Search Results (8,320)

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

In light of the increasing focus on global climate change and environmental issues, countries around the world are collaboratively working towards the establishment of a low-carbon economy (LCE). As the most populous developing nation, China is proactively advocating for low-carbon economic development as a means to achieve sustainable growth. Nevertheless, the efficiency of the low-carbon economy (LCEE) exhibits considerable variation across different regions within China. This article seeks to explore the regional disparities in LCEE throughout the country and to identify the factors that contribute to these variations. Firstly, this paper examines the advancements in LCEE research, concentrating on an analysis of 30 Chinese provinces. Employing the Multi-directional Efficiency Analysis (MEA) framework alongside the global Malmquist (GM) index, this study evaluates the efficiency of the low-carbon economy across the 30 provinces from 2010 to 2021. Secondly, by integrating spatial autocorrelation analysis techniques, the research encompasses a multifaceted examination, including spatiotemporal analysis, regional disparities, driving factors, and potential for improvement. The findings indicate significant discrepancies in LCEE among various provinces in China. Notably, LCEE tends to be higher in the eastern coastal regions, attributed to their advanced economic development, whereas the western inland areas generally exhibit lower efficiency levels due to comparatively limited economic progress. Thirdly, LCEE exhibits significant spatial heterogeneity, with clear high–high and low–low clustering patterns, revealing systemic coordination gaps between eastern coastal and central/western regions. Fourthly, from the decomposition results of the global Malmquist index, it can be seen that efficiency change (EC) is less than 1 and technology change (TC) is greater than 1, which promotes the improvement of LCEE. Technical efficiency is the main factor affecting the improvement of LCEE. Full article

Rhizoctonia solani root rot (RSRR) is a major disease that significantly reduces soybean yields, causing substantial economic losses to global soybean production. To elucidate the genetic basis of RSRR resistance, 310 soybean germplasm accessions were evaluated using the disease severity index (DSI) following inoculation with R. solani. Among these accessions, 46.13% were susceptible, and only 2.26% exhibited high resistance. Utilizing resequencing data consisting of 738,561 Single Nucleotide Polymorphism (SNP) loci, a genome-wide association study (GWAS) was performed by integrating both general linear model (GLM) and mixed linear model (MLM) approaches, resulting in the identification of 21 SNPs significantly associated with resistance on chromosomes 3, 13, 15, 16, 17, and 18, and six candidate genes. RT-qPCR expression analysis revealed that four genes, including Glyma.03G166300, Glyma.03G168100, Glyma.13G212700, and Glyma.13G212300, were significantly upregulated in resistant genotypes after inoculation. Furthermore, Cleaved Amplified Polymorphic Sequences (CAPS) and Kompetitive Allele Specific PCR (KASP) molecular markers were successfully developed based on the RSRR-associated SNPs S3_38086892, S3_38247290, and S13_32595026, providing effective tools for marker-assisted selection (MAS). The findings strengthen our genetic knowledge concerning RSRR resistance and contribute to the molecular breeding of resistant soybean cultivars. Full article

Objectives: Adolescence is a pivotal stage of development characterized by significant physical, psychological, and social changes. Establishing healthy lifestyle habits during this period is crucial for long-term health and the prevention of chronic diseases. Despite this, global trends show a marked decline in physical activity among adolescents, particularly girls, who are more susceptible to sedentary behaviors. One potential site for intervention to eliminate physical inactivity at the population level is the school educational setting during childhood. Traditional school-based physical exercise programs often adopt a one-size-fits-all approach, which may not address the diverse needs and interests of students, leading to reduced motivation and participation. Personalized physical exercise programs, tailored to individual capabilities and preferences, offer a promising alternative to enhance physical fitness and foster lifelong engagement in physical activity. Methods: A total of 170 Hungarian high school girls (mean age ≈ 15.3 years) were randomly assigned to either a personalized physical exercise group or a control group following the standard curriculum. The intervention spanned two academic years and consisted of five traditional gym classes per week (control group) or three traditional and two individually tailored classes with cardiorespiratory and resistance training per week (intervention group), each lasting 45–60 min. Individual goals were set based on baseline assessments, emphasizing self-referenced progress. Results: The personalized physical exercise group showed significant improvements in body mass index (BMI), body fat percentage, maximum oxygen uptake capacity (VO₂max), muscular strength, and flexibility (p < 0.05), while the control group exhibited minimal or negative changes. Conclusions: The personalized physical exercise program has been shown to be more effective in achieving higher cardiorespiratory performance and favorable body composition among adolescent girls than a traditional school physical education class, highlighting its potential role in school settings. Full article

To promote sustainable development and guide the responsible use of natural ecosystems, the United Nations introduced the concept of ecosystem accounting. Ecosystem services are key components of ecosystem accounting. Water-related ecosystem services (ES) are of primary importance for Armenia due to relatively dry climate, and dependence on irrigation water for agriculture. This study aims to conduct a pilot-level quantitative scoping assessment and mapping of key water-related regulating ES in accordance with the SEEA-EA guidelines, and to offer recommendations to initiate their accounting in Armenia. We used three Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models—Seasonal Water Yield, Sediment Delivery Ratio, and Urban Flood Risk Mitigation. Input data for these models were sourced from global and national databases, as well as ESRI land cover datasets for 2017 and 2023. Government-reported data on river flow and water consumption were used to assess the ES supply–use balance. The results show that natural ecosystems contribute between 11% and 96% of the modeled ES, with the strongest impact on baseflow supply and erosion prevention. The average current erosion is estimated at 2.3 t/ha/year, and avoided erosion at 46.4 t/ha/year. Ecosystems provide 93% of baseflow, with an average baseflow index of 34%, while on bare ground it is only 3%. Changes in land cover from 2017 to 2023 have resulted in alterations across all assessed ES. Comparison of total water flow and baseflow with water consumption revealed water-deficient provinces. InVEST models show their general operability at the scoping phase of ecosystem accounting planning. Advancing ES accounting in Armenia requires model calibration and validation using local data, along with the integration of InVEST and hydrological and meteorological models to account for the high diversity of natural conditions in Armenia, including terrain, geological structure, soil types, and regional climatic differences. Full article

The Hunga-Tonga volcanic eruption on 15 January 2022, produced significant atmospheric and ionospheric disturbances that may degrade global navigation satellite system (GNSS) and precise point positioning (PPP) accuracy. Using data from the GEONET GNSS network and Soratena barometric pressure sensors across Japan, we analyzed the eruption’s effects through the gradient ionospheric index (GIX) and the rate of TEC index (ROTI) to characterize the propagation and effects of these disturbances on ionospheric total electron content (TEC) gradients. Our analysis identified two separate ionospheric disturbance events. The first event, coinciding with the arrival of atmospheric Lamb waves, was characterized by wave-like pressure anomalies, differential TEC (dTEC) fluctuations, and modest horizontal gradients of vertical TEC (VTEC). In contrast, the second, more pronounced disturbance was driven by equatorial plasma bubbles (EPBs), which generated severe ionospheric irregularities and large TEC gradients. Further analysis revealed that these two disturbances had markedly different impacts on GNSS positioning accuracy. The Lamb wave–induced disturbance mainly caused moderate TEC fluctuations with limited effects on positioning accuracy, and mid-latitude stations maintained both average and 95th percentile positioning ($\mathrm{p}\mathrm{p}\mathrm{p},{\mathrm{P}}_{95}$) errors below 0.1 m throughout the event. In contrast, the EPB-driven disturbance had a substantial impact on low-latitude regions, where the average horizontal PPP error peaked at 0.5 m and the horizontal and vertical $\mathrm{p}\mathrm{p}\mathrm{p},{\mathrm{P}}_{95}$ errors exceeded 1 m. Our findings reveal two episodes of spatial-gradient enhancement and successfully estimate the propagation speed and direction of the Lamb waves, supporting the potential application of ionospheric gradient monitoring in forecasting GNSS performance degradation. Full article

This study investigates long-term changes in thermal comfort across the Italian and Iberian Peninsulas from 1940 to 2024, using the Universal Thermal Climate Index (UTCI) derived from ERA5-HEAT reanalysis. We apply a dual analytical framework combining structural break detection to identify regime shifts and Sen’s slope estimation with confidence intervals to quantify monotonic trends. Results reveal pronounced seasonal asymmetries. Summer exhibits abrupt regime shifts in both regions: in 1980 for Italy (slope shifting from −0.039 °C/year before 1980 to +0.06 °C/year after) and 1978 for Iberia (from −0.054 °C/year to +0.050 °C/year). Winter, by contrast, shows no structural breaks but a persistent, spatially uniform warming trend of ~0.030–0.033 °C/year across the 1940–2024 period, consistent with a gradual erosion of cold stress. Transitional seasons display more nuanced responses. Spring reveals detectable breakpoints in 1987 for Italy (shifting from −0.028 °C/year to +0.027 °C/year) and 1986 for Iberia (from −0.047 °C/year to +0.024 °C/year), indicating the early acceleration of warming. Autumn shows a breakpoint in 1970 for Italy, with trends intensifying from +0.011 °C/year before to +0.052 °C/year after, while Iberia exhibits no clear breakpoint but a consistent positive slope. These findings highlight spring as an early-warning season, where warming acceleration first emerges, and autumn as a consolidating phase that extends summer-like heat into later months. Overall, the results demonstrate that Mediterranean thermal regimes evolve through both abrupt and gradual processes, with summer defined by non-linear regime shifts, winter by steady accumulation of warming, and spring and autumn by transitional dynamics that bridge these extremes. The methodological integration of breakpoint detection with Sen’s slope estimation provides a transferable framework for detecting climate regime transitions in other vulnerable regions under accelerated global warming. Full article

This study examined the sustainability of global table grape exports from 2020 to 2024, focusing on two key dimensions: market diversification and international competitiveness. Using data from Trade Map and applying the Herfindahl–Hirschman Index (HHI) and the Revealed Comparative Advantage Normalized Index (RCAN), the research analyzed the export performance of major grape-exporting countries, including Peru, Chile, the Netherlands, Italy, the United States, South Africa, and China. The results showed significant differences in both market structure and competitive positioning. Countries like Peru and South Africa demonstrated rapid export growth and high competitiveness in certain markets, but faced elevated levels of market concentration, exposing them to external shocks. In contrast, Italy and the Netherlands maintained more diversified portfolios but showed modest competitiveness. The study concluded that no country achieved an ideal balance between diversification and competitiveness. As a result, it is recommended that governments pursue integrated trade strategies that promote geographic expansion alongside measures to enhance export competitiveness. Investments in logistics, quality certifications, and market intelligence are essential to reduce vulnerability and ensure long-term export sustainability. Full article

This paper provides a comprehensive bibliographic and bibliometric review of water, sanitation, and hygiene (WASH) in global urban areas, employing the Proknow-C methodology. The study categorizes WASH into four main themes: sustainability, urban areas, indicators and index, and urban planning, allowing for a detailed analysis of several multidimensional aspects. The review underscores the importance of providing basic infrastructure to adopt an integrated, sustainable, and socially inclusive approach, showcasing the resilience and adaptability of the WASH sector in tackling the dynamic challenges of urbanization. It is noticeable that the WASH area has undergone significant development, moving from a focus primarily on infrastructure to a more holistic approach. In general, the WASH framework is globally characterized by high irregularity/inequality in provision and access. The relationship between urban vulnerabilities and WASH is very clear, but also multifaceted and complex, and there is a crucial need to combine behavior change with infrastructure development while addressing economic challenges and prioritizing investments in WASH. The improvement of WASH conditions in urban areas should focus the interplay between urban development policies and the provision of WASH services, while focusing also on the role of multi-sectoral collaboration, stakeholder engagement, and policy implementation in overcoming barriers to effective WASH delivery. Full article

With the rapid development of edge computing and deep learning, the efficient deployment of deep neural networks (DNNs) on resource-constrained terminal devices faces multiple challenges (background), such as execution delay, high energy consumption, and resource allocation costs. This study proposes an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for PSO. Unlike the conventional PSO, our approach integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy to overcome its limitations in this application scenario. First, an end–edge–cloud collaborative computing framework is constructed. Within this framework, a multi-objective optimization model is established, aiming to minimize time delay, energy consumption, and cloud configuration cost. To solve this model, an optimization method is designed that integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy into the MOPSO algorithm. Experiments on six types of DNNs, including the Visual Geometry Group (VGG) series, have shown that this algorithm reduces execution time by an average of 58.6%, the average energy consumption by 61.8%, and optimizes cloud configuration costs by 36.1% compared to traditional offloading strategies. Its Global Search Capability Index (GSCI) reaches 92.3%, which is 42.6% higher than the standard PSO algorithm. This method provides an efficient, secure, and stable cooperative computing solution for multi-constraint task unloading in an edge computing environment. Full article

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements—up to an 8128-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors. Full article

This research presents a fully automated two-step method for segmenting rice seedlings and assessing their health by integrating spectral, morphological, and textural features. Driven by the global need for increased food production, the proposed method enhances monitoring and control in agricultural processes. Seedling locations are first identified by the excess green minus excess red index, which enables automated point-prompt inputs for the segment anything model to achieve precise segmentation and masking. Morphological features are extracted from the generated masks, while spectral and textural features are derived from corresponding red–green–blue imagery. Health assessment is conducted through anomaly detection using a one-class support vector machine, which identifies seedlings exhibiting abnormal morphology or spectral signatures suggesting stress. The proposed method is validated by visual inspection and Silhouette score, confirming effective separation of anomalies. For segmentation, the proposed method achieved mean dice scores ranging from 72.6 to 94.7. For plant health assessment, silhouette scores ranged from 0.31 to 0.44 across both datasets and various growth stages. Applied across three consecutive rice growth stages, the framework facilitates temporal monitoring of seedling health. The findings highlight the potential of advanced segmentation and anomaly detection techniques to support timely interventions, such as pruning or replacing unhealthy seedlings, to optimize crop yield. Full article

Lunar digital elevation models (DEMs) are a fundamental data source for lunar research and exploration. However, high-resolution DEM products for the Moon are only available in some local areas, which makes it difficult to meet the needs of scientific research and missions. To this end, we have previously developed a deep learning-based method (LDEMGAN1.0) for single-image lunar DEM reconstruction. To address issues such as loss of detail in LDEMGAN1.0, this study leverages the inherent structural self-similarity of different DEM data from the same lunar terrain and proposes an improved version, named LDEMGAN2.0. During the training process, the model computes the self-similarity graph (SSG) between the outputs of the LDEMGAN2.0 generator and the ground truth, and incorporates the self-similarity loss (SSL) constraint into the network generator loss to guide DEM reconstruction. This improves the network’s capacity to capture both local and global terrain structures. Using the LROC NAC DTM product (2 m/pixel) as the ground truth, experiments were conducted in the Apollo 11 landing area. The proposed LDEMGAN2.0 achieved mean absolute error (MAE) of 1.49 m, root mean square error (RMSE) of 2.01 m, and structural similarity index measure (SSIM) of 0.86, which is 46.0%, 33.4%, and 11.6% higher than that of LDEMGAN1.0. Both qualitative and quantitative evaluations demonstrate that LDEMGAN2.0 enhances detail recovery and reduces reconstruction artifacts. Full article

Resonant acoustic technology utilizes low-frequency vertical harmonic vibrations to induce full-field mixing effects in processed materials, and it is regarded as a “disruptive technology in the field of energetic materials”. Although numerous scholars have investigated the mechanisms of resonant acoustic mixing, there remains a lack of parameter selection methods for improving product quality and production efficiency in engineering practice. To address this issue, this study employs phase-field modeling and fluid–structure coupling methods to numerically simulate the transport process of glycerol during resonant acoustic mixing. The research reveals the mass transfer mechanism within the flow field, establishes a liquid-phase distribution index for quantitatively characterizing mixing effectiveness, and clarifies the enhancement effect of fluid transport on solid particle mixing through particle tracking methods. Furthermore, parameter studies on vibration frequency and amplitude were conducted, yielding a critical curve for guiding parameter selection in engineering applications. The results demonstrate that Faraday instability first occurs at the fluid surface, generating Faraday waves that drive large-scale vortices for global mass transfer, followed by localized mixing through small-scale vortices. The transport process of glycerol during resonant acoustic mixing comprises three distinct stages: stable Faraday wave oscillation, rapid mass transfer during flow field destabilization, and localized mixing upon stabilization. Additionally, increasing either vibration frequency or amplitude effectively enhances both the rate and effectiveness of mass transfer. These findings offer theoretical guidance for optimizing process parameters in resonant acoustic mixing applications. Full article

Background: Tuberculosis (TB) remains a global health priority, with current interventions like the Bacille Calmette–Guérin (BCG) vaccine lacking efficacy against latent infection and drug-resistant strains. Novel vaccines targeting both latent and active TB are urgently needed. Objective: This study aims to design a multi-epitope vaccine (MEV) and evaluate its immunogenicity, structural stability, and interactions with toll-like receptor 2/4 (TLR-2/4) via computational biology approaches. Methods: We designed MEV using bioinformatics tools, prioritizing immunodominant epitopes from Mycobacterium tuberculosis antigens. Structural stability was optimized through disulfide engineering, and molecular docking/dynamics simulations were used to analyze interactions and conformational dynamics with TLR-2/4. Antigenicity, immunogenicity, population coverage, and immune responses were computationally assessed. Results: The MEV candidate, CP91110P, exhibited 86.18% predicted global human leukocyte antigen (HLA)-I/II coverage, high antigenicity (VaxiJen: 0.8789), and immunogenicity (IEDB: 4.40091), with favorable stability (instability index: 33.48) and solubility (0.485). Tertiary structure analysis indicated that 98.34% residues were located in favored regions. Molecular docking suggested strong TLR-2 (−1535.9 kcal/mol) and TLR-4 (−1672.5 kcal/mol) binding. Molecular dynamics simulations indicated stable TLR-2 interactions (RMSD: 6–8 Å; Rg: 38.50–39.50 Å) and flexible TLR-4 binding (RMSD: 2–6 Å; Rg: 33–36 Å). Principal component analysis, free energy landscapes, and dynamic cross-correlation matrix analyses highlighted TLR-2’s structural coherence versus TLR-4’s adaptive flexibility. Immune simulations predicted potential robust natural killer cell activation, T helper 1 polarization (interferon-gamma/interleukin-2 dominance), and elevated IgM/IgG levels. Conclusions: CP91110P is predicted to stably bind to TLR-2 and flexibly interact with TLR-4, with prediction of its high antigenicity and broad coverage across immune populations. However, this conclusion requires confirmation through experimental validation. Therefore, it may provide a promising candidate for experimental validation in the development of tuberculosis vaccines. Full article