Search Results (1,788)

This study systematically benchmarks the performance of four single soft magnetic powders—water-atomized Fe–Si–Cr (FeSiCr), silica-coated reduced iron powder (RIP), silica-coated carbonyl iron powder (CIP), and phosphate-coated CIP (CIP-P)—to establish quantitative relationships between powder attributes, deformation substructure, and high-frequency loss for molded power inductors (100 kHz–1 MHz). We prepared toroidal compacts at 200 MPa and characterized them by initial permeability (μ_i), core-loss (P_cv(f)), partitioning (P_cv(f) = K_hf + K_ef², K_h, $K_{\mathrm{e}}$: hysteresis and eddy-current loss coefficients), and EBSD (electron backscatter diffraction)-derived microstrain metrics (Kernel Average Misorientation, KAM; low-/high-angle grain-boundary fractions). Corrosion robustness was assessed using a 5 wt% NaCl, 35 °C, 24 h salt-spray protocol. Our findings reveal that FeSiCr achieves the highest μ_i across the frequency band, despite its lowest compaction density. This is attributed to its coarse particle size (D₅₀ ≈ 18 µm) and the resulting lower intragranular pinning. The loss spectra are dominated by hysteresis over this frequency range, with FeSiCr exhibiting the largest K_h, while the fine, silica-insulated Fe powders (RIP/CIP) most effectively suppress K_e. EBSD analysis shows that the high coercivity and hysteresis loss in CIP (and, to a lesser extent, RIP) are correlated with dense, deformation-induced subgrain networks, as evidenced by higher mean KAM and a lower low-angle grain boundary fraction. In contrast, FeSiCr exhibits the lowest KAM, with strain confined primarily to particle contact regions. Corrosion testing ranked durability as FeSiCr ≳ CIP ≈ RIP ≫ CIP-P, which is consistent with the Cr-rich passivation of FeSiCr and the superior barrier properties of the SiO₂ shells compared to low-dose phosphate. At 15 A, inductance retention ranks CIP (67.9%) > RIP (55.7%) > CIP-P (48.8%) > FeSiCr (33.2%), tracking a rise in effective anisotropy and—for FeSiCr—lower M_s that precipitate earlier roll-off. Collectively, these results provide a microstructure-informed selection map for single-powder formulations. We demonstrate that particle size and shell chemistry are the primary factors governing eddy currents (K_e), while the KAM-indexed substructure dictates hysteresis loss (K_h) and DC-bias superposition characteristics. This framework enables rational trade-offs between magnetic permeability, core loss, and environmental durability. Full article

Water resources are fundamental to human survival, as well as critical to the sustainable progress of the economy and society. This study selects representative indicators and employs the TOPSIS model to evaluate the water resources carrying capacity (WRCC) in the Chang–Zhu–Tan region (2006–2022). Based on this, kernel density estimation and Moran’s I are applied to analyze the spatiotemporal distribution and evolution trends of WRCC. Additionally, the Lorenz curve, Gini coefficient, and imbalance index are utilized to examine the alignment between WRCC and socio-economic growth. Finally, a system dynamics model is used to simulate WRCC and matching dynamics under different scenarios. The findings reveal the following: (1) The overall WRCC is favorable but exhibits a declining temporal trend, with widening inter-district disparities and strong spatial agglomeration. (2) The match between WRCC and economic development is unbalanced, though alignment has gradually improved over time. (3) The WRCC varies across different scenarios. In current development scenario, WRCC declines significantly. In economic priority development and industrial restructuring scenarios, this reduction is slowed. Specifically, in water resource policy control scenario, WRCC can be enhanced. Aside from the industrial restructuring scenario, all other scenarios contribute to improving the coordination between WRCC and economic development. Full article

To systematically investigate the diversity and distribution patterns of Dendrocalamus in Yunnan Province, we integrated field surveys, literature reviews, specimen records, and existing research data to compile and analyze the distribution of Dendrocalamus species across the region. The results revealed the following: (1) A total of 3730 valid distribution points were compiled, representing 38 taxa of Dendrocalamus (including 32 species, 3 varieties, and 3 forms), reflecting remarkably high species diversity. These account for approximately 52% (38/73) of the global species and 69% (38/55) of those recorded in China. (2) Horizontal Distribution Pattern: In terms of distribution points, Pu’er had the highest count (929), followed by Xishuangbanna (759) and Lincang (586). Honghe, Wenshan, and Dehong also showed substantial records. Regarding species richness, Xishuangbanna ranked highest with over 20 species, while Pu’er and Honghe contained 15–20 species. Yuxi and Kunming supported 10–15 species, and Baoshan, Nujiang, Chuxiong, Wenshan, Qujing, and Zhaotong each hosted 5–10 species. In contrast, Dali, Lijiang, and Diqing recorded only 0–5 species. (3) Vertical Distribution Pattern: Distribution points were predominantly concentrated in the 1000–1500 m elevation range, whereas species richness peaked in the 500–1000 m band. Both the number of distribution points and species richness were lowest at elevations above 2500 m. (4) Based on the collected 3730 distribution points, kernel density analysis and hot spot analysis (Getis-Ord Gi*) were performed in ArcGIS 10.8. Both analyses indicated that southern Yunnan (centered on Xishuangbanna and Pu’er) exhibits significant spatial clustering characteristics, identifying it as the core distribution area for Dendrocalamus species in Yunnan Province. (5) During field surveys, a distinctive new species characterized by swollen internodes was discovered. Morphological comparison and phylogenetic analysis confirmed it as a new species of Dendrocalamus and named Dendrocalamus turgidinodis C.M.Hui, M.S.Sun & J.W.Li, it is similar to D. hamiltonii, D. fugongensis, and D. sinicus, but can be easily distinguished by culm diameter 13–16 cm, intranode swollen, culm leaf sheath deciduous, culm blade erect, culm leaf ligule ca. 5 mm tall., Foliage leaf ligule 1–1.5 mm tall (vs. 1.5–2 mm). In conclusion, this study demonstrates that Yunnan Province serves as a major distribution center for Dendrocalamus, with the genus primarily distributed from the southeastern to southwestern parts of the region, and concentrated most densely in the southern area encompassing Xishuangbanna and Pu’er. Full article

Kernel row number (KRN) is a pivotal determinant for yield in maize breeding programs. However, the genetic basis underlying KRN remains largely elusive. To identify candidate genes regulating KRN, a population of 318 BC₄F₄ chromosomal segment substitution lines (CSSLs) was developed via backcrossing, with Baimaya (BMY) as the donor parent and B73 as the recurrent parent. Furthermore, a high-density genetic linkage map containing 2859 high-quality single-nucleotide polymorphism (SNP) markers was constructed for quantitative trait locus (QTL) mapping of KRN. Notably, 19 QTLs controlling KRN were detected across three environments and in the Best Linear Unbiased Prediction (BLUP) values; among these, a major-effect QTL (qKRN4.09-1) was consistently identified across all three environments and BLUP. Then, the integration of linkage mapping and transcriptome analysis of 5 mm immature ears from near-isogenic lines (NILs) uncovered a candidate gene, Zm00001eb205550. This gene exhibited significant downregulation in qKRN4.09-1^BMY, and two missense variants were detected between qKRN4.09-1^BMY and qKRN4.09-1^B73. Zm00001eb205550 exhibited preferential expression in developing ears. Moreover, the pyramiding of favorable alleles from the five stable QTLs significantly increased KRN in maize. These findings advance our genetic understanding of maize ear development and provide valuable genetic targets for improving KRN in maize breeding. Full article

The identification of anomalous data objects within massive datasets is a critical technique in financial auditing. Most existing methods, however, focus on global outlier anomalies detection with less effective in contexts such as Chinese financial budget auditing, where local outliers are often more prevalent and meaningful. To overcome this limitation, a unified outlier detection framework is proposed that integrates both global and local detection mechanisms using k-nearest neighbors (KNN) and kernel density estimation (KDE) methodologies. The global outlier score is redefined as the sum of the distances to the k-nearest neighbors, while the local outlier score is computed as the ratio of the average cluster density to the kernel density—replacing the cutoff distance employed in Density Peak Clustering (DPC). Furthermore, an adaptive adjustment coefficient is further incorporated to balance the contributions of global and local scores, and outliers are identified as the top-ranked objects based on the combined outlier scores. Extensive experiments on synthetic datasets and benchmarks from Python Outlier Detection (PyOD) demonstrate that the proposed method achieves superior detection accuracy for both global and local outliers compared to existing techniques. When applied to real-world Chinese financial budget data, the approach yields a substantial improvement in detection precision-with 38.6% enhancement over conventional methods in practical auditing scenarios. Full article

From the perspective of “dynamic supply–demand coordination,” this study evaluates the development level of China’s economic “dual circulation” across 30 provinces during 2001–2020. Employing Kernel density estimation, natural breakpoint method, and exploratory spatial–temporal data analysis (ESTDA), we provide a comprehensive examination of the spatiotemporal evolution and developmental dynamics of China’s “dual circulation” economy. Furthermore, a nested matrix linking the quantile response types of driving factors with spatiotemporal transition types is constructed to uncover the mechanisms underlying these transitions, in order to form a unified understanding of the significance of China’s implementation of the economic “dual circulation” strategy against the background of high-quality development and lay a solid theoretical foundation for the empirical measurement of China’s economic “double circulation”. The results reveal the following: (1) Despite the “dual circulation” development level of Chinese provinces steadily improving over time, a marked east-to-west gradient of regional imbalance remains; (2) The spatial correlation of the “dual circulation” development level across provinces is significant, with changing trends influenced by neighboring provinces, showing both “concentration” and “differentiation” characteristics; (3) The spatial agglomeration trend of China’s “dual circulation” economy continues to strengthen, with distinct characteristics of “high rigidity + low mobility.” The low mobility of provinces locked in low-level spatial patterns will become a key limiting factor for their overall transition; (4) The quantile response types of the driving factors for the “dual circulation” development level in each province exhibit nest ability with their spatiotemporal transition types. The driving and constraining patterns of various driving factors coexist and interact. Full article

Distributed measurement networks, from semiconductor testing arrays to environmental sensor grids, medical diagnostic systems, and agricultural monitoring stations, face a fundamental challenge: undetected site-to-site variations that silently corrupt data integrity. These variations create systematic biases between supposedly identical measurement units, which undermine scientific reproducibility and yield. The current site-to-site variation detection methods require extensive sampling or make rigid distributional assumptions, making them impractical for many applications. We introduce a novel framework that transforms measurement data into density-based feature vectors using Kernel Density Estimation, followed by anomaly detection with Isolation Forest. To automate the final classification, we then apply a novel probabilistic thresholding method using Gaussian Mixture Models, which removes the need for user-defined anomaly proportions. This approach identifies problematic measurement sites without predefined anomaly proportions or distributional constraints. Unlike traditional methods, our method works efficiently with limited samples and adapts to diverse measurement contexts. We demonstrate its effectiveness using semiconductor multisite testing as a case study, where our approach consistently outperforms state-of-the-art methods in detection accuracy and sample efficiency when validated against industrial testing environments. Full article

A 2014Al matrix composite reinforced with 0.8 wt.% graphene nanoplatelets (GNPs) was prepared by pre-dispersion and ultrasonic melt casting. Subsequently, the as-cast 2014Al-GNP composite was subjected to hot extrusion under different parameters, followed by a comparative analysis of the microstructure and properties of the various alloys. Microstructure and phase composition of the prepared samples were characterized using OM, SEM, EDS, EBSD and TEM inspections. The results indicate that the addition of GNPs effectively promoted the refinement of the as-cast matrix alloy microstructure, while hot extrusion with appropriate parameters further refined the microstructure of the as-cast matrix alloy. At an extrusion ratio of 16, the Al₂Cu, Al₂CuMg, and GNPs in the microstructure displayed a band-like distribution along the extrusion direction, with reduced size and enhanced uniformity. Concurrently, the dislocation density and Kernel Average Misorientation (KAM) values of the composite increased significantly, dynamic recrystallization intensified, and the texture was further enhanced. The tensile strength reached 572.1 MPa, hardness was 369.6 HV, and elongation was 11.9%, representing improvements of 89.0%, 92.0%, and 142.9%, respectively, compared to the as-cast matrix alloy. Fracture surface analysis exhibited brittle fracture characteristics in the matrix alloy, while the extruded composite with optimal parameters displayed distinct ductile fracture features. In the extruded aluminum matrix composite, the interface between GNPs and the matrix was clean, with mutual diffusion of Al and C atoms, achieving an excellent interfacial bonding state. The significant enhancement in mechanical properties of the extruded alloy was primarily attributed to grain refinement strengthening, dislocation strengthening, and load transfer strengthening by GNPs. Full article

Over the past decade, the rapid development of geospatial tools has significantly expanded the scope of archaeobotanical research, enabling unprecedented insights into ancient plant domestication, agricultural practices, and human-environment interactions. Within the Chinese context, where rich archaeobotanical records intersect with complex socio-ecological histories, GIS-driven approaches have revealed nuanced patterns of crop dispersal, settlement dynamics, and landscape modification. However, despite these advances, current applications remain largely exploratory, constrained by fragmented datasets and underutilized spatial-statistical methods. This paper argues that a more robust integration of large-scale archaeobotanical datasets with advanced ArcGIS functionalities—such as kernel density estimation, least-cost path analysis, and predictive modelling—is essential to address persistent gaps in the field. By synthesizing case studies from key Chinese Neolithic and Bronze Age sites, we demonstrate how spatial analytics can elucidate (1) spatiotemporal trends in plant use, (2) anthropogenic impacts on vegetation, and (3) the feedback loops between subsistence strategies and landscape evolution. Furthermore, we highlight the challenges of data standardization, scale dependency, and interdisciplinary collaboration in archaeobotanical ArcGIS. Ultimately, this study underscores the imperative for methodological harmonization and computational innovation to unravel the intricate relationships between ancient societies, agroecological systems, and long-term environmental change. Full article

Reliable schedule-risk estimation in hybrid software development lifecycles is strategically important for organizations adopting AI in software engineering. This study addresses that need by transforming routine process telemetry (CI/CD, SAST, traceability) into explainable, quantitative predictions of completion time and rework. This paper introduces an integrated probabilistic model of the hybrid software development lifecycle that combines Generalized Evaluation and Review Technique (GERT) network semantics with I-AND synchronization, explicit artificial-intelligence (AI) interventions, and a fuzzy treatment of epistemic uncertainty. The model embeds two controllable AI nodes–an AI Requirements Assistant and AI-augmented static code analysis, directly into the process topology and applies an analytical reduction to a W-function to obtain iteration-time distributions and release-success probabilities without resorting solely to simulation. Epistemic uncertainty on critical arcs is represented by fuzzy intervals and propagated via Zadeh’s extension principle, while aleatory variability is captured through stochastic branching. Parameter calibration relies on process telemetry (requirements traceability, static-analysis signals, continuous integration/continuous delivery, CI/CD, and history). A validation case (“system design → UX prototyping → implementation → quality assurance → deployment”) demonstrates practical use: large samples of process trajectories are generated under identical initial conditions and fixed random seeds, and kernel density estimation with Silverman’s bandwidth is applied to normalized histograms of continuous outcomes. Results indicate earlier defect detection, fewer late rework loops, thinner right tails of global duration, and an approximately threefold reduction in the expected number of rework cycles when AI is enabled. The framework yields interpretable, scenario-ready metrics for tuning quality-gate policies and automation levels in Agile/DevOps settings. Full article

In recent years, China has faced the dual challenge of achieving high-quality economic development (HQED) alongside carbon emission reduction (CER), with new-quality productivity (NQP) emerging as a key driver integrating both agendas. Research on the coordinated development of these three dimensions remains limited but is critical for effective policy-making. Based on panel data from 30 Chinese provinces (2014–2023), this study constructs the NQP-CER-HQED evaluation indicator system; calculates the composite index using the entropy weight method and composite index calculation model; computes the coupling coordination degree (CCD) of the three components via the CCD model; analyzes the temporal evolution and future trends of CCD using kernel density and GM(1,1) models; examines the spatial evolution of CCD through Moran’s I index; employs traditional Markov chains and spatial Markov chains to investigate the spatial-temporal evolution patterns of CCD; and applies the geographic detector method to analyze the influencing factors of CCD among NQP, CER and HQED. The findings reveal that (1) the CCD of China’s NQP-CER-HQED has undergone six levels, showing an overall upward trend; (2) temporally, CCD levels improve annually, with all provinces expected to achieve coordinated development by 2026; (3) spatially, the CCD exhibits a “high-east, low-west” tiered distribution; (4) spatially/temporally, the transition of the CCD levels is primarily gradual rather than leapfrogging; and (5) the level of opening up and new-quality labor resources are identified as dominant influencing factors, with the interaction between new-quality labor resources and government support showing the strongest explanatory power. This study provides an analytical framework for understanding the NQP-CER-HQED synergy and offers a scientific basis for sustainable policy formulation. Full article

This study develops an integrated bi-level operations–assignment model to optimise express service on the Gyeongin Line, a core corridor connecting Seoul and Incheon. The upper level jointly selects express stops and time-of-day headways under coverage constraints—a minimum share of key stations and a maximum inter-stop spacing—while the lower level assigns passengers under user equilibrium using a generalised time function that incorporates in-vehicle time, 0.5× headway wait, walking and transfers, and crowding-sensitive dwell times. Undergrounding and alignment straightening are incorporated into segment run-time functions, enabling the co-design of infrastructure and operations. Using automatic-fare-collection-calibrated origin–destination matrices, seat-occupancy records, and station-area population grids, we evaluate five rail scenarios and one intermodal extension. The results indicate substantial system-wide gains: peak average door-to-door times fall by approximately 44–46% in the AM (07:00–09:00) and 30–38% in the PM (17:30–19:30) for rail-only options, and by up to 55% with the intermodal extension. Kernel density estimation (KDE) and cumulative distribution function (CDF) analyses show a leftward shift and tail compression (median −8.7 min; 90th percentile (P90) −11.2 min; ≤45 min share: 0.0% → 47.2%; ≤60 min: 59.7% → 87.9%). The 45-min isochrone expands by ≈12% (an additional 0.21 million residents), while the 60-min reach newly covers Incheon Jung-gu and Songdo. Backcasting against observed express/local ratios yields deviations near the ±10% band (PM one comparator within and one slightly above), and the Kolmogorov–Smirnov (KS) statistic and Mann–Whitney (MW) test results confirm significant post-implementation shifts. The most cost-effective near-term package combines mixed stopping with modest alignment and capacity upgrades and time-differentiated headways; the intermodal express–transfer scheme offers a feasible long-term upper bound. The methodology is fully transparent through provision of pseudocode, explicit convergence criteria, and all hyperparameter settings. We also report SDG-aligned indicators—traction energy and CO₂-equivalent (CO₂-eq) per passenger-kilometre, and jobs reachable within 45- and 60-min isochrones—providing indicative yet robust evidence consistent with SDG 9, 11, and 13. Full article

The optimization of the microstructural homogeneity and residual stress distribution in Ti sputtering targets for OLED 6G applications is essential for improving dimensional stability, durability, and deposition performance. Herein, 3N Ti plates were hot-rolled at 730 °C and then annealed at 600 °C and 700 °C for different durations to investigate the effects of annealing parameters on microstructural evolution and stress relaxation. X-ray diffraction analysis revealed that hexagonal α-Ti with progressive development of the (002) orientation was produced during annealing under all the conditions. Electron backscatter diffraction analyses showed that short-time annealing at 600 °C (≤30 min) generated heterogeneous grains, high dislocation density, and mixed grain boundary character, whereas extended annealing (≥60 min) produced a more uniform microstructure. However, residual stress differences between the plate center and edge remained significant under this condition. Conversely, annealing at 700 °C promoted progressive recrystallization, as indicated by increased high-angle grain boundary fractions and decreased kernel average misorientation values, and facilitated grain growth stabilization across the plate. Prolonged annealing improved microstructural and residual stress uniformity significantly, and near-complete homogenization was achieved after 5 h. These findings demonstrate that annealing at 700 °C for sufficient time is optimal for producing homogeneous microstructures and uniform residual stress distributions, providing valuable guidelines for Ti sputtering target processing. Full article

Studying the coordination between urban economic resilience (ER) and high-quality tourism development (HQTD) is crucial to understanding tourism’s role in responding to economic shifts and driving urban economic transformation. Using 2010–2023 panel data from the Yangtze River Economic Belt (YREB) and a “measurement—evolution—disparity—diagnosis” framework, this study examines their coupling coordination via the coupling coordination degree (CCD) model, kernel density estimation, Gini coefficient decomposition, and influence coordination force index, elucidating spatiotemporal evolution, regional disparities, and drivers. The results show: (1) YREB synergies strengthened significantly, with ER and HQTD increasingly reinforcing each other; (2) Eastern coordination levels markedly exceeded central and western ones, reflecting persistent regional imbalances; (3) Coupling coordination converged toward higher levels, with inter-city gaps narrowing. Recommendations include enhancing regional coordination, balancing ecology and economy, fostering industrial innovation, and promoting social participation. This study provides empirical support for integrated, sustainable regional economic-tourism development. Full article

Typhoons pose severe threats to coastal regions through destructive winds and extreme rainfall, with rainfall-induced flooding often causing more casualties and economic damage than wind damage alone. Accurate precipitation forecasting is therefore paramount for effective disaster risk management. This study proposes a trajectory-based framework for predicting cumulative rainfall from typhoon events, based on the premise that cyclones with similar tracks yield comparable precipitation due to topographic interactions. An extensive dataset of typhoons over East Asia (1979–2022) is analyzed, and two new similarity metrics—the Kernel Density Similarity Index (KDSI) and the Comprehensive Index (CI)—are introduced to quantify track resemblance. Their predictive skill is benchmarked against existing indices, including fuzzy C-means, convex hull area, and triangle mesh methods. Optimal performance is achieved using an ensemble of 13 analogous cyclones, which minimizes root-mean-square error (RMSE). Validation across a large sample demonstrates that the proposed model overcomes limitations of earlier approaches, providing a robust and efficient tool for forecasting typhoon-induced rainfall. Full article