Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.

Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.

This research explores the role of digital economy in driving agricultural development in the BIMSTEC region, which includes Thailand, Myanmar, Sri Lanka, Nepal, India, Bangladesh and Bhutan (with Bhutan excluded due to data limitations) with a particular focus on mobile technologies, computing capacity and internet connectivity which were the most readily available data points for BIMSTEC. Using a combination of document analysis, and panel data analysis with the data covering 10 years (2012–2021), the study examines the interplay of key digital technologies with agricultural growth while controlling for factors including water usage, fertilizer consumption, and land temperature and agricultural land area. The analysis incorporates additional variables such as infrastructure development, credit to agriculture, investment in agricultural research, and education level. The findings reveal a strong positive correlation between mobile technology, Internet and computing capacity in BIMSTEC. This study underscores that digital tools are pivotal in enhancing agricultural productivity, yet their impact is significantly combined with investment in infrastructure and education. This study suggests that digital solutions, when strategically integrated with broader socio-economic factors can effectively challenges in developing countries, particularly in rural and underserved regions. This research contributes to the growing body of literature on digital economy in agriculture, highlighting how digital technologies can foster agricultural productivity in developing countries.

Phytochemical and antioxidant analysis of some varieties of Capsicum was evaluated. Mature Capsicum varieties were collected across the State. The seeds were removed, sun-dried for 3 days, stored for 2 weeks at 15 ºC–25 ºC in polythene bags before planting. Saponins, tannins, flavonoids, alkaloids and cardiac glycosides were present in abundant, moderate and trace amounts. Combined anthraquinones were absent in all varieties. Yellow (0.810 ± 0.0006 µg/mL), red long dry (0.211 ± 0.0006 µg/mL) and round peppers (2.527 ± 0.0003 µg/mL) had the largest values for total phenol, flavonoids and tannins. Shombo and yellow peppers had the largest (0.270 ± 0.002 µg/mL) and least (0.102 ± 0.001 µg/mL) capsaicin content. The antioxidant activities varied across the varieties. At 100 µg/mL of methanol, yellow (45%) and round peppers (45%) had largest mean absorbances for 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) Radical Scavenging Activity while sub-shombo pepper (23%) had the least. For Ferric Reducing Antioxidant Power (FRAP), yellow (0.63 ± 0.001 µg/mL) and sub-shombo peppers (0.55 ± 0.001µg/mL) had the largest and least values at 100 µg/mL of methanol. At 100 µg/mL of methanol, red long dry (0.112 ± 0.001) and shombo peppers (0.101 ± 0.001) had the largest and least values for the nitric oxide scavenging activity. This study shows that Capsicum varieties exhibit bioactive componds similarities and variations with implications in hybridization, taxonomy and conservation.

Introduction: New energy vehicles (NEVs) refer to automobiles powered by alternative energy sources to reduce reliance on fossil fuels and mitigate environmental impacts. They represent a sustainable transportation solution, aligning with global efforts to promote energy efficiency in the automotive sector. Aim: The purpose of this research is to investigate the influence of social demand on the business model of NEVs. Through a comprehensive analysis of consumer preferences and market dynamics, the research aims to identify strategies for driving the sustainable growth of the NEV industry in respond to societal demands. Research methodology: We conduct a questionnaire survey on 2415 individuals and evaluated that questionnaire data by multifactor analysis of variance to examine individual consumer characteristics. We employed NOVA to evaluate the differences in market penetration factors. Additionally, a regression analysis model is utilized to examine accessibility element’s effects on the consumer’s intensions to buy, addressing categorical and ordered data requirements effectively. Research findings: This research demonstrates that middle-aged and adolescent demographics show the highest willingness to purchase NEV’s, particularly emphasizing technological advancements. Consumer preferences vary based on focus like NEV type, model and brand, necessitating tailored marketing strategies. Conclusion: Improving perception levels and addressing charging convenience and innovative features are vital for enhancing market penetration and sustainable business growth in the NEV industry.