Search Results (2,806)

Optimizing the regional spatial pattern of land use and high-quality economic development requires an accurate understanding of the multifunctional evolution of land use. Based on remote sensing data and socio-economic data from 2000 to 2023, this study utilizes a land transfer matrix, an evaluation index system, an obstacle degree model, and regression analysis to deeply explore the spatial distribution characteristics and influencing factors of the production–living–ecological functions (PLEF) in the Yangtze River Economic Belt (YREB) over the 23-year period. The results show the following: ① the living function area of the YREB has increased by 22,400 km², while the production function area has decreased by 20,600 km², and the ecological function area has decreased by 1800 km². ② The production and living function spaces are characterized by high values in the eastern region and low values in the western region, and the ecological function space is characterized by high values in the western region and low values in the eastern region. ③ In the YREB, production function was the main obstacle to the PLEF between 2000 and 2023. ④ Population growth, economic development, agricultural technology, and agricultural efficiency are the main factors that influence the spatial and temporal evolution of the PLEF. This study suggests exploring an interactive compensation mechanism of the PLEF that combines the government and the market to form a differentiated development strategy. Full article

Miao embroidery holds significant cultural, economic, and aesthetic value. However, its transmission faces numerous challenges: a high learning threshold, a lack of interest among younger generations, and low production efficiency. These factors have created obstacles to its sustainable development. In the age of artificial intelligence (AI), generative AI is expected to improve the efficiency of pattern innovation and the adaptability of the embroidery industry. Therefore, this study proposes a Miao embroidery pattern generation and application method based on Stable Diffusion and low-rank adaptation (LoRA) fine-tuning. The process includes image preprocessing, data labeling, model training, pattern generation, and embroidery production. Combining objective indicators with subjective expert review, supplemented by feedback from local artisans, we systematically evaluated five representative Miao embroidery styles, focusing on generation quality and their social and business impact. The results demonstrate that the proposed model outperforms the original diffusion model in terms of pattern quality and style consistency, with optimal results obtained under a LoRA scale of 0.8–1.2 and diffusion steps of 20–40. Generated patterns were parameterized and successfully implemented in digital embroidery. This method uses AI technology to lower the skill threshold for embroidery training. Combined with digital embroidery machines, it reduces production costs, significantly improving productivity and increasing the income of embroiderers. This promotes broader participation in embroidery practice and supports the sustainable inheritance of Miao embroidery. It also provides a replicable technical path for the intelligent generation and sustainable design of intangible cultural heritage (ICH). Full article

This study addresses this gap by proposing a multilevel fuzzy evaluation model combined with an analytic hierarchy process (AHP) to quantify the greenness of furniture products across their entire lifecycle. Focusing on an office desk as a case study, we developed an indicator system encompassing environmental attributes, resource efficiency, energy consumption, economic costs, and quality performance. Weighting results revealed that environmental attributes (27.2%) and resource efficiency (27.2%) dominated the greenness evaluation, with material recycling rate (33.5%) and solid waste pollution (24.3%) as critical sub-indicators. The prototype achieved a moderate greenness score of 70.38/100, highlighting optimization potential in renewable material adoption (10% current rate) and modular design for disassembly. Mechanically recycled materials could reduce lifecycle emissions by 18–25% in key categories. The model demonstrates scalability for diverse furniture types and informs policy-making by prioritizing high-impact areas such as toxic material reduction and energy-efficient manufacturing, thus amplifying its global and interdisciplinary multiplier effects. Full article

Capsicum is one of the most economically significant vegetable crops worldwide, owing to its high content of bioactive compounds with nutritional, pharmacological, and industrial relevance. However, research has focused on C. annuum, often disregarding local diversity and secondary gene pools, which may contain hidden variation for quality traits. Therefore, this study evaluated the genetic and phenotypic diversity of 283 accessions from the Colombian germplasm collection in the agrobiodiversity hotspot of northwest South America, representing all five domesticated species of the genus. A total of 18 morphological, physicochemical, and biochemical fruit traits were assessed, including texture, color, capsaicinoid, and carotenoid content. The phenotypic data were integrated with genomic information obtained through genotyping-by-sequencing (GBS) using the C. annuum reference genome and a multispecies pangenome. Fixed-and-Random-Model-Circulating-Probability-Unification (FarmCPU) and Bayesian-information-and-Linkage-disequilibrium-Iteratively-Nested-Keyway (BLINK) genome-wide association studies (GWAS) were performed on both alignments, respectively, leading to the identification of complex polygenic architectures with 144 and 150 single nucleotide polymorphisms (SNPs) significantly associated with key fruit quality traits. Candidate genes involved in capsaicinoid biosynthesis were identified within associated genomic regions, terpenoid and sterol pathways, and cell wall modifiers. These findings highlight the potential of integrating pangenomic resources with multi-omics approaches to accelerate Capsicum improvement programs and facilitate the development of cultivars with enhanced quality traits and increased agro-industrial value. Full article

Mastitis, an inflammatory disease caused by the invasion of various pathogenic microorganisms into mammary gland tissue, is a core health issue plaguing the global dairy industry. The consequences of this disease are manifold. In addition to directly compromising the health and welfare of dairy cows, it also precipitates a substantial decline in lactation function, a precipitous drop in raw milk production, and alterations in milk composition (e.g., increased somatic cell counts and imbalanced ratios of milk protein to fat). These changes result in a marked degradation of milk quality and safety, and in turn, engender significant economic losses for the livestock industry. Therefore, the establishment and implementation of a comprehensive prevention and control system is a key strategy to effectively curb the occurrence of mastitis, reduce its incidence rate, and minimise economic losses. This review systematically explores the complex etiological factors and pathogenic mechanisms of mastitis in dairy cows, and summarises various diagnostic methods, including milk apparent indicators monitoring, pathogen detection, physiological parameter monitoring, omics technologies, and emerging technologies. Furthermore, it undertakes an analysis of treatment protocols for mastitis in dairy cows, with a particular emphasis on the significance of rational antibiotic use and alternative therapies. Moreover, it delineates preventive measures encompassing both environmental and hygiene management, and dairy cow health management. The objective of this paper is to provide a comprehensive and scientific theoretical basis and practical guidance for dairy farming practices. This will help to improve the health of dairy cows, ensure a stable supply of high-quality dairy products, and promote the sustainable and healthy development of the dairy farming industry. Full article

Organic agriculture is a production system based on environmentally friendly practices that promote the conservation of natural resources, biodiversity, and the production of high-quality food. Its tenets are linked to the concept of sustainable development, which integrates environmental, social, and economic goals. In the face of global competition and changes in food systems, studying their competitiveness of organic agriculture is essential. It is key to assessing its potential for long-term development and competition with conventional agriculture. The purpose of this study is to identify and assess the spatial differentiation in the competitiveness of organic agriculture in EU countries. This study assessed the level of input and output competitiveness of organic agriculture in selected EU countries using the author’s synthetic taxonomic indicators consisting of several sub-variables. The competitiveness of organic farming in twenty-three countries (Cyprus, Latvia, Portugal, and Finland were not included due to a lack of statistical data) was analysed using one of the linear ordering methods, i.e., a non-pattern method with a system of fixed weights. The research has shown significant spatial differentiation in both the input competitiveness and the outcome competitiveness of organic agriculture in EU countries. In 2023, Estonia had the highest level of input competitiveness, followed by Austria, the Czech Republic, and Sweden. In 2023, Estonia had the highest synthetic indicator of outcome competitiveness, followed by The Netherlands and Denmark. In addition, an assessment was made of changes in EU organic agriculture in 2014–2023 by analysing the direction and dynamics of changes in selected measures of the development potential of organic agriculture in all member states (27 countries). This sector is characterised by high growth dynamics, including both the area under cultivation and the number of producers and processors of organic food. This study identified several important measures to support the development of organic farming (especially in countries where this type of activity is relatively less competitive) through targeted support mechanisms, such as policy and regulatory measures, financing, agricultural training and advisory services, scientific research, encouraging cooperation, and stimulating demand for organic products. Full article

In the context of the rapid digitalization of higher education, proactive artificial intelligence (AI) agents embedded within multi-agent systems (MAS) offer new opportunities for personalized learning, improved quality of education, and alignment with sustainable development goals. This study aims to analyze how such AI solutions are perceived by students at Narxoz University (Kazakhstan) prior to their practical implementation. The research focuses on four key aspects: the level of student trust in AI agents, perceived educational value, concerns related to privacy and autonomy, and individual readiness to use MAS tools. The article also explores how these solutions align with the Sustainable Development Goals—specifically SDG 4 (“Quality Education”) and SDG 8 (“Decent Work and Economic Growth”)—through the development of digital competencies and more equitable access to education. Methodologically, the study combines a bibliometric literature analysis, a theoretical review of pedagogical and technological MAS concepts, and a quantitative survey (n = 150) of students. The results reveal a high level of student interest in AI agents and a general readiness to use them, although this is tempered by moderate trust and significant ethical concerns. The findings suggest that the successful integration of AI into educational environments requires a strategic approach from university leadership, including change management, trust-building, and staff development. Thus, MAS technologies are viewed not only as technical innovations but also as managerial advancements that contribute to the creation of a sustainable, human-centered digital pedagogy. Full article

Liriope muscari is a medicinal and ornamental herbaceous plant with significant economic value, as its tuberous roots are used for medicinal purposes. However, the current production of medicinal plants is characterized by wasteful use of resources and ecological risks caused by the unreasonable application of nitrogen fertilizers. In this study, based on uniform application of phosphorus and potassium fertilizers, six nitrogen application levels were set in pot experiments (expressed as N): N0: 0 kg/ha, N1: 208.33 kg/ha, N2: 416.66 kg/ha, N3: 625 kg/ha, N4: 833.33 kg/ha, N5: 1041.66 kg/ha). The morphological characteristics, photosynthetic physiology, tuber yield and quality, and seven nitrogen fertilizer utilization indices of L. muscari were analyzed and measured. Correlation analysis and structural equation modeling (SEM) were employed to investigate the mechanism by which nitrogen influences its growth and development, photosynthetic characteristics, tuber yield and quality, and nitrogen fertilizer utilization efficiency. The results showed that (1) nitrogen significantly promoted plant height, crown width, tiller number, and chlorophyll synthesis, with the N3 treatment (625 kg/ha) reaching the peak value, and the crown width and tiller number increasing by 26.44% and 38.90% compared to N0; the total chlorophyll content and net photosynthetic rate increased by 39.67% and 77.04%, respectively, compared to N0; high nitrogen (N5) inhibited photosynthesis and increased intercellular CO₂ concentration; (2) Fresh weight of tuberous roots, polysaccharide content, and saponin C content peaked at N3 (34.67 g/plant, 39.89%, and 0.21%), respectively, representing increases of 128.69%, 28.37%, and 33.66% compared to N0; (3) Nitrogen uptake, nitrogen fertilizer utilization efficiency, agronomic utilization efficiency, and apparent utilization efficiency were optimal at N3, while high nitrogen (N4–N5) reduced nitrogen fertilizer efficiency by 40–60%; (4) SEM analysis indicated that tiller number and transpiration rate directly drive yield, while stomatal conductance regulates saponin C synthesis. Under the experimental conditions, 625 kg/ha is the optimal nitrogen application rate balancing yield, quality, and nitrogen efficiency. Excessive nitrogen application (>833 kg/ha) induces photosynthetic inhibition and “luxury absorption”, leading to source-sink imbalance and reduced accumulation of secondary metabolites. This study provides a theoretical basis and technical support for the precise management of nitrogen in Liriope-type medicinal plants. It is expected to alleviate the contradictions of “high input, low output, and heavy pollution” in traditional fertilization models. Full article

This paper presents a comprehensive economic and environmental analysis of the utilization of recycled ceramic demolition materials in the construction sector, considering three distinct applications: erecting vertical partitions, constructing road bases, and producing decorative finishes. The findings demonstrate significant economic advantages when using recycled ceramic materials in structural applications, specifically vertical partitions and road base layers, with cost reductions of approximately 14.1% and 23.9%, respectively, compared to new materials. Conversely, the economic viability of using recycled materials for decorative finishes (“old brick”) proved limited due to high labor intensity and significant waste generation during processing, resulting in higher costs than using new materials. From an environmental perspective, the recycling of construction ceramics provides substantial benefits, notably in reducing carbon footprints. The greatest environmental benefit observed was a reduction in carbon footprint by about 90% in vertical partition applications, and about 70% for decorative finishes. Despite these benefits, practical implementation faces substantial technological and regulatory barriers, including labor-intensive recovery processes and the absence of unified quality standards. Overcoming these challenges requires further development of advanced sorting and processing technologies, clear regulations, unified quality standards, and educational efforts targeted at the construction industry and investors. Full article

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes and evaluates the existing research on seawater quality in the Greek islands, with emphasis on pollution sources, monitoring methodologies, and socio-environmental impacts, while highlighting the gaps in addressing emerging contaminants and aligning with sustainable development goals. Methods: A systematic literature search was conducted in Scopus, Google Scholar, ResearchGate, Web of Science, and PubMed for English- and Greek-language studies published over the last two to three decades. The search terms covered physical, chemical, and biological aspects of seawater quality, as well as emerging pollutants. The PRISMA-ScR guidelines were followed, resulting in the inclusion of 178 studies. The data were categorized by pollutant type, location, water quality indicators, monitoring methods, and environmental, health, and tourism implications. Results: This review identifies agricultural runoff, untreated wastewater, maritime traffic emissions, and microplastics as key pollution sources. Emerging contaminants such as pharmaceuticals, PFASs, and nanomaterials have been insufficiently studied. While monitoring technologies such as remote sensing, fuzzy logic, and Artificial Neural Networks (ANNs) are increasingly applied, these efforts remain fragmented and geographically uneven. Notable gaps exist in the quantification of socio-economic impact, source apportionment, and epidemiological assessments. Conclusions: The current monitoring and management strategies in the Greek islands have produced high bathing water quality in many areas, as reflected in the Blue Flag program, yet they do not fully address the spatial, temporal, and technological challenges posed by climate change and emerging pollutants. Achieving long-term sustainability requires integrated, region-specific water governance linked to the UN SDGs, with stronger emphasis on preventive measures, advanced monitoring, and cross-sector collaboration. Full article

Government debt can potentially enhance high-quality economic development, yet its effects and risks diverge substantially under the interplay of scale economies and diseconomies. Against the backdrop of the 20th CPC Central Committee’s Third Plenary Session, which emphasized coordinated development-security integration and local debt risk resolution, this study investigates the debt-development nexus through the lens of dual-scale economies in explicit/implicit local government debt. We innovatively incorporate resource allocation efficiency and investment levels as mediating factors. Empirical results demonstrate the following: (1) An inverted U-shaped relationship between local debt scale and economic development quality during two debt rectification periods, with implicit debt exhibiting a more pronounced curvilinear pattern; (2) Both resource allocation efficiency and investment levels significantly moderate the scale economies of explicit/implicit debt, yet paradoxically constrain development quality. Key obstacles include short-term adjustment costs, income disparity, and innovation suppression. Notably, while government debt currently operates within scale economies, implicit debt possesses greater borrowing capacity than explicit debt. Debt-driven economies of scale exhibit significant regional heterogeneity. In coastal areas, these effects are more sustainable, whereas in inland areas it is relatively weak. Policy implications suggest the following: (1) Recognizing debt’s nonlinear developmental impacts; (2) Optimizing resource allocation to improve investment quality; (3) Clarifying central-local fiscal responsibility demarcation; (4) A regionally differentiated collaborative strategy is needed for coordinating debt, investment, and resource allocation. Full article

Digital transformation is gradually emerging as a core driver of high-quality economic development and the achievement of sustainable development goals. As the core engine of the digitalization process, digital innovation is becoming a key supporting force for activating the value of digital elements and fostering a new development pattern. Based on panel data from 31 provincial-level administrative regions in China spanning from 2019 to 2023, we undertook an in-depth exploration of how information ecosystems and digital transformation drivers synergistically enhance regional digital innovation capabilities and their substitutive relationships. The results show the following: Firstly, technological, subject, information, and environmental conditions cannot individually constitute the necessary conditions for a high level of regional digital innovation capacity. Secondly, three types of conditional configurations, namely technology–environment-driven, subject–environment-driven, and balance-driven, are its main driving paths, with significant substitution effects among these conditions. This study provides theoretical and empirical evidence for formulating region-specific strategies to optimize digital innovation capacity and helps formulate differentiated digital development strategies based on regional resource endowments and institutional characteristics, ultimately promoting the construction of a more balanced and resilient digital innovation ecosystem. Full article

Given the growing adverse effects of drought on water resources, agriculture, and various sectors, assessing and evaluating drought and producing high-quality drought maps despite the data scarcity to better understand its impacts and develop effective mitigation strategies is essential. Considering the existing gaps related to drought evaluation, especially in scarce data regions, this research aims to evaluate the efficiency of acceptable time period for drought studies (10–20 years), evaluate the performance of ERA5-LAND and IMERG-NASA precipitation data in estimating the Standardized Precipitation Index (SPI) using different statistical metrics and the innovative drought classification matrix (IDCM), and finally produce and compare high-quality and accurate drought characteristics maps resulted from in situ stations, ERA5-LAND, and IMERG-NASA. The Kocaeli province in Türkiye, which has limited data and is a scarce data region, has been selected as an application. The results ensure that an acceptable time period can be sufficient and provide reliable accuracy for assessing drought with RMSE ranging between 0.09 and 0.23 standard deviation and IDCM ranging between 85% and 97%. NASA IMERG data gave more accurate drought results than ERA5-LAND, and the Pearson correlation ranges between 0.57 and 0.89. Also, in situ data showed longer drought duration, while ERA5-LAND and NASA had higher intensity. This article enables policymakers and decision-makers to manage and plan water resources within the city boundary, ensuring sustainable agricultural, economic, and industrial activities and supporting effective climate change adaptation strategies. 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

The paper presents the results of research focused on configuring a system for stereoscopic view capturing and processing. The system is being developed for use in staff training scenarios based on Virtual Reality (VR), where high-quality, distortion-free imagery is essential. This research addresses key challenges in image distortion, including the fish-eye effect and other aberrations. In addition, it considers the computational and bandwidth efficiency required for effective and economical streaming and real-time display of recorded content. Measurements and calculations were performed using a selected set of cameras, adapters, and lenses, chosen based on predefined criteria. A comparative analysis was conducted between the nearest-neighbour linear interpolation method and a third-order polynomial interpolation (ABCD polynomial). These methods were tested and evaluated using three different computational approaches, each aimed at optimizing data processing efficiency critical for real-time image correction. Images captured during real-time video transmission—processed using the developed correction techniques—are presented. In the final sections, the paper describes the configuration of an innovative VR-based training system incorporating an edge computing device. A case study involving a factory producing wheel rims is also presented to demonstrate the practical application of the system. Full article