This study delves into the concept of the “cultural bomb” within the framework of non-military defense empowerment strategies in Indonesia. This approach can potentially change society’s views and attitudes towards various security threats as a realization of strengthening the defense and security system of the universal people (Sishankamrata) per article 30 paragraph (2) of the 1945 constitution. By leveraging media, education, and information technology, the cultural bomb acts as a social weapon that operates powerfully in the “space of mind,” shaping behavior and actions nonviolently. The issue of cultural threats pertains to the infiltration and imposition of foreign cultural values and practices that undermine local traditions and national identity, leading to social fragmentation and weakness. This study proposes the concept of a “cultural bomb” as a policy framework to address and mitigate these cultural threats. The research employs a qualitative approach using the Delphi technique, engaging experts from cultural studies and defense strategies to reach a consensus on the strategic application of the cultural bomb. The results indicate that the cultural bomb can effectively strengthen national identity and awareness of national defense by promoting local values and cultural resilience, thus enhancing societal cohesion and mitigating the impact of foreign cultural influences. The paper outlines the components of a cultural bomb, analyzes its application in international contexts, and discusses its implications in efforts to strengthen national identity and foster a sense of national defense awareness. Focusing on the “war over space of mind” ideology, it introduces “cultural hacking” as a strategic initiative to address cultural power imbalances in the post-truth era.

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.

Delay is the leading challenge in completing Engineering, Procurement, and Construction (EPC) projects. Delay can cause excess costs, which reduces company profits. The relationship between subcontractors and the main contractor is a critical factor that can support the success of an EPC project. The problematic financial condition of the main contractor can cause delay in payments to subcontractors. This research will set a model that combines the system dynamics and earned value method to describe the impact of subcontractor advance payments on project performance. The system dynamics method is used to model and analyze the impact of interactions between variables affecting project performance, while the earned value method is applied to quantitatively evaluate project performance and forecast schedule and cost outcomes. These two methods are used complementarily to achieve a holistic understanding of project dynamics and to optimize decision-making. The designed model selects the optimum scenario for project time and costs. The developed model comprises project performance, costs, cash flow, and performance forecasting sub-models. The novelty in this research is a new model for optimizing project implementation time and costs, adding payment rate variables to subcontractors and subcontractor performance rates. The designed model can provide additional information to assist project managers in making decisions.

Lead halide perovskites are the new rising generation of semiconductor materials due to their unique optical and electrical properties. The investigation of the interaction of halide perovskites and light is a key issue not only for understanding their photophysics but also for practical applications. Hence, tremendous efforts have been devoted to this topic and brunch into two: (i) decomposition of the halide perovskites thin films under light illumination; and (ii) influence of light soaking on their photoluminescence (PL) properties. In this review, we for the first time thoroughly compare the illumination conditions and the sample environment to correlate the PL changes and decomposition of perovskite under light illumination. In the case of vacuum and dry nitrogen, PL of the halide perovskite (MAPbI_3–xCl_x, MAPbBr_3–xCl_x, MAPbI₃) thin films decreases due to the defects induced by light illumination, and under high excitations, the thin film even decomposes. In the presence of oxygen or moisture, light induces the PL enhancement of halide perovskite (MAPbI₃) thin films at low light illumination, while increasing the excitation, which causes the PL to quench and perovskite thin film to decompose. In the case of mixed halide perovskite ((MA)Pb(Br_xI_1-x)₃) light induces reversible segregation of Br domains and I domains. 

The technological development and the rise of artificial intelligence are driving a significant transformation of the labor market. The technological unemployment predicted by Keynes poses challenges for the global labor market that require new solutions. Basic income research has become a significant field of study, attracting attention from various disciplines such as political science, law, economics, and sociology. The aim of this paper is to explore on the basis of a literature review, what factors influence the support for basic income among the population. A systematic literature review based on the Web of Science and Scopus databases, after screening 2623 publications, identified 23 articles that contained findings relevant to the research question. A significant number of authors (12/23) analyzed data from the same source, the European Social Survey 2016 (ESS Round 8, 2020), conducted in 2016, first published in 2017 and updated several times since then. The paper shows that the study of the topic has a strong European focus. The social, economic, social and cultural diversity of European countries makes these studies important from a European and EU perspective, but from an international perspective, further research on the topic is needed.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.