The sustainability of the creative industry through creative-based tourism in the Laweyan Tourism Village requires the support of a sustainable and inclusive development model for local communities. This research aims to present the design of a tourist village development model that applies the eco-cultural city concept as a Surakarta City Perspective through creative-based tourism towards creative industries. This research uses a qualitative approach with a descriptive exploratory method. Data collection techniques use interviews with key informants. Empirical observation using cultural mapping as identification of physical mapping of spatial layout, build ings and environment, as well as cultural landscapes for tangible and intangible cultural assets of the community in the local landscape in the Laweyan tourist village. Content analysis is applied as a research data analysis method. The research results provide an overview of the design of the creative-based tourism village development model towards a sustainable creative industry including aspects attraction, accessibility, amenities, and ancillary, and green tourism. Model design requires commitment and participation from the government and private sector in collaborating with sustainable tourist village development forums.

The significance of financial literacy is garnering worldwide attention across all age groups. Financial literacy has been defined by certain scholars as a necessary skill for individuals to possess in order to effectively navigate their future financial endeavors. The aim of this article is to perform a bibliometric analysis and systematic literature review in order to investigate the present corpus of scholarship on the application of Financial Literacy. The present study entailed a comprehensive analysis of existing research papers to ascertain the principal contributors to this specific domain, noteworthy subthemes, and prospective directions for further investigation. There has been a noticeable rise in the quantity of literature pertaining to this topic during the period spanning from 2020 to 2023. Furthermore, the utilization of network analysis was employed to chart research clusters. The aforementioned discovery yielded a cumulative total of 84 scholarly publications. The findings of the analysis indicate that there exists a gap in the comprehensive research of the keywords “Financial Behavior”, “Financial Attitude”, and “Financial Inclusion”.

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.

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.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.