Search Results (491)

Licorice is an important specialty crop that links agricultural production, processing and trade, and rural livelihoods in the arid and semi-arid regions of China. Using UN Comtrade data for HS 130212 from 1990 to 2024, this study evaluates the international Trade Competitiveness of China’s licorice exports and identifies the sources of export growth. A multi-indicator static framework is constructed, combining International Market Share (IMS), the Trade Competitiveness Index (TC), the Revealed Symmetric Comparative Advantage index (RSCA) and the Revealed Competitive Advantage index (CA). The results show that China maintains a relatively large and stable global market share and a persistent net export position, but its comparative and net Competitive Advantages are weaker than those of high-end suppliers such as France and Israel, revealing a pattern of “large scale but weak competitiveness”. To capture dynamic drivers, an extended Constant Market Share (CMS) model is applied to decompose China’s licorice exports into world demand, structural and competitiveness effects. The decomposition indicates that export growth has gradually shifted from being mainly driven by global demand expansion to relying more on improvements in product competitiveness and market reconfiguration, particularly in emerging markets. These findings suggest that upgrading product quality and processing, strengthening standards and branding, and promoting more inclusive value-chain development are essential for transforming China’s licorice exports from scale expansion to high-quality growth and for enhancing rural incomes in producing regions. Full article

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects. Full article

The radial sand-ridge field off the Jiangsu coast is a distinctive landform in a strongly tide-dominated environment, where sediment supply and geomorphic patterns have been profoundly altered by Yellow River course changes, reduced Yangtze-derived sediment, and large-scale reclamation. Focusing on a typical nearshore sector off Dongtai, this study integrates multi-source data from 1979 to 2025, including historical nautical charts, high-precision engineering bathymetry, full-tide hydro-sediment observations, and surficial sediment samples, to quantify seabed erosion–deposition over 46 years and clarify linkages among tidal currents, suspended-sediment transport, and surface grain-size patterns. Surficial sediments from Maozhusha to Jiangjiasha channel systematically fine from north to south: sand-ridge crests are dominated by sandy silt, whereas tidal channels and transition zones are characterized by silty sand and clayey silt. From 1979 to 2025, Zhugensha and its outer flank underwent multi-meter accretion and a marked accretion belt formed between Gaoni and Tiaozini, while the Jiangjiasha channel and adjacent deep troughs experienced persistent scour (local mean rates up to ~0.25 m/a), forming a striped “ridge accretion–trough erosion” pattern. Residual and potential maximum currents in the main channels enhance scour and offshore export of fines, whereas relatively strong depth-averaged flow and near-bed shear on inner sand-ridge flanks favor frequent mobilization and short-range trapping of coarser particles. Suspended-sediment concentration and median grain size are generally positively correlated, with suspension coarsening in high-energy channels but dominated by fine grains on nearshore flats and in deep troughs. These findings refine understanding of muddy-coast geomorphology under strong tides and may inform offshore wind-farm foundation design, navigation-channel maintenance, and coastal-zone management. Full article

Reality-capture has made point clouds a primary spatial data source, yet processing and integration limits hinder their potential. Prior reviews focus on isolated phases; by contrast, Smart Point Clouds (SPCs)—augmenting points with semantics, relations, and query interfaces to enable reasoning—received limited attention. This systematic review synthesizes the state-of-the-art SPC terminology and methods to propose a modular pipeline. Following PRISMA, we searched Scopus, Web of Science, and Google Scholar up to June 2025. We included English-language studies in geomatics and engineering presenting novel SPC methods. Fifty-eight publications met eligibility criteria: Direct (n = 22), Indirect (n = 22), and New Use (n = 14). We formalize an operative SPC definition—queryable, ontology-linked, provenance-aware—and map contributions across traditional point cloud processing stages (from acquisition to modeling). Evidence shows practical value in cultural heritage, urban planning, and AEC/FM via semantic queries, rule checks, and auditable updates. Comparative qualitative analysis reveals cross-study trends: higher and more uniform density stabilizes features but increases computation, and hybrid neuro-symbolic classification improves long-tail consistency; however, methodological heterogeneity precluded quantitative synthesis. We distill a configurable eight-module pipeline and identify open challenges in data at scale, domain transfer, temporal (4D) updates, surface exports, query usability, and sensor fusion. Finally, we recommend lightweight reporting standards to improve discoverability and reuse. Full article

This article aims to find out if pre-exit financial (FP) and internationalization (IP) performance indicators can be used for predicting full de-internationalization (ceasing all export activities; CE). To achieve that, a theoretical concept focusing on the behavior of these predictors is built, and three research questions are postulated. Full de-internationalization is an under-researched topic in international business studies, while quantitative studies focusing on its predictors are especially rare. This study fills both gaps by providing population-level evidence for the theoretical concept. The dataset is composed of Estonian exporters that ceased or continued exporting in 2010–2022. IP variables focus on export scale, intensity and scope, while FP variables focus on liquidity, solvency, profitability and revenue-creation capability. The variables cover the timespan of three (pre-exit) years. To outline the significance of predictors and accuracies in the whole population and for different types of exporters, initially, logistic regression is applied, after which the prediction models are also composed with neural networks. Before CE, IP is in a gradual decline, while the bulk of this decline is concentrated shortly before the exit. Before CE, exporters are constantly liquidity- and solvency-constrained, while the problems with revenue creation and profitability are much shorter-lived. That population-level behavior is subject to substantial variation for different types of exporters, especially regarding FP. Prediction models incorporating the full set of variables achieve high accuracy; however, predictive performance declines as the time to exit increases and varies across exporter types. IP variables are more beneficial for predicting CE. The latter also serve as the main practical implications of the paper. Full article

Significant achievements have been made in the control of point source pollution. However, agricultural non-point source pollution (AGNPSP) has become a serious threat to ecological environment quality and is now the main source of pollution in the Yangtze River Basin. The topographical features of the upper Yangtze River region are primarily characterised by hilly and mountainous terrain, marked by steep slopes and pronounced undulations. This renders the land susceptible to soil erosion, thereby becoming a significant conduit for the entry of AGNPSP into water bodies. Consequently, there is an urgent need to identify critical sources, areas and periods of AGNPSP and to promote the effective prevention and control of such pollution. The present study adopted the Yongchuan District of Chongqing, a region characterised by hilly and mountainous terrain in the upper reaches of the Yangtze River, as a case study. The research, conducted from 2018 to 2021, sought to identify the “critical sources—areas—periods“ of AGNPSP. In order to surmount the challenge posed by the absence of fundamental data, the study constructed and integrated three models. The export coefficient model was used to calculate the pollution load, the pollutant load intensity model was used for spatial analysis, and the equal-scale pollution load equation was used to assess the contribution degree of different pollutants. Furthermore, the study developed a monthly pollutant flux model to accurately identify the critical pollution periods within the year. In conclusion, the research results have indicated the necessity of a governance strategy that is to be implemented with utmost priority. This strategy is to be based on the following hierarchy: critical sources, areas, and periods. The results of the study indicate the following: (1) The pollutants that exhibit the greatest contribution in Yongchuan District are total nitrogen (TN)and chemical oxygen demand (COD), accounting for 34% and 33%, respectively. The primary source of pollution is attributed to livestock and poultry breeding, accounting for 49.7% of the total pollution load. (2) The critical area of AGNPSP in Yongchuan District is located in the south of the district and primarily comprises Zhutuo Town, Hegeng Town and Xianlong Town. Among the critical areas identified, livestock and poultry farming accounts for 68% of the pollution load. (3) The monthly variation of pollutant fluxes demonstrates a single peak pattern, with the peak occurring in June. The data indicates that the flux of pollutants in June and July accounted for 37% of the total, thus identifying these months as critical periods for the management of AGNPSP in Yongchuan District. The critical source–area–period analysis indicates that the comprehensive management strategy for AGNPSP should focus on critical sources, areas and periods. Furthermore, it should adopt a prioritised, zoned and phased management approach. This approach has the potential to promote cost-effective and efficient prevention and control, thereby facilitating the achievement of sustainable agricultural development. Full article

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages. Full article

The rapid growth of electric vehicles (EVs) is expected to create a substantial stream of retired automotive batteries over the coming decades, offering an opportunity for low-cost stationary storage deployment. This paper quantifies how much additional photovoltaic (PV) capacity can be unlocked in Greece through the systematic use of second-life EV batteries under the new self-consumption and zero feed-in regulatory framework. First, a deterministic cohort model is developed to estimate the annual potential of second-life batteries, considering parameters like EV sales, first-life duration, repurposing eligibility, and second-life operational lifetime. The results indicate that Greece could accumulate from 3.5 GWh to 12.1 GWh of second-life batteries until 2050, depending on future EV growth rates. Next, to link battery capacity with PV unlocked potential, an hourly time-series simulation is implemented under a zero feed-in scheme, i.e., without exporting energy to the grid, indicating that each kilowatt-hour of second-life battery can unlock 0.33 kW of PVs in residential zero feed-in systems. On this basis, second-life batteries could unlock from 1.1 GW to 3.9 GW of additional PV capacity that would otherwise be infeasible. For comparison, the peak load of Greece is about 10 GW. Importantly, unlike large-scale grid-connected PV plants—where transmission system operators increasingly impose curtailments—zero feed-in installations can operate seamlessly without creating additional operational stress for the grid. Full article

Rare earth elements (REE) are vital for renewable energy, electronics, and advanced technologies; however, the process-related evolution of REE research has not been systematically quantified. This study conducts the first large-scale bibliometric analysis of 76,768 REE-related publications (1975–2024) from Web of Science, using the Cross-Disciplinary Publication Index (CDPI) and Technology–Economic Linkage Model (TELM). Results reveal three development phases: publication growth from <300 (1975–1990) to >5000 after 2008, driven by China’s export restrictions and the global clean energy transition; China leads with 24.1% of publications, followed by the U.S. (11.7%) and Germany (6.4%). Interdisciplinary mapping identifies materials science as the central field (CDPI = 0.81) linked to nanotechnology (0.75) and environmental science (0.66). Four thematic clusters dominate: (i) deposit geology, (ii) material applications, (iii) green extraction technologies, and (iv) circular economy strategies. Recent emphasis on sustainable practices and unconventional sources—such as phosphorites, bauxite, coal fly ash, and urban mining—reflects a shift toward green innovation. The findings guide policies to diversify REE supply through unconventional deposits (~50 Mt coal-hosted REE), eco-friendly extraction, and recycling. Future priorities include AI-driven exploration, lifecycle assessment of secondary sources, and stronger global collaboration to secure resilient, sustainable REE supply chains. Full article

Background: Emerging Asian economies face a critical policy dilemma: macroeconomic and sustainability factors affect high-performing and struggling logistics exporters in fundamentally different ways. Methods: Analysing transportation trade data from China, South Korea, India, Vietnam, Malaysia, and Indonesia (2000–2023) using Panel Quantile Autoregressive Distributed Lag (P-QARDL) methodology, this study investigates asymmetric relationships between macroeconomic indicators (real GDP, inflation, real effective exchange rate), sustainability variables (energy intensity, energy prices, CO₂ emissions), and logistics performance measured through transportation trade flows. Results: The results reveal striking performance-dependent heterogeneities that conventional approaches overlook. Economic growth provides 55% larger benefits to high performers (0.345) versus strugglers (0.222), confirming scale advantages. Energy constraints intensify for successful exporters, with energy intensity penalties 12% larger in upper quantiles. CO₂ emissions correlate positively with logistics performance, with effects doubling from lower (0.142) to upper quantiles (0.341), highlighting an intensifying sustainability trade-off. Error correction operates 39% faster during high-performance periods. Conclusions: These asymmetric relationships challenge one-size-fits-all policies, necessitating targeted energy efficiency interventions for high performers and growth-enabling support for struggling exporters. Full article

Geomagnetic disturbances are an emerging sustainability challenge for modern, low-carbon and highly interconnected power systems, affecting both grid stability and market performance. We develop a deep causal neural network that fuses geomagnetic observatory measurements with national operational indicators and, via counterfactual inference, traces shock and no-shock trajectories to estimate instantaneous and cumulative impacts. Using Switzerland as a case, shocks significantly change national load, canton-level consumption, cross-border flows, and balancing prices. East–west disturbances have stronger effects than north–south, highlighting the role of grid topology. At the regional scale, the canton of Aargau shows pronounced cumulative consumption responses, revealing spatial heterogeneity. In cross-border exchanges, imports rise after shocks while exports contract and transit flows decline; balancing prices increase markedly, suggesting that market mechanisms can amplify physical stress into economic impacts. The approach goes beyond correlation and exposure metrics by providing system-level, decision-relevant effect sizes. The main contributions are as follows: (i) a deep causal framework that identifies and quantifies the causal effects of geomagnetic disturbances on grid operations and prices; (ii) topology-linked empirical evidence of directional and spatial asymmetries across national, canton-level, and cross-border indicators; and (iii) actionable levers for system operation and market design. These findings inform risk-aware reserve procurement, topology-aware dispatch, and cross-border coordination in highly interconnected, low-carbon grids, helping to enhance reliability, maintain affordability, and facilitate clean-energy integration. Full article

Background: The Na⁺/K⁺-ATPase (NKA) maintains electrochemical gradients by exporting Na+ and importing K⁺ at the expense of ATP hydrolysis. Although NKA inhibition is a well-established strategy for increasing cardiac contractility, existing inhibitors such as cardiotonic steroids (CTS) are limited by serious adverse effects. N106 is a small molecule previously shown to enhance cardiac lusitropy by promoting SERCA2a SUMOylation and, intriguingly, also exerts positive inotropic effects, suggesting additional mechanisms of action. Methods: To test whether N106 directly modulates NKA, we combined ATPase activity assays with molecular docking and microsecond-scale molecular dynamics simulations. Results: Biochemical measurements showed that N106 partially inhibits NKA, achieving ~80% maximal inhibition with an IC50 of 7 ± 1 µM, while leaving the pump’s apparent affinity for Na⁺, K⁺, and ATP unchanged. Computational analyses suggest that N106 binds within the canonical CTS-binding pocket but undergoes intermittent unbinding events, consistent with the partial inhibition observed experimentally. Conclusions: These findings identify N106 as a first-in-class dual modulator of cardiac ion pumps, partially inhibiting NKA while previously shown to activate SERCA2a through enhanced SUMOylation. This combined mechanism likely underlies its positive inotropic and lusitropic effects and positions the N106 scaffold as a promising lead for developing next-generation dual-target therapeutics for heart failure. Full article

Soil erosion threatens agricultural sustainability and water quality in Mediterranean watersheds, necessitating effective Nature-Based Solutions (NBSs) for mitigation. This study applied the InVEST Sediment Delivery Ratio (SDR) model to assess erosion patterns and evaluate NBS effectiveness in the Carapelle watershed (506 km²). The SDR model was calibrated and validated using measured sediment yield data from 2007 and 2008. Model validation achieved a 4.3% deviation from observed data after parameter optimization. Four NBS scenarios were evaluated: contour farming (CF), no-tillage (NT), cover crops (CCs), and combined practices (Comb). Baseline soil loss varied from 2.43 t ha⁻¹ yr⁻¹ (2007) to 3.88 t ha⁻¹ yr⁻¹ (2008), with sediment export ranging from 0.86 to 1.30 t ha⁻¹ yr⁻¹. NT demonstrated the highest individual effectiveness, reducing sediment export by 72.2% on average. The Comb approach (NT + CCs) achieved a superior performance with a 75.9% sediment export reduction and a 70.5% soil loss reduction. Spatial analysis revealed that high-retention zones were concentrated in forest and shrubland, while agricultural zones showed the greatest potential for NBS implementation. NBSs significantly enhance sediment retention services in Mediterranean agricultural watersheds. The InVEST SDR model proves to be effective for watershed-scale assessment. The results provide actionable guidance for sustainable land management and soil conservation policy in erosion-prone Mediterranean environments. Full article

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production. Full article

As a new engine for promoting the high-quality development of China’s foreign trade, digital trade provides new opportunities for enterprises’ low-carbon transition. Based on samples of export industrial enterprises listed in China from 2010 to 2023, this paper uses the digital trade policy represented by the cross-border e-commerce (CBEC) comprehensive pilot zone as a quasi-natural experiment and employs a multi-period difference-in-differences (DID) model to empirically analyze the policy effect of digital trade development on firms’ carbon emission intensity. This research finds that (1) digital trade policies represented by the pilot policy can significantly reduce firms’ carbon emission intensity and (2) the pilot policy can achieve the emission intensity reduction effect through dual paths of “internal innovation deepening” and “external environment optimization”. The internal innovation deepening refers to the green awareness formation and green production implementation of enterprises. External environment optimization refers to financial support resources for enterprises and institutional safeguards for innovation rights of enterprises. (3) Further analysis indicates that the policy effects are more pronounced in firms with higher risk preference, with larger scale, in heavily polluting and high-tech industries, and in the central and northeastern regions. Additionally, the policy demonstrates synergistic effects with the Belt and Road Initiative and exhibits significant spatial spillover effects, benefiting neighboring non-pilot areas. Full article