Search Results (2,469)

Large-scale monitoring of land rights remains constrained by substantial time requirements, limited spatial coverage, and considerable human resource demands associated with conventional image interpretation and field verification. This study develops an operational Geospatial Artificial Intelligence (GeoAI) workflow for indicative monitoring of land-use compliance by integrating semantic segmentation of land cover within registered land rights (HAT) boundaries with spatial planning information. U-Net and SF-Net architectures were evaluated for temporal monitoring using two models: M3 based on 10 m Sentinel-2 imagery from 2017–2023 and M4 based on 0.07 m orthophotos from 2022–2023. Data augmentation increased the number of M3 samples from 445 to 3560 and M4 samples from 1106 to 8848. The U-Net M3 model achieved an F1-score of 0.961 and an IoU of 0.927, whereas the SF-Net M4 model achieved an F1-score of 0.969 and an IoU of 0.940. At Keera Plantation, the 28–29% difference in estimated oil palm area between M3 and field verification reflects inter-method variation in a heterogeneous landscape rather than model error alone. M3 processed the entire 680,380.99 ha administrative area of East Luwu Regency in only 4 min 31 s to assess operational scalability and monitoring efficiency. The proposed GeoAI workflow is effective for large-scale initial screening and prioritization. However, GeoAI outputs are intended as decision-support evidence and do not replace field verification or legally accountable administrative decision-making. Full article

Background: Polyploidization generates extensive gene redundancy, but how duplicated metabolic genes are retained and subsequently diversified remains poorly understood. UDP-glycosyltransferases (UGTs) provide a suitable system for examining this process because they participate in specialized metabolism, plant development, and environmental responses. This study aimed to characterize the retention, expansion, and regulatory divergence of the UGT family in allotetraploid Panax ginseng at subgenome resolution. Methods: We integrated telomere-to-telomere (T2T) genome annotation, phylogenetic and chromosomal analyses, duplication classification, collinearity and Ka/Ks analyses, promoter cis-acting element prediction, developmental co-expression networks, and transcriptomic responses to biotic and abiotic treatments. Results: A total of 212 PgUGT genes were identified, including 104 and 108 members in the A and B subgenomes, respectively. The family exhibited an overall near-mirrored retention pattern between the two subgenomes, accompanied by local copy-number asymmetry. Whole-genome and segmental duplication accounted for 64.2% of the family, and 99.5% of the gene pairs with valid Ka/Ks estimates had values below 1, suggesting pervasive purifying selection. PgUGT-containing co-expression modules were associated with bud, stem, leaf, and fruit developmental conditions, while promoter cis-acting element compositions exhibited member-specific variation. Transcriptional responses to fungal pathogens and abiotic, hormone, and chemical treatments were concentrated in particular members and local gene arrays rather than being coordinated across entire clades or subgenomes. Conclusions: The PgUGT family is characterized by extensive ancestral copy retention accompanied by local copy-number changes and copy-specific regulatory divergence. These findings provide a subgenome-resolved framework for understanding UGT family evolution in allotetraploid ginseng and prioritize candidate PgUGT genes for subsequent functional validation. Full article

Ultrasound assessment of developmental dysplasia of the hip (DDH) in infants is highly operator-dependent, particularly during standard plane acquisition and Graf angle measurement. This paper proposes DDH-Net, a fully automated computer-aided assessment method for infant hip ultrasound that follows the complete Graf workflow. First, YOLOv8n-pose is used to detect eight anatomical structures and two ilium orientation keypoints for standard plane determination. Regions of interest (ROIs) are then cropped according to the detection results, and U-Net is employed to perform fine segmentation of the ilium, labrum, and lower limb of the ilium. Finally, Graf reference lines are constructed from the segmentation results to enable automatic measurement of the α and β angles. The study included 1409 infants, comprising 2648 ultrasound images and 50 ultrasound videos. On an independent test set of 529 images, the accuracy, sensitivity, and specificity of standard plane detection were 96.8%, 94.5%, and 100.0%, respectively. The model automatically saved 187 candidate frames from the videos, of which 93.1% were rated by experts as having no or only minor clinically relevant discrepancies; 48 of the 50 videos (96.0%) contained at least one clinically acceptable candidate frame. Compared with full-image segmentation, ROI-based segmentation reduced processing time by 33.7%. In 307 standard plane images, the mean absolute errors (MAE) for the α and β angles were 1.61° and 2.13°, respectively, with corresponding intraclass correlation coefficients (ICC) of 0.913 and 0.766. The complete pipeline achieved a processing speed of 28.87 frames per second, indicating that DDH-Net has the potential to provide efficient and interpretable analysis of infant hip ultrasound images. Full article

Publicly available bearing vibration datasets are widely used as benchmarks for developing condition monitoring and prognostic algorithms, yet the extent to which they represent the signal characteristics of real operational machinery has not been systematically investigated. This study presents a systematic comparison of five public benchmark datasets with vibration data acquired from industrial and maritime machinery operating under real service conditions. A unified signal-processing framework was applied across all datasets, including standardised segmentation, normalisation, and feature extraction in the time, frequency, and time–frequency domains. Degradation behaviour was further characterised using geometric descriptors of trajectory shape capturing trajectory regularity and smoothness. The analysis revealed consistent differences between laboratory-generated and operational vibration data, with public benchmark datasets generally exhibiting lower operating-condition variability and smoother degradation trajectories. Importantly, dataset realism emerged as a continuous characteristic rather than a binary property, with individual datasets occupying different positions along a realism continuum. An unexpected finding was that non-stationarity during nominal operation discriminated strongly between the two groups but in the direction opposite to that hypothesised, an effect attributed to latent degradation drift during accelerated laboratory testing. To quantify these aspects of realism along this continuum, a composite Realism Index (RI) was developed by combining two physically motivated and empirically complementary signal dimensions: operating-condition variability and degradation irregularity. Across 41 laboratory and 10 operational bearing runs, the RI separated the two groups with a large effect size (Cliff’s δ = 0.61, 95% CI [0.43, 1.00]), providing a quantitative framework for comparatively assessing the signal and operating-condition representativeness of benchmark datasets, with potential relevance to benchmark selection and evaluation practice in condition monitoring research. Full article

Increasing soil salinisation challenges food production, since most crops are highly sensitive to salinity. The identification of wild halophytes adapted to saline environments with nutritional value represents a promising strategy for food production on salt-affected farmlands. Assessing the nutritional potential of such species requires evaluating their proximate composition, mineral profile, and antioxidant properties. In parallel, citizen-science approaches complement and enhance scientific research by actively engaging potential final consumers in the research process, thereby improving the societal relevance, dissemination, and potential impact of scientific findings. In this study, we characterised the nutritional profile of the facultative halophyte Cakile maritima and found it to be comparable, and in some respects superior, to that of other conventional leafy vegetables, particularly regarding its mineral composition and bioactive compounds. Moreover, similar though not identical nutritional characteristics were observed in two C. maritima leaf morphotypes analysed, since differences in dry matter, ashes, total proteins and carbohydrates were identified. Interestingly, vitamin C accumulation was organ- and morphotype-dependent. Finally, the biochemical characterisation was complemented with an online survey that showed a clear predisposition of consumers towards the incorporation of wild edible plants into their diet, together with a sensory evaluation in which 32 participants assessed the acceptability of up to 11 dishes prepared using C. maritima. Sensory evaluation revealed a prominent bitter flavour amongst dishes containing C. maritima, with weighted scores for negative perceptions slightly exceeding those for positive ones, suggesting that its sensory profile may limit its acceptance by the general public while offering potential for specific consumer segments. Overall, our findings highlight the nutritional potential of C. maritima and support its valorisation as a sustainable species for saline agriculture. Full article

The Afar Triple Junction, where the Arabian, Nubian, and Somali plates diverge, is one of the most volcanically and tectonically active region on Earth. However, the east–west and vertical deformation field across the Erta Ale volcanic segment remains poorly constrained because previous InSAR studies have largely focused on individual volcanoes or line-of-sight deformation, limiting the characterization of horizontal and vertical ground motions across the entire segment. Here, we derive cumulative horizontal (east–west) and vertical displacements across the Erta Ale volcanic segment using a four-year (2022 to 2025) Sentinel-1 InSAR time series. Time-series analysis and two-dimensional decomposition were performed using the open-source LiCSBAS version 1.17 software to separate ascending and descending line-of-sight observations into horizontal and vertical displacement components. Deformation time series were generated for all six volcanoes to identify temporal trends and episodic deformation and to interpret the underlying magmatic and tectonic processes. The results reveal widespread subsidence across all volcanoes during the study period. Erta Ale and Alu Bagu exhibit westward horizontal motion, whereas Hayli Gubbi, Geda Ale, Alu Dalafilla, and Bora Ale show eastward displacement. The opposing horizontal motions produce an east–west separation of approximately 110 mm between Erta Ale and Hayli Gubbi, consistent with active rift extension. This 4 years intrusion-dominated separation and the long-term Red Sea spreading rate refer to different temporal scales and are therefore compared only qualitatively. The largest subsidence occurred at Erta Ale (−301.49 mm) and Hayli Gubbi (−271.37 mm), which is consistent with caldera-floor collapse and magma withdrawal associated with the mid-2025 dike intrusion event. The spatial variability of the horizontal and vertical deformation fields indicates the combined influence of shallow magmatic processes and regional tectonic extension. These results provide the first comprehensive characterization of east–west and vertical deformation components across the entire Erta Ale volcanic segment and offer new constraints on tectono-magmatic interactions and the ongoing evolution of the Afar Triple Junction. Full article

We propose an end-to-end differentiable framework for hedonic agricultural land pricing that integrates deep learning-based land cover classification with spatial econometric modeling into a single neural architecture. Traditional hedonic pricing approaches typically separate land cover extraction from price regression, leading to suboptimal feature representations that fail to capture the spatial spillover effects inherent to agricultural markets. In our system, a Swin Transformer-based semantic segmentation network extracts pixel-level land cover features from high-resolution multispectral imagery, which are then aggregated within parcel boundaries to produce composition vectors. These features are combined with static parcel attributes and fed into a graph isomorphism network that models spatial dependencies among neighboring parcels through message passing. The central methodological innovation is a differentiable Moran’s I operator that computes spatial autocorrelation from predicted parcel prices and incorporates this statistic into the training objective as a regularizing loss term. This constraint explicitly penalizes deviations from empirically observed target levels of positive spatial autocorrelation in agricultural land markets, thereby ensuring that the learned land cover features are optimized to explain spatial price clustering rather than generic class categories. The complete pipeline, including the segmentation backbone, graph neural network, and spatial autocorrelation computation, is fully differentiable, allowing gradients from the spatial loss to flow backwards and update pixel-level features. This design transforms land cover classification from a mere preprocessing step into an economically informed feature-learning process. The unified framework thereby produces parcel valuations that are both pixel-accurate and spatially coherent, capturing complex nonlinear dependencies such as irrigation network effects or soil-type continuity that conventional spatial econometric models cannot represent. By jointly optimizing segmentation features and their spatial spillover effects on market prices, our approach represents a significant departure from the two-stage hedonic pricing methodology. Full article

Background: Accurate pain assessment in children remains challenging because conventional behavioural assessment tools are subjective and provide only intermittent observations. This study evaluated a vision-based sensing system for automated paediatric pain detection using two-dimensional facial landmark trajectories derived from three-dimensional geometric normalisation of frontal facial video recordings. Methods: In a prospective observational study, 125 children aged 6–17 years undergoing elective surgery had frontal facial videos recorded pre- and postoperatively using a custom-developed iOS mobile application. Video frames were processed using the MediaPipe Face Mesh framework to estimate 478 facial landmarks with three-dimensional coordinates. After three-dimensional normalisation, the normalised x- and y-coordinates were segmented into one-second trajectories, and analysed using a previously developed Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) framework. Reference pain labels (pain vs. no pain) were derived from observer-rated revised Face, Legs, Activity, Cry, Consolability (r-FLACC) scores. Results: The sensing system analysed 971 annotated clips and was trained on 6438 balanced landmark trajectories. On an independent validation dataset, it achieved an area under the receiver operating characteristic curve of 0.994, with an accuracy of 97.15%, precision of 86.78%, recall of 97.83%, and F1-score of 91.97% for binary pain classification. Conclusions: A landmark-based, non-contact vision sensor combined with a STA-LSTM network achieved high performance for automated detection of observer-labelled pain states in children, supporting facial landmark trajectories as a practical signal representation for privacy-conscious continuous pain monitoring. Full article

Expansion of a polymer chain released from a confined state under theta conditions is investigated by means of Langevin dynamics simulations in both three- and two-dimensional geometries. Using a bead–spring chain representation, the $\theta$-temperature is first identified through a scaling analysis of the chain size that exhibits the expected power-law behavior. Extensive simulations then carried out at this temperature reveal that the time evolution of the average chain size during expansion displays sigmoidal curves on log–log plots, indicating a two-stage process: an initial rapid power-law expansion from the confining size, followed by a slower exponential relaxation toward the final equilibrium size. By examining the regularity of these curves, we determine the scaling laws that govern the characteristic times and expansion exponents in each stage. The 2D expansion is found to proceed with slower dynamics than the 3D case, owing to the restriction of the available chain motion on a plane. Plotting the expansion rate against chain size provides a direct test of the kinetic equations, producing master evolutionary trajectories for both stages. Integrating these kinetic equations yields the scaling forms of the free energy for each stage, from which several relationships between the scaling exponents are obtained. This study demonstrates that incorporating local steric constraints and prohibiting segment crossing is essential for accurately describing chain expansion kinetics at the theta point, thereby exposing the limitations of idealized freely jointed chain models. Full article

The structural, processing, and mechanical response of a multiphase poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)/poly(butylene adipate-co-terephthalate) (PBAT)/thermoplastic starch (TPS) matrix to two distinct glass fillers was investigated. Glass fibers and hollow glass spheres were incorporated by melt compounding and injection molding, while the unfilled blend served as the reference. Differential scanning calorimetry, X-ray diffraction, melt-flow-rate measurements, helium pycnometry, scanning electron microscopy with deep-learning-based segmentation, tensile, and Charpy impact tests were applied. At higher filler contents, the composite-level XRD-based crystallinity index decreased to approximately 47%, whereas the Scherrer-derived PHBV (110) coherent-domain size remained within approximately 21–24 nm. Preferred orientation, assessed independently from the PHBV reflection-intensity ratio, varied with filler type and content. Glass fibers progressively reduced melt flow and were associated with an increase in tensile modulus from 2.06 to 3.45 GPa and maximum tensile stress from 23.46 to 27.32 MPa at the highest investigated fiber content. Hollow glass spheres produced a non-monotonic melt-flow response, while the reduction in tensile performance at higher contents coincided with decreasing interparticle spacing and increasing specific external polymer–glass interfacial area. Within the analyzed SEM fields, no pronounced filler-rich clustering was evident. Notched specimens remained brittle, whereas unnotched specimens retained impact strength above 10 kJ × m⁻². Overall, the two filler geometries exhibited distinct relationships among apparent melt flowability, crystalline organization, quantitative microstructural descriptors, and mechanical response. Full article

The global expansion of interconnected edge network components requires immediate strategies for securing low-power computing nodes. Cryptographic algorithms executing over binary extension fields yield considerable computational benefits because their carry-free arithmetic significantly optimizes dynamic power consumption. However, general-purpose silicon architectures lack the dedicated hardware structures to run these finite-field operations efficiently, resulting in severe processing throughput bottlenecks. This study addresses this limitation by introducing a parallelized modular multiplier framework designed to integrate smoothly with the multicore execution environments of modern embedded platforms. Our approach deploys a progressive multiplier reduction (PMR) protocol that segments dense mathematical workloads into distributed structural thread groups. This architectural alignment allows multiplication matrices and spatial field reductions to take place concurrently, balancing localized workloads while decreasing intermediate data buffering demands. We present two distinct topological styles based on column division and row division techniques, deriving comprehensive analytical formulations to capture precise silicon area footprints, critical path delays, and total operational cycle counts. The resulting hardware metrics demonstrate that the parallel PMR design achieves a highly competitive area–delay product alongside optimized dynamic consumption characteristics. This structural paradigm delivers a scalable and robust security alternative for general edge hardware, ensuring system runtime stability while meeting tight environmental power constraints, protecting vital industrial assets, and sustaining emerging macroeconomic infrastructure. Full article

Intelligent systems deployed in smart cities, smart grids, environmental monitoring, and other data-driven applications increasingly depend on reliable multivariate time-series forecasting. Recent deep forecasting models have achieved strong performance on various benchmarks, but their final predictions are often generated through a fixed forecast-generation process. This one-size-fits-all approach may be suboptimal because input windows exhibit different values, trends, and periodic patterns. We propose gap-aware residual selection (GARS), a plug-in correction module for time-series forecasting that is attached to a base forecasting model and adjusts its initial forecast rather than replacing the model. From the observed input window and the initial forecast alone, GARS constructs deterministic reference forecasts, uses their differences from the initial forecast as gap-aware residual information, and forms three component forecasts: the initial forecast, a gap-aware residual component, and a direct residual component. GARS combines these components with soft weights over segments of the forecast horizon, and future target values are used only for training and evaluation. Experiments on five multivariate benchmark datasets related to solar energy, weather, electricity consumption, traffic, and exchange rates, using four representative forecasting models trained on the complete chronological training splits, show that GARS reduces the normalized-scale mean squared error by an average of $6.6\%$ across the 80 evaluated settings. This comparison is between GARS trained jointly with each base model and the same base models trained without it. The mean absolute error is not consistently improved, and the difference between the two metrics is associated with a redistribution of error across the test samples. Under the same protocol, a direct conditional mixture without the gap signal performs at least as well as GARS on average and a parameter-matched control also improves on the base models, and thus the gain cannot be attributed to the gap-based construction. On two datasets not used elsewhere in this study, the same configuration increased the mean squared error, and thus the improvements are not established beyond the evaluated benchmarks. Full article

Quantitative motion analysis is important for understanding the characteristics of climbing movement and for providing objective feedback in sport climbing. Although optical motion capture systems can measure three-dimensional body motion with high accuracy, their application to climbing is limited by the need for markers, multiple cameras, and a controlled measurement space, as well as by occlusion caused by the wall, holds, and body segments. A markerless measurement method using a compact sensor configuration would therefore be advantageous for analyzing climbing motion in more practical environments. This study presents a markerless three-dimensional motion measurement method for sport climbing using a single RGB-D camera and iterative closest point (ICP)-based body-part model fitting. The proposed method first separates the human region from the RGB image using image segmentation and then detects two-dimensional body keypoints using OpenPose. The detected keypoints are projected onto the depth image to reconstruct an initial three-dimensional posture. To refine the reconstructed posture, predefined body-part models are fitted to the segmented human point cloud using the ICP algorithm. The joint points included in the fitted body-part models are then used as the refined three-dimensional joint coordinates. Rather than relying solely on the accuracy of the image-based pose estimator, the proposed method introduces a model-based correction process that compensates for uncertainty in the initial keypoint-based reconstruction. This design aims to reduce the influence of background objects, unstable depth measurements, and incorrect or missing two-dimensional keypoints, which are common difficulties in climbing environments. The proposed method was quantitatively evaluated using five successfully completed trials from two participants under a low-occlusion condition and eight successfully completed trials from four participants under an occlusion-prone condition. The proposed method reduced the average three-dimensional RMSE across six representative body points relative to the initial RGB-D reconstruction under both conditions, with a more pronounced reduction under the occlusion-prone condition. The results indicate that ICP-based body-part model fitting is a useful correction step for single-camera RGB-D motion measurement in sport climbing. Full article

Stereo vision measurement in industrial robot environments fails on thin structures such as needles and gripper tips, because both traditional and deep-learning-based stereo matching produce boundary errors there. We identify and verify experimentally that ground-truth disparity inflation in public datasets is a systematic error source that pushes networks toward biased boundary estimates. From this, we classify the resulting measurement deviations into four categories: edge errors, intra-segment expansion, small-object loss, and uniform-region distortion. We then propose a geometric-constraint-driven measurement refinement method that corrects each category in turn. Since every correction step is geometric rather than learned, the method is unaffected by ground-truth inflation, a property that conventional post-processing filters do not offer. A GUM-based uncertainty propagation analysis of the measurement model $D=f·B/d$ shows that disparity uncertainty dominates the depth uncertainty budget when f and B are exactly known. Experiments on KITTI 2015, Middlebury, and a custom UE4 synthetic industrial dataset (100 stereo pairs) with nine stereo baselines (seven deep learning and two traditional) show that, on inflation-free ground truth, the refinement imposes a near-zero systematic penalty on deep learning output while clearly improving traditional methods. On KITTI, the predictable metric shift confirms that the method is unaffected by LiDAR ground-truth inflation. On a real industrial robot scene, the refined disparity recovers the gripper tip and needle that the baseline LEAStereo loses. These results position geometric-constraint-driven refinement as an effective, training-data-independent complement to end-to-end stereo matching for precision industrial measurement, within the tested scenes and methods. Full article

Ulva prolifera detection is of great significance for marine ecological monitoring and green tide disaster prevention and control. Current single-modal detection methods have inherent limitations. Optical RGB imagery is highly vulnerable to cloud occlusion, while synthetic aperture radar (SAR) data is contaminated by severe speckle noise. Furthermore, existing multimodal detection algorithms struggle to address the prominent multimodal feature heterogeneity between optical and SAR remote sensing data. To overcome these challenges, we develop a decoupled multimodal detection network (DMDNet) for fine-grained Ulva prolifera segmentation. First, a dual-branch feature extraction module with parallel alignment encoding is constructed to adapt to heterogeneous inputs of two modalities. Second, a dedicated convolutional layer unifies the dimensions of the two-modality feature streams. The processed features are subsequently passed to the encoder–decoder module, where the network exploits available features from both optical and SAR modalities for segmentation. Third, a multimodal comprehensive loss function is designed to mitigate the segmentation accuracy degradation caused by class imbalance and blurry target boundaries. In addition, a multimodal joint training strategy is adopted to train the model with optical and SAR samples simultaneously in each iteration. Equipped with a shared encoder and independent task-specific decoder heads, DMDNet accepts either a single optical image or a single SAR image as input and generates stable and reliable segmentation results. Comprehensive experiments are conducted on FIO-EP and CODC datasets, which demonstrate that DMDNet outperforms other baseline models. Full article