF) is proposed to suppress interference elements by accounting for feature motion patterns. A long-term temporal enhancement module, based on spatial aggregation, efficiently leverages global temporal information to model the performer’s holistic limb motion trajectories. Extensive experiments on three benchmark datasets demonstrate significant performance improvements, with a reduction in Word Error Rate (WER) of up to 7.0% on the CE-CSL dataset, showcasing the superiority and competitive advantage of the proposed DCA algorithm. Full article

Against the dual backdrop of rapid population aging and legacy neighborhood renewal, morphologically planning-locked legacy neighborhoods in high-density cities face persistent imbalances in outdoor activity spaces that undermine aging-in-place participation and health equity. This study advances a Spatial Pathology framework. Using nine representative communities in Longtan Subdistrict, Dongcheng District, Beijing, we develop a GIS-assisted spatial audit, a systematic behavioral observation protocol with temporal-intensity metrics, and a validated perception instrument. These tools form a closed evidentiary loop with explicit indicator definitions, formulas, and decision thresholds, alongside a reproducible analytic and visualization pipeline. Tri-dimensional baselines revealed substantial inter-community disparities: Spatial Quality Index (SQI) ranged from 43.3 to 77.0; activity intensity varied from 1.5 to 15.7 persons/100 m²·hour; and overall satisfaction scores spanned 3.88–4.49. It quantifies and identifies three core paradoxes in outdoor activity spaces within this context: (1) the Functional Failure Paradox with FFI exceeding +0.5 and ELR surpassing 60% in dormant communities; (2) the Value Misalignment Paradox where Facilities & Equipment showed the strongest satisfaction impact (β = 0.344) yet the largest unmet-need gap (VQGI > +8); (3) the Practice–Perception Decoupling Paradox evidenced by a negative correlation (r = −0.38) between usage intensity and satisfaction. These paradoxes reveal the spatial roots of planning-locked legacy neighborhoods—compound mechanisms of planning inertia, decision–demand information gaps, and elderly adaptability masking environmental deficits. We translate the diagnosis into typology-specific prescriptions—reactivating dormant spaces via “route–node–plane” continuity and proximal micro-spaces; decongesting peak periods through elastic zoning and equipment redistribution; and precision calibration of facilities and walking loops—implemented through co-creation and light-touch stewardship. This provides evidence-based, precision-targeted intervention pathways for micro-renewal of aging neighborhoods, supporting localized implementation of UN Sustainable Development Goals (SDG 11 Sustainable Cities; SDG 10 Reduced Inequalities). This methodological framework is transferable to other high-density aging cities, offering theoretical scaffolding and empirical reference for multi-source geographic data-driven urban spatial analysis and equity-oriented age-friendly retrofitting. Full article

Credit risk refers to the possibility that a borrower fails to meet contractual repayment obligations, posing potential losses to lenders. This study aims to enhance post-loan default prediction in credit risk management by constructing a time-series modeling framework based on repayment behavior data, enabling the capture of repayment risks that emerge after loan issuance. To achieve this objective, a Residual Enhanced Encoder Bidirectional Long Short-Term Memory (ResE-BiLSTM) model is proposed, in which the attention mechanism is responsible for discovering long-range correlations, while the residual connections ensure the preservation of distant information. This design mitigates the tendency of conventional recurrent architectures to overemphasize recent inputs while underrepresenting distant temporal information in long-term dependency modeling. Using the real-world large-scale Freddie Mac Single-Family Loan-Level Dataset, the model is evaluated on 44 independent cohorts and compared with five baseline models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) across multiple evaluation metrics. The experimental results demonstrate that ResE-BiLSTM achieves superior performance on key indicators such as F1 and AUC, with average values of 0.92 and 0.97, respectively, and demonstrates robust performance across different feature window lengths and resampling settings. Ablation experiments and SHapley Additive exPlanations (SHAP)-based interpretability analyses further reveal that the model captures non-monotonic temporal importance patterns across key financial features. This study advances time-series–based anomaly detection for credit risk prediction by integrating global and local temporal learning. The findings offer practical value for financial institutions and risk management practitioners, while also providing methodological insights and a transferable modeling paradigm for future research on credit risk assessment. Full article

The Gravity Recovery and Climate Experiment (GRACE) tracks drought events by detecting changes in the global gravitational field and capturing abnormal information on the reserves of surface water, soil water, and groundwater, which makes it possible for a more comprehensive and unified global and regional monitoring of groundwater drought. This study adopted the gravity satellite GRACE data and combined it with the hydrological model dataset. Additionally, we assessed the temporal evolution and spatial pattern of groundwater drought in the Yangtze River Basin (YRB) and its sub-basins from 2003 to 2022, determined the change points of the hidden seasonal and trend components in groundwater drought, and identified the direct/indirect driving contributions of the main climatic and circulation factors to groundwater drought. The results show that (1) as a normalized index, the groundwater drought index (GDI) can reflect direct evidence of any surplus and deficit in groundwater availability. During the study period, the minimum value (−1.66) of the GDI occurred in July 2020 (severe drought). (2) The average value of GDI in the entire basin ranged from −1.66 (severe drought) to 0.52 (no drought). (3) The average Z_s values (Mann–Kendall Z-statistic) of GDI were −0.23, −0.16, −0.43, and 0.14, respectively, and the proportions of areas with aggravated drought reached 65.21%, 61.05%, 89.70% and 43.67%, respectively. (4) Partial wavelet coherence analysis can simultaneously reveal the local correlations of time series at different time scales and frequencies. Based on partial wavelet analysis, precipitation was the best factor for explaining the dynamic changes in groundwater drought. (5) The North Pacific Index (NPI), the Pacific/North American Index (PNA), and the Sunspot Index (SSI) can serve as the main predictors that can effectively capture the drought changes in groundwater in the YRB. The GRACE satellite can provide a new tool for monitoring, tracking, and assessing groundwater drought situations, which is of great significance for guiding the development of the drought early warning system in the YRB and effectively preventing and responding to drought disasters. Full article

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

Creatinine (Crn) is a clinically relevant biomarker commonly used for the diagnosis and monitoring of kidney disease. In this work, we report the fabrication of reduced-graphene-oxide-based field-effect transistors (rGO FETs) for Crn detection. These devices were functionalized using a layer-by-layer (LbL) assembly, in which polyethyleneimine (PEI) and creatinine deiminase (CD) were alternately deposited. This LbL strategy allows for the effective incorporation of CD without compromising its structural or functional integrity, while also taking advantage of the local pH changes caused by creatinine hydrolysis. It also benefits from the use of a polyelectrolyte that can amplify the enzymatic signal. Furthermore, it enables scalable and efficient fabrication. These transistors also address the challenges of point-of-care implementation in single-use cartridges. It is worth noting that the devices showed a linear relationship between the Dirac-point shift and the logarithm of the creatinine concentration in the 20–500 µM range in diluted simulated urine. The sensor response improved with increasing numbers of PEI/CD bilayers. Furthermore, the functionalized FETs demonstrated rapid detection dynamics and good long-term stability. Present results confirm the potential of these devices as practical biosensors for sample analysis under real-world conditions, making them ideal for implementation in practical settings. Full article

This study investigates the electromagnetic field enhancement and optical response of self-assembled monolayer (SAM)-coated iron nanoparticles under laser excitation, with the aim of advancing optical gas sensing technologies. Using finite element method (FEM) simulations, we model the interaction of laser beams in both the visible (400–700 nm) and infrared (1000–2500 nm) spectral ranges with SAM-coated and uncoated nanoparticles. The results reveal that SAM coatings significantly amplify localized electromagnetic fields—reaching up to ~60 V/m in the visible range—while providing stable, wavelength-independent field distributions. In contrast, uncoated nanoparticles exhibit weaker but more variable field responses. Angular dependence analysis indicates maximal field enhancement at perpendicular (90°) detection, suggesting an orientation-sensitive design consideration for optical sensors. These findings demonstrate that SAM coatings enable stable, wavelength-independent electromagnetic responses, offering a promising pathway toward miniaturized and highly sensitive laser-based optical gas sensors. Full article

Quantum dots (QDs) composed of the narrow-bandgap semiconductor PbTe were incorporated into the depletion region of p–n junctions based on wide-bandgap II–VI semiconductors (p-ZnTe/n-CdTe). The heterostructures were grown by molecular beam epitaxy (MBE) on semi-insulating GaAs (100) substrates. The depletion region was engineered by depositing 20 alternating thin layers of CdTe and PbTe, then thermal annealing under ultrahigh vacuum. As revealed by cross-sectional scanning electron microscopy (SEM), the initially continuous PbTe layers transformed into arrays of zero-dimensional nanostructures, namely PbTe QDs. The formation of PbTe QDs in a CdTe matrix arises from the structural mismatch between the zinc blende and rock-salt crystal structures of the two materials. Electron beam-induced current (EBIC) scans confirmed that the QDs are localized within the depleted charge region between the p-ZnTe and n-CdTe layers. The resulting wide-gap diodes containing narrow-band QDs show pronounced sensitivity to infrared radiation in the spectral range of 1–4.5 μm, with a peak responsivity of approximately 8 V/W at a wavelength of ~2.0 μm and a temperature of 200 K. A red-shift in the cutoff wavelength when temperature decreases indicates that the infrared (IR) response is governed by band-to-band optical transitions in the PbTe QDs. In addition, the devices show sensitivity to visible radiation, with a maximum responsivity of 20 V/W at 0.69 μm. These results demonstrate that wide-bandgap p–n junctions incorporating narrow-bandgap QDs can function as dual-wavelength (visible and infrared) photodetectors, with potential applications in two-color detection and infrared solar cells. Full article