Search Results (31,506)

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. 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

Rapid socioeconomic development has continuously driven urban land expansion at the expense of other land types, leading to significant changes in land use and environment. However, existing studies still lack fine-resolution, long-term projections of urban land. Using seven periods of land use data from 1990 to 2020, this study projects urban land in the Yangtze River Delta (YRD) region under the framework of Shared Socioeconomic Pathways (SSPs). A multiple linear regression model and the land use change scenario simulation model (GeoSOS-FLUS) were employed to make projection at a high spatial resolution of 1 km. The findings are as follows: (1) From 1990 to 2020, the rate of urban land expansion in the study area showed a pattern of initial acceleration followed by deceleration, with the average annual expansion rate decreasing from 1.36 × 10³ km² to 0.24 × 10³ km². The center of gravity shifted toward the southeast. (2) Future urban land expansion is projected to increase by 14 × 10³ km² (SSP3) to 48 × 10³ km² (SSP5). The northern and central parts of the region will experience more significant growth, and the center of gravity is projected to shifting northwest. (3) Under SSP2 and SSP5, the urban land will increase continuously. The findings can offer a valuable insight for regional planning and sustainable development. Full article

The rapid growth of 5G base stations (BSs) and electric vehicles (EVs) introduces significant challenges for distribution network operation due to high energy consumption and variable loads. This paper proposes a coordinated day-ahead scheduling framework that integrates 5G BS task migration, storage utilization, and EV charging or discharging with mobility constraints. A mixed-integer second-order cone programming (MISOCP) model is formulated to optimize network efficiency while ensuring reliable power supply and maintaining service quality. The proposed approach enables dynamic load adjustment via 5G computing task migration and coordinated operation between 5G BSs and EVs. Case studies demonstrate that the proposed method can effectively generate an optimal day-ahead scheduling strategy for the distribution network. By employing the task migration strategy, the computational workloads of heavily loaded 5G BSs are dynamically redistributed to neighboring stations, thereby alleviating computational stress and reducing their associated power consumption. These results highlight the potential of leveraging the joint flexibility of 5G infrastructures and EVs to support more efficient and reliable distribution network operation. Full article

Model-based approaches provide increasingly advanced opportunities for optimizing and accelerating bioprocess development. However, to accurately capture the complexity of biotechnological processes, continuous refinement of suitable models remains essential. A crucial gap in this field has been the lack of suitable model for describing Escherichia coli growth in cultivation media containing yeast extract, while accounting for key bioprocess parameters such as biomass, substrate, acetate, and oxygen. To address this, a published mechanistic macro-kinetic model for E. coli was extended with a set of mathematical equations that describe key aspects of the uptake of yeast extract. The underlying macro-kinetic approach is based on the utilization of amino acids in E. coli, where growth is primarily influenced by two distinct classes of amino acids. Using fed-batch cultivation data from an E. coli K-12 strain supplemented with yeast extract, it was demonstrated that the proposed model extensions were essential for accurately representing the bioprocess. This approach was further validated through fitting the model on cultivation data from five different yeast extracts sourced from various manufacturers. Additionally, the model enabled reliable predictions of growth dynamics across a range of yeast extract concentrations up to 20 g L⁻¹. Further differentiation of the data into batch and fed-batch revealed that for less complex datasets, such as those obtained from a batch phase, a simplified model can be sufficient. Due to its modular structure, the developed model provides the necessary flexibility to serve as a tool for the development, optimization, and control of E. coli cultivations with and without yeast extract. Full article

Background: Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy with poor clinical outcomes. microRNA-7-5p (miR-7-5p) has been described as both a tumour suppressor and an oncomiR depending on the tissue context, but its role in HNSCC remains unclear. This study aimed to clarify the clinical significance and biological function of miR-7-5p in HNSCC by integrating data from multiple sources. Methods: A systematic review of the literature was conducted to identify studies analysing miRNA expression in human head and neck tissues. A meta-analysis of individual patient data from Gene Expression Omnibus (GEO), ArrayExpress, and The Cancer Genome Atlas (TCGA) was performed to assess miR-7-5p expression in tumours and normal tissues, and its associations with clinical parameters and prognostic outcomes. Bioinformatics analyses were used to predict miR-7-5p target genes, classify hub genes, and perform gene ontology enrichment analysis. MicroRNA in situ hybridisation (miRNA ISH) and real-time quantitative PCR (RT-qPCR) were conducted on tissue samples, HNSCC cell lines, and an in vitro model of oral oncogenesis to validate miR-7-5p expression patterns. Results: miR-7-5p was significantly upregulated in tumours compared to normal tissues and associated with larger tumour size, HPV-negative status, poor disease-specific survival, and shorter progression-free intervals. Bioinformatics analysis highlighted miR-7-5p target genes enriched in pathways related to cell growth, survival, and tumourigenesis. Despite evidence supporting the anti-cancer role of exogenous miR-7-5p in preclinical models, the observed endogenous upregulation in tumours suggests that miR-7-5p expression may represent a compensatory or stress-responsive mechanism during tumourigenesis, rather than acting as a primary oncogenic driver. Conclusions: This study provides new insights into the complex role of miR-7-5p in HNSCC, supporting its potential as both a biomarker and a therapeutic target. Understanding the context-specific functions of miR-7-5p is essential for its development as an RNA-based therapeutic in HNSCC. 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

The advancement of green energy is a crucial mechanism for balancing economic growth with environmental sustainability, helping to mitigate conflicts between development and ecological preservation. This paper assesses the policy effects of the Belt and Road Initiative (BRI) on China’s overseas green energy projects (including gas) using the difference-in-difference (DID) model from 2009 to 2022. The findings show that, overall, the BRI has notably augmented China’s green energy projects in the BRI countries. This result remains robust after excluding potential interference from Nationally Determined Contributions (NDCs). Specifically, its promotional effect shows heterogeneity. Firstly, the BRI has shown significant regional differences in promoting the development of China’s overseas green energy projects. Secondly, the BRI is more effective in promoting green energy projects in developing and low-risk countries compared to developed and high-risk countries. Additionally, it indicates that the BRI boosts green energy projects in BRI countries by enhancing their infrastructure quality, encompassing transportation, energy, communication, and financial infrastructure. Finally, based on the above findings, this paper provides context-specific recommendations aimed at enhancing the effectiveness of the BRI in promoting sustainable green energy cooperation. Full article

Survival rates for those with metastatic osteosarcoma (OS) have not improved over the last four decades. It is imperative that novel approaches to treating and curing OS be sought. We, therefore, turned our attention to Brusatol (Bru), a naturally occurring Nrf2 inhibitor reported to elicit anti-cancer effects in a multitude of tumour models. Importantly there is emerging evidence that Nrf2 is implicated in chemoradiotherapy resistance in OS and that inhibiting Nrf2 may represent a desirable route to treating OS. Surprisingly, using the human OS cell line, MG63, we actually found that Bru promoted cell growth. Compared to control, normoxic cultures, the application of Bru (50 nM) over 3 days led to an increase in cell number by approximately 1.7-fold. A similar outcome occurred for cells under hypoxic conditions, although the extent of cell growth was significantly less at around 1.3-fold. Furthermore, Bru prevented MG63 differentiation in response to co-treatment with the calcitriol analogue, EB1089, and the lipid growth factor, lysophosphatidic acid. The extent of inhibition was profound at approximately 2.8-fold. The application of the Nrf2 activator, dimethyl fumarate, did not rescue these phenotypes. Whilst Bru has shown promise in other cancer models, it would appear, from our findings, that this agent may not be suitable for the treatment of OS. Full article

This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r₁, r₂) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X₆ and import dependency (X₅) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development. Full article

Background: In late January 2025, a measles outbreak began in Gaines County, Texas, USA, and the outbreak extended to New Mexico. We used a variety of mathematical models to estimate the growth rate of the Texas–New Mexico measles outbreak of 2025. Methods: We used both empirical and mechanistic models based on differential equations to make the estimations that allow us to characterize this measles outbreak. Regarding empirical models, we used the exponential growth model to compute and estimate the growth rate, basic reproduction number, ${\mathcal{R}}_0$, and effective reproduction number ${\mathcal{R}}_t$. With regard to mechanistic models, we use the SIR and SEIR models to estimate the growth rate, basic reproduction number ${\mathcal{R}}_0$, and effective reproduction number ${\mathcal{R}}_t$. We used new weekly measles cases and also cumulative cases. Results: Using the exponential growth model, we estimated a basic reproduction number between 32 and 40. For the classical SIR model, we estimated that the basic reproduction number is approximately 30. Conclusion: We found that the current Texas–New Mexico measles outbreak of 2025 has a slightly higher growth rate and effective reproduction number ${\mathcal{R}}_t$ compared to several previous measles outbreaks around the world. Full article

The aim of the present study is to evaluate the impact of chromium (Cr) supplementation on glucose and lipid metabolism in breast muscle in broilers under heat stress. A total of 220 day-old broiler chicks were reared in cages. At 29 days old, 180 birds were randomly assigned to three treatments (0, 400, and 800 µg Cr/kg, as chromium picolinate) and transferred to climate chambers (31 ± 1 °C, 60 ± 7% humidity) for 14 days. Growth performance, carcass traits, serum biochemical indices, fasting glucose and insulin, homeostasis model assessment of insulin resistance (HOMA-IR), as well as muscle metabolomic profiles and gene expression related to energy and lipid metabolism were analyzed. The results showed that, compared with the heat stress group, the groups supplemented with 400 and 800 µg Cr/kg showed higher dry matter intake and average daily gain, breast muscle ratio, and lower feed conversion ratio and abdominal fat ratio; chickens supplemented with 400 and 800 µg Cr/kg showed significantly lower serum corticosterone (CORT), free fatty acids, and cholesterol levels compared with the heat stress (HS) group (p < 0.05). Fasting blood glucose and HOMA-IR were also significantly reduced, while fasting insulin was significantly increased in the Cr-supplemented groups (p < 0.05). Metabolomic analysis revealed that Cr supplementation regulated lipid and amino acid metabolism by altering key metabolites such as citric acid, L-glutamine, and L-proline, and modulating pathways including alanine, aspartate, and glutamate metabolism, and glycerophospholipid metabolism. Furthermore, Cr supplementation significantly upregulated the expression of Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1 α (PGC-1α), ATP Binding Cassette Subfamily A Member 1 (ABCA1), Peroxisome Proliferator-Activated Receptor α (PPARα), and ATP Binding Cassette Subfamily G Member 1 (ABCG1) in both the hepatic and muscle tissue. This paper suggested that chromium supplementation may enhance energy metabolism and lipid transport like the findings of our study suggested. Full article

While it is undisputed that Poisson GLMs represent the gold standard for counting COVID-19 deaths, recent studies have analyzed the seasonal growth and decline trends of these deaths in Italy using a simple segmented linear regression. They found that, despite an overall decreasing trend throughout the entire period analyzed (2021–2025), rising mortality trends from COVID-19 emerged in all summers and winters of the period, though they were more pronounced in winter. The technical reasons for the general unsuitability of using linear regression for the precise counting of deaths are well-known. Nevertheless, the question remains whether, under certain circumstances, the use of linear regression can provide a valid and useful tool in a specific context, for example, to highlight the slopes of seasonal growth/decline in deaths more quickly and clearly. Given this background, this paper presents a comparison between the use of linear regression and a Poisson GLM with the aforementioned death data, leading to the following conclusions. Appropriate statistical hypothesis testing procedures have demonstrated that the conditions of a normal distribution of residuals, their homoscedasticity, and the lack of autocorrelation were essentially guaranteed in this particular Italian case (weekly COVID-19 deaths in Italy, from 2021 to 2025) with very rare exceptions, thus ensuring the acceptable performance of linear regression. Furthermore, the development of a Poisson GLM definitively confirmed a strong agreement between the two models in identifying COVID-19 mortality trends. This was supported by a Kolmogorov–Smirnov test, which found no statistically significant difference between the slopes calculated by the two models. Both the Poisson and the linear model also demonstrated a comparably high accuracy in counting COVID-19 deaths, with MAE values of 62.76 and a comparable 88.60, respectively. Based on an average of approximately 6300 deaths per period, this translated to a percentage error of just 1.15% for the Poisson and only a slightly higher 1.48% for the linear model. Full article

Identifying suitable areas for ecosystem services (ES) development is essential for balancing economic growth with environmental sustainability in ecologically fragile regions. However, existing studies often neglect integrating future climate and socioeconomic drivers into ES optimization, hindering the design of robust strategies for sustainable resource management. In this study, we propose a novel framework integrating the System Dynamics (SD) model, the Patch-based Land Use Simulation (PLUS) model, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and the Dynamic Bayesian Network (DBN) to optimize ES patterns in the Sanjiangyuan region under three climate scenarios (SSP126, SSP245, and SSP585) from 2030 to 2060. Our results show the following: (1) Ecological land (forest) expanded by 0.86% under SSP126, but declined by 11.54% under SSP585 due to unsustainable land use intensification. (2) SSP126 emerged as the optimal scenario for ES sustainability, increasing carbon storage and sequestration, habitat quality, and water conservation by 3.2%, 1%, and 1.4%, respectively, compared to SSP585. (3) The central part of the Sanjiangyuan region, characterized by gentle topography and adequate rainfall, was identified as a priority zone for ES development. This study provides a transferable framework for aligning ecological conservation with low-carbon transitions in global biodiversity hotspots. Full article

Antiviral peptides (AVPs) are promising therapeutic candidates, yet the rapid growth of sequence data and the field’s emphasis on predictors have left a gap: the lack of an integrated view linking peptide chemistry with biological context. Here, we map the AVP landscape through interactive data mining using Half-Space Proximal Networks (HSPNs) and Metadata Networks (MNs) in the StarPep toolbox. HSPNs minimize edges and avoid fixed thresholds, reducing computational cost while enabling high-resolution analysis. A threshold-free HSPN resolved eight chemically and biologically distinct communities, while MNs contextualized AVPs by source, function, and target, revealing structural–functional relationships. To capture diversity compactly, we applied centrality-guided scaffold extraction with redundancy removal (90–50% identity), producing four representative subsets suitable for modeling and similarity searches. Alignment-free motif discovery yielded 33 validated motifs, including 10 overlapping with reported AVP signatures and 23 apparently novel. Motifs displayed category-specific enrichment across antimicrobial classes, and sequences carrying multiple motifs (≥4–5) consistently showed higher predicted antiviral probabilities. Beyond computational insights, scaffolds provide representative “entry points” into AVP chemical space, while motifs serve as modular building blocks for rational design. Together, these resources provide an integrated framework that may inform AVP discovery and support scaffold- and motif-guided therapeutic design. Full article