Research on the Measurement of the Development Level of China's Provincial Digital Economy and Its Temporal and Spatial Differentiation Based on the AHP-Entropy Weight Method

In the wave of global digital transformation, the scale of China's digital economy has continued to expand. However, due to differences in resource endowments, industrial bases, and policy orientations among different provinces, there are significant temporal and spatial disparities in its development level. This has affected the release of the overall effectiveness of the digital economy and regional coordinated development. This study constructs an evaluation system covering 4 first - level dimensions and 11 second - level indicators, including digital infrastructure, digital industry development, digital innovation, and the digital economy environment. By using the AHP - entropy weight method, it measures and analyzes the development level of the digital economy in 31 provinces of China from 2015 to 2024. The results show that: the eastern region has outstanding advantages and has formed an “eastern innovation pole”, while the central, western, and northeastern regions mostly rank in the middle - low echelons; the development of each dimension is unbalanced, with problems such as a disconnection between innovation and industry, large regional differences in infrastructure, and insufficient support from the digital economy environment; the potential for regional collaboration has not been fully exploited, cross - regional data circulation is hindered, industrial collaboration is characterized by “strong points but weak chains”, and the matching degree between computing power supply and demand is low

Adenylyl cyclase 1 mitigates doxorubicin-induced cardiotoxicity by reducing oxidative stress and myocardial damage

Doxorubicin (DOX), a commonly used ant-cancer drug, has its clinical application severely restricted due to its serious cardiotoxic side-effects. Adenylyl cyclase 1 (ADCY1) is recognized for its pivotal role in cardiac pathophysiology. Nonetheless, the involvement of ADCY1 in DOX-induced cardiotoxicity (DIC) remains not well-studied. This study aimed to elucidate the role of ADCY1 in DIC, in particular whether ADCY1 affects oxidative stress and myocardial injury. Our findings indicate that ADCY1 expression is downregulated in DOX-treatment AC16 cardiomyocytes with a DOX concentration- dependent manner. Silencing of ADCY1 further exacerbated DIC. It was manifested by the elevated levels of ANP, BNP, LDH oxidative stress compared with DOX treatment group. Conversely, over expression of ADCY1 mitigated these adverse effects induced by DOX. Our study elucidates the cardioprotective effects of ADCY1 by attenuating DOX-induced myocardial injury, oxidative stress . It provides a novel perspective for the development of therapeutic strategies aimed at reducing cardiac toxicity in patients undergoing DOX treatment

Optimizing Supply Chain Management to Reduce Food Waste and Loss in Agriculture

Food excess and damage in agriculture contemporary substantial experiments to worldwide food safekeeping and supply organization. In the existing situation, a considerable serving of food does not ever influence clients due to inadequacies in collecting, packing, conveyance, and dispensation. This question not only outcomes in commercial fatalities for farmers but also subsidizes conservational difficulties such as greenhouse gas discharges from decomposing surplus. The problematic declaration highlights that despite progress in cultivated methods, food leftover residues are a determined matter. Several approaches have been working to report this, including enhanced gathering techniques, healthier packing clarifications, well-organized source sequence organization, enhanced processing and packaging, and education and training for farmers. However, the efficiency of these approaches varies, and an incorporated method is essential to accomplish momentous consequences. This investigation recommends augmenting source sequence administration as a crucial approach to diminish food excess and damage in agriculture. By reorganizing logistics and enhancing coordination from farm to market, the proposed system aims to minimize delays and spoilage, ensuring that food products reach regulars in optimum condition. The incorporation of supply chain management with other approaches, such as enhanced loading resolutions and superior handling, is also discovered to deliver a wide-ranging solution. The consequences of this investigation determine that well-organized supply chain management suggestively diminishes food excess, leading to better-quality agricultural efficiency and sustainability. This method offers an accessible and adjustable resolution that can be applied transversely to numerous agricultural contexts, eventually subsidizing worldwide determinations to augment food safekeeping and source proficiency

Strengthening Coastal Fishing Communities: Lessons from Global Responsible Market Systems for Resilience in Bangladesh

Coastal fishing communities in Bangladesh are grappling with significant challenges posed by climate change and economic difficulties. These include burdensome loans, restricted market access, and the aftermath of natural disasters. This article seeks to introduce a tailored Responsible Market Systems (RMS) model that aims to bolster the resilience of these communities, drawing inspiration from successful initiatives in Southeast Asia. We explore effective RMS frameworks in Southeast Asia, highlighting essential components such as financial inclusion and market access, cooperatives, Community Revolving Funds (CRFs), and Public-Private Partnerships (PPPs). These elements are adapted to fit the unique context of Bangladesh’s coastal fishing communities. The proposed model advocates for financial inclusion through CRFs, enhanced market access via cooperatives, and resilient infrastructure development through PPPs. Key strategies include skill development, using digital financial tools, and creating diversified income streams. For successful execution, it is vital to strengthen local governance, foster community organization and financial literacy, and tackle environmental issues. A gradual approach with pilot projects and continuous monitoring is crucial. Implementing this RMS model in stages, with a focus on fair income distribution and environmental sustainability, is vital for achieving long-lasting success and equitable outcomes in Bangladesh’s coastal fishing communities

Effect of Vacuum Filtration Pressure on The Harvesting Efficiency and Phycocyanin Quality of

Spirulina platensis, a blue-green microalgae classified as cyanobacteria, is valued for its nutritional content and pigments, particularly phycocyanin. This study aims to determine the effect of differences in vacuum filtration pressure on the filtration performance and quality of the Spirulina platensis produced. The experimental stages involved cultivation, vacuum filtration, drying, and phycocyanin extraction. The vacuum pressure treatments were 0, 5, 10, and 15 kPa. Vacuum filtration could improve quality speed and harvest time parameters compared to without vacuum filtration (0 kPa pressure). The vacuum pressures were not significantly differences in filtration time, rejection value, dry biomass weight, and water content. However, flux at 15 kPa showed a significantly different value than 5 and 10 kPa. Wet biomass weight was significantly different in the 10 kPa treatment. The results of phycocyanin extraction did not show significant differences in phycocyanin pigment concentration and yield. Analysis of antioxidant activity using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method also showed no significant differences

AI-Powered Soil Nutrient Mapping for Optimized Crop Rotation Strategies with Advanced Multispectral Analysis and Precision variable Rate Technology

Soil nutrient management is an important factor for the maximum crop production and sustainable agricultural practices. To cater to this need, the research explores the integration of the highest technology on cutting edge AI-powered technologies to boost soil nutrient mapping in tandem with optimising crop rotation strategies. The study apply Variable Rate Technology (VRT), the application of fertilizers that are both precise and adaptive to on going, real time gain on soil it allows savings in fertiliser and reduces wastes. To make data analysis more dynamic and hence more accurate in nutrient predictions, and allow for more responsive management practices, dynamic time warping and gradient boosting machines have been taken into consideration. Socket on MQTT (Message Queuing Telemetry Transport) is ideal for the continuous and real time communication of data between the soil sensors and the decision making platforms to allow for a timely adjustment of the nutrient application. Through Analysis of light in multiple wavelengths known as Multispectral Analysis, a complete overview of soil health is given and specific nutrient deficiencies can be identified and relevant fertilization procedures modified. In addition to this, Sigfox technology also enables long range and reliable communication for the remote soil monitoring, allowing a large scale or geographically dispersed agricultural operations to be manageable. And the proposed approach achieved significant improvement in Nutrient Uptake Efficiency Ratio (NUER) whereas it increased by 7% on average across field zones, Soil Nutrient Variability Coefficient (SNVC) was reduced by 15%, and Soil Nutrient Response Factor (SNRF) has better efficiency in 12%. The findings thus support the successful merging of these technologies to facilitate the improvement of soil nutrient management and support the practice of sustainable agriculture

AI-Based Linguistic Frameworks for Monitoring Physical Health, Sports Performance, and Educational Contexts

Advancements in discourse analysis techniques on health and sports data streams are challenging traditional conceptions of diagnostic validity and educational feedback systems, and in the process, opening up windows of opportunity for redefining the interpretive logics associated with language-mediated monitoring systems. As little is known about where computational linguistics-based health inference is gaining momentum beyond clinical diagnostics and performance analytics, the purpose of this study is to map in what clusters of application domains it is perceived to gain traction. Drawing on data from network-based visualizations and structural equation models in multimodal datasets, we identify a long tail of embedded constructs and relational dependencies in which a total of 76 unique conceptual nodes operate, including predictors such as lexical calibration, contextual sentiment attribution, and semantic load dispersion. Our findings reveal a strong, positive correlation coefficient (r = 0.82) between semantic coherence and predictive decision consistency. However, target users do not passively comply. Rather, their perceptual feedback loops and adaptive interpretations are integrated into the iterative refinement of monitoring algorithms. The paper concludes by identifying emerging linguistic bottlenecks, reflecting on the application of AI-driven linguistic inference in the field of education and sports physiology, and proposing suggestions for scalable framework deployment. The resulting insights enrich understandings of the workings of semantic computing architectures in experiences of personalized health diagnostics and intelligent educational environments

The Impact of Subsistence Insurance on Employment

This study investigates the impact of subsistence insurance on employment within China’s urban social security and welfare network (SSWN), focusing on long-term dependency patterns and their underlying causes. Subsistence insurance is an essential constituent of the SSWN, however; its effect on employment remains underexplored. Prior research has primarily examined employment willingness, leaving gaps in understanding the role of “receipt duration” in fostering dependency. This study uses event history analysis to assess the relationship between subsistence insurance and employment outcomes, offering insights into welfare dependency in a rapidly transitioning economy. The study leverages data from the Urban Household Survey (2010-2020), analyzing employment status, demographic factors, and regional economic indicators. Regression and fixed-effects models reveal that while subsistence insurance provides crucial financial support, prolonged reliance may negatively impact labor market engagement. Regional disparities are also examined, with stronger negative employment effects in more developed areas. The findings have significant policy implications, suggesting that reforming welfare policies to promote long-term employability and reduce dependency is essential. This study contributes to the broader understanding of welfare mechanisms in transitional economies and informs policymakers on enhancing social support systems