Search Results (2,962)

A double-cavity vertical-cavity surface-emitting laser (VCSEL) can effectively suppress high-order transverse modes and achieve a high side-mode suppression ratio (SMSR). However, the double cavity also results in increased fundamental mode loss, reducing output power. In this study, both p-type and n-type oxide layers were simultaneously incorporated into a double-cavity VCSEL and the structure was numerically simulated using Pics3D (2024) software. The simulation results indicate that this approach can significantly enhance the output power, strengthen the single-transverse-mode characteristic, and thus improve the side-mode suppression ratio (SMSR). Generally, as the number of oxide layers increases, their ability to confine the optical field also enhances, trapping more high-order transverse modes within the oxide aperture, leading to a decrease in SMSR. However, in this study, the introduction of an n-type layer resulted in an abnormal increase in the SMSR, because the n-type oxide layer is situated between the active region and the second cavity. When the optical field oscillates between these two regions, some high-order transverse modes are blocked by the n-type oxide holes and cannot participate in mode competition, thereby increasing the SMSR. Full article

In parametric statistics, it is well established that the canonical measures of estimator performance—such as bias, variance, and mean squared error—are inherently dependent on the parameterization of the model. Consequently, these quantities describe the behavior of an estimator only relative to a particular parameterization, rather than representing intrinsic properties of either the estimator itself or the underlying probability distribution it seeks to estimate. Some years ago, the authors introduced a framework, termed the intrinsic analysis of point estimation, in which tools from information geometry were employed to construct analogues of classical statistical notions that are intrinsic to both the estimator and the associated probability measure. Within this framework, a contravariant vector field was introduced to define the intrinsic bias, while the squared Riemannian distance naturally emerged as the intrinsic analogue of the classical squared distance. Intrinsic counterparts of the Cramér–Rao inequalities, as well as the Rao–Blackwell and Lehmann–Scheffé theorems, were also established. The present work extends the intrinsic analysis—originally founded on the concept of intrinsic risk, a fundamentally local measure of estimator performance—to an approach that characterizes the estimator over an entire region of the parameter space, thereby yielding an intrinsically global perspective. Building upon intrinsic risk, two indices are proposed to evaluate estimator performance within a bounded region: (i) the integral of the intrinsic risk with respect to the Riemannian volume over the specified region, and (ii) the maximum intrinsic risk attained within that region. The Riemannian volume induced by the Fisher information metric on the manifold associated with the parametric model provides a natural means of averaging the intrinsic risk. Using variational methods, integral inequalities of the Cramér–Rao type are derived for the mean squared integrated Rao distance of the estimators, thereby extending previous contributions by several authors. Furthermore, lower bounds for the maximum intrinsic risk are obtained through corresponding integral formulations. Full article

The combined application of straw biochar and nitrogen fertilizer is an increasingly studied strategy to enhance soil fertility and crop yield. Optimizing the biochar-nitrogen interaction could be a choice for increasing nitrogen use efficiency (NUE) and reducing nitrogen loss in dryland agriculture. However, the mechanisms by which it regulates nitrogen allocation and absorption in foxtail millet (Setaria italica) are still limited in terms of mechanical understanding. Based on preliminary experiments, the optimal biochar-nitrogen interaction for soil nutrient absorption was identified. A field experiment was conducted with six treatments in an arid region of northwestern China: N1C1 (N1: 130 kg ha⁻¹ + C1: 100 kg ha⁻¹, control group), N2C4 (N2: 195 kg ha⁻¹ + C4: 250 kg ha⁻¹), N3C1 (N3: 260 kg ha⁻¹ + C1: 100 kg ha⁻¹), N3C2 (N3: 260 kg ha⁻¹ + C2: 150 kg ha⁻¹), N3C3 (N3: 260 kg ha⁻¹ + C3: 200 kg ha⁻¹), and N3C4 (N3: 260 kg ha⁻¹ + C4: 250 kg ha⁻¹). The results demonstrated that the biochar–nitrogen ratio significantly influenced topsoil total nitrogen, microbial biomass carbon (SMBC), and microbial biomass nitrogen (SMBN). All biochar-to-nitrogen combinations sharply increased soil total nitrogen by 133.11–151.52% compared to pre-sowing levels, providing a fundamental base for microbial-driven nitrogen transformation. Low nitrogen addition is more conducive to biomass accumulation, with N2C4 significantly increasing by 62.82%. Although a high biochar-to-nitrogen ratio reduced leaf relative chlorophyll content (SPAD) by 5.72–16.18% and net photosynthetic rate (Pn) by 16.09–52.65% at the heading stage, these did not compromise final yield. Importantly, N2C4, N3C1, and N3C4 significantly increased spike ¹⁵N abundance by 71.45%, 13.21%, and 19.43%, respectively. N2C4 grain production increases by 53.77–110.57% in two years and was positively correlated with spike ¹⁵N abundance, reflecting high nitrogen partial factor productivity. In conclusion, a reasonable biochar-nitrogen interaction enhances nitrogen allocation and grain yield by stimulating microbial activity and strengthening soil–plant synergy, the certified strategy effectively supports sustainable dryland agriculture by simultaneously increasing productivity and improving soil health. Full article

The rapid advancement of energy collection and storage systems (ECSSs) is fundamentally reshaping global energy markets and accelerating the transition toward low-carbon energy systems. This study provides a comprehensive assessment of the economic benefits and systemic effects of advanced ECSS technologies, including photovoltaic-thermal (PV/T) hybrid systems, advanced batteries, hydrogen-based storage, and thermal energy storage (TES). Through a mixed-methods approach combining techno-economic analysis, macroeconomic modeling, and policy review, we evaluate the cost trajectories, performance indicators, and deployment impacts of these technologies across major economies. The paper also introduces a novel economic-mathematical model to quantify the long-term macroeconomic benefits of large-scale ECSS deployment, including GDP growth, job creation, and import substitution effects. Our results indicate significant cost reductions for ECSS by 2050, with battery storage costs projected to fall below USD 50 per kilowatt-hour (kWh) and green hydrogen production reaching as low as USD 1.2 per kilogram. Large-scale ECSS deployment was found to reduce electricity costs by up to 12%, lower fossil fuel imports by up to 25%, and generate substantial GDP growth and job creation, particularly in regions with supportive policy frameworks. Comparative cross-country analysis highlighted regional differences in economic effects, with the European Union, China, and the United States demonstrating the highest economic gains from ECSS adoption. The study also identified key challenges, including high capital costs, material supply risks, and regulatory barriers, emphasizing the need for integrated policies to accelerate ECSS deployment. These findings provide valuable insights for policymakers, industry stakeholders, and researchers aiming to design effective strategies for enhancing energy security, economic resilience, and environmental sustainability through advanced energy storage technologies. Full article

Using a method inspired by quantum principles, this study estimates the composition of various types of tax contributions expected from foreign trade operations. The estimation approach is proposed considering the superposition of expectations and disturbances—fundamental elements of quantum methods—that add complexity to the forecasts of tax collections. For instance, the contributions of international trade-related taxes may be determined not only by the country’s degree of regional integration but also by the composition of tax revenue that depends on the kind and use of merchandise. Using the case of Mexico’s imports, the methodology illustrates how the expectations of collecting certain taxes—like the General Import Tariff (GIT) and the Value Added Tax (VAT)—would be impacted by fluctuations in others—such as the Special Tax on Production and Services (STPS). The hypothesis of this study is that, through the proposed quantum-inspired methodology, it is possible to establish future scenarios of tax revenue compositions while maintaining fiscal consistency by anticipating potential outcomes in the adjustments of contributions if the recently proposed fiscal reform is approved by the Mexican Government. This work contributes to the academic literature on public finance management by advancing a methodology that can support the strategic formulation of fiscal expectations and policy. Full article

The Shenzhen–Zhongshan Link is a key cross-sea corridor in the Guangdong–Hong Kong–Macao Greater Bay Area. As a representative ultra-wide cross-section undersea tunnel, it exhibits smoke spread behaviors that differ fundamentally from those of traditional road tunnels. In particular, the radial flow region of fire smoke is more pronounced, resulting in substantial lateral variations in smoke dynamics parameters. These characteristics render classical one-dimensional ceiling jet temperature rise theories insufficient for capturing the multidimensional thermal behavior in such geometries. In this study, the immersed-tunnel section of the Shenzhen–Zhongshan Link was investigated through a combination of full-scale fire experiments and Fire Dynamics Simulator (FDS) simulations. The longitudinal attenuation and lateral distribution characteristics of hot smoke temperature rise during spread in an ultra-wide tunnel were systematically obtained. Based on a simplified one-dimensional ceiling jet concept, differences in hot smoke diffusion distance were employed to characterize the lateral temperature rise ratio at any longitudinal location, from which a lateral distribution model was developed. The classical one-dimensional average temperature rise decay model was further reformulated to derive a modified longitudinal decay model applicable to the tunnel centerline of ultra-wide cross-sections. By integrating these characteristic models, a two-dimensional longitudinal prediction framework for hot smoke temperature rise in ultra-wide tunnels was established. Validation against full-scale fire experiments demonstrates that the proposed model can predict the two-dimensional thermal field with an accuracy within 25%. The findings of this study provide a theoretical basis for fire scenario reconstruction in the Shenzhen–Zhongshan undersea tunnel and offer a technical foundation for optimizing emergency ventilation strategies during fire incidents. Full article

The construction and operation of reservoirs have made hydrological processes complex, posing challenges to flood modeling. While many hydrological models have incorporated reservoir operation schemes to improve discharge estimation, the influence of reservoir representation on model calibration has not been sufficiently evaluated—an issue that fundamentally affects the spatial reliability of distributed modeling. Additionally, the limited availability of reservoir regulation data impedes dam-inclusive flood simulation. To overcome these limitations, this study proposes a synergistic modeling framework for data-scarce dammed basins. It integrates a satellite-based reservoir operation scheme into a distributed hydrological model and incorporates reservoir processes into the model calibration procedure. The framework was tested using the coupled version of the DRIVE flood model (DRIVE-Dam) in the Nandu River Basin, southern China. Two calibration configurations, with and without dam operation (CWD vs. CWOD), were compared. Results show that reservoir dynamics were effectively reconstructed by combining satellite altimetry with FABDEM topography, successfully supporting the development of the reservoir scheme. Multi-site comparisons indicate that, while CWD slightly improved streamflow estimation (NSE and KGE > 0.75, similar to CWOD) on the calibrated outlet gauge, it enhanced basin-internal process representation, as evidenced by the superior peak discharge and flood event capture with reduced bias, boosting flood detection probability from 0.54 to 0.60 and reducing false alarms from 0.28 to 0.15. The improvements stem from refined parameterization enabled by a physically complete model structure. In contrast, CWOD leads to subdued flood impulses and prolonged recession due to spurious parameters that distort baseflow and runoff response. The proposed methodology provides a practical reference for flood forecasting in dam-regulated basins, demonstrating that reservoir representation enhances model parameterization and underscoring the strong potential of satellite observations for hydrological modeling in data-limited regions. Full article

In semi-arid areas like Southeastern Anatolia, where agricultural productivity and water supply are extremely climate-sensitive, drought is a significant environmental and socioeconomic problem. Comprehensive assessment of drought and soil moisture dynamics is fundamental to sustainable agriculture and water security in semi-arid regions. This study analyzes drought patterns across seven provinces in the Southeastern Anatolia (GAP) region of Türkiye (Adıyaman, Diyarbakır, Gaziantep, Kilis, Mardin, Siirt, and Şanlıurfa) from 1963 to 2022, employing four drought indices (SPI, SPEI, CZI, and RDI) at multiple timescales (1-, 3-, and 12-month) to support evidence-based strategies for sustainable water and agricultural resource management. A more thorough evaluation is made possible by this multi-index and multi-scale method, which is rarely used concurrently at the provincial level. Additionally, the drought characterization was validated and enhanced through the analysis of ERA5-Land soil moisture data (1950–2022). According to the findings, the provinces with the lowest median index values and the highest frequency of extreme drought episodes are Diyarbakır and Şanlıurfa. The SPEI-12 (THW) median values showed a neutral long-term drought–wetness balance with seasonal changes, ranging from −0.0714 (Adıyaman) to 0.188 (Şanlıurfa). Particularly after 2009, soil moisture levels decreased to as low as 2–3 mm during the summer, indicating heightened evapotranspiration stress. RDI-12’s reliability in long-term drought evaluation was confirmed by its strongest correlation with other indices (r = 0.87–0.97). According to spatial research, the frequency of moderate droughts in the southwest was as high as 39%, whilst the eastern provinces experienced severe and intense droughts as high as 8%. However, with frequency above 53%, wet occurrences were more common in the east, particularly in Siirt. By clarifying long-term drought and soil moisture patterns, this study provides essential insights for sustainable irrigation planning and agricultural water allocation in the GAP region. Full article

As the digital economy becomes increasingly integrated with the real economy, agricultural production is experiencing fundamental transformation. Digital–real integration has emerged as strategically important for cultivating agricultural new quality productive forces and safeguarding national food security. This study examines provincial panel data from 13 major grain-producing regions in China between 2012 and 2023. We develop an evaluation index system to assess both digital–real integration and agricultural new quality productive forces. Using the entropy weight method, we quantify the development levels of these two dimensions. Our empirical analysis employs fixed effects models, mediation effect models, and spatial econometric approaches to investigate how digital–real integration influences agricultural new quality productive forces in major grain-producing regions. The research findings indicate the following: (1) Digital–real integration demonstrates a robust positive correlation with agricultural new quality productive forces in major grain-producing regions. (2) Both agricultural industrial structure upgrading and agricultural green total factor productivity serve as significant mediating channels through which digital–real integration enhances agricultural new quality productive forces. (3) The impact exhibits notable heterogeneity across three dimensions: regional characteristics, industrial structure levels, and fiscal decentralization levels. (4) Digital–real integration generates substantial positive spatial spillover effects on agricultural new quality productive forces, facilitating coordinated improvements in neighboring regions. (5) A significant threshold effect exists in how digital–real integration promotes agricultural new quality productive forces. Specifically, the promotional effect intensifies once innovation level and human capital level exceed certain critical thresholds. These findings offer both theoretical insights and practical guidance for advancing high-quality development in agriculture within major grain-producing regions while strengthening the national food security strategy. Full article

Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and effective methodology for short-term hourly peak load forecasting that is both simple and highly accurate. The methodology is based on the load forecast used for electricity market purposes, together with fine-tuning dynamic estimation. As a case study, the methodology was applied and tested in Mexico’s interconnected power system. It was implemented across various regions and at both regional and load-\ zone levels of this interconnected power system and, even under a variety of standard and extreme load conditions, achieved outstanding results. Full article

Background/Objective: Accurate biological profile estimation from skeletal remains is fundamental to forensic identification. While the humerus demonstrates considerable sexual dimorphism, population-specific validation data for Thai populations remain limited. This study aimed to develop and independently validate population-specific equations for sex and stature estimation from humeral measurements in Northeastern Thai populations. Methods: This cross-sectional study examined 300 adult humeri (150 male, 150 female) from the Khon Kaen University skeletal collection. Four osteometric measurements (maximum length, midshaft circumference, epicondylar breadth, superior–inferior head diameter) and weight were recorded. The sample was randomly divided into development (n = 200) and validation (n = 100) datasets. Logistic regression for sex estimation and linear regression for stature estimation were developed using stepwise selection. Results: Sex classification achieved 93.5% accuracy in development and 93.0% in independent validation. The optimal model incorporated midshaft circumference, superior–inferior head diameter, and weight, with an area under the curve of 0.977 (95% CI: 0.953–1.000), sensitivity 90.0%, specificity 96.0%, and Cohen’s kappa 0.86. Stature estimation demonstrated a correlation coefficient of 0.81 with a mean absolute error of 4.36 cm (2.74% of the mean stature). Independent validation confirmed minimal performance deterioration for both models. Conclusions: These independently validated, population-specific equations provide accurate and reliable methods for biological profile estimation in Northeastern Thai forensic contexts. The rigorous validation framework supports confident operational application and provides a methodological model for developing regional forensic standards. Full article

Genetic diversity and kinship information of cotton germplasm resources are fundamental to breeding, providing a theoretical basis for the rational selection of hybrid parents and further breeding of new varieties with high yield, high quality, and multi-resistance. This study utilized cotton varieties that have been used for variety improvement or are widely planted in the Northern Xinjiang cotton region as materials. Genotyping was performed using the ZJU CottonSNP40K chip to analyze genetic diversity and kinship relationships. A total of 26,852 high-quality SNP markers were obtained, including 15,222 SNPs in subgenome A and 11,630 SNPs in subgenome D. The number of SNPs per chromosome ranged from 547 (A04) to 2168 (A08). Based on phylogenetic tree and principal component analysis, the 83 materials were clustered into 3 major subgroups. Group I contained varieties introduced from the former Soviet Union and the United States, which have become important parents for cotton breeding in Northern Xinjiang. Among them, as many as 27 varieties were derived and selected from the introduced US variety ‘Beiersinuo’ as a parent. While playing an important role in cotton breeding in Northern Xinjiang, this has also led to the current situation where the genetic base of Northern Xinjiang varieties is relatively narrow (average kinship coefficient 0.72). It clarifies the significant role of introduced American variety ‘Beiersinuo’ in the breeding of Northern Xinjiang cultivars and provides theoretical guidance for broadening the genetic base of Northern Xinjiang cotton varieties. Full article

For multi-robot cooperative coverage, an effective spatial division strategy is essential to ensure balanced and spatially continuous task regions for each robot. Traditional grid-based partitioning approaches like DARP (Divide Areas based on Robots’ Positions) and TASR (Task Allocation based on Spatial Regions) often generate discontinuous sub-regions and imbalanced workloads, particularly in irregular or fragmented task spaces. To mitigate these issues, this paper introduces HGTA (Hexagonal Grid-based Task Allocation), a novel method that employs hexagonal tessellation for environmental representation. The hexagonal grid structure provides uniform neighbor connectivity and minimizes boundary fragmentation, yielding smoother partitions. HGTA integrates a multi-stage wavefront expansion algorithm with an iterative region-correction mechanism, jointly ensuring spatial contiguity and load equilibrium across robots. Extensive evaluations in 2D environments with varying obstacle densities and robot distributions show that HGTA enhances spatial continuity—achieving improvements of 18.2% in connectivity and 17.8% in boundary smoothness over DARP, and 7.5% and 9.5% over TASR, respectively—while also improving workload balance (variance reduction up to 18.5%) without compromising computational efficiency. The core contribution lies in the synergistic coupling of hexagonal tessellation, wavefront expansion, and dynamic correction, a design that fundamentally advances partition smoothness and convergence speed. HGTA thus offers a robust foundation for multi-robot cooperative coverage, area surveillance, and underwater search applications where connected and balanced partitions are critical. Full article

Image inpainting, a pivotal technology for restoring damaged regions of images, has emerged as a significant research focus in computer vision. This review systematically surveys recent advances in deep learning-based image inpainting. We begin by categorizing prevailing methods into three groups based on their core architectures: Convolutional Neural Networks (CNNs), Generative Models, and Transformers. Through a comparative analysis of their symmetric versus asymmetric network architectures, applicable scenarios, and performance bottlenecks, we provide a critical discussion of the strengths and limitations inherent to each approach. The evolution of underlying design principles, such as symmetry, and the corresponding solutions to core challenges are also discussed. Furthermore, we introduce key benchmark datasets and commonly used image quality assessment metrics, offering a multidimensional framework for evaluation. We highlight that mainstream datasets collectively foster a greenhouse-like evaluation environment detached from real-world complexities and that existing metrics are critically misaligned with the fundamental objective of inpainting: generating plausible new content. Finally, we summarize the prevailing challenges in current deep learning-based inpainting research and outline promising future directions. We highlight critical issues, such as enhancing restoration quality, reducing computational costs, and broadening application scenarios, thereby providing valuable insights for subsequent research. Full article

Accurately delineating soil elements from satellite imagery is fundamental for regional geological mapping and survey. However, vegetation cover and complex geomorphological conditions often obscure diagnostic surface information, weakening the visibility of key geological features. Additionally, long-term tectonic deformation and weathering processes reshape the spatial organization of soil elements, resulting in substantial within-class variability, inter-class spectral overlap, and fragmented structural patterns—all of which hinder reliable segmentation performance for conventional deep learning approaches. To mitigate these challenges, this study introduces a Reinforced Feature and Multiscale Feature Fusion Network (RFMFFNet) tailored for semantic interpretation of soil elements. The model incorporates a rectangular calibration attention (RCA) module into a ResNet101 backbone to recalibrate feature responses in critical regions, thereby improving scale adaptability and the preservation of fine geological structures. A complementary multiscale feature fusion (MFF) component is further designed by combining sparse self-attention with pyramid pooling, enabling richer context aggregation while reducing computational redundancy. Comprehensive experiments on the Landsat-8 and Sentinel-2 datasets verify the effectiveness of the proposed framework. RFMFFNet consistently achieves superior segmentation performance compared with several mainstream deep learning models. On the Landsat-8 dataset, the oPA and mIoU increase by 2.4% and 2.6%, respectively; on the Sentinel-2 dataset, the corresponding improvements reach 4.3% and 4.1%. Full article