The scientific objective of this study is to demonstrate how a hybrid photovoltaic-grid-generator microsystem responds under transient regime to varying loads and grid disconnection/reconnection. The object of the research was realized by acquiring the electrical magnitudes from the three PV systems (25 kW, 40 kW, and 60 kW) connected to the grid and the consumer (on-grid), during the technological process where the load fluctuated uncontrollably. Similar recordings were also made for the transient regime caused by the grid disconnection, diesel generator activation (450 kVA), its synchronization with PV systems, power supply to receivers, and grid voltage restoration after diesel generator shutdown. Analysis of the data focused on power supply continuity, voltage stability, and frequency variations. Findings indicated that on-grid photovoltaic systems had a 7.9% maximum voltage deviation from the standard value (230 V) and a frequency variation within ±1%. In the transient period caused by the grid disconnection and reconnection, a brief period with supply interruption was noted. This study contributes to the understanding of hybrid system behavior during transient regimes.

Cities play a key role in achieving the climate-neutral supply of heating and cooling. This paper compares the policy frameworks as well as practical implementation of smart heating and cooling in six cities: Munich, Dresden and Bad Nauheim in Germany; and Jinan, Chengdu and Haiyan in China, to explore strategies to enhance policy support, financial mechanisms, and consumer engagement, ultimately aiming to facilitate the transition to climate-neutral heating and cooling systems. The study is divided into three parts: (i) an examination of smart heating and cooling policy frameworks in Germany and China over the past few years; (ii) an analysis of heating and cooling strategies in the six case study cities within the context of smart energy systems; and (iii) an exploration of the practical solutions adopted by these cities as part of their smart energy transition initiatives. The findings reveal differences between the two countries in the strategies and regulations adopted by municipal governments as well as variations within each country. The policy frameworks and priorities set by city governments can greatly influence the development and implementation of smart heating and cooling systems. The study found that all six cities are actively engaged in pioneering innovative heating and cooling projects which utilise diverse energy sources such as geothermal, biomass, solar, waste heat and nuclear energy. Even the smaller cities were seen to be making considerable progress in the adoption of smart solutions.

This study investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

This study examines the economic feasibility of the environment-friendly farmland use policy to improve water quality. Conventional highland farming, polluting the Han River basin in South Korea, can be converted into environment-friendly farming through land acquisition or application of pesticide-free or organic farming practices. We estimate the welfare measures of improvement in water quality and the costs of policy implementation for economic analysis. To estimate the economic benefit of improvement in water quality experienced by the residents residing in mid-and-downstream areas of the Han River, the choice experiment was employed with a pivot-style experimental design approach. In the empirical analysis, we converted the household perception for water quality grades into scientific water quality measures using Water Quality Standard to estimate the value of changes in water quality. To analyze the costs required to convert conventional highland farmlands into environment-friendly farmlands, we estimated the relevant cost of land acquisition and the subsidy necessary for farm income loss for organic agricultural practice. We find that the agri-environmental policy is economically viable, which suggests that converting conventional highland farming into environment-friendly farming would make the improvement in water quality visible.

The present study is designed to analyse how the Public-Private Partnership (PPP) model is helping to create sustainable livelihood opportunities for women. It draws an inference from ‘Marudhara Rangsaaz’, a producer company operating in the textile sector in Rajasthan, India. It explains how this woman-based organisation operates in a PPP model to create economic value for women. It also tries to understand the specific role of the Rajasthan Grameen Aajeevika Vikas Parishad (RAJEEVIKA), The Rajasthan Government partner and ‘Rang Sutra’, the private partner, and the women members of ‘Marudhara Rangsaaz’ in the PPP model. The paper adopted a case study research design. The data was collected using in-depth interviews with all stakeholders and analysis of the documents. The findings indicate that in the said PPP model, Government took the role of mobilizer, financer, mentor, and private player, took the responsibility of building up capacity and arranging market links, and the women members worked together to help themselves sustain the project.