Search Results (155)

Human activities and climate change are influencing the survival and distribution of species, threatening the current distribution pattern of biodiversity and potentially leading to the “sixth mass extinction.” The forest musk deer (Moschus berezovskii) is among the most numerous and widely distributed musk deer species in China. However, its habitat is severely threatened by human activities and climate change. Due to the lack of field surveys and research data, it is difficult to assess the threats posed by human activities and climate change effectively. In this study, we integrate the new records of forest musk deer with climate and human activity data, and apply the MaxEnt species distribution model to evaluate the impact of human activities and climate change on the forest musk deer under current conditions and future scenarios (SSP1-2.6 and SSP5-8.5 for the 2030s, 2050s, and 2070s). Our results showed that the forest musk deer prefer areas with high vegetation cover (NDVI > 0.7), low GDP, and low levels of human activity disturbance. The areas of high-suitability habitats are 90.10 × 10⁴ km², 72.85 × 10⁴ km², and 30.43 × 10⁴ km², respectively. The optimal climatic conditions are an annual precipitation (BIO12) of 750–1500 mm and a seasonal temperature variation (BIO4) of 500–600. Their occurrence probability is highest at elevations between 1500 and 3000 m. Under the current climate conditions, the area of high-suitability habitats is estimated at 5.54 × 10⁴ km², primarily distributed across central–northern Sichuan, northwestern Guangxi, and southern Gansu. Under the future climate scenarios, low and medium-suitability habitats are projected to shrink to varying degrees, whereas the high-suitability area is expected to expand, particularly under the SSP5-8.5-2030s scenario where it is projected to increase by 2.88 × 10⁴ km². The centroid of suitable habitat is projected to shift toward higher-elevation areas in northwestern China, with regional hotspots emerging in southwestern regions such as central–northern Sichuan and northwestern Guangxi. These elevational and distributional shifts highlight the vulnerability of current habitats and the importance of adaptive conservation strategies to strengthen species protection, including continuously advancing forest protection programs, mitigating the impact of human activities in high-altitude areas, and strengthening the protection of key areas in the southwestern region. Full article

Background/Objectives: Novel hotspot displacement radiomic features (normalized hotspot-to-centroid distance [NHOC]/normalized hotspot-to-perimeter distance [NHOP]) are robust against image resampling and spatial resolution variations. However, their reproducibility under respiratory motion remains unvalidated. This study aimed to evaluate the reproducibility, reliability, and survival prognostic value of NHOC/NHOP features in thoracic ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) images with and without respiratory motion correction and to determine whether these features maintain stability and predictive performance for overall survival (OS) compared with respiratory-stable reference features. Methods: We analyzed 138 patients (203 lesions) who underwent ¹⁸F-FDG PET/CT with and without data-driven respiratory gating. Reproducibility and reliability were assessed using the coefficient of variation (CoV) and intraclass correlation coefficient (ICC), respectively. OS prediction was evaluated using Cox regression and concordance index (c-index) analyses. Results: Except for NHOCmax and NHOPpeak, which showed ICC values of 0.782 and 0.93, respectively, the novel morphological features generally exhibited poor reproducibility and moderate reliability (CoV > 20% and ICC < 0.75). In contrast, reference features (entropy-based and sphericity) demonstrated excellent robustness. Motion-corrected NHOCmax showed significant OS prediction for both spatially resampled and non-resampled images. No significant differences in c-indices were observed between motion-corrected and non-corrected features. Conclusions: The marked sensitivity of novel hotspot-displacement features to respiratory motion substantially limits their clinical applicability in thoracic disease. To ensure reproducibility and generalizability in future research, prioritizing inherently robust radiomic parameters, such as entropy-based features, is strongly recommended. Full article

This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace operation robot. It employs a multi-feature fusion framework combining foreground and background saliency maps with center prior maps. Foreground saliency maps are generated through spatial compactness and local contrast computations, enhancing discriminative features while suppressing shared foreground–background characteristics. Background saliency maps are constructed via sparse reconstruction to exploit redundant features. Then method integrates edge extraction and density clustering to generate center prior maps that emphasize foreground target centroids and mitigate background noise. Comprehensive evaluations on both a specialized operational hole dataset and six public datasets demonstrate superior performance compared to other methods. On the specialized dataset, it achieves a precision of 0.8954, a maximum F-measure of 0.8994, and an S-measure of 0.8662. While maintaining operational robustness, the method offers a practical solution for furnace monitoring and robotic operation guidance in metallurgical processes. Full article

Carbon emissions (CEs) are a primary driver of global climate change, particularly pronounced in China’s Yangtze River Delta (YRD) region, where rapid economic development and urbanization have led to a substantial increase in CEs. At fine spatial scales (e.g., county level) or in regions with limited statistical data, traditional methods for CE accounting are constrained by data gaps and inconsistencies, which hinders the accurate characterization of regional disparities. Therefore, this study proposes a CE spatial downscaling method based on nighttime light (NTL) data. By integrating remote sensing data with the IPCC emission inventory model, energy consumption-related carbon emissions (ECCEs) across the YRD region from 2000 to 2020 were quantified. Through global spatial autocorrelation analysis and standard deviation ellipse (SDE) analysis, the spatial distribution characteristics and temporal variation trends of ECCEs were revealed. Results indicate that total CEs increased significantly over the study period. CE hotspots were concentrated in the Hangzhou Bay area and the Shanghai–Nanjing corridor, while coldspots were identified in southwestern Anhui and Zhejiang. From 2010, the CE centroid shifted toward the southwest or northwest, and the regional CE distribution evolved from a point pattern to a band-shaped pattern. These findings offer a novel approach for CE monitoring and can provide scientific support for low-carbon development policies and precise emission reduction strategies in data-scarce regions of developing countries. Full article

Climate change, together with intensifying human activities, is reshaping global plant invasion dynamics and increasingly threatening ecosystem stability and biodiversity. Cockleburs are highly invasive weeds with strong ecological plasticity and dispersal capacity, causing widespread impacts on agricultural systems and native ecosystems. Here, we used the maximum entropy (MaxEnt) model to assess the current (2001–2020) and future (2021–2040, 2041–2060, and 2061–2080) potential distributions, key driving factors, and centroid shifts of four invasive cocklebur species—Cyclachaena xanthiifolia (=Iva xanthiifolia), Xanthium chinense, Xanthium italicum, and Xanthium spinosum—at the global scale under current climate conditions and three Shared Socioeconomic Pathway scenarios (SSP126, SSP245, and SSP585). Species occurrence records were integrated with climatic, topographic, and anthropogenic variables to project habitat suitability. Model performance was robust, with mean training and testing area under the receiver operating characteristic curve (AUC) values > 0.8 for all species and mean true skill statistic (TSS) values > 0.8 for three species (0.660 for Xanthium spinosum). Suitable habitats were jointly shaped by climatic and anthropogenic factors, although the dominant drivers differed among species. Cyclachaena xanthiifolia and Xanthium spinosum were primarily constrained by temperature and precipitation, whereas Xanthium italicum and Xanthium chinense were more strongly associated with human activity. At present, suitable habitat areas for Cyclachaena xanthiifolia, Xanthium chinense, Xanthium italicum, and Xanthium spinosum were 1196.92 × 10⁴, 358.76 × 10⁴, 888.34 × 10⁴, and 1985.14 × 10⁴ km², respectively. Future projections indicated overall contractions in suitable habitat, with pronounced interspecific variation. Xanthium chinense showed the largest mean decline (−161.23 × 10⁴ km² relative to the present), whereas Cyclachaena xanthiifolia experienced the smallest reduction (−53.15 × 10⁴ km² on average). Centroid analyses further suggested overall shifts toward higher latitudes and elevations under warming scenarios. Despite uncertainties related to climate scenario variability and assumptions inherent in species distribution modelling, these findings provide quantitative evidence to support global invasion risk assessment and climate-adaptive management of invasive cockleburs. Full article

Background/Objectives: An accurate description of facial sexual dimorphism is essential in clinical, forensic, and anthropological contexts to support accurate diagnosis of craniofacial dysmorphisms and differences, treatment planning and evaluation, as well as biological profiling, craniofacial reconstruction, and personal identification. This study investigates sexual dimorphism of the facial soft tissues in a sample of healthy Italian adults, providing reference data and deepening our understanding of normal craniofacial variation. Methods: Three-dimensional stereophotogrammetric facial images of 342 Italian adults (172 males and 170 females; 18–40 years old) were analyzed using a 3D spatially dense geometric morphometric approach to assess both shape and form. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were used to explore facial variation and to quantify sex-related differences. Results: Centroid size was significantly larger in males. While PCA revealed that sex is a significant factor in facial shape and form variation, PLSR highlighted the existence of significant associations between sex and both shape and form. Color-coded morphometric maps underlined the most sexually dimorphic traits: males exhibited bigger faces with deep-set eyes and central facial projection extending from the supraorbital rims to the chin, whereas females display smaller faces with fuller cheeks, and a more vertical forehead profile. Conclusions: While our results are consistent with those of previous studies, our study revealed important, distinctive group-specific traits: flatter labiomandibular folds in males and wider temples and fuller cheeks in the infraorbital region extending to zygomatic and mandibular areas in females. Thus, this study provides high-resolution reference data supporting related applications. Full article

Taxus, a relic plant genus from the Tertiary period, contains taxane compounds that are crucial in anti-cancer drug development and have significant medicinal and ecological value. Evaluation of the potential distribution range and shifts for this genus considering global climate change is vital for conserving wild resources, supporting artificial propagation, and ensuring sustainable development. We analyzed the potential geographic distribution patterns and key environmental factors affecting four Taxus species (Taxus cuspidata, Taxus wallichiana var. mairei, Taxus wallichiana, and Taxus wallichiana var. chinensis) under current climate conditions and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) across three future periods (2050s, 2070s, and 2090s) using the regularization multiplier and feature combination parameters of the MaxEnt model. We also explored their responses to climate change over time. The area under the curve of models built using the ENMeval package exceeded 0.9, demonstrating high accuracy. Environmental analysis indicated that the coldest monthly minimum temperature was the main environmental factor influencing the species distribution, except in Taxus cuspidata, for which the human footprint was the primary factor. Currently, the habitats of the four Taxus species exhibit spatial variation, with Taxus wallichiana var. chinensis having the largest suitable area in China, covering approximately 200.89 × 10⁴ km², accounting for 21.17% of China’s land area. Habitat trends varied under future climate scenarios, with the suitable area expanding for Taxus wallichiana and Taxus wallichiana var. chinensis, and showing expansion and contraction for Taxus wallichiana var. mairei and Taxus cuspidata. The distribution centroids were predicted to shift to higher latitudes over time, with Taxus wallichiana var. chinensis showing particularly clear migration trends. These results offer a vital reference for developing conservation strategies and introduction and cultivation initiatives for these Taxus species. Full article

Accurate apple detection and precise three-dimensional (3D) localisation are essential for autonomous robotic harvesting in orchard environments, where occlusion, illumination variation, depth noise, and the similar colour appearance of fruits and surrounding leaves present significant challenges. This paper proposes a dual-detector vision framework combined with depth-aware back-projection to achieve robust apple detection and metric 3D localisation in real time. The method integrates the complementary strengths of YOLOv8 and Mask R-CNN through confidence-weighted fusion of bounding boxes and pixel-wise union of segmentation masks, producing stabilised two-dimensional (2D) apple representations under visually ambiguous conditions. The fusion results are converted into dense 3D representations through depth-guided projection within the camera coordinate system representing the visible fruit surface. A depth-consistency weighting strategy assigns higher influence to depth-reliable pixels during centroid computation, thereby suppressing noisy or occluded depth measurements and improving the stability of 3D fruit centre estimation, while local intensity normalisation standardises neighbourhood-level pixel intensities to reduce the impact of shadows, highlights, and uneven lighting, enabling more consistent segmentation and detection across varying illumination conditions. Experimental results demonstrate an accuracy of 98.9%, an mAP of 94.2%, an F1-score of 93.3%, and a recall of 92.8%, while achieving real-time performance at 86.42 FPS, confirming the suitability of the proposed method for robotic harvesting in challenging orchard environments. Full article

Background and Objectives: Psychosocial symptoms and oral behaviors can complicate routine dental care, yet available screeners yield multiple separate scores. Explainable artificial intelligence offers a pragmatic way to integrate such multidomain measures into a single, auditable output that can support screening-oriented stratification and standardized documentation (non-diagnostic). Therefore, we aimed to develop an interpretable, deterministic Mamdani fuzzy inference system (FIS) integrating GAD-7, PHQ-9, and OBC-21 into a 0–10 psychobehavioral composite score (PCS) to support screening-oriented stratification and standardized documentation (non-diagnostic). Materials and Methods: Cross-sectional multicenter study in 18 private dental clinics in Romania (October 2024–March 2025; n = 460). A rule-based Mamdani Type-1 FIS was specified a priori (48 rules; triangular membership functions; centroid defuzzification) without supervised training. Internal evaluation assessed coherence across severity strata, robustness to predefined input perturbations (±1 point; ±5%) and membership-function variation (±10%), and benchmarking against linear composites (Z-mean; PCA PC1). Results: Median PCS was 2.30 (IQR 2.03–3.56). PCS correlated with GAD-7 (Spearman ρ = 0.886), PHQ-9 (ρ = 0.792), and OBC-21 (ρ = 0.687) (all p < 0.001), increased monotonically across anxiety and depression severity strata, and was higher in high OBC-21 risk. Robustness was excellent under input perturbations (ICC(3,1) = 0.983 for ±1 point; 0.992 for ±5%) and high under ±10% membership-function variation (ICC(3,1) = 0.959). Concordance with linear baselines was high (Spearman ρ = 0.956 for Z-mean; 0.955 for PCA PC1), with a small systematic nonlinearity at higher scores. Conclusions: PCS provides a fully auditable, rule-based integration of three patient-reported measures with coherent internal behavior and robustness to plausible measurement noise and specification changes. This study reports internal evaluation of a deterministic, rule-based aggregation; external clinical validation against independent outcomes is required before any clinical utility claims. Full article

Despite advances in high-resolution topographic survey technologies, abstracting static 3D data into physically meaningful indicators remains critical for river management. This study introduces a geometric moment technique to reflect river curvature and hydraulic characteristics within an integrated framework. Analysis was conducted on a reach of the Nakdong River using first-, second-, and third-order moments, W/D ratios, asymmetry indicators, and D50 data. Key findings are: First, the moment-based approach precisely quantified asymmetric variations and localized bed changes by utilizing centroid deviation (M1), dispersion (M2), and mass bias (M3), addressing the limitations of traditional average-based indices. This effectively transforms vast 3D datasets into “compressed records” for tracing hydraulic drivers. Second, sinuosity (S) analysis revealed that reaches with higher curvature (S ≥ 1.5) exhibited intensified variability in third-order moments and asymmetry due to imbalanced hydraulic forcing. Specifically, the horizontal misalignment between the velocity core and the thalweg was identified as a key mechanism driving geometric imbalance in curves. Third, a W/D-asymmetry quadrant analysis categorized reach-scale morphological types and identified hydraulically vulnerable zones. By integrating sectional geometry, velocity distribution, and sinuosity into a unified system, this study provides a quantitative framework for scientific river management and decision-making. Full article

Considering the nonlinear trends, multi-scale variations, and capacity regeneration phenomena exhibited by battery capacity degradation under real-world conditions, accurately predicting its trajectory remains a critical challenge for ensuring the reliability and safety of electric vehicles. To address this, this study proposes a hybrid prediction framework based on Variational Mode Decomposition and a Transformer–Long Short-Term Memory architecture. Specifically, the proposed Variational Mode Decomposition–Transformer for Time Series–Long Short-Term Memory (VMD–TTS–LSTM) framework first decomposes the capacity sequence using Variational Mode Decomposition. The resulting modal components are then aggregated into high-frequency and low-frequency parts based on their frequency centroids, followed by targeted feature analysis for each part. Subsequently, a simplified Transformer encoder (Transformer for Time Series, TTS) is employed to model high-frequency fluctuations, while a Long Short-Term Memory (LSTM) network captures the long-term degradation trends. Evaluated on charging data from 20 commercial electric vehicles under a long-horizon setting of 20 input steps predicting 100 steps ahead, the proposed method achieves a mean absolute error of 0.9247 and a root mean square error of 1.0151, demonstrating improved accuracy and robustness. The results confirm that the proposed frequency-partitioned, heterogeneous modeling strategy provides a practical and effective solution for battery health prediction and energy management in real-world electric vehicle operation. Full article

Background/Objectives: The identification of individuals from human remains is crucial in any scenario where their identity is unknown. The study of ontogenetic allometry, which refers to proportional changes in the shape and size of bones during growth, provides important baseline information for constructing biological profiles. Methods: This study focuses on the analysis of the ontogenetic allometry of the scapula in Portuguese reference skeletal samples, using geometric morphometric techniques. The sample includes 140 individuals (67 females, 73 males), ranging from birth to 89 years old. Scapulae were photographed, and seven landmarks and forty semi-landmarks were digitized using the “tps” programs. Statistical analyses were performed using the MorphoJ (v. 1.08.02) and PAST (v. 5.2) programs. Results: The results point to a significant and continuous growth of the scapula in the early stages of life, with a tendency to stabilize after adolescence. Centroid size significantly influenced shape variation across the full sample. Conclusions: These findings provide a descriptive baseline of scapular development that can aid future anthropological and forensic research, including studies on population variation and age-related morphological trajectories. Full article

Reliability assessment of conical shells in the chemical industry commonly relies on point cloud registration. Thus, accurate edge detection from 3D laser scan data is crucial for high-precision registration. However, existing edge detection methods often misclassify or omit gradual edge points on conical shell structures, significantly compromising registration accuracy and subsequent integrity assessment. This paper proposes an edge point detection method integrating Principal Component Analysis (PCA) and wavelet transform. First, characteristic curves are constructed by computing the ratio of PCA eigenvalues at all points to generate preliminary candidates for gradual edge points. Subsequently, distance vectors are calculated between the centroid of each characteristic curve and its sampled points. These vectors are then encoded via multi-level wavelet transform to produce mapping vectors that capture curvature variations. Finally, gradual edge points are discriminated effectively using these mapping vectors. Experimental results demonstrate that the proposed method achieves superior edge detection performance on complex conical shell surfaces and significantly enhances the accuracy of point cloud registration. Full article

Regional economic development serves as a crucial indicator of societal vitality and the efficiency of resource allocation. Nighttime light (NL) remote sensing data is a reliable reflection of regional economic activities, making it essential to analyze its spatiotemporal variations and influencing factors for economic growth. This study employs space–time cubes, incorporating hotspot and outlier analysis, to explore the dynamics of NL in the Dianchi Lake basin between 2000 and 2022, focusing on shifts in centroids, temporal patterns, and spatial clustering. Various machine learning models were tested, with the most effective model utilizing the SHAP algorithm to uncover the nonlinear relationships between explanatory variables and NL. The findings reveal that economic hotspots are predominantly concentrated around Dianchi Lake, exhibiting high–high spatial clustering, whereas cold spots are mainly distributed in the northern and southern regions and are characterized by low–low clustering. In addition, human activity indicators (GDP, road density, and population) and climatic factors (temperature and precipitation) are positively associated with economic development, while topographic factors (DEM and slope) show negative associations. Full article

Objectives: The present study compared the amplitude and spatial distribution of muscle excitation between a seated row performed with a fixed scapular position (fixed-SR) and a free scapular position (free-SR) in resistance-trained men, analyzing concentric and eccentric phases separately using high-density surface EMG (HD-sEMG). Methods: Fourteen resistance-trained males (age: 25 ± 4 years; stature: 1.74 ± 0.06 m; body mass: 76.22 ± 5.73 kg) performed fixed-SR and free-SR in a randomized cross-over design using 8-repetition maximum as the load for both variations. HD-sEMG grids recorded the activity from the upper/middle/lower trapezius, latissimus dorsi, lateral/posterior deltoid, biceps brachii, triceps brachii, and erector spinae. Normalized root mean squared (RMS) amplitude and excitation centroids in the mediolateral and craniocaudal planes were computed for the concentric and eccentric phases. Data were analyzed using repeated-measures statistical models, with significance set at p < 0.05. Results: During the concentric phase, nRMS amplitude was greater for the posterior deltoid in fixed-SR compared with free-SR (effect size [ES] = 0.66), whereas no between-condition difference was observed for the remaining muscles. During the eccentric phase, nRMS amplitude was greater in the fixed-SR for the middle trapezius (ES = 0.67) and the latissimus dorsi (ES = 0.85), with no between-condition differences detected for the remaining muscles. The centroid position analysis revealed that, during the eccentric phase, the middle trapezius centroid was located more laterally in the fixed-SR condition (ES = 0.54), while the posterior deltoid centroid was positioned more caudally in the fixed-SR compared with the free-SR condition (ES = 0.22). Conclusions: The fixed-SR and free-SR conditions produce comparable overall muscle excitation patterns, while showing some quantitative and spatial differences in selected upper-back muscles. These results suggest that scapular constraint influences the distribution of muscular excitation rather than overall excitation levels. Accordingly, both variations can be effectively used in resistance training, selecting to fix or free the scapulae depending on the emphasis on the scapular movements rather than a substantial difference in muscle excitation. Full article