Search Results (71)

This study examines how green finance influences high-quality economic development, with a particular focus on its spatial spillover mechanisms. Specifically, we investigate the competing roles of technology spillover and the pollution haven effect. Using provincial panel data from China (2010–2021) and applying a Spatial Durbin Model (SDM), we deconstruct the total effect of green finance into three distinct components: the local technological progress effect, the positive technology spillover effect, and the negative pollution haven effect. While acknowledging limitations related to the macro-level data granularity and the indirect nature of the mechanism tests, our analysis yields three main findings. First, green finance development shows significant regional disparities. It has progressed most rapidly in the eastern region, remained relatively stable in the central region, and declined in the western region. Second, green finance exerts a strong positive direct effect on local high-quality economic development. This promoting effect becomes even stronger in more developed regions. Third, green finance generates significant negative spatial spillovers on neighboring regions. These are primarily driven by the pollution haven effect, which involves the cross-regional relocation of polluting industries. However, local technological progress partially mitigates these adverse externalities. Overall, our findings reveal the dual nature of the spatial externalities associated with green finance. They also highlight the urgency of coordinated regional environmental governance to prevent “green leakage” and to promote balanced, high-quality economic development. Full article

The Yangtze-to-Huaihe Water Diversion (YHWD) project has raised concerns about balancing economic benefits and ecological impacts in Lake Caizi, a nationally protected wetland recognized by the World Wildlife Fund. To assess post-diversion contamination and ecological risks, seasonal variation in polycyclic aromatic hydrocarbons (PAHs) was investigated in surface sediments from Lake Caizi. Total PAH concentrations were 103–565 ng/g dw in the wet season, marginally exceeding the 97.1–526 ng/g dw observed in the dry season. The lowest levels occurred in the western sub-lake (Lake Xizi), showing marked declines relative to a decade ago, attributable to enhanced wastewater treatment, farmland-to-lake restoration, and a 10-year fishing ban. Conversely, PAH concentrations in the main lake, particularly the southeastern and northern sectors of the Caizi route, have increased, reflecting pollutant inflows from Zongyang County via the Yangtze River and accumulation driven by the diversion flows. The diagnostic ratio and positive matrix factorization model indicated biomass burning as the dominant PAH source in Lake Xizi across seasons. In contrast, PAH in the main lake were primarily derived from petroleum combustion and leakage, with coal combustion during the wet season shifting to coal combustion dominance in the dry season due to the seasonal halt of shipping activity. Although overall ecological risk remains low in Lake Caizi, localized hotspots near the Caizi routes and industrial zones pose moderate-to-high risks, necessitating continuous monitoring in the future. Full article

With the increase in urban cableization rate and cable length, the overvoltage problem caused by the capacitive effect becomes more and more serious. To limit overvoltage and achieve regional reactive power balance, shunt reactors are installed in substations. Based on a series of previous research, a type of superconducting toroidal air-core reactor is presented in this paper. The aim is to improve the power density of reactive power compensation and reduce magnetic leakage and noise pollution. In this paper, the structural optimized design of a 10 kV/1 MVar reactor is carried out based on COMSOL and MATLAB. In consideration of the usage of high-temperature superconducting tapes and AC loss of the reactor, combined with critical current, this paper uses corresponding finite element method (FEM) models and the optimal solution set is obtained via multi-objective genetic algorithm (MOGA). Finally, the solutions are analyzed economically and the set of solutions with the lowest cost is obtained, which provides a reference for the actual fabrication of a toroidal reactor in Shanghai, and can be used in the design of superconducting reactors at higher voltage levels. Full article

This study evaluates the technical, design, and economic feasibility of large-scale hydrogen storage in deep water-bearing geological formations (aquifers), presenting it as a scalable solution for seasonal energy storage within the European Union’s decarbonization framework. A techno-economic model was developed for a 1 BCM facility, integrating geomechanical, microbial, and thermodynamic criteria. The results indicate a recoverable hydrogen fraction of 70–85%, with dissolution and microbial conversion losses limited to below 10% under optimized operational regimes. Geochemical and microbiological modelling demonstrated that sulfate-reducing and methanogenic bacterial activity can be reduced by 80–90% through controlled salinity and pH management. The proposed design, incorporating high-permeability sandstone reservoirs (100–300 mD), hydrogen-resistant materials, and fibre-optic monitoring ensures stable containment at 60–100 bar pressure and enables multi-cycle operation with minimal leakage (<0.05% per year). Economically, the baseline Levelized Cost of Hydrogen Storage (LCOHS) for aquifers was found to be ~0.29 EUR/kWh, with potential reductions to ~0.18 EUR/kWh through optimized drilling, modularized compression systems, and microbial mitigation. The lifecycle carbon footprint (0.20–0.36 kg CO₂-eq/kg H₂) is competitive with other geological storage methods, while offering superior scalability and strategic flexibility. Full article

This review surveys how contemporary machine learning is reshaping financial and economic forecasting across markets, macroeconomics, and corporate planning. We synthesize evidence on model families, such as regularized linear methods, tree ensembles, and deep neural architecture, and explain their optimization (with gradient-based training) and design choices (activation and loss functions). Across tasks, Random Forest and gradient-boosted trees emerge as robust baselines, offering strong out-of-sample accuracy and interpretable variable importance. For sequential signals, recurrent models, especially LSTM ensembles, consistently improve directional classification and volatility-aware predictions, while transformer-style attention is a promising direction for longer contexts. Practical performance hinges on aligning losses with business objectives (for example cross-entropy vs. RMSE/MAE), handling class imbalance, and avoiding data leakage through rigorous cross-validation. In high-dimensional settings, regularization (such as ridge/lasso/elastic-net) stabilizes estimation and enhances generalization. We compile task-specific feature sets for macro indicators, market microstructure, and firm-level data, and distill implementation guidance covering hyperparameter search, evaluation metrics, and reproducibility. We conclude in open challenges (accuracy–interpretability trade-off, limited causal insight) and outline a research agenda combining econometrics with representation learning and data-centric evaluation. Full article

Moss in container seedling nurseries competes with seedlings for water and nutrients while blocking light, thereby inhibiting growth. This study aimed to address this issue by evaluating the moss control efficacy of 11 chemical compounds, including terpinyl acetate (TA), limonene, and Hinoki essential oil (HEO). The plate experiment results led to the selection of 6 substances (TA, limonene, HEO, pine leaf extract, baking soda, pelargonic acid) that stably controlled both Polytrichum commune Hedw. and Marchantia. Polymorpha L. When TA, limonene, and HEO were combined with surfactants, moss control rates increased and showed stable performance. In the container seedling experiment, TA, limonene, and HEO demonstrated high moss control effects while exhibiting low growth inhibition. When these three substances were combined with surfactants, the electrolyte leakage index (ELI) decreased, indicating minimal cell membrane damage. Additionally, TA treatment maintained stable soil physicochemical properties with no significant changes in pH or nutrient levels. Microscopic analysis of moss cells showed cell wall deformation and expansion of intercellular spaces in the three substance treatment groups. Future verification of long-term effectiveness, expansion of application targets, and assessment of economic feasibility could lead to the development of eco-friendly moss removal agents for improving container seedling quality. Full article

Fire incident reports contain detailed textual narratives that capture causal factors often overlooked in structured records, while financial damage amounts provide measurable outcomes of these events. Integrating these two sources of information is essential for uncovering interpretable links between descriptive causes and their economic consequences. To this end, we develop a data-driven framework that constructs a composite Risk Index, enabling systematic quantification of how specific keywords relate to property damage amounts. This index facilitates both the identification of high-impact terms and the aggregation of risks across semantically related clusters, thereby offering a principled measure of fire-related financial risk. Using more than a decade of Korean fire investigation reports on the chemical industry classified as Special Buildings (2013–2024), we employ topic modeling and network-based embedding to estimate semantic similarities from interactions among words, and subsequently apply Lasso regression to quantify their associations with property damage amounts, thereby estimating the fire risk index. This approach enables us to assess fire risk not only at the level of individual terms, but also within their broader textual context, where highly interactive related words provide insights into collective patterns of hazard representation and their potential impact on expected losses. The analysis highlights several domains of risk, including hazardous chemical leakage, unsafe storage practices, equipment and facility malfunctions, and environmentally induced ignition. The results demonstrate that text-derived indices provide interpretable and practically relevant insights, bridging unstructured narratives with structured loss information and offering a basis for evidence-based fire risk assessment and management. The derived Risk Index provides practical reference data for both safety management and insurance underwriting by enabling the prioritization of preventive measures within industrial sites and offering quantitative guidance for assessing facility-specific risk levels in insurance decisions. An R implementation of the proposed framework is openly available for public use. Full article

Hospital fraud detection has often relied on periodic audits that miss evolving, internet-mediated patterns in electronic claims. An artificial intelligence and machine learning pipeline is being developed that is leakage-safe, imbalance aware, and aligned with operational capacity for large healthcare datasets. The preprocessing stack integrates four tables, engineers 13 features, applies imputation, categorical encoding, Power transformation, Boruta selection, and denoising autoencoder representations, with class balancing via SMOTE-ENN evaluated inside cross-validation folds. Eight algorithms are compared under a fraud-oriented composite productivity index that weighs recall, precision, MCC, F1, ROC-AUC, and G-Mean, with per-fold threshold calibration and explicit reporting of Type I and Type II errors. Multilayer perceptron attains the highest composite index, while CatBoost offers the strongest control of false positives with high accuracy. SMOTE-ENN provides limited gains once representations regularize class geometry. The calibrated scores support prepayment triage, postpayment audit, and provider-level profiling, linking alert volume to expected recovery and protecting investigator workload. Situated in the Future Internet context, this work targets internet-mediated claim flows and web-accessible provider registries. Governance procedures for drift monitoring, fairness assessment, and change control complete an internet-ready deployment path. The results indicate that disciplined preprocessing and evaluation, more than classifier choice alone, translate AI improvements into measurable economic value and sustainable fraud prevention in digital health ecosystems. Full article

Gas leakages pose significant safety risks in urban environments and industrial sectors like the Oil and Gas Industry (OGI), leading to accidents, fatalities, and economic losses. This paper introduces a novel generative AI framework, the Multi-Scale Dual Discriminator Generative Adversarial Network (MSDD-GAN), designed to detect and localize gas leaks by generating thermal images from RGB input images. The proposed method integrates three key innovations: (1) Attention-Guided Masking (AttMask) for precise gas leakage localization using saliency maps and a circular Region of Interest (ROI), enabling pixel-level validation; (2) Multi-scale input processing to enhance feature learning with limited data; and (3) Dual Discriminator to validate the thermal image realism and leakage localization accuracy. A comprehensive dataset from laboratory and industrial environment has been collected using a FLIR thermal camera. The MSDD-GAN demonstrated robust performance by generating thermal images with the gas leakage indications at a mean accuracy of $81.6\%$, outperforming baseline cGANs by leveraging a multi-scale generator and dual adversarial losses. By correlating ice formation in RGB images with the leakage indications in thermal images, the model addresses critical challenges of OGI applications, including data scarcity and validation reliability, offering a robust solution for continuous gas leak monitoring in pipeline. Full article

Cotton Verticillium wilt is a disease that significantly impacts the cotton industry, severely affecting cotton quality and the economic well-being of farmers. Bacillus atrophaeus YL84 is a biocontrol bacterium with broad-spectrum antagonistic and growth-promoting characteristics, previously isolated by our laboratory. This study aimed to elucidate the antagonistic effects of sterilized fermentation filtrate from Bacillus atrophaeus YL84 on cotton Verticillium wilt pathogen Verticillium dahliae and its growth-promoting effects on cotton. The experiments were conducted in vitro and in vivo to assess these effects comprehensively. Using the dual culture method, it was found that Bacillus atrophaeus YL84 exhibited a high inhibition rate on mycelial growth of V. dahliae, with an inhibition rate of 84.11%. The undiluted YL84 sterilized fermentation filtrate and its 10% volume fraction dilution (fermentation filtrate diluted to 10%) exhibited inhibition rates of 80.25% and 72.16% for conidial germination and mycelial growth of V. dahliae, respectively. Scanning electron microscopy showed increased branching, swelling, and shortened internodes in the antagonized mycelia. Conductivity measurements revealed a significant enhancement caused by the YL84 filtrate, with conductivity increasing by 8.94 times compared to the control at a 250 μg/mL concentration. Similarly, protein leakage peaked at 9.47 times the control level at 250 μg/mL, demonstrating the filtrate’s potent impact on mycelial cell membrane permeability. The enzymatic activities of polygalacturonase (PG), cellulase (CL), and β-glucosidase (β-GC) were significantly reduced following treatment with YL84 sterilized fermentation filtrate, with reductions from control levels of 15.78, 10.11, and 5.01 U/mL to treatment levels of 11.81, 6.96, and 1.44 U/mL, respectively. Indoor pot experiments demonstrated that different concentrations of YL84 sterilized fermentation filtrate significantly suppressed the occurrence of cotton Verticillium wilt while promoting plant growth. Compared to the control group, application of 250 μg/mL YL84 sterilized fermentation filtrate resulted in a control efficacy of 66.69% for cotton Verticillium wilt, with increases in plant height, root length, fresh weight, and dry weight of 9.36–33.85%, 17.33–29.49%, 16.79–28.24%, and 25–58.33%, respectively. These findings underscore the potential of the YL84 filtrate as both a biocontrol agent and a promoter of cotton plant growth in agricultural settings. These results indicate that Bacillus atrophaeus YL84 sterilized fermentation filtrate possesses both disease-suppressing and growth-promoting activities, making it a promising candidate for development and use as a biocontrol agent and plant growth promoter. Full article

Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our research group conducted a study on mechanical harvesting technology for fresh jujubes. A pneumatic jujube harvester was designed. This harvester is composed of a self-regulating picking mechanism, a telescopic conveying pipe, a negative pressure generator, a cleaning mechanism, a double-chamber collection box, a single-door shell, a control assembly, a generator, a towing mobile chassis, etc. During the harvest, the fresh jujubes on the branches are picked under the combined effect of the flexible squeezing of the picking roller and the suction force of the negative pressure air flow. They then enter the cleaning mechanism through the telescopic conveying pipe. Under the combined effect of the upper and lower baffles of the cleaning mechanism and the negative-pressure air flow, the fresh jujubes are separated from impurities such as jujube leaves and branches. The clean fresh jujubes fall into the collection box. We considered the damage rate of fresh jujubes, impurity rate, leakage rate, and harvesting efficiency as the indexes, and the negative-pressure suction wind speed, picking roller rotational speed, and the inclination angle of the upper and lower baffles of the cleaning and selection machinery as the test factors, and carried out the harvesting test of fresh jujubes. The test results show that when the negative-pressure suction wind speed was 25 m/s, the picking roller rotational speed was 31 r/min, and the inclination angles of the upper and lower baffle plates for cleaning and selecting were −19° and 19.5°, respectively, the breakage rate of fresh jujube harvesting was 0.90%, the rate of impurity was 1.54%, the rate of leakage was 2.59%, and the efficiency of harvesting was 73.37 kg/h, realizing the high-efficiency and low-loss harvesting of fresh jujubes. This study provides a reference for the research and development of fresh jujube mechanical harvesting technology and equipment. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

Betula platyphylla Sukaczev (white birch) is a cold-tolerant tree species native to northeastern Asia, valued for its ecological adaptability and economic utility. While its responses to various abiotic stresses have been studied, the physiological and biochemical mechanisms underlying its cold stress tolerance remain insufficiently explored. In this study, we investigated the effects of prolonged cold exposure (6 °C for up to 27 days) on key physiological and biochemical traits of B. platyphylla seedlings, including plant height, chlorophyll content, electrolyte leakage (EL), malondialdehyde (MDA), proline levels, and antioxidant enzyme activities (SOD, CAT, POD). Cold stress resulted in visible phenotypic changes, reduced growth, and significant declines in chlorophyll content, suggesting inhibited photosynthesis. EL and MDA levels increased with exposure duration, indicating progressive membrane damage and oxidative stress. In response, antioxidant enzyme activities and proline accumulation were significantly enhanced, reflecting a coordinated defense strategy. Correlation analyses further revealed strong associations among antioxidant enzymes, MDA, proline, and EL under cold stress. These findings advance our understanding of the adaptive responses of B. platyphylla to low-temperature stress and provide a physiological and biochemical basis for future breeding programs aimed at improving cold tolerance. Full article

Glycyrrhiza uralensis Fisch. is an important economic plant. With its wild populations on the brink of extinction and the area of salinized soil increasing sharply, farmers have gradually used saline soil to carry out artificial cultivation of the licorice. However, the salt stress has led to a significant decrease in the yield and quality of its medicinal organ (root), seriously restricting the sustainable development of the licorice industry. Therefore, we investigated zinc oxide nanoparticles (ZnO NPs) as a nano-fertilizer to enhance root biomass and bioactive compound accumulation under salinity. Our results indicate that under 160 mM NaCl stress, the application of 30 mg/kg ZnO NPs increased the root biomass of the licorice and the contents of glycyrrhizic acid, glycyrrhizin, and total flavonoids in the roots by 182%, 158%, 87%, and 201%, respectively. And the ZnO treatment made the enzyme activities of SOD, CAT, and POD exhibit increase, and made the levels of superoxide anions, electrolyte leakage, soluble sugar, and proline reduce. These results demonstrate that ZnO NPs not only enhance salt tolerance but also redirect metabolic resources toward medicinal compound biosynthesis. Our findings provide a mechanistic basis for utilizing nanotechnology to sustainably cultivate the licorice in marginal saline environments, bridging agricultural productivity and pharmacological value. Full article

Nano-hydroxyapatite (n-HAP) and Fe₃O₄ NPs (Fe₃O₄ NPs) offer effective and economical approaches for reducing Cd toxicity, which presents considerable risks to both environmental and human health. We examined the mechanisms through which these NPs mitigate Cd toxicity in bamboo, Pleioblastus pygmaeus. The plants were exposed to Cd (0, 50, 100, and 150 mg L⁻¹) and received foliar sprays of 100 mg L⁻¹ n-HAP, 100 mg L⁻¹ Fe₃O₄ NPs, and a combination of both treatments. The findings indicated that Cd exposure led to oxidized molecules in bamboo, as evidenced by elevated levels of reactive oxygen species (ROS) and lipoperoxidation. Foliar treatments utilizing n-HAP and Fe₃O₄ NPs markedly diminished these effects. H₂O₂, O₂•−, malondialdehyde (MDA), and electrolyte leakage (EL) levels decreased by 56%, 71%, 65%, and 72%, respectively, compared to the controls. The application of n-HAP and Fe₃O₄ NPs significantly enhanced the enzymes, including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), glutathione reductase (GR), and phenylalanine ammonia-lyase (PAL), with increases observed between 28% and 56%. Furthermore, there was an enhancement in proline accumulation, total phenolic content (TPC), flavonoids (TFC), nitric oxide levels, relative water content (RWC), chlorophyll concentration, and photosynthetic parameters. The combination of n-HAP and Fe₃O₄ NPs was most effective in improving bamboo tolerance to Cd, especially at moderate Cd concentrations of 50 and 80 mg L⁻¹. The results indicate that n-HAP and Fe₃O₄ NPs, particularly in combination, may mitigate Cd toxicity by decreasing Cd uptake, improving antioxidant capacity, and preserving plant water balance. Full article