Search Results (117)

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article

The hot deformation discipline of typical 45CrNi steel under a strain rate ranging from 0.01 s⁻¹ to 1 s⁻¹ and deformation temperature between 850 °C and 1200 °C was investigated through isothermal hot compression tests. The activation energy involved in the high-temperature deformation process was determined to be 361.20 kJ·mol⁻¹, and a strain-compensated constitutive model, together with dynamic recrystallization (DRX) kinetic models, was successfully established based on the Arrhenius theory. An improved second-phase (SP) cellular automaton (CA) model considering the influence of the pinning effect induced by SP particles on the DRX process was developed, and the established SP-CA model was further utilized to predict the evolution behavior of parent austenite grain in regard to the studied 45CrNi steel. Results show that the average absolute relative error (AARE) associated with the austenite grain size and the DRX volume fraction achieved through the simulation and experiment was overall below 5%, indicating good agreement between the simulation and experiment. The pinning force intensity could be controlled by regulating the size and volume fraction of SP particles involved in the established SP-CA model, and the DRX behavior and the average grain size of the studied 45CrNi steel treated by high-temperature compression could also be predicted. The established SP-CA model exhibits significant potential for universality and is expected to provide a powerful simulation tool and theoretical foundation for gaining deeper insights into the microstructural evolution of metals or alloys during high-temperature deformation. Full article

Detecting and quantifying barely visible impact damage (BVID) in carbon fiber-reinforced polymer (CFRP) materials is a key challenge in maintaining the safety and reliability of composite structures. This study presents the application of low-power vibrothermography to identify and quantify such defects. Using a long-wave infrared (LWIR) camera, thermal data were captured from the CFRP specimens that inhibit BVID. How image processing, specifically principal component analysis (PCA) and sparse principal component analysis (SPCA), can enhance thermal contrast and improve the accuracy of defect size is also explored. By combining low-energy excitation with advanced data analysis, this research aims to develop a more accessible and reliable approach to non-destructive testing (NDT) for composite materials. Full article

Wetlands are natural ecosystems of great ecological and economic value. They provide undeniable ecosystem services that contribute to promoting sustainable development. Exposed to different pressures, these limnic ecosystems are particularly vulnerable to climate change. Thus, assessing wetland vulnerability is of utmost importance. Based on a systematic selection of relevant peer-reviewed studies, this paper helps to develop a general vision of the methods used to assess wetland vulnerability in different contexts, emphasizing the use of advanced computational approaches. Hence, an overview of different cases of wetlands all across the five continents and of different types of habitats is presented. Whether the wetland is permanently or seasonally flooded, coastal, or tropical, this study enables the analysis of diverse, already established vulnerability evaluation index systems. Some of these indices were computed using geographic information systems (GISs), artificial intelligence (AI), machine learning (ML), spatial principal component analysis (SPCA) and driver–pressure–state–impact–response (DPSIR) as evaluation models. Indeed, given the adoption of different methods, diverse models, and analytical approaches under different scenarios, the vulnerability assessment process should be seen as an iterative rather than a definitive process. An accurate wetland vulnerability assessment is essential for ensuring the sustainability of wetland ecosystems and for informing effective conservation and management strategies. Full article

European black pine (Pinus nigra Arn. subsp. nigra) persists in scattered montane stands across Greece, where isolated populations harbour genetic variation shaped by local environments and demographic history. In this study, we assessed the genetic diversity and population structure of P. nigra using nuclear microsatellite markers (nSSRs) across four populations: Mt. Athos, Sithonia, Thassos, and Perama. A total of 67 individuals were genotyped, and seven high-quality polymorphic loci were retained after rigorous filtering. The Mt. Athos population exhibited the highest allelic richness and heterozygosity, with all loci being polymorphic and a low inbreeding coefficient after null allele correction. In contrast, the Perama population displayed reduced diversity, fewer polymorphic loci, and persistent heterozygote deficits. Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC) revealed weak overall population structure, with Perama genetically distinct from the other sites. Spatial Principal Component Analysis (sPCA) further uncovered an east–west cline within Athos and localized structure potentially shaped by both natural isolation and human influence. These findings highlight regional variation in genetic diversity within P. nigra and identify Athos as a genetically rich population of particular interest. The results provide a foundation for long-term monitoring and support informed strategies for the management and conservation of P. nigra in Greece. Full article

Portfolios incorporating environmental, social, and governance (ESG) criteria present distinct, unobserved risks, the empirical quantification of which has proven challenging. This difficulty stems from sustainable investment strategies being guided by both financial objectives and investors’ non-pecuniary preferences, which fundamentally alter a portfolio’s risk and return characteristics. To address this, we propose a novel methodology that identifies latent, ESG-specific risk factors by applying sparse principal component analysis (SPCA) to two-dimensional portfolio returns. Unlike approaches that rely on subjective judgment, our method extracts risk dimensions inherent to the return data itself. Our analysis reveals that the resulting firm-specific SPCA beta plays a dual role: it explains performance differentials across ESG-rated portfolios and exhibits a statistically significant, negative association with expected individual stock returns. The robust predictive performance of this SPCA-based risk factor confirms its practical utility for analyzing and managing diversification in ESG investing. Full article

This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP (q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor (q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems. Full article

Ecological network construction has been widely accepted and applied to guide regional ecological conservation and restoration. For arid regions, ecological networks proposed based on ecological risk assessments are better aligned with the sensitive and fragile characteristics of local ecosystems. This study assesses landscape ecological risk in Wensu County, located on the southern slope of the Tianshan Mountains in the arid region of northwestern China, and it further proposes an optimized ecological network. A multidimensional framework composed of the natural environment, human society, and landscape patterns was employed to construct an ecological risk assessment system. Spatial principal component analysis (SPCA) was applied to identify the spatial pattern of ecological risk. Morphological spatial pattern analysis (MSPA) and a minimum cumulative resistance (MCR) model integrated with circuit theory were used to extract the ecological sources and delineate the ecological corridors. The results reveal significant spatial heterogeneity in terms of ecological risk: Low-risk zones (16.26%) are concentrated in the southwestern forest and water areas. In comparison, high-risk zones (28.27%) are mainly distributed in the northern mountainous mining region. A total of 24 ecological source patches (4105.24 km²), 44 ecological corridors (313.6 km), 39 ecological pinch points, and 38 ecological barriers were identified. Following optimization, the Integral Index of Connectivity (IIC) increased by 89.04%, and the Landscape Coherence Probability (LCP) rose by 105.23%, indicating markedly enhanced ecological connectivity. The current ecological network exhibits weak connectivity in the south and fragmentation in the central region. Targeted restoration of critical nodes, optimization of corridor configurations, and expansion of ecological sources are recommended to improve landscape connectivity and promote biodiversity conservation. Full article

Protein phosphorylation is one of the most common and important post-translational modifications (PTMs) and is highly involved in various biological processes. Ideal adsorbents with high sensitivity and specificity toward phosphopeptides with large coverage are therefore essential for enrichment and mass spectroscopy-based phosphoproteomics analysis. In this study, a newly designed IMAC adsorbent composite was constructed on the graphene matrix coated with mesoporous silica. The outer functional 3D-network layer was prepared by free radical polymerization of the phosphonate-functionalized vinyl imidazolium salt monomer and subsequent metal immobilization. Due to its unique structural feature and high content of Ti⁴⁺ ions, the resulting phosphonate-immobilized adsorbent composite G@mSiO₂@PPFIL-Ti⁴⁺ exhibits excellent performance in phosphopeptide enrichment with a low detection limit (0.1 fmol, tryptic β-casein digest) and superior selectivity (molar ratio of 1:15,000, digest mixture of β-casein and bovine serum albumin). G@mSiO₂@PPFIL-Ti⁴⁺ displays high tolerance to loading and elution conditions and thus can be reused without a marked decrease in enrichment efficacy. The captured phosphopeptides can be released globally, and mono-/multi-phosphopeptides can be isolated stepwise by gradient elution. When applying this material to enrich phosphopeptides from human lung cell lysates, a total of 3268 unique phosphopeptides were identified, corresponding to 1293 phosphoproteins. Furthermore, 2698 phosphorylated peptides were found to be differentially expressed (p < 0.05) between human lung adenocarcinoma cells (SPC-A1) and human normal epithelial cells (Beas-2B), of which 1592 were upregulated and 1106 were downregulated in the cancer group. These results demonstrate the material’s superior enrichment efficiency in complex biological samples. Full article

Pneumothorax is a critical condition that requires rapid and accurate diagnosis from standard chest radiographs. Identifying and segmenting the location of the pneumothorax are essential for developing an effective treatment plan. nnUNet is a self-configuring, deep learning-based framework for medical image segmentation. Despite adjusting its parameters automatically through data-driven optimization strategies and offering robust feature extraction and segmentation capabilities across diverse datasets, our initial experiments revealed that nnUNet alone struggled to achieve consistently accurate segmentation for pneumothorax, particularly in challenging scenarios where subtle intensity variations and anatomical noise obscure the target regions. This study aims to enhance the accuracy and robustness of pneumothorax segmentation in low-contrast chest radiographs by integrating spatial prior information and attention mechanism into the nnUNet framework. In this study, we introduce the spatial prior contrast adapter (SPCA)-enhanced nnUNet by implementing two modules. First, we integrate an SPCA utilizing the MedSAM foundation model to incorporate spatial prior information of the lung region, effectively guiding the segmentation network to focus on anatomically relevant areas. In the meantime, a probabilistic atlas, which shows the probability of an area prone to pneumothorax, is generated based on the ground truth masks. Both the lung segmentation results and the probabilistic atlas are used as attention maps in nnUNet. Second, we combine the two attention maps as additional input into nnUNet and integrate an attention mechanism into standard nnUNet by using a convolutional block attention module (CBAM). We validate our method by experimenting on the dataset CANDID-PTX, a benchmark dataset representing 19,237 chest radiographs. By introducing spatial awareness and intensity adjustments, the model reduces false positives and improves the precision of boundary delineations, ultimately overcoming many of the limitations associated with low-contrast radiographs. Compared with standard nnUNet, SPCA-enhanced nnUNet achieves an average Dice coefficient of 0.81, which indicates an improvement of standard nnUNet by 15%. This study provides a novel approach toward enhancing the segmentation performance of pneumothorax with low contrast in chest X-ray radiographs. Full article

Accurate prediction of tool wear plays a vital role in improving machining quality in intelligent manufacturing. However, traditional Gaussian Process Regression (GPR) models are constrained by linear assumptions, while conventional filtering algorithms struggle in noisy environments with low signal-to-noise ratios. To address these challenges, this paper presents an innovative tool wear prediction method that integrates a nonlinear mean function and a multi-kernel function-optimized GPR model combined with an LSTM-enhanced particle filter algorithm. The approach incorporates the LSTM network into the state transition model, utilizing its strong time-series feature extraction capabilities to dynamically adjust particle weight distributions, significantly enhancing the accuracy of state estimation. Experimental results demonstrate that the proposed method reduces the mean absolute error (MAE) by 47.6% and improves the signal-to-noise ratio by 15.4% compared to traditional filtering approaches. By incorporating a nonlinear mean function based on machining parameters, the method effectively models the coupling relationships between cutting depth, spindle speed, feed rate, and wear, leading to a 31.09% reduction in MAE and a 42.61% reduction in RMSE compared to traditional linear models. The kernel function design employs a composite strategy using a Gaussian kernel and a 5/2 Matern kernel, achieving a balanced approach that captures both data smoothness and abrupt changes. This results in a 58.7% reduction in MAE and a 64.5% reduction in RMSE. This study successfully tackles key challenges in tool wear monitoring, such as noise suppression, nonlinear modeling, and non-stationary data handling, providing an efficient and stable solution for tool condition monitoring in complex manufacturing environments. Full article

Dog training is an unregulated profession in Canada without licensing or standardized practices, yet professional dog trainers greatly influence how guardians interact with their dogs and, by extension, dog welfare. We conducted an online survey to characterize the demographics, qualifications, services, methods, and views of dog trainers in Canada. Of the 706 valid respondents, most (65%) had completed at least one structured dog training program, while 33% were self-educated. Respondents held qualifications from 138 training programs and 39 exam-based certifications that differed in their curriculum, duration, and scope. We identified over 80 different themes or terms that trainers use to describe their practices, with the most frequent relating to reward-based methods. Most respondents also indicated that they would be unlikely to use aversive collars. These findings suggest that reward-based methods are likely the most prevalent in Canada. Two-thirds (62%) supported some regulation of dog training. The quantity and variety of training programs, certifications, and terminology utilized by dog trainers could present challenges for dog guardians in selecting trainers, and/or result in the use of harmful training methods. These findings can inform further development of best practices, educational programs, and advocacy to advance the use of humane training methods. Full article

Background and Objectives: Despite the growing amount of new research, the pathophysiology of glaucoma remains unclear. The aim of this study was to determine the relationship between intracranial pressure (ICP), ocular blood flow and structural optic nerve parameters. Materials and Methods: A prospective clinical study was conducted involving 24 patients with open-angle glaucoma and 25 healthy controls. Routine clinical examination was performed. Swept-source optical coherence tomography (SS-OCT) and OCT angiography (OCTA) images were taken (DRI-OCT Triton, Topcon). The vessel density (VD) values of the ONH were calculated around the optic nerve head (ONH). An orbital Doppler device (Vittamed 205, Kaunas, Lithuania) was used for non-invasive ICP measurements. Color Doppler imaging (CDI) (Mindray M7, Shenzhen, China) was used for retrobulbar blood flow measurements in the ophthalmic artery (OA), central retinal artery (CRA) and short posterior ciliary arteries (SPCAs). Results: ICP was 8.35 ± 2.8 mmHg in the glaucoma group and 8.45 ± 3.19 mmHg in the control group (p = 0.907). In the glaucoma group, the VD of the superficial vascular plexus in the inferior-nasal (NI) sector of the ONH showed a correlation with ICP (r = 0.451, p = 0.05). In contrast, the control group exhibited weaker correlations. CRA peak systolic velocity (PSV) demonstrated significant moderate correlations with VD in multiple retinal layers, including the avascular retina layer in the temporal (T) sector (r = 0.637, p = 0.001). Conclusions: Lower ICP was significantly associated with the lower VD of the superficial plexus layer in the inferior-nasal sector in the glaucoma group, with the control group exhibiting weaker correlations in all sectors. Further longitudinal studies with larger sample sizes are needed to establish associations between intracranial pressure, ocular blood flow and ONH parameters. Full article

Many critical applications exhibit a growing dependency on Global Navigation Satellite Systems (GNSS), which has led to GNSS jamming and spoofing becoming an increasing threat to society. The Structural Power Content Analysis (SPCA) algorithm is a pre-despreading, low-complexity, and effective method to detect spoofing events, but in practice it is seen that false alarms are being generated in certain jamming scenarios. To mitigate these effects, alternative filtering techniques are evaluated and tested on both simulated data and publicly available spoofing datasets. Effective false alarm reduction with only a minor degradation in spoofing detection sensitivity is demonstrated. Full article

Objective: This study compares machine learning (ML) and logistic regression (LR) algorithms in developing a predictive model for sPCa using the seven predictive variables from the Barcelona (BCN-MRI) predictive model. Method: A cohort of 5005 men suspected of having PCa who underwent MRI and targeted and/or systematic biopsies was used for training, testing, and validation. A feedforward neural network (FNN)-based SimpleNet model (GMV) and a logistic regression-based model (BCN) were developed. The models were evaluated for discrimination ability, precision–recall, net benefit, and clinical utility. Both models demonstrated strong predictive performance. Results: The GMV model achieved an area under the curve of 0.88 in training and 0.85 in test cohorts (95% CI: 0.83–0.90), while the BCN model reached 0.85 and 0.84 (95% CI: 0.82–0.87), respectively (p > 0.05). The GMV model exhibited higher recall, making it more suitable for clinical scenarios prioritizing sensitivity, whereas the BCN model demonstrated higher precision and specificity, optimizing the reduction of unnecessary biopsies. Both models provided similar clinical benefit over biopsying all men, reducing unnecessary procedures by 27.5–29% and 27–27.5% of prostate biopsies at 95% sensitivity, respectively (p > 0.05). Conclusions: Our findings suggest that both ML and LR models offer high accuracy in sPCa detection, with ML exhibiting superior recall and LR optimizing specificity. These results highlight the need for model selection based on clinical priorities. Full article