Search Results (18,856)

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

Economic implications of livestock diseases extend far beyond direct treatment costs and affect productivity, trade, and public health. Despite the growing recognition of animal health economics, a comprehensive analysis of its research landscape has been lacking. Therefore, this study employs bibliometric techniques to systematically analyze research on the economics of animal health between 2000 and 2024 using data extracted from the Web of Science Core Collection. A total of 1070 peer-reviewed publications were analyzed to map publication trends, influential authors, research themes, and international collaborations. The results showed that after 2014, the research output increased steadily to a peak in 2018, thus illustrating the increased global interest in economic evaluations of livestock diseases. The USA, UK, and the Netherlands emerged as key contributors, whereas low-income regions showed low research output, indicating an equity gap for animal health economics studies. The most frequently used keywords were “economics”, “cost–benefit analysis”, “economic impact”, “foot-and-mouth disease”, and “vaccination”, with increasing focus on zoonotic diseases. Coauthorship network analysis demonstrated that the institutions are well connected in Europe and North America, but research from developing countries has remained mostly fragmented. However, notable research gaps were discovered: advanced modelling approaches were underutilized, and the translation of economic research into policy was limited. This work highlights the increasing interdisciplinary nature of animal health economics, while emphasizing the need for broader species coverage, stronger international collaboration, and deeper methodological innovation. These insights provide a foundation for guiding future research priorities and shaping evidence-based policies in animal health economics. Full article

In a competitive global economy, the efficient use of human capital is a key determinant of productivity, growth, and sustainable development. This study assesses the efficiency of human capital in the Mexican manufacturing sector, with a focus on three strategic subsectors: the chemical industry, the food industry, and the transport equipment industry. The purpose is to analyze how human capital—measured through training, average wages, and daily working hours—relates to firm performance across different branches of economic activity and company sizes. Firm-level data from the National Institute of Statistics and Geography (INEGI) for the period 2009–2021 are analyzed using an input-oriented Data Envelopment Analysis (DEA) with CCR and BCC models. The results reveal significant differences in human capital efficiency across branches of economic activity within each—micro, small, and medium and large—firm size. Overall, the results highlight the central role of human capital investment in enhancing firm competitiveness and advancing the sustainable development of strategic industries. Policy implications underscore the need for training and wage strategies that improve efficiency and strengthen the long-term resilience of the Mexican manufacturing sector. Full article

For more than a decade (2014–2025), the Consortium “3D for Digital Humanities” has been advancing the use of 3D technologies in the Humanities and Social Sciences (HSS) while structuring and supporting the research community. It now brings together more than 30 teams, primarily from academic research, but also increasingly from the cultural sector. Under its coordination, significant achievements have been realized, including best-practice guides, an infrastructure for the publication of 3D data, and dedicated software for documentation, dissemination, and archiving, as well as a metadata schema, all fully aligned with FAIR principles. The Consortium has developed national training programs, particularly on metadata and ethical practices, and contributed to important initiatives such as the reconstruction of Notre-Dame de Paris, while actively engaging in European projects. It has also fostered international collaborations to broaden perspectives, share methodologies, and amplify impacts. Looking ahead (2025–2033), the Consortium aims to address the environmental impact of 3D data production and storage by proposing best practices for digital sustainability and efficiency. It is also expanding the National 3D Data Repository, enhancing interoperability, and adopting emerging standards to meet evolving scientific needs. Building on its past achievements, the Consortium intends to further advance 3D research and its applications across disciplines, positioning 3D data as a key component of future scientific data clouds. Full article

Food legumes play a pivotal role in China’s food security, nutritional health, and green development strategies due to their unique advantages. This paper presents an empirical study on the economic evaluation of scientific research on pest and disease control for food legumes. Using panel data from 31 Chinese provinces from 2008 to 2023, we employ a Double Machine Learning (DML) approach to identify the impact of investment in plant protection research on food legume outputs. The results indicate a steady increase in China’s investment in this field, with an average annual growth rate of 5.19% from 2008 to 2023, and the total investment in 2023 was 2.14 times that of 2008. Investment in plant protection research effectively mitigates output losses and leads to significant production increases. Specifically, a 1% increase in research investment corresponds to a 0.2% increase in food legume output. This effect remains robust across various algorithms, time windows, and control variable settings. Based on these findings, we recommend: (1) increasing financial support and talent acquisition for research on food legume pests and diseases to enhance the stability and sustainability of research investment; (2) strengthening cooperation mechanisms between research institutions and enterprises to leverage their respective strengths and promote the commercialization of research outcomes and regional variety extension; (3) establishing a diversified research investment system that explores a co-construction model guided by the government, involving enterprises, and utilizing public–private partnerships to reconcile the conflict between long research cycles and market demands; (4) fostering a dual-track linkage between regional technological innovation and enterprise product commercialization to improve the efficiency of technology transfer and application; and (5) strengthening R&D in cutting-edge fields like Artificial Intelligence to improve the efficiency and precision of pest and disease control. Full article

Solar-energy-driven membrane distillation provides a sustainable pathway to mitigate freshwater scarcity by utilizing an abundant renewable heat source. This study develops a two-dimensional axisymmetric computational fluid dynamics (CFD) model to simulate the transient performance of a hollow fiber water gap membrane distillation (HF-WGMD) module integrated with flat-plate solar collectors (FPCs). A lumped-parameter transient FPC model is coupled with the CFD framework to predict feed water temperature under time-varying solar irradiation, evaluated across four representative days in a Mediterranean city. The model is validated against experimental data, showing strong agreement. A comprehensive parametric analysis reveals that increasing the collector area from 10 to 50 m² enhances the average water flux by a factor of 6.4, reaching 10.9 kg/(m²h), while other parameters such as collector width, tube number and working fluid flow rate exert comparatively minor effects. The module flux strongly correlates with solar intensity, achieving a maximum instantaneous value of 18.4 kg/(m²h) with 35 m² collectors. Multistage HF-WGMD configurations are further investigated, demonstrating substantial reductions in solar energy demand due to internal thermal recovery by the cooling stream. A 40-stage system operating with only 10 m² of solar collectors achieves an average specific thermal energy consumption of 424 kWh/m³, while the overall solar desalination efficiency improves dramatically from 2.6% for a single-stage system with 50 m² collectors to 57.5% for the multistage configuration. The proposed system achieves a maximum freshwater productivity of 51.5 kg/day, highlighting the viability and optimization potential of solar-driven HF-WGMD desalination. Full article

Background: Cinnarizine (CIN) is a poorly soluble drug used in the treatment of vestibular disorders. Its solubility can be improved by complexation with cyclodextrins (CDs). This study focused on the preparation of 1:1 CIN/CD complexes with β-cyclodextrin (βCD) and its derivatives hydroxypropyl-β-cyclodextrin (HPβCD) and sulfobutylether-β-cyclodextrin (SBEβCD) by mechanical activation. Methods: Complexes were obtained under optimized grinding conditions using a high-energy vibrational mill with ZrO₂ grinding media. Solid products were characterized by DSC, TGA, XRPD, and FTIR spectroscopy. Dissolution studies were performed in phosphate buffer (pH 4.5). The effect of βCD and HPβCD on CIN stability was assessed under hydrolytic (acidic, neutral, and basic) and oxidative conditions. A stability-indicating UHPLC-DAD-HRMS method was developed and validated, enabling CIN quantification in the presence of degradation products, whose structures were proposed based on HRMS/MS data. Potential toxicity, bioaccumulation, and mutagenicity of degradation products were predicted using QSAR modeling. Accelerated stability studies (40 °C, 75% RH) were conducted to evaluate long-term stability. Results: Solid-state analyses confirmed CIN/CD interactions in the ground products. The highest dissolution efficiency was observed for CIN/HPβCD complexes, while CD complexation did not alter CIN permeability in biomimetic membrane assays. CIN and its complexes demonstrated satisfactory chemical stability, with no degradation products detected under accelerated conditions. Conclusions: Solid-state complexes of CIN with CDs enhanced dissolution without compromising stability, supporting their potential as promising candidates for novel pharmaceutical formulations. Full article

Green industry, as an emerging industry, plays an important role in improving regional economic and environmental performance and promoting green sustainable development. This study calculates carbon productivity using panel data from 30 Chinese provinces between 2013 and 2022. It employs the location quotient index to measure green industrial agglomeration (GIA) levels and utilizes the Spatial Durbin Model (SDM) and spatial mediation effect model to empirically examine the impact of GIA on carbon productivity (CP), its spatial effects, and the role of technological innovation therein. The results are as follows: (1) GIA not only directly enhances local CP but also exerts positive effects on surrounding regions through spatial spillover effects. (2) Spatial mediation analysis indicates that technological innovation mediates effects within regions and amplifies the positive impact of GIA on CP in surrounding areas through spatial spillover effects. (3) Heterogeneity analysis shows that regional differences in green productivity level leads to different effects of GIA on CP. Based on empirical findings, this study provides practical evidence for optimizing the spatial layout of green industries and offers policy implications for advancing China’s green and low-carbon development. Full article

The objective of this article is to identify and analyze the factors shaping the behavior of Chinese consumers in the organic product market, with a particular focus on young members of Generations Y and Z. These factors are examined in the context of organic consumption and sustainable development, taking into account global and local trends in the organic food market as well as the role of consumers in stimulating clean production and a circular economy. The article applies a research approach that combines a review of the literature with an analysis of quantitative data. In 2022, an online survey was conducted among 1012 Chinese users of the most popular social media platforms, primarily WeChat and Sina Weibo. The respondents were young consumers from Generations Y and Z. The sample was drawn from the IMAS International online panel. The study identified the characteristics attributed to organic food, the frequency and structure of purchases (product categories and share of organic products in the shopping basket), key motives and choice criteria, barriers to purchase, sources of information on organic products, and the role of promotional tools in shaping attitudes and behaviors. The results show that pro-environmental consumption fosters sustainable development and cleaner production, with younger generations emerging as the driving force behind sustainable consumption. The analysis revealed both stimulating and limiting factors influencing the development of sustainable consumption, and highlighted the critical role of digital channels in shaping consumer attitudes and decisions. The study also discusses implications for market stakeholders (producers, distributors, educational institutions, and policymakers) in leveraging the potential of young Chinese consumers as a catalyst for cleaner production and the circular economy. Full article

The fracturing and driving oil technology used in shale oil provides a new approach for the development of offshore low-permeability reservoirs. However, the main control role of technical parameters is unclear, resulting in unsatisfactory accuracy and effectiveness of the enhanced oil recovery plan. For this reason, this study is based on the production and process data of five wells in the WZ oilfield. Fuzzy analytic hierarchical process analysis method (FAHP) was used to evaluate the parameter weights. Combined with numerical simulation technology, the evaluation results were verified by geological-engineering integration. The results show that in offshore low-permeability oilfields, the reservoir pressure coefficient has the greatest influence on the fracturing and oil repelling effect. The comprehensive weight reaches 0.450 compared to not adopting hydraulic fracturing oil displacement technology. This improves the recovery rate by 10.19% in 5 years. The surfactant concentration and the residual oil saturation of the reservoir rank are second, with a comprehensive weight of 0.219. Finally is the effective thickness of the reservoir, with a comprehensive weight of 0.113. In this study, the key parameters of fracturing and oil repelling in offshore low-permeability reservoirs are clarified. It provides theoretical basis and practical support for improving the success rate of well selection, layer selection and recovery capacity. Full article

With rapid economic development and the growing global demand for water resources, the relationship between water demand and economic growth has become a critical international concern. This study investigates the role of water resources in China’s economic growth by extending the Cobb–Douglas production function to include investment, labor, energy, land, and water resources. Using national and regional data from 1949 to 2023, we quantify the spatiotemporal dynamics of factor contributions across primary, secondary, and tertiary industries. Results show that investment remains the dominant growth driver, with rising contributions from energy and land, while labor is increasingly substituted. Water resources exhibit marked industrial and regional heterogeneity: since 2013, water constraints have intensified in the primary sector of the Yellow River basin and Northeast China, and in the secondary sector of the inland northwest and Yellow River provinces. Considering national food security imperatives and given the complementary nature of water–land resources and the fixed nature of land, we propose strategic water network planning based on land productivity patterns to optimize resource coordination and drive high-quality economic development. Full article

The transition to renewable energy is essential for mitigating climate change and promoting sustainable development, particularly in Latin America and the Caribbean (LAC). Despite its vast potential, the region faces structural and economic challenges that hinder a sustainable energy transition. Understanding scientific production in this field is key to shaping policy, investment, and technological progress. The primary objective of this study is to conduct a large-scale, data-driven analysis of renewable energy research in LAC, mapping its thematic evolution, collaboration networks, and key research trends over the past three decades. To achieve this, machine learning-based topic modeling and network analysis were applied to examine research trends in renewable energy in LAC. A dataset of 18,780 publications (1994–2024) from Scopus and Web of Science was analyzed using Latent Dirichlet Allocation (LDA) to uncover thematic structures. Network analysis assessed collaboration patterns and regional integration in research. Findings indicate a growing focus on solar, wind, and bioenergy advancements, alongside increasing attention to climate change policies, energy storage, and microgrid optimization. Artificial intelligence (AI) applications in energy management are emerging, mirroring global trends. However, research disparities persist, with Brazil, Mexico, and Chile leading output while smaller nations remain underrepresented. International collaborations, especially with North America and Europe, play a crucial role in research development. Renewable energy research supports Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy) and 13 (Climate Action). Despite progress, challenges remain in translating research into policy and addressing governance, financing, and socio-environmental factors. AI-driven analytics offer opportunities for improved energy planning. Strengthening regional collaboration, increasing research investment, and integrating AI into policy frameworks will be crucial for advancing the energy transition in LAC. This study provides evidence-based insights for policymakers, researchers, and industry leaders. Full article

This study explores the application of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy combined with various multivariate calibration techniques to detect the presence of cannabis in tobacco samples and tobacco in herbal smoking products. Both MIR and NIR spectra were recorded for self-prepared samples, followed by data exploration using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and the calculation of binary classification models with Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). PCA demonstrated a clear differentiation between tobacco samples containing and not containing cannabis. On the other hand, based on PCA, only NIR was able to distinguish herbal smoking products adulterated and not adulterated with tobacco. HCA further clarified these results by revealing distinct clusters within the data. Modelling results indicated that MIR and NIR spectroscopy, particularly when paired with preprocessing techniques like Standard Normal Variate (SNV) and autoscaling, demonstrated high classification accuracy in SIMCA and PLS-DA, achieving correct classification rates of 90% to 100% for external test sets. Comparison of MIR and NIR revealed that NIR spectroscopy resulted in slightly more accurate models for the screening of tobacco samples for cannabis and herbal smoking products for tobacco. The developed approach could be useful for the initial screening of tobacco samples for cannabis, e.g., in a night life setting by law enforcement, but also for inspectors visiting shops selling tobacco and/or herbal smoking products. Full article

The topic proposal concerns the axes of climate operation and modification, the consequences and/or benefits of the flow of the economy, as well as the risks to social security, amidst the evolution of human interventions, which the Anthropocene highlights. Atmospheric data demonstrates the interaction of gaseous pollutants and aerosols, with the contribution of different emission and pollution sources to its chemical composition. At the same time, satellite remote sensing of precipitation and the water cycle reveal an imbalance in components and effects, in an environment of rapid rates of commercial production and human mobility in the developed world. How does mobility prevent the full observation and modeling of the elements involved (in atmospheric and hydrological data)? What is the role of multi-sensor technologies for detecting gases and what are their applications in decontamination? With sources from bibliographic reviews, data were collected from the detection of point sources of gases and dynamic analyses of the extent of the water surface, in order to highlight the descriptive characteristics of the meteorological phenomena and their activity. The scientific approach to analyzing the individual data is based on the techno-scientific Actor-Network Theory, in order to test their connection and contribution to the overall problematic result. The aim of this study is to build an interdisciplinary analysis with documentation of vulnerabilities in the expression of weather phenomena, of the present geological time. The ambition of the study is to propose principles of regulation and precaution, related to the sustainable development of geo-resources and ways to reduce vulnerability. Full article

Accurate apple orchard mapping plays a vital role in managing agricultural resources. However, national-scale apple orchard mapping faces challenges such as the “same spectrum with different objects” phenomenon between apple trees and other crops, as well as difficulties in sample collection. To address the above issues, this study proposes a knowledge-assisted apple mapping framework that automatically generates samples using agronomic knowledge and employs a random forest algorithm for classification. Firstly, an apple mapping composite index (AMCI) was developed by integrating the chlorophyll content and leaf structural characteristics of apple trees. In a single Sentinel-2 image, a novel natural vegetation phenolic compounds index was applied to systematically exclude natural vegetation, and based on this, the AMCI was used to generate an initial apple distribution map. Using this initial map, apple samples were obtained through random point selection and visual interpretation, and other samples were constructed based on land cover products. Finally, a 10 m-resolution apple orchard map of China was generated with the random forest algorithm. The results show an overall accuracy of 90.7% and a kappa of 0.814. Moreover, the extracted area shows an 82.11% consistency with official statistical data, demonstrating the effectiveness of the proposed method. This simple and robust framework provides a valuable reference for large-scale crop mapping. Full article