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.

Distributed Energy Resources (DERs), such as solar photovoltaic (PV) systems, wind turbines, and energy storage systems, offer many benefits, including increased energy efficiency, sustainability, and grid reliability. However, their integration into the smart grid also introduces new vulnerabilities to cyber threats. The smart grid is becoming more digitalized, with advanced technologies like Internet of Things (IoT) devices, communication networks, and automation systems that enable the integration of DER systems. While this enhances grid efficiency and control, it creates more entry points for attackers and thus expands the attack surface for potential cyber threats. Protecting DERs from cyberattacks is crucial to maintaining the overall reliability, security, and privacy of the smart grid. The adopted cybersecurity strategies should not only address current threats but also anticipate future dangers. This requires ongoing risk assessments, staying updated on emerging threats, and being prepared to adapt cybersecurity measures accordingly. This paper highlights some critical points regarding the importance of cybersecurity for Distributed Energy Resources (DERs) and the evolving landscape of the smart grid. This research study shows that there is need for a proactive and adaptable cybersecurity approach that encompasses prevention, detection, response, and recovery to safeguard these critical energy systems against cyber threats, both today and in the future. This work serves as a valuable tool in enhancing the cybersecurity posture of utilities and grid-connected DER owners and operators. It allows them to make informed decisions, protect critical infrastructure, and ensure the reliability and security of grid-connected DER systems in an evolving energy landscape.

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.

During and after any disaster, a situation report (SITREP) is prepared, based on the Daily Incident Updates (DIU), as an initial decision support information base. It is observed that the decision support system and best practices are not optimized through the available formal reporting on disaster incidents. The rapidly evolving situation, misunderstood terms, inaccurate data and delivery delays of DIU are challenges to the daily SITREP. Multiple stakeholders stipulated with different tasks should be properly understood for the SITREP to initiate relevant response tasks. To fill this research gap, this paper identifies the weaknesses of the current practice and discusses the upgrading of the incident-reporting process using a freely available software tool, enabling further visualization, and producing a comprehensive timely output to share among the stakeholders. In this case, “Power-BI” (a data visualization software) is used as a 360-degree view of useful metrics—in a single place, with real-time updates while being available on all devices for operational decision-making. When a dataset is transformed into several analytical reports and dashboards, it can be easily shared with the target users and action groups. This article analyzed two sources of data, namely the Disaster Management Center (DMC) and the National Disaster Relief Service Center (NDRSC) of Sri Lanka. Senior managers of disaster emergencies were interviewed and explored social media to develop a scheme of best practices for disaster reporting, starting from just before the occurrence, and following the unfolding sequence of the disasters. Using a variety of remotely acquired imageries, rapid mapping, grading, and delineating impacts of natural disasters, were made available to concerned users.

Green cosmetics made from organic ingredients are becoming increasingly popular due to their environmentally friendly nature. However, research on consumer behavior towards green cosmetics is rare, especially in developing countries like Pakistan. Previous studies have primarily focused on female consumers, and little is known about the behavior of male consumers. Therefore, this research aims to investigate the behavior of both male and female consumers towards green cosmetic products and analyze the factors that affect their purchase behavior. This study employs a quantitative approach with deductive reasoning and collects data through a questionnaire from major cities in Pakistan. The study finds that eco-awareness, social influence, price-quality instructions, health consciousness, and the need for uniqueness significantly influence consumer purchase behavior when buying green cosmetics. Interestingly, price sensitivity does not significantly affect consumer purchase behavior as consumers are willing to pay for high-quality green cosmetics. Based on the findings, the study recommends promoting eco-awareness and health consciousness among consumers through educational campaigns and workshops launched by the government and the private sector. Future research can explore factors such as age, gender, and specific generations like millennials and Generation Z, as well as packaging, branding, and product design to promote environmentally friendly and health-conscious products. Additionally, comparative studies between countries can identify universal and region-specific factors, and examining the overall impact of green cosmetic products on the environment can highlight areas for improvement in sustainability.