This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

Catastrophes, like earthquakes, bring sudden and severe damage, causing fatalities, injuries, and property loss. This often triggers a rapid increase in insurance claims. These claims can encompass various types, such as life insurance claims for deaths, health insurance claims for injuries, and general insurance claims for property damage. For insurers offering multiple types of coverage, this surge in claims can pose a risk of financial losses or bankruptcy. One option for insurers is to transfer some of these risks to reinsurance companies. Reinsurance companies will assess the potential losses due to a catastrophe event, then issue catastrophe reinsurance contracts to insurance companies. This study aims to construct a valuation model for catastrophe reinsurance contracts that can cover claim losses arising from two types of insurance products. Valuation in this study is done using the Fundamental Theorem of Asset Pricing, which is the expected present value of the number of claims that occur during the reinsurance coverage period. The number of catastrophe events during the reinsurance coverage period is assumed to follow a Poisson process. Each impact of a catastrophe event, such as the number of fatalities and injuries that cause claims, is represented as random variables, and modeled using Peaks Over Threshold (POT). This study uses Clayton, Gumbel, and Frank copulas to describe various dependence characteristics between random variables. The parameters of the POT model and copula are estimated using Inference Functions for Margins method. After estimating the model parameters, Monte Carlo simulations are performed to obtain numerical solutions for the expected value of catastrophe reinsurance based on the Fundamental Theorem of Asset Pricing. The expected reinsurance value based on Monte Carlo simulations using Indonesian earthquake data from 1979–2021 is Rp 10,296,819,838.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

This study aimed to examine the impact of Environmental, Social, and Corporate Governance (ESG) scores and Country Governance Indicators (CGI) on companies’ value. The study procedures were carried out by creating a linear empirical model where the dependent variable was companies’ value. In addition, the variables of interest in the model were ESG scores and CGI. Analysis was carried out on annual data from 278 non-financial Asian companies spanning 11 years from 2011–2021. The feasible generalized least squares (FGLS) method was used for estimation due to the presence of serial correlation and heteroscedasticity in the data obtained. The results showed the presence of a positive relationship and correlation between ESG scores and companies’ value. Meanwhile, CGI had a negative impact, revealing the potential difficulties caused by country governance framework. This study also found a positive correlation between CGI and ESG on company value. These findings have important practical contributions emphasizing the significance of ESG factors in improving companies’ value and the complex relationship between country governance and corporate valuation.

Heat transfer enhancement (HTE) is a topic of everlasting importance in thermal engineering research. The latest focuses in this field are on nanosolutions for more efficient thermal transmission fluids (a) and designs of metallic foams (b) Metallic foams provide extended surfaces for HTE and possess advantages such as a high value of Cp, high thermal conductivity (TC) and being light weight. nanosolutions, on the other hand, can be used as an efficient HT medium as they exhibit higher TCs in comparison to base fluids. This review paper summarizes the physical properties of nanosolutions and or within the metal foam, focusing on HT and flow properties of nanosolutions, metal foam and combined NS-metal foam systems. The inspiration novelty for this review is the basic transference identifications for the HT enhancement of nanosolutions in porous media. The aim of the work is to provide insight on how nanosolutions in conjunction with porous media can be useful for HTE.

In the teaching of professional courses, the introduction of information technology teaching mode, currently the most widely used is blended teaching. This teaching mode highlights the student's learning subject status, and the overall teaching effect is significant. Linux course is a highly practical course, and the introduction of blended teaching mode in specific course teaching is of great significance for promoting curriculum reform and development. This article provides a brief introduction to Linux courses, analyzes the importance of blended teaching methods, and explores strategies for effectively applying online and offline mixed teaching modes in Linux courses.