Search Results (12,737)

Persistent scatterer (PS) pixel selection is crucial in the PS-InSAR technique, ensuring the quality and quantity of PS pixels for accurate deformation measurements. However, traditional methods like the amplitude dispersion index (ADI)-based method struggle to balance the quality and quantity of PS pixels. To adequately select high-quality PS pixels, and thus improve the deformation measurement performance of PS-InSAR, the multi-temporal feature extraction network (MFN) is constructed in this paper. The MFN combines the 3D U-Net and the convolutional long short-term memory (CLSTM) to achieve time-series analysis. Compared with traditional methods, the proposed MFN can fully extract the spatiotemporal characteristics of complex SAR images to improve PS pixel selection performance. The MFN was trained with datasets constructed by reliable PS pixels estimated by the ADI-based method with a low threshold using ∼350 time-series Sentinel-1A SAR images, which contain man-made objects, farmland, parkland, wood, desert, and waterbody areas. To test the validity of the MFN, a deformation measurement experiment was designed for Tongzhou District, Beijing, China with 38 SAR images obtained by Sentinel-1A. Moreover, the similar time-series interferometric pixel (STIP) index was introduced to evaluate the phase stability of selected PS pixels. The experimental results indicate a significant improvement in both the quality and quantity of selected PS pixels, as well as a higher deformation measurement accuracy, compared to the traditional ADI-based method. Full article

The study explores long-term changes in the maximum number of consecutive hours per day of heat-related discomfort in Athens over the period 1960–2024, using the Universal Thermal Climate Index (UTCI). This index includes a four-category scale to represent heat stress intensity, ranging from ‘moderate’ to ‘extreme’, as part of its broader multi-category classification system. The analysis indicated a clear increase in the frequency of days with a large number of consecutive discomfort hours over the past decades. Almost 70% of the total number of days with 11 consecutive hours under at least ‘strong heat stress’ and 7 consecutive hours under at least ‘very strong heat stress’ were detected after the year 2000. Full article

Background/Objectives: Low back pain (LBP) remains one of the leading causes of disability globally and contributes significantly to healthcare expenditures. Discogenic LBP, a subtype stemming from intervertebral disc degeneration, often provesrefractory to conventional treatment modalities. Regenerative orthobiologic therapies, including platelet-rich plasma (PRP), platelet lysate (PL), and mesenchymal stem cells (MSCs), have emerged as promising alternatives, though long-term outcomes and safety profiles are not yet well understood. Methods: This case series reports 13 patients treated between 2015 and 2016 at an outpatient interventional pain center who received intradiscal culture-expanded MSC injections with or without additional injections to other surrounding vertebral structures. There was no control group. Inclusion required patients to have discogenic LBP with or without radiculopathy and at least six years of completed follow-up data. Outcomes were assessed using Numeric Rating Scale (NRS), Functional Rating Index (FRI), and modified Single Assessment Numeric Evaluation (SANE) scores at multiple time points up to 10 years post treatment. Results: Thirteen patients met the inclusion criteria. Significant reductions in NRS and FRI scores were observed at 6 months, 3 years, and 6 years (p < 0.01). At 6 years, the average NRS score decreased by 2.50 points, FRI by 24.14 points, and SANE showed a 60% improvement. At 10 years, among the seven patients who responded, average SANE improvement was 78.1%. No adverse events were reported. Conclusions: This study presents the longest known follow-up data for intradiscal MSC therapy for discogenic LBP, demonstrating sustained improvements in pain and function. These findings support further investigation into combination orthobiologic therapies as a viable long-term treatment option for chronic LBP. Full article

The Qinghai Lake Basin (QLB), as a key component of the ecological security barrier on the Tibetan Plateau, is crucial for regional sustainable development due to the stability of its alpine agro-pastoral ecosystems. This study aims to systematically analyze the spatiotemporal evolution patterns and underlying driving mechanisms of eco-environmental quality (EEQ) in the QLB from 2001 to 2022. Based on Google Earth Engine (GEE) and long-term MODIS data, we constructed a Remote Sensing Ecological Index (RSEI) model to evaluate the EEQ dynamics. Geodetector (GD) was applied to quantitatively identify key driving factors and their interactions. The findings reveal: (1) The mean RSEI value increased from 0.46 in 2001 to 0.51 in 2022, showing a fluctuating improvement trend with significant transitions toward higher ecological quality grades; (2) spatially, a distinct “high-north-south, low-center” pattern emerged, with excellent-grade areas (4.77%) concentrated in alpine meadows and poor-grade areas (5.10%) mainly in bare rock regions; (3) 47.81% of the region experienced ecological improvement, whereas 16.34% showed degradation, predominantly above 3827 m elevation; and (4) GD analysis indicated natural factors dominated EEQ differentiation, with temperature (q = 0.340) and elevation (q = 0.332) being primary drivers. The interaction between temperature and precipitation (q = 0.593) exerted decisive control on ecological pattern evolution. This study provides an efficient monitoring framework and a spatially explicit governance paradigm for maintaining differentiated management and ecosystem stability in alpine agro-pastoral regions. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

In this paper, we analyse drought variability in Spain (1950–2024) using the Standardised Precipitation–Evapotranspiration Index (SPEI) at 6-, 12-, and 24-month scales. Using 43 long-record meteorological observatories (AEMET), we compute SPEI from quality-controlled (QC), homogenised series, and derive coherent drought regions via clustering and assess trends in the frequency, duration, and intensity of dry episodes (SPEI ≤ −1.5), including seasonality and statistical significance (p < 0.05). Short-term behaviour (SPEI-6) has become more complex in recent decades, with the emergence of a “Catalonia” type and stronger June–October deficits across the northern interior; Mediterranean coasts show smaller or non-significant changes. Long-term behaviour (SPEI-24) is more structural, with increasing persistence and duration over the north-eastern interior and Andalusia–La Mancha, consistent with multi-year drought. Overall, short and long scales converge on rising drought severity and persistence across interior Spain, supporting multi-scale monitoring and region-specific adaptation in agriculture, water resources, and forest management. Key figures are as follows: at 6 months—frequency 0.09/0.08 per decade (Centre–León/Catalonia), duration 0.59/0.50 months per decade, intensity −0.12 to −0.10 SPEI per decade; at 24 months—frequency 0.5 per decade (Cantabrian/NE interior), duration 0.8/0.7/0.4 months per decade (Andalusia–La Mancha/NE interior/Cabo de Gata–Almería), intensity −0.06 SPEI per decade; Mediterranean changes are smaller or non-significant. Full article

Objectives: To examine long-term outcomes and predictors of structural and functional remission in rheumatoid arthritis (RA) after 10-year disease-modifying antirheumatic drug (DMARD) therapy under tight control. Methods: We used real-world cohort data from RA patients who completed 10-year DMARD therapy toward remission or low disease activity based on every-3-month measurements between April 2001 and July 2024. Baseline characteristics, disease control during follow-up, and outcomes after 10 years were examined. Results: Among 204 patients, 76% received biological and/or non-biological targeted DMARDs. Clinical remission, structural remission defined as an increase in modified total Sharp score (mTSS) ≤ 5 per 10 years, and functional remission defined as health assessment questionnaire-disability index (HAQ-DI) ≤ 0.5 were achieved by 68.1%, 73.0%, and 81.4% of patients, respectively. The mean increase (∆) in mTSS was 5.4 for 10 years (∆erosion score, 1.2; ∆joint space narrowing [JSN] score, 4.2), and 28.9% of patients had no structural progression (51% for erosion score and 34.8% for JSN score). Mean HAQ-DI was 0.26. During a 10-year follow-up, 8.8% of patients experienced high or moderate disease activity lasting for ≥12 months and they had a low structural remission rate (11.1%) and functional remission rate (16.6%). According to multivariable logistic regression analysis, baseline mTSS and JNS score (but not erosion score) were strong predictors for structural and functional remission after 10 years. Conclusions: Structural damage progression and functional loss are limited during 10-year tightly controlled DMARD therapy. Compared with bone erosion, JSN appears to be of much higher relevance to structural and functional outcomes. Full article

Background/Objectives: Cardiac implantable electronic device-related infective endocarditis (CIED-RIE) is a serious condition with significant morbidity and mortality. Although recent advances in imaging and therapeutic approaches have improved management, diagnosing and treating CIED-RIE continues to be challenging. This study aimed to identify factors associated with mortality in CIED-RIE patients. Methods: We conducted a retrospective, multicenter international study of adult patients diagnosed with CIED-RIE between January 2014 and June 2024. Data on demographics, clinical presentation, microbiological findings, imaging results, treatment modalities, and outcomes were collected and analyzed to determine predictors of short-term mortality. Results: A total of 197 patients (mean age: 65.3 ± 14.4 years; 75.1% male) were included. The most common device type was permanent pacemaker (48.2%). Staphylococcus species were the predominant pathogens (62.4%). Surgical intervention was performed in 67.5% of patients, and 90-day mortality occurred in 19.3%. Multivariable analysis identified higher Charlson comorbidity index (HR: 1.31), tricuspid valve involvement (HR: 2.35), vegetation size ≥ 10 mm (HR: 2.53), pulmonary embolism (HR: 3.92), and absence of surgical intervention (HR: 2.90) as independent predictors of increased 90-day mortality. Conclusions: Early identification of high-risk patients and prompt multidisciplinary management, including surgical intervention when indicated, are critical to improving outcomes in patients with CIED-RIE. Full article

An experiment with a two interaction modalities (traditional: touch; novel: touch–speech) × two task complexities (low: visual single task; high: audio–visual dual task) within-subjects design was conducted to observe alterations in crew performance (including task performance, subjective workload, and eye responses) in a typical planning task-based on a high-fidelity special vehicle simulation platform. The results revealed that (1) compared to the traditional interaction modality, the novel interaction modality significantly improved task performance, reduced subjective workload, increased mean peak saccade velocity, and decreased fixation entropy; (2) under high task complexity, subjective workload, mean pupil diameter, and the nearest neighbor index showed significant increases, while no significant decline in task performance was observed; (3) no interaction effect of crew performance was observed between interaction modality and task complexity. The foregoing results imply that (1) the novel interaction modality incorporating speech input exhibits advantages over the traditional touch-based modality in terms of enhancing task performance (over 45% improvement) and reducing cognitive workload; (2) leveraging dual-channel audio–visual information processing facilitates the maintenance of task performance under high task complexity and multi-tasking demands; (3) eye movement characteristics may serve as informative indicators for evaluating the benefits of speech-based interaction and the effectiveness of cognitive resource allocation under high-complexity task conditions. The results can provide a basis for the design of the display and control interface in special vehicles. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

Accurate estimation of flow velocities in torrents and steep rivers is essential for flood risk assessment, sediment transport analysis, and the sustainable management of water resources. While deep learning models are increasingly applied to such tasks, their evaluation often depends on statistical metrics that may yield conflicting interpretations. The objective of this study is to clarify how different evaluation metrics influence the interpretation of hydrological deep learning models. We analyze two models of flow velocity prediction in a torrential creek in Taiwan. Although the models differ in architecture, the critical distinction lies in the datasets used: the first model was trained on May–June data, whereas the second model incorporated May–August data. Four performance metrics were examined—root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Willmott’s index of agreement (d), and mean absolute percentage error (MAPE). Quantitatively, the first model attained RMSE = 0.0471 m/s, NSE = 0.519, and MAPE = 7.78%, whereas the second model produced RMSE = 0.0572 m/s, NSE = 0.678, and MAPE = 11.56%. The results reveal a paradox. The first model achieved lower RMSE and MAPE, indicating predictions closer to the observed values, but its NSE fell below the 0.65 threshold often cited by reviewers as grounds for rejection. In contrast, the second model exceeded this NSE threshold and would likely be considered acceptable, despite producing larger errors in absolute terms. This paradox highlights the novelty of the study: model evaluation outcomes can be driven more by data variability and the choice of metric than by model architecture. This underscores the risk of misinterpretation if a single metric is used in isolation. For sustainability-oriented hydrology, robust assessment requires reporting multiple metrics and interpreting them in a balanced manner to support disaster risk reduction, resilient water management, and climate adaptation. Full article

This work presents the development of an intelligent system designed to support the predictive maintenance of the Colombian Navy’s maritime vessels through the estimation of remaining useful life and unsupervised anomaly detection, within the framework of the project called “Colombian Integrated Platform Supervision and Control System” (SISCP-C). This project seeks to guarantee the sustainability of the vessels over time, increase their operational availability, and optimize their life cycle cost, in accordance with the institution’s strategic direction established in the Naval Development Plan 2042. The system provides useful information to the crew, enabling informed decision-making for intelligent and efficient maintenance strategies. To address the limited availability of normal operating data, synthetic data generation techniques with seeding are implemented, including tabular variational autoencoders, conditional tabular generative adversarial networks, and Gaussian copulas. Among these, tabular variational autoencoders achieved the best performance and are used to generate synthetic datasets under normal conditions for the Wärtsilä 6L26 diesel engine (manufactured by Wärtsilä Italia S.p.A., Trieste, Italy). These datasets are used to train several unsupervised anomaly detection models, including one-class support vector machines, classical autoencoders, and long short-term memory-based autoencoders. The long short-term memory autoencoders outperformed the others in terms of detection metrics. Dedicated multivariate long short-term memory autoencoders are subsequently trained for each engine subsystem. By calculating the mean absolute error of the reconstructions, a subsystem-specific health index is computed, which is used to estimate the remaining useful life. Full article

The swift advancement of the digital economy presents new pathways toward achieving common prosperity in China. Based on microdata derived from the China Family Panel Studies (2010–2022), this study employs the “Broadband China” pilot policy as a quasi-natural experiment to explore how digital economy development influences multidimensional relative poverty. We develop a multidimensional relative poverty index encompassing economic, health, education, and living condition aspects utilizing the Alkire–Foster dual cutoff method and employ a staggered Difference-in-Differences design for empirical analysis. Results show that the policy leads to an average decrease of 1.8 percentage points in the probability of multidimensional relative poverty across households. The effect is more pronounced in central and western regions, rural households, and those with a high proportion of non-labor force, particularly in the dimensions of economic, health, and living conditions dimensions. Mechanism analysis via interaction term regression indicates that increased population mobility and improved informal employment are key channels. These findings suggest that enhancing digital infrastructure and tailoring mobility and employment policies to fit regional and urban–rural contexts can effectively alleviate multidimensional relative poverty. This study contributes empirical evidence connecting the advancement of the digital economy to poverty alleviation and aligns with the United Nations Sustainable Development Goal 1 (No Poverty). Full article

Prosopis cineraria (L.) Druce is a keystone tree species in the arid and semi-arid regions of West and South Asia, with critical ecological, cultural, and conservation significance. In the United Arab Emirates (UAE) and other regions of the Arabian Peninsula, this beneficial tree is called Ghaf. Despite its importance, genomic resources and population-level diversity data for the tree remain limited. Here, we present the first comprehensive population genomics study of Ghaf based on whole-genome resequencing of 204 individual trees collected across the UAE. Following Single-Nucleotide Polymorphism (SNP) discovery and stringent filtering, we analyzed 57,183 high-quality LD-pruned SNPs to assess population structure, diversity, and gene flow. Principal component analysis (PCA), sparse non-negative matrix factorization (sNMF), and discriminant analysis of principal components (DAPC) revealed four well-defined genetic clusters, broadly corresponding to geographic origins. The genetic diversity varied significantly among the groups, with observed heterozygosity (Ho), inbreeding coefficients (F), and nucleotide diversity ($\pi$) showing strong population-specific trends. Genome-wide fixation index F_ST scans identified multiple highly differentiated genomic regions, enriched for genes involved in stress response, transport, and signaling. Functional enrichment using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam annotations indicated overrepresentation of protein kinase activity, ATP binding, and hormone signaling pathways. TreeMix analysis revealed gene flow into one of the genetic clusters from both others, suggesting historical admixture and geographic connectivity. This work provides foundational insights into the population genomic profile of P. cineraria, supporting conservation planning, restoration strategies, and long-term genetic monitoring in arid ecosystems. Full article