Search Results (26,748)

The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article

With global climate change, urbanization, and agricultural resource limitations, precision agriculture and crop monitoring are crucial worldwide. Integrating multi-source remote sensing data with deep learning enables accurate crop mapping, but selecting optimal network architectures remains challenging. To improve remote sensing-based fruit planting classification and support orchard management and rural revitalization, this study explored feature selection and network optimization. We proposed an improved CF-EfficientNet model (incorporating FGMF and CGAR modules) for fruit planting classification. Multi-source remote sensing data (Sentinel-1, Sentinel-2, and SRTM) were used to extract spectral, vegetation, polarization, terrain, and texture features, thereby constructing a high-dimensional feature space. Feature selection identified 13 highly discriminative bands, forming an optimal dataset, namely the preferred bands (PBs). At the same time, two classification datasets—multi-spectral bands (MS) and preferred bands (PBs)—were constructed, and five typical deep learning models were introduced to compare performance: (1) EfficientNetB0, (2) AlexNet, (3) VGG16, (4) ResNet18, (5) RepVGG. The experimental results showed that the EfficientNetB0 model based on the preferred band performed best in terms of overall accuracy (87.1%) and Kappa coefficient (0.677). Furthermore, a Fine-Grained Multi-scale Fusion (FGMF) and a Condition-Guided Attention Refinement (CGAR) were incorporated into EfficientNetB0, and the traditional SGD optimizer was replaced with Adam to construct the CF-EfficientNet architecture. The results indicated that the improved CF-EfficientNet model achieved high performance in crop classification, with an overall accuracy of 92.6% and a Kappa coefficient of 0.830. These represent improvements of 5.5 percentage points and 0.153, compared with the baseline model, demonstrating superiority in both classification accuracy and stability. Full article

The construction sector is a major contributor to climate change, with embodied carbon emissions from building materials representing a critical share of its environmental footprint. Selecting zero-carbon materials is therefore essential for reducing life-cycle emissions while advancing global climate goals. This study investigates six decision-making factors, including cost-effectiveness, durability, buildability, embodied carbon, availability, and aesthetics, and evaluates four alternative materials (wood, hemp, rammed earth, and straw bale) in the New Zealand context. A survey of 203 industry professionals was analysed using descriptive statistics, one-sample t-tests, and structural equation modelling (SEM). Using a 5-point Likert scale, the survey assessed six factors affecting material choice: cost-effectiveness, durability, buildability, embodied carbon, aesthetics, and material availability. Descriptive and inferential analyses were performed using SEM via Partial Least Squares analysis. The results revealed that embodied carbon and material availability were the most influential factors shaping zero-carbon material selection. Among the available alternatives, hemp emerged as the most preferred material, while cost-effectiveness and wood showed moderate impacts, and aesthetic considerations had the least influence. These findings highlight that environmental performance and practical accessibility are central drivers of decision-making when adopting zero-carbon materials. This study contributes to developing effective strategies for promoting the widespread adoption of zero-carbon materials, thereby supporting New Zealand’s progress toward achieving the Sustainable Development Goals and the 2030 Agenda for reducing greenhouse gas emissions. Full article

In the Republic of Korea, the problems of low birth rate and population aging are accelerating population decline at the regional level, leading to the phenomena of local extinction and urban shrinkage. These phenomena, coupled with the projected nationwide population decline, pose a fundamental threat to the sustainability of essential infrastructure such as urban parks. The conventional growth-oriented paradigm of urban planning has shown clear limitations in quantitatively forecasting future demand, constraining proactive management strategies for the era of population decline. To address this gap, this study develops a policy-decision-support framework that integrates long-term population projections, grid-based population data, the DEGURBA urban classification system—a global standard for delineating urban and rural areas— and network-based accessibility analysis. For the entire Republic of Korea, we (1) constructed a 1 km resolution time-series population dataset for 2022–2072; (2) applied DEGURBA to quantify transitions among urban, semi-urban, and rural types; and (3) assessed changes in potential user populations within the defined service catchments. The results indicate that while population concentration in the Seoul Capital Area persists, under the low-variant scenario, a projected average decline of 40% in potential user populations by 2072 will lead to significant functional changes, with 53.6% of municipalities nationwide transitioning to “semi-urban” or “rural” areas. This spatial shift is projected to decrease the proportion of urban parks located in “urban” areas from 83.3% to 75.0%, while the total potential user population is expected to plummet from approximately 44.4 million to 25.8 million, a 42.0% reduction. This study underscores the need for urban park policy to move beyond quantitative expansion and toward quality-oriented management based on selection and concentration. By uniquely integrating long-term demographic scenarios, the Degree of Urbanization (DEGURBA), and spatial accessibility analysis, this study provides a foundational scientific basis for forecasting future demand and supports the formulation of sustainable, data-driven strategies for urban park restructuring under conditions of demographic change. Full article

Food spoilage and contamination remain pressing global challenges, undermining food security and safety while driving economic losses. Conventional preservation strategies, including thermal treatments, refrigeration, and synthetic additives, often compromise nutritional quality and raise sustainability concerns, thereby necessitating natural, effective alternatives. Curcumin, a polyphenolic compound derived from Curcuma longa, has demonstrated broad-spectrum antimicrobial, antioxidant, and anti-inflammatory activities, making it a promising candidate for food preservation. However, its poor solubility, instability, and low bioavailability limit direct applications in food systems. Advances in nanotechnology have enabled the development of nanoformulated curcumin, enhancing solubility, stability, controlled release, and functional efficacy. This review examines the antimicrobial mechanisms of curcumin and its nanoformulations, including membrane disruption, oxidative stress via reactive oxygen species, quorum sensing inhibition, and biofilm suppression. Applications in active and smart packaging are highlighted, where curcumin nanoformulation not only extends shelf life but also enables freshness monitoring through pH-responsive color changes. Evidence across meats, seafood, fruits, dairy, and beverages shows improved microbial safety, oxidative stability, and sensory quality. Multifunctional systems, such as hybrid composites and stimuli-responsive carriers, represent next-generation tools for sustainable packaging. However, challenges remain with scale-up, migration safety, cytotoxicity, and potential promotion of antimicrobial resistance gene (ARG) transfer. Future research should focus on safety validation, advanced nanocarriers, ARG-aware strategies, and regulatory frameworks. Overall, nanoformulated curcumin offers a natural, versatile, and eco-friendly approach to food preservation that aligns with clean-label consumer demand. Full article

A reassessment of the risk-benefit balance of the two lipid nanoparticle (LNP)-based vaccines, Pfizer’s Comirnaty and Moderna’s Spikevax, is currently underway. While the FDA has approved updated products, their administration is recommended only for individuals aged 65 years or older and for those aged 6 months or older who have at least one underlying medical condition associated with an increased risk of severe COVID-19. Among other factors, this change in guidelines reflect an expanded spectrum and increased incidence of adverse events (AEs) and complications relative to other vaccines. Although severe AEs are relatively rare (occurring in < 0.5%) in vaccinated individuals, the sheer scale of global vaccination has resulted in millions of vaccine injuries, rendering post-vaccination syndrome (PVS) both clinically significant and scientifically intriguing. Nevertheless, the cellular and molecular mechanisms of these AEs are poorly understood. To better understand the phenomenon and to identify research needs, this review aims to highlight some theoretically plausible connections between the manifestations of PVS and some unique structural properties of mRNA-LNPs. The latter include (i) ribosomal synthesis of the antigenic spike protein (SP) without natural control over mRNA translation, diversifying antigen processing and presentation; (ii) stabilization of the mRNA by multiple chemical modification, abnormally increasing translation efficiency and frameshift mutation risk; (iii) encoding for SP, a protein with multiple toxic effects; (iv) promotion of innate immune activation and mRNA transfection in off-target tissues by the LNP, leading to systemic inflammation with autoimmune phenomena; (v) short post-reconstitution stability of vaccine nanoparticles contributing to whole-body distribution and mRNA transfection; (vi) immune reactivity and immunogenicity of PEG on the LNP surface increasing the risk of complement activation with LNP disintegration and anaphylaxis; (vii) GC enrichment and double proline modifications stabilize SP mRNA and prefusion SP, respectively; and (viii) contaminations with plasmid DNA and other organic and inorganic elements entailing toxicity with cancer risk. The collateral immune anomalies considered are innate immune activation, T-cell- and antibody-mediated cytotoxicities, dissemination of pseudo virus-like hybrid exosomes, somatic hypermutation, insertion mutagenesis, frameshift mutation, and reverse transcription. Lessons from mRNA-LNP vaccine-associated AEs may guide strategies for the prediction, prevention, and treatment of AEs, while informing the design of safer next-generation mRNA vaccines and therapeutics. Full article

Random walks are considered in a one-dimensional monotonously decreasing energy landscape. To reach the minimum within a region ${\Omega }_{ϵ}$, a number of downhill steps have to be performed. A stochastic model is proposed which captures this random downhill walk and to make a prediction for the average number of steps, which are needed to hit the target. Explicit expressions in terms of a recurrence relation are derived for the density distribution of a downhill random walk as well as probability distribution functions to hit a target region ${\Omega }_{ϵ}$ within a given number of steps. For the case of stochastic optimisation, the number of rejected steps between two successive downhill steps is also derived, providing a measure for the average total number of trial steps. Analytical results are obtained for generalised random processes with underlying polynomial distribution functions. Finally the more general case of non-monotonously decreasing energy landscapes is considered for which results of the monotonous case are transferred by applying the technique of decreasing rearrangement. It is shown that the global stochastic optimisation can be fully described analytically, which is verified by numerical experiments for a number of different distribution and objective functions. Finally we discuss the transition to higher dimensional objective functions and discuss the change in computational complexity for the stochastic process. Full article

Land-use carbon emissions denote the release or sequestration of greenhouse gases (e.g., CO${}_2$, N${}_2$O) resulting from human land-use activities, with land-use changes exerting a major influence on land-use carbon emissions. Revealing the coupling mechanism between land-use changes and carbon emissions is of crucial theoretical significance for achieving “dual carbon” goals and mitigating global climate change. Based on the land-use change data of Jiangxi Province, this study explored the Spatial–temporal relationship between land-use carbon emissions and land-use changes in Jiangxi Province from 2000 to 2020 using a model of land-use dynamic degrees, a model of land-use transfer matrices, and the IPCC carbon emission accounting model. In this study, the factors influencing changes in land-use carbon emissions were comprehensively analyzed using an LMDI model and the Tapio decoupling model. The results indicated that: (1) Jiangxi Province’s land-use changes show a “two-increase, four-decrease” trend, with construction land and unused land experiencing the most significant shifts, while water, grassland, cropland, and forestland changes stayed near 1%. (2) Net land-use carbon emissions exhibit a rapid then gradual increase, with higher emissions in the north/south and lower levels in central regions. While overall land-use carbon emission intensity is declining, per capita emissions continue to rise. (3) Land-use carbon emission changes are primarily driven by emission intensity, land-use structure, efficiency, and economic level. In Jiangxi, economic growth mainly increases land-use carbon emissions, while land-use efficiency enhancement counters this trend. Jiangxi Province shows weak land-use carbon emission–economic growth decoupling, with land-use carbon emissions rising more slowly than economic growth. This study not only provides a typical case analysis and methodological framework for understanding the carbon emission effects of human–land relationships in rapidly urbanizing regions but also offers a specific scientific basis and policy insights for Jiangxi Province and other similar regions to formulate differentiated territorial spatial planning, promote ecological protection and restoration, and achieve green and low-carbon development pathways under the “dual carbon” goals. Full article

Food insecurity, financial loss, and a decline in agricultural output are among the significant challenges to the global food chain caused by extreme climatic events, high variability and change, rapid urbanization, and land degradation. Therefore, it is essential to explore alternative, sustainable agricultural practices to meet the growing population’s food needs. Sustainable agriculture is foundational to farm management, rural development, and water conservation. This includes sustainable practices such as crop rotation, intercropping, and planting crops with varying rooting depths to maximize soil moisture absorption, as well as mulching to improve nutrient recycling and enhance productivity in smallholder cropping systems. The adoption of sustainable agricultural practices has become a priority for smallholder farmers, policymakers, extension agents, and agricultural experts to improve agricultural productivity, contribute to food security, and generate income. However, adoption rates have been slow, especially in Southern Africa, due to a lack of access to technology, financial constraints, limited information, and limited knowledge. This review was conducted using a comprehensive literature search on the adoption of sustainable agricultural practices by legume smallholders, examining various factors that contribute to the failure of legume smallholder farmers to adopt new agricultural practices. The timeframe of the reviewed literature was from 2010 to 2024. The results showed that smallholder farmers face numerous challenges, including limited access to technology, inadequate knowledge, and insufficient financial resources. Research conducted by the Water Research Commission (WRC) indicates that commercial farmers have access to technology, and this group of farmers possesses more substantial financial resources compared to smallholder farmers. In the adoption of sustainable agricultural practices. It is essential to strengthen the linkage between researchers, agricultural extension, and legume smallholder farmers to promote sustainable agricultural practices (SAPs). Smallholder farmers must be informed about such interventions and sustainable agricultural practices to improve rural livelihoods and enhance resilience, adaptation, and responsiveness. Full article

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article

Under the global climate change, variations in climatic elements such as temperature, precipitation, and sunshine duration significantly impact the growth, development, and yield formation of winter wheat. A precise understanding of the impact of climate change on winter wheat growth and the scientific use of meteorological resources are crucial for ensuring food security, optimizing agricultural planting structures and agricultural sustainability. This study uses statistical methods and focuses on the Beijing–Tianjin–Hebei region, utilizing data from 25 meteorological stations from 1961 to 2010 and winter wheat yield data from 1978 to 2010. Twelve refined indicators encompassing temperature, precipitation, and sunshine duration were constructed. Path analysis was employed to determine their weights, establishing a comprehensive climate indicator model. Results indicate: Temperature indicators in the region show an upward trend, with accumulated temperature of the whole growth period increasing at a rate of 61.1 °C·d/10a. Precipitation indicators reveal precipitation of the whole growth period rising at 3.9 mm/10a and pre-winter precipitation increasing at 4.2 mm/10a. Sunshine duration exhibits a declining trend, decreasing at 72.7 h/10a during the whole growth period. Comprehensive climate indicators decrease from south to north, with the southwest region exhibiting the highest tendency rate (18.41), while the central and southern regions show greater variability. This study provides scientific basis for optimizing winter wheat planting patterns and rational utilization of climate resources in the Beijing–Tianjin–Hebei region. It recommends prioritizing cultivation in western southern Hebei and improving water conditions in the central and northern areas through irrigation technology to support sustainable crop production. Full article

Cryptococcus neoformans (C. neoformans) has emerged as a global pathogen of concern. While much is known about its pathobiology, its management is complicated by strains displaying non-fluconazole susceptibility. This contribution assessed the repurposing of artemisinin (ART) as an anti-Cryptococcus antifungal. An in vitro susceptibility assay was performed to assess the drug response of cells. To establish the ART mode of action, assays examining mitochondrial health were set up. The phagocytosis efficiency of a murine macrophage cell line towards ART-treated and non-treated cells was determined. To complement this, the immunomodulatory effects of ART were further characterised in Galleria mellonella (G. mellonella) by assessing haemocytes’ phagocytosis and expression of immune genes, i.e., insect metalloproteinase inhibitor (IMPI) and hemolin, essential for the insect antimicrobial response. In the end, the survival rate of infected larvae was calculated. We established that ART was antifungal, with cell death triggered by the uncoupling of the cytochrome c (cyt c) from the mitochondria, leading to activation of caspase-3-dependent-like apoptosis. Moreover, treatment induced ultrastructural changes with treated cells appearing more deformed than non-treated cells (p < 0.05). Treatment also increased the susceptibility of cells towards both macrophage and haemocyte phagocytosis compared to non-treated cells (p < 0.05). Importantly, treatment seemed to weaken the cells, decreasing their virulence potential based on analysis of the expression of the immune gene markers, which translated into treatment rescuing 75% of the larvae infected with 0.1 ART-treated cells. These preliminary findings support the repurposing of ART as an anti-Cryptococcus antifungal. Full article

Under global warming, drought frequency and its severity have risen notably, posing considerable challenges to vegetation growth. Central Asia (CA), recognized as the largest non-zonal arid zone globally, features dryland ecosystems that are particularly vulnerable to drought stress. This research examines how plant life in CA reacts to prolonged dry spells by analyzing multiple datasets, including drought indices and satellite-derived NDVI measurements, spanning four decades (1982–2022). This study also delves into the compound impact of drought, revealing how its influence on vegetation unfolds through both cumulative stress and delayed ecological responses. Based on the research results, the vegetation coverage in CA exhibited a notable rising tendency from 1982 to 1998. Specifically, it increased at a rate of 4 × 10⁻³ per year (p < 0.05). On the other hand, the direction of this trend shifted to a downward one during the period from 1999 to 2022. During this latter phase, the vegetation coverage decreased at a rate of −4 × 10⁻³ per year (p > 0.05). Vegetation changes in the study area underwent a fundamental reversal around 1998, shifting from widespread greening during 1982–1998 to persistent browning during 1999–2022. Specifically, 98.6% of the region underwent pronounced summer drought stress, which triggered a substantial rise in vegetation browning. The vegetation response to the accumulated and lagged effects of drought varied across seasons, with summer exhibiting the strongest sensitivity, followed by spring and autumn. The lagged effect of drought predominantly influences the vegetation during the growing season and spring, affecting 59.44% and 79.27% of CA, respectively. In contrast, the accumulated effect of drought is more prominent in summer and autumn, affecting 54.92% and 56.52% of CA. These insights offer valuable guidance for ecological restoration initiatives and sustainable management of dryland ecosystems. Full article

On Wednesday, 25 June 2025, the Federal Reserve voted to make changes to the Tier 1 Capital requirements for eight global systemically important banks. The changes include a cut to the enhanced supplementary leverage ratio (eSLR). The purpose of this study is to examine the immediate impact of this announcement on the stock prices of these eight systemically important banks. Using event study analysis and controlling for interest rate movements and general market conditions, we find that most of these banks generated superior returns during the event period. When compared to the KBW Index, however, results are mixed. Some banks do have superior returns even on pre-event day. The heterogeneous effects among banks emphasize that the benefits of capital regulation changes depend on a bank’s size, structure, and scope of operations. Full article

Understanding the relationship between vegetation transpiration and root-zone soil moisture is essential for assessing eco-hydrological processes under global change. However, past studies often looked at only one side, and traditional field observations have the limitations of high cost and poor spatial–temporal continuity. Using daily GLDAS Catchment data from 2004 to 2023, this study investigates the spatiotemporal patterns and interactions between vegetation transpiration and root-zone soil moisture in Fujian Province. The results show that transpiration decreased before 2016 and increased thereafter temporally, with an overall spatial decline. In contrast, the root-zone soil moisture increased before 2016 and then decreased temporally, showing overall spatial growth with significant heterogeneity. A strong negative correlation was found between vegetation transpiration and root-zone soil moisture, particularly in summer and autumn. Among them, vegetation transpiration strongly influenced soil moisture, with increases (or decreases) in transpiration corresponding to decreases (or increases) in soil moisture. Moreover, transpiration changes preceded those in soil moisture, and a significant resonance relationship with a 1- to 2-year cycle was identified. These findings offer insights into the vegetation–soil moisture dynamics in humid subtropical regions, supporting eco-hydrological management under climate change. Full article