Search Results (15,342)

Climatic factors have always played a key role in the construction of food-related architecture: mitigation of outdoor temperatures or winds, adoption of raining waters in the productive processes, etc. However, sometimes, climate change has impacted the profitability of those structures and eventually caused their abandonment. Today, historical food-related architectures are significant elements of local rural heritage, and they are also tangible symbols of all the values connected to the corresponding typical food productions. When the cultural value of rural cultural assets and the historical management of climatic factors coexist, this potential can be investigated, and the results can ultimately be included in the corresponding enhancement processes. To exploit this potential, the paper introduces the theoretical concept of food-related architecture as climatic indicators, with reference to the changes in the climate that have occurred during their construction, as well as their abandonment. According to the thesis of the research, the adoption of the concept of climatic indicators can implement the value of selected minor cultural assets, support sustainable rural regeneration plans and integrate missing historical climate series and data. In the Materials and Methods section, two theoretical charts have been introduced, and the pyramid of the Mediterranean diet was analyzed to allow for the selection of some food-related architectures to test the theoretical approach developed. Then, three Italian case studies have been analyzed: the concept of climate indicators was tested, and some potential focus points of actions connected to this aspect were elucidated. The case studies are the Pietragalla wine district in the Basilicata Region, the Apulian rock-cut oil mills and Mills’s Valley in the Campania Region. Full article

EPS module buildings are prefabricated, low-rise systems with high thermal insulation that are widely used in rural self-built houses in northern China, yet their indoor thermal environments often suffer from instability. This study experimentally verified the effectiveness of microcapsule phase change mortar (PCM plaster) in improving winter indoor temperatures of EPS module houses. In addition, based on simulation data from 350 design combinations across five representative cold-climate cities and four envelope design variables, the study provides quantitative design guidance for EPS module walls and PCM plaster in rural houses, offering a practical approach to improve indoor thermal stability that has not been previously reported. The main findings are as follows: (1) The thermal transmittance of EPS module walls is the dominant factor influencing indoor thermal performance. For climate adaptability, Type II walls are recommended for severely cold regions, while Type I walls are suitable for cold regions. The application of PCM plaster is not recommended in solar-rich cold regions such as Lhasa due to limited effectiveness. (2) Optimal PCM plaster parameters exist, with the phase change temperature recommended to be 2–4 °C higher than the average indoor operative temperature during the heating period. Specifically, 18 °C is optimal for Type I walls in Yinchuan, Beijing, and Dalian, while 15 °C is more appropriate for Type II walls in Shenyang and Harbin. The corresponding optimal thicknesses are 20 mm for Harbin, Shenyang, and Dalian; 30 mm for Yinchuan; and 40 mm for Beijing, achieving a balance between indoor temperature improvement and construction cost. (3) Operative temperature and discomfort hours are introduced to assess indoor thermal stability, especially in buildings with interior PCM plaster. Full article

Weeds are a primary factor affecting sugarcane production and productivity in Thailand. During the late-rainy season, when cultivation is carried out under rainfed conditions, weed competition becomes increasingly severe, prompting farmers to perform secondary weed control using post-emergence herbicides. Therefore, to guide farmers on the appropriate use of herbicides for effective weed management and long-term control during the critical period of sugarcane growth, this study evaluates the effectiveness of pre- and post-emergence herbicides. Conducted in Northeast Thailand using a randomized complete block design (RCBD) with four replications, the experiment revealed that several pre-emergence herbicides, namely pendimethalin + imazapic (825 + 75 g a.i. ha⁻¹), indaziflam (62.5 g a.i. ha⁻¹), and sulfentrazone (875 g a.i. ha⁻¹), and a combination of indaziflam + sulfentrazone (46.88 + 750 g a.i. ha⁻¹) were applied one day after sugarcane planting, demonstrating high weed control efficacy. These treatments significantly reduced the summed dominance ratio (SDR) of both total weed (41.65–78.54%) and dominant weeds (70.13–86.04%), including Digitaria ciliaris (Retz.) Koel., Dactyloctenium aegyptium (L.), Brachiaria distachya (L.) Stapf, and Cyperus rotundus, compared with the no-weeding treatment. In summary, effective weed management in sugarcane fields under late-rainy season can be achieved through the application of pendimethalin + imazapic at 825 + 75 g a.i. ha⁻¹, which produced the highest sugarcane yield (a 139.00% increasing compared with no weeding) and net profit (a 79.75% increasing compared with hand weeding) in loamy sand soil conditions, where D. ciliaris, D. aegyptium, and C. rotundus were dominant weeds. Similarly, indaziflam at 62.5 g a.i. ha⁻¹ yielded the best results (a 71.68% increasing compared with no weeding) and net profit (a 121.04% increasing compared with no weeding) in sandy loam soil, where B. distachya was the only dominant weed. This weed management strategy is potentially transferable to sugarcane production systems in other regions that share comparable soil properties, climatic conditions, and dominant weed species. Full article

This study presents a bibliometric and thematic analysis of economic convergence analysis from 1982 to 2025, based on a corpus of 2924 Scopus-indexed articles. Using VOSviewer and the bibliometrix R package, this research maps the field’s intellectual structure, identifying five main thematic clusters: (1) formal statistical models, (2) institutional-contextual approaches, (3) theoretical–statistical foundations, (4) nonlinear historical dynamics, and (5) normative and policy assessments. These reflect a shift from descriptive to explanatory and prescriptive frameworks, with growing integration of sustainability, spatial analysis, and institutional factors. The most productive journals include Journal of Econometrics (121 articles), Applied Economics (117), and Journal of Cleaner Production (81), while seminal contributions by Quah, Im et al., and Levin et al. anchor the co-citation network. International collaboration is significant, with 25.99% of publications involving cross-country co-authorship, particularly in European and North American networks. The field has grown at a compound annual rate of 14.4%, accelerating after 2000 and peaking in 2022–2024, indicating sustained academic interest. These findings highlight the maturation of convergence analysis as a multidisciplinary domain. Practically, this study underscores the value of composite indicators and spatial econometric models for monitoring regional, environmental, and technological convergence—offering policymakers tools for inclusive growth, climate resilience, and innovation strategies. Moreover, the emergence of clusters around sustainability and digital transformation reveals fertile ground for future research at the intersection of transitions in energy, digital, and institutional domains and sustainable development (a broader sense of structural change). Full article

Comprehending the effects of climate change on the range of endangered species is essential for formulating successful conservation strategies. This research examines two nationally protected dung beetle species (Heliocopris dominus and Heliocopris bucephalus) in China to forecast their probable habitat range under present and future climate scenarios. Employing MaxEnt modeling with validated occurrence records and environmental variables, we discerned critical factors affecting their distribution and anticipated changes in habitat suitability. Results reveal that isothermality, temperature seasonality, maximum temperature of the warmest month, and annual precipitation are the principal environmental drivers. Presently, appropriate habitats are primarily located in southern Yunnan and Hainan, with future forecasts indicating a northward extension into additional areas. These findings offer critical insights for choosing conservation zones for these vulnerable species amid shifting climate conditions. Full article

Alexandrium spp. blooms produce a range of toxins, including spirolides, goniodomins, and paralytic shellfish toxins (PSTs). Of these, PSTs are the most impactful due to their high affinity for voltage-gated sodium ion channels in nerve cell membranes. This interaction can cause neurological effects such as paralysis and, in severe cases, may lead to death. Given the implications of Alexandrium blooms on public health, all mitigation, prevention, and treatment strategies aim to reduce their socioeconomic impacts. However, monitoring harmful algal blooms remains difficult due to confounding influences such as pollution, climate change, and the inherent variability of environmental conditions. These factors can complicate early detection and management efforts, especially as the intensity and frequency of blooms continue to rise, further exacerbating their socioeconomic consequences. This review offers insights into several management approaches to prevent and control Alexandrium blooms, focusing on modified nano-clays as a promising emergency mitigation measure for low-density toxic algal blooms, especially in areas predominantly used for recreational fishing. However, it is recommended that treatment be coupled with monitoring to alleviate reliance on treatment alone. Full article

This manuscript is aimed at analyzing how operating factors may affect the durability of thermal insulation in building partitions located underground. It examines the durability of inverted insulation systems where thermal insulation is installed above the waterproofing layer and used in residential foundation slabs. The article demonstrates that, despite their popularity due to cost efficiency, the long-term success of these systems depends on thorough investigations of thermal isolation, especially considering different climate conditions. The analysis was based on an extensive literature review (2016–2024), supplemented with laboratory test results on extruded (XPS) and expanded (EPS) polystyrene boards. Additional tests examined the water penetration mechanism into insulation layers that are in direct contact with groundwater, revealing that cyclic freezing and thawing significantly increase moisture levels. The findings highlight the need for updated region-specific guidelines for the underground insulation in Central and Eastern Europe. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

Climate change continues to threaten global biodiversity, making it essential to assess how keystone species may shift their distributions and to use these findings to inform conservation planning. This study evaluated the current and future habitat suitability of D. macropodum, an important tree species within subtropical evergreen broad-leaved forests in China, using 354 occurrence records and a suite of environmental variables. A parameter-optimized MaxEnt model (calibrated with ENMeval; RM = 4, FC = QHPT) was applied to simulate the species’ present distribution and projected changes under three climate scenarios (SSP126, SSP245, SSP585). The main factors influencing distribution were determined to be moisture and temperature seasonality, with the precipitation of the coldest quarter (Bio19, 36.3%), the mean diurnal range (Bio2, 37.5%), and the precipitation of the warmest quarter (Bio18, 14.2%) jointly contributing 88.0% of the total influence. The model projections indicated a 40.1% reduction in the total number of suitable habitats under high-emission scenarios (SSP585) by the 2090s, including a loss of over 80% of highly suitable areas. Centroid movements also diverged across the scenarios: a southwestern shift under SSP126 and SSP245 contrasted with a southeastern shift under SSP585, with each accompanied by significant habitat fragmentation. Key climate refugia were identified primarily in central Taiwan Province and the mountainous zones of Zhejiang and Fujian Provinces, which should be prioritized for conservation activities. These insights offer a foundational understanding for the conservation of D. macropodum and other ecologically similar subtropical evergreen species. However, direct extrapolation to other taxa should be made cautiously, as specific responses may vary based on differing ecological tolerances and dispersal capacities. Further research is needed to test the generalizability of these patterns across diverse plant functional types. Full article

Gasoline is the most common ignitable liquid used to initiate fires, making its detection and identification in fire debris crucial for determining incendiary origins. Fire debris is typically collected after extinguishment and safety clearance, often resulting in gasoline weathering, especially when delayed. Most research on gasoline weathering has been conducted in controlled laboratory settings in temperate climates. However, the effects of tropical conditions on the rate of gasoline weathering and the resulting chemical composition of volatiles remain largely unexplored. Understanding how tropical environmental factors alter gasoline weathering is essential for accurate fire debris interpretation in such regions. This study investigates how tropical climates impact gasoline weathering indoors and outdoors. Weathered samples were prepared by volume reduction method, gradually evaporating gasoline from 10% to 95%. Indoor samples were exposed to room temperature, while outdoor samples were left in open space under natural tropical conditions. Gas Chromatography/Mass Spectrometry (GC-MS) analysis revealed chromatographic shifts in heavier compounds (C₃–C₄ alkylbenzenes) compared to lighter ones like toluene as weathering progressed. Correlation between indoor and outdoor samples was high (>0.970) at 10–50% weathering but declined (<0.600) at 90–95%, indicating differing patterns. All target compounds remained detectable across all samples. Full article

Seawater intrusion into coastal river systems poses increasing challenges for freshwater availability and estuarine ecosystem integrity, especially under evolving climatic and anthropogenic pressures. This study presents a multidisciplinary investigation of marine intrusion dynamics within the Magra River estuary (Northwest Italy), integrating field monitoring, isotopic tracing (δ¹⁸O; δD), and multivariate statistical modeling. Over an 18-month period, 11 fixed stations were monitored across six seasonal campaigns, yielding a comprehensive dataset of water electrical conductivity (EC) and stable isotope measurements from fresh water to salty water. EC and oxygen isotopic ratios displayed strong spatial and temporal coherence (R² = 0.99), confirming their combined effectiveness in identifying intrusion patterns. The mass-balance model based on δ¹⁸O revealed that marine water fractions exceeded 50% in the lower estuary for up to eight months annually, reaching as far as 8.5 km inland during dry periods. Complementary δD measurements provided additional insight into water origin and fractionation processes, revealing a slight excess relative to the local meteoric water line (LMWL), indicative of evaporative enrichment during anomalously warm periods. Multivariate regression models (PLS, Ridge, LASSO, and Elastic Net) identified river discharge as the primary limiting factor of intrusion, while wind intensity emerged as a key promoting variable, particularly when aligned with the valley axis. Tidal effects were marginal under standard conditions, except during anomalous events such as tidal surges. The results demonstrate that marine intrusion is governed by complex and interacting environmental drivers. Combined isotopic and machine learning approaches can offer high-resolution insights for environmental monitoring, early-warning systems, and adaptive resource management under climate-change scenarios. Full article

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points. Full article

Species distribution models were built for three short-palped crane fly species of the genus Dicranomyia: Dicranomyia affinis, Dicranomyia chorea, and Dicranomyia mitis. The main objective of this study was to assess potential habitat suitability in undersampled regions and explore differences in environmental space. Dicranomyia affinis was historically considered a variety of Dicranomyia mitis due to their morphological similarity. In contrast, Dicranomyia chorea is a widespread species. The biology and ecology of these species remain poorly understood. Models were developed using Maxent, a widely used tool. Our results indicate that Dicranomyia affinis and Dicranomyia chorea share highly similar predicted habitat suitability, with high suitability across the Mediterranean, Central, and Northern Europe, moderate suitability in Eastern Europe, and low suitability in Central Asia. In contrast, Dicranomyia mitis is predicted to have greater habitat suitability in Eastern Europe and Scandinavia, with lower suitability in Mediterranean regions. Analysis of variable importance revealed possible ecological differences between the species. While climatic factors primarily influenced the models for Dicranomyia affinis and Dicranomyia chorea, Dicranomyia mitis was more strongly influenced by the variable pH. These findings may provide insights into potential distributions in undersampled areas and improve our understanding of the species’ ecology. Full article