Search Results (71)

Green finance is often viewed as being linked to sustainable growth, yet its effects may be uneven across regions with different industrial legacies. This paper examines how green finance correlates with green total factor productivity (GTFP) in China, with a focus on the country’s legacy industrial regions (broadly referred to as the “Rust Belt” in this paper), spanning Northeastern and Central China. Using a province–year panel for 30 mainland provinces over 2006–2023, we measure GTFP with a Slacks-Based Measure–Global Malmquist–Luenberger (SBM–GML) index that accounts for undesirable outputs. To reduce simultaneity concerns, we estimate two-way fixed-effects models and conduct robustness checks, including lag-based specifications; nevertheless, the observational design implies that the estimates should be interpreted as stable associations rather than definitive causal effects. We reveal a concerning stylized fact: despite rapid growth in green finance, GTFP in legacy industrial provinces exhibits a nonlinear pullback. More formally, we document pronounced regional heterogeneity: green finance is positively related to GTFP in eastern coastal provinces but negatively related to GTFP in central and northeastern legacy industrial provinces. Our findings are consistent with the theoretical prediction of an intertemporal mismatch in Schumpeterian creative destruction: standardized green-credit tightening coincides with tighter liquidity conditions for incumbent high-carbon sectors, while green entrants in these regions may scale up only gradually, leaving a temporary output and productivity “valley” during the transition. The results suggest that uniform green-finance policies may amplify transition risks in legacy industrial regions, motivating a shift from purely “green finance” toward complementary “transition finance” tools. Full article

Corrosion difficulties have emerged as a key obstacle to the safe functioning of offshore wind turbine towers, prompting academics to focus on rust removal maintenance studies. Existing research focuses mostly on automated rust removal systems, which have limitations such as limited flexibility and poor performance. In contrast, intelligent laser rust removal has emerged as a revolutionary solution due to its great efficiency and environmental friendliness. As a result, this paper examines and summarizes the strengths and limits of laser rust removal research before presenting a new offshore wind power operation and maintenance solution—the drone-based intelligent laser rust removal system. This technology uses solar and wave energy coupling to power the drone, resulting in a completely green operation that includes power supply, rust removal, and corrosion avoidance. The results show that this system has major advantages, such as high efficiency, environmental friendliness, safety, and advanced intelligence, making it an efficient solution for intelligent operation and maintenance in the offshore wind power industry. Full article

Distinct breeding populations of Coffea canephora often exhibit genetic divergence, yet the biological pathways underlying yield and leaf rust resistance in contrasting populations remain poorly understood. Here, we performed a comparative genomic analysis of two populations (Premature and Intermediate) to dissect the genetic architecture of coffee bean production, green bean yield, and leaf rust incidence. By integrating single-SNP association, machine learning (Bootstrap Forest), and Gene Ontology (GO) pathway analysis, we found that the Premature population’s traits were linked to specialized metabolic pathways, particularly lipid modification and organelle lumen–associated processes. In contrast, the Intermediate population was governed by core cellular machinery, with significant enrichment for actin cytoskeleton regulation and salicylic acid signaling. These findings demonstrate that distinct breeding populations achieve agronomic success through fundamentally different biological strategies and provide a reusable resource of ranked SNP lists for targeted, population-aware breeding. Full article

Barley (Hordeum vulgare L.) is a major fodder crop whose productivity is often reduced by phytopathogens, especially during early growth. Understanding how soil fertility management and microbial communities influence disease outcomes is critical for developing sustainable strategies that reduce fungicide dependence and enhance crop resilience. This study evaluated the resistance of the winter barley cultivar “Zemela” to powdery mildew (Blumeria graminis f. sp. hordei), brown rust (Puccinia hordei), and net blotch (Pyrenophora teres f. maculata). The crop was cultivated under two soil management systems—green manure and conventional—and five fertilisation regimes: mineral, vermicompost, combined, biochar, and control. Phytopathological assessment was integrated with functional predictions of soil microbial communities. Field trials showed high resistance to powdery mildew (RI = 95%) and brown rust (RI = 82.5%), and moderate resistance to net blotch (RI = 60%). While ANOVA indicated no significant treatment effects (p > 0.05), PCA explained 82.3% of the variance, revealing clear clustering of microbial community functions by soil management system and highlighting the strong influence of fertilisation practices on disease-related microbial dynamics. FAPROTAX analysis suggested that organic amendments enhanced antifungal functions, whereas conventional systems were dominated by nitrogen cycling. FUNGuild identified higher saprotrophic and mycorrhizal activity under organic and combined treatments, contrasting with greater pathogen abundance in conventional plots. Overall, results demonstrate that soil fertilisation practices, together with microbial functional diversity, play a central role in disease suppression and crop resilience, supporting sustainable barley production with reduced reliance on chemical inputs. Full article

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application. Full article

Excessive intake of fluorine (F) over time can lead to acute or chronic fluorosis. In this study, a novel Fe^III–Ce^IV-based layered hydroxide composite (DD-LHC) was synthesized and applied in both batch and column modes to develop new adsorbent materials and to obtain efficient removal of fluorine (F) anions from wastewater. DD-LHC achieved better adsorption results and material stability compared to green rusts (GR, Fe^II–Fe^III hydroxide). The maximum adsorption capacity of DD-LHC for F⁻ was 44.68 mmol·g⁻¹, obtained at an initial pH of 5 and initial concentration of 80 mM. The substitution of Ce^IV for Fe^II in the intercalated layered structure of GR potentially changed the reaction pathways for F⁻ removal, which are typically dominant in the layered double hydroxides (LDHs) of Fe^II–Fe^III. The molecular structure of layered hydroxides combined with the three-dimensional (3D) metal frame of Fe-O-Ce was integrated into DD-LHC, resulting in nanoscale particle morphologies distinct from those of GR. The pseudo-first-order kinetic model effectively described the whole adsorption process of DD-LHC for F⁻. DD-LHC exhibited notable selectivity for F⁻ across a wide pH range. The removal process of F⁻ by DD-LHC was dominated by Ce–F coordination bonds, with additional influences from auxiliary pathways to different extents. Full article

This study focuses on the nameplate of Vila D. Bosco, a modern building in Macau from the time of Portuguese rule, and looks at the types of metal materials and surface coatings used, as well as how they corrode due to the tropical marine climate affecting the building’s metal parts. The study uses different techniques, such as X-ray fluorescence spectroscopy (XRF), scanning electron microscopy/energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR), and cross-sectional microscopic analysis, to carefully look at the metal, corrosion products, and coating of the nameplate. The results show that (1) the nameplate matrix is a resulfurized steel with a high sulfur content (Fe up to 97.3% and S up to 1.98%), and the sulfur element is evenly distributed inside, which is one of the internal factors that induce corrosion. (2) Rust is composed of polycrystalline iron oxides such as goethite (α-FeOOH), hematite (α-Fe₂O₃), and magnetite (Fe₃O₄) and has typical characteristics of atmospheric oxidation. (3) The white and yellow-green coatings on the nameplate are oil-modified alkyd resin paints, and the color pigments are TiO₂, PbCrO₄, etc. The surface layer of the letters is protected by a polyvinyl alcohol layer. The paint application process leads to differences in the thickness of the paint in different regions, which directly affects the anti-rust performance. The study reveals the deterioration mechanism of resulfurized steel components in a subtropical polluted environment and puts forward repair suggestions that consider both material compatibility and reversibility, providing a reference for the protection practice of modern and contemporary architectural metal heritage in Macau and even in similar geographical environments. Full article

Lesion mimic phenotypes, characterized by leaf spots formed in the absence of pathogens or pests, are often associated with reactive oxygen species (ROS) accumulation and cell necrosis. This study identified a novel and stable homozygous spotted phenotype (HSP) from the F₈ population of common wheat (XN509 × N07216). The yellow spots that appeared at the booting stage were light-sensitive, and accompanied by cell necrosis and H₂O₂ accumulation. Compared with homozygous normal plants (HNPs), HSPs exhibited enhanced resistance to stripe rust and powdery mildew without compromising yield. RNA-Seq analysis at three stages revealed that differentially expressed genes (DEGs) between HSPs and HNPs were significantly enriched in KEGG pathways related to photosynthesis and photosynthesis-antenna proteins. GO analysis highlighted chloroplast and light stimulus-related down-regulated DEGs. Fine mapping identified TaSpl1 within a 0.91 Mb interval on chromosome 3DS, flanked by the markers KASP188 and KASP229, using two segregating populations comprising 1117 individuals. The candidate region contained 42 annotated genes, including 14 DEGs based on previous BSR-Seq data. PCR amplification and qRT-PCR verification identified the expression of TraesCS3D02G022100 was consistent with RNA-Seq data. Gene homology analysis and silencing experiments confirmed that TraesCS3D02G022100 was associated with stay-green traits. These findings provide new insights into the genetic regulation of lesion mimics, photosynthesis, and disease resistance in wheat. Full article

Geological structures known as alkaline hydrothermal vents (AHVs) likely displayed dynamic energy characteristics analogous to cellular chemiosmosis and contained iron-oxyhydroxide green rusts in the early Earth. Under specific conditions, those minerals could have acted as non-enzymatic catalysts in the development of early bioenergetic chemiosmotic energy systems while being integrated into the membrane of AHV-produced organic vesicles. Here, we show that the simultaneous addition of two probable AHV components, namely nickel and amino acids, impacts green rust’s physico-chemical properties, especially those required for its incorporation in lipid vesicle’s membranes, such as decreasing the mineral size to the nanometer scale and increasing its hydrophobicity. These results suggest that such hydrophobic nano green rusts could fit into lipid vesicle membranes and could have functioned as a primitive, inorganic precursor to modern chemiosmotic metalloenzymes, facilitating both electron and proton transport in early life-like systems. Full article

Protected cultivation is the sustainable approach to horticultural crop production under adverse climates. In this study, the performance of yard-long beans under three protected cultivations, including single-span polyhouse (SSP), five-span polyhouse (FSP), and insect-proof net house (IPN), is examined and compared to open field cultivation. The above protected cultivation can extend the harvest period of pods by 6–10 days, improve their quality, and increase yield by 15.6% to 25.1%, reducing the incidence and severity of thrips and Cercospora leaf spot, rust, and powdery mildew. Among them, yard-long beans grown in SSP are longer and straighter in shape and have the lowest incidence and severity of pests and diseases and the highest levels of total polyphenols, total sugar, soluble protein, starch, and fiber. This indicates that protected cultivation has broad application in the production of yard-long beans. Through full subset regression analysis (FSRA), we report here that the yield and of yard-long bean occurrences of pests and diseases were highly impacted by climatic factors, especially UV radiation intensity and air temperature. These results have considerable implications for improving pod yield and quality and green prevention and control of pests and diseases through optimizing facility structure and fertilizer management. Full article

The microbial reduction of Fe(III) is a major component of Fe cycling in terrestrial and aquatic environments and is affected by the Fe(III) mineralogy of the system. The majority of the research examining the bioreduction of Fe(III) oxides by Fe(III)-reducing bacteria (IRB) has focused on the reduction of poorly crystalline Fe(III) phases, primarily ferrihydrite; however, crystalline Fe(III) oxides like goethite (α-FeOOH) and hematite (α-Fe₂O₃) comprise the majority of Fe(III) oxides in soils. This study examined the bioreduction of goethite and hematite of geogenic and synthetic origin by Shewanella putrefaciens CN2, a well-studied model IRB, in laboratory incubations. Overall, the rate and extent of Fe(II) production were greater for goethite than for hematite, and for geogenic Fe(III) oxides relative to their synthetic analogs. Although there was substantial production of Fe(II) (i.e., >5 mM Fe(II)) in many of the systems, X-ray diffraction analysis of the solids at the end of the incubation did not indicate the formation of any Fe(II)-bearing secondary minerals (e.g., magnetite, siderite, green rust, etc.). The results of this study demonstrate the variability in the extent of bioreduction of geogenic goethite and hematite, and furthermore, that synthetic goethite and hematite may not be good analogs for the biogeochemical behavior of Fe(III) oxides in aquatic and terrestrial environments. Full article

The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and eXplainable Artificial Intelligence (XAI) come into play to facilitate an understanding of the inner workings of models. Consequently, alternatives have emerged, such as class activation mapping (CAM) techniques aimed at identifying regions of importance for an image classification model. However, the behavior of such models can be highly dependent on the type of architecture and the different variants of convolutional neural networks. Accordingly, this paper evaluates three Convolutional Neural Network (CNN) architectures (VGG16, ResNet50, ConvNext-T) against seven CAM models (GradCAM, XGradCAM, HiResCAM, LayerCAM, GradCAM++, GradCAMElementWise, and EigenCAM), indicating that the CAM maps obtained with ConvNext models show less variability among them, i.e., they are less dependent on the selected CAM approach. This study was performed on an image dataset for the classification of coffee leaf rust and evaluated using the RemOve And Debias (ROAD) metric. Full article

Rapid urbanization is universally acknowledged to degrade ecosystem services, posing significant threats to human well-being. However, the effects of urban shrinkage, a global phenomenon and a counterpart to urbanization, on ecosystem services (ESs) remain unclear. This study focuses on China’s Rust Belt during the period from 2000 to 2020, constructing a comprehensive analytical framework based on long-term remote sensing data to reveal the temporal and spatial patterns of ESs and their associations with cities experiencing varying degrees of shrinkage. It employs a random forest (RF) model and a Shapley additive explanation (SHAP) model to measure and visualize the significance and thresholds of socioeconomic factors influencing changes in ESs. Our findings highlight the following: (1) Since 2010, the three provinces of Northeast China (TPNC) have begun to shrink comprehensively, with the degree of shrinkage intensifying over time. Resource-based cities have all experienced contraction. (2) Regional urban shrinkage has been found to enhance the overall provision capacity of ESs, with the most significant improvements in cities undergoing continuous shrinkage. (3) The impact of the same socioeconomic drivers varies across cities with different levels of shrinkage; increasing green-space ratios and investing more in public welfare have been identified as effective measures to enhance ESs. (4) Threshold analysis indicates that the stability of the tertiary sector’s proportion is critically important for enhancing ESs in cities undergoing intermittent shrinkage. An increase of 10% to 15% in this sector can allow continuously shrinking cities to balance urban development with ecological improvements. This research highlights the positive aspects of urban shrinkage, demonstrating its ability to enhance the provision capacity of ESs. It offers new insights into the protection and management of regional ecosystems and the urban transformation of the three eastern provinces. Full article

Accurate detection of early diseased plants is of great significance for high quality and high yield of crops, as well as cultivation management. Aiming at the low accuracy of the traditional deep learning model for disease diagnosis, a crop disease recognition method was proposed based on multi-source image fusion. In this study, the adzuki bean rust disease was taken as an example. First, color and thermal infrared images of healthy and diseased plants were collected, and the dynamic thresholding excess green index algorithm was applied to extract the color image of the canopy as the reference image, and the affine transformation was used to extract the thermal infrared image of the canopy. Then, the color image was fused with the thermal infrared image by using a linear weighting algorithm to constitute a multi-source fusion image. In addition, the sample was randomly divided into a training set, validation set, and test set according to the ratio of 7:2:1. Finally, the recognition model of adzuki bean rust disease was established based on a novel deep learning model (ResNet-ViT, RMT) combined with the improved attention mechanism and the Squeeze-Excitation channel attention mechanism. The results showed that the average recognition rate was 99.63%, the Macro-F1 was 99.67%, and the recognition time was 0.072 s. The research results realized the efficient and rapid recognition of adzuki bean rust and provided the theoretical basis and technical support for the disease diagnosis of crops and the effective field management. Full article

Zero-valent iron (Fe⁰) has been extensively used in hexavalent chromium (Cr(VI)) removal from groundwater, but its treatment suffers from interference of humic acid (HA) and ferrochrome precipitate. In this study, a biotic Fe⁰ system was established to address these problems in Cr(VI) removal from HA-rich groundwater by introducing a combination of heterotrophic and hydrogen-autotrophic microorganisms. Due to the formation of HA-Fe complexes and ferrochrome precipitates on the Fe⁰ surface, the HA-abiotic Fe⁰ system obtained a slight Cr(VI) removal of 10.5%. While in the HA-biotic Fe⁰ system, heterotrophic microbes could effectively eliminate HA through biodegradation and decrease HA-Fe complex generation; autotrophic microbes used H₂ from iron corrosion as electron donors for their metabolism and promoted iron corrosion and active secondary mineral generation (e.g., magnetite and green rust) for Cr(VI) adsorption and reduction. Therefore, a much higher Cr(VI) removal of 84.9% was achieved. Additionally, increasing HA content and extra electron acceptors (e.g., sulfate and nitrate) both boosted Cr(VI) removal, further proving the role of heterotrophic microbes in biodegrading HA for enhanced Cr(VI) elimination. This work presented a feasible strategy to achieve efficient Cr(VI) removal with Fe⁰ by diminishing HA interference and ferrochrome precipitate passivation through the synergistic effect of heterotrophic and hydrogen-autotrophic microorganisms. Full article