The purpose of this overview is to discover materials commonly used in the automotive industry and provide an overview of optimized composites to reduce weight, cost, fuel consumption and CO2 emissions. The cost of carbon fiber, Al and Mg lightweight composites is much higher than conventional materials. It is therefore important for research and development in the area of reducing costs, increasing recyclability, enabling integration and maximizing the fuel economy benefits of automobiles. In order to meet these characteristics, natural fibers have better properties and, in addition to being environmentally friendly, will become the material of choice for the future automotive industry. Composites can reduce weight by 10-60%. Researchers are already working with bio composites, investigating not only the economic aspects, but also the properties and associated manufacturing processes for environmentally friendly transportation and CO2 reduction.

A pine tree, namely Pinus merkusii is an indigenous species from Indonesia which grows extensively in the Island of Java, Sumatera, and Sulawesi. This plant produces both timber and non-timber forest products (TFP and NTFP). Resin or oleum pine resin, as the main NTFP of Pinus merkusii, becomes the raw material for the gum rosin and turpentine oil industry. Globally, Indonesia is ranked 3rd as a producer of pine products after China and Brazil, in which Perhutani as a State Owned Forestry Enterprise plays a major role in this industry. On average, Perhutani manufactures 65,000 tons of gum rosin and 14,000 turpentine oil per year. Entire volume of both pine products is produced by nine factories with various maximum capacities. Therefore, this research aims to measure efficiency and/or inefficiency score of each factory using data envelopment analysis (DEA) method, which is then complemented by a single bootstrap technique with 2.000 iterations to eliminate bias scores. Cost of raw material, labour, energy, and general affairs are employed as input variables, while the output variables are total revenue and production volume. As result, 27.3% inefficiency (efficiency score = 72.7%) is generally found in all Perhutani’s pine chemical factories. To resolve this inefficiency issue, analytical hierarchy process (AHP) pairwise comparison questionnaire is distributed to 13 expert respondents to determine prioritized operational capability to focus on in optimizing efficiency of production performance. Dimensions of Cost, Quality, Flexibility, Innovation, and Sustainability are selected to construct the AHP questionnaires.

This paper aims to investigate the impact of vehicle velocity on the rollover stability of a fully loaded tractor semi-trailer during lane changes and turning. Specifically, the study focuses on velocities ranging from 30 km/h to 60 km/h. The investigation found that vehicle velocity is a critical parameter that can affect the potential for rollover during lane changes or turning. The results showed that to ensure the vehicle moves steadily and does not roll over during these maneuvers, the steering angle must be controlled and kept within certain limits. The study provided specific recommendations for maximum steering angles at different velocities. For instance, to ensure stability when the vehicle is moving at 60 km/h, the maximum steering angle should be less than 4 degrees. Similarly, at 50 km/h, the maximum steering angle is recommended to be less than 6 degrees, and at 44 km/h, it should be less than 8 degrees. At lower speeds, the recommended maximum steering angle increases, with the maximum recommended angle at 36 km/h being 12 degrees. These findings highlight the importance of carefully controlling vehicle velocity and steering angle to minimize the risk of rollover accidents. By providing specific recommendations for different velocities, this study can inform the design and safety testing of vehicles to improve their stability and safety during lane changes and turning.

Stock is one of the investment assets that has its charm for investors. It is very liquid and has a high rate of return, but it has a high risk. The strategy commonly used to minimize investment risk is to diversify through portfolio formation. A good allocation of funds must be determined in forming an optimal portfolio. In addition, the method of stock selection needs to be considered so the stocks are well diversified and the portfolio developed has good performance. This study aims to compare stock selection between K-Means and Average Linkage clustering approaches in forming an investment portfolio. Clustering analysis is used to group IDX80 stocks based on their attributes. In forming a portfolio with the Mean-VaR model, the stock selection decision criteria used are by selecting stocks with the highest positive returns from each cluster. As a result, the two clustering techniques show the superiority of the Silhouette score for a certain number of clusters, but there are still more advantages in Average Linkage. The portfolio approached by Average Linkage resulted in a better performance than the portfolio approached by K-Means. Therefore, Average Linkage clustering can be used as a better recommendation in decision-making to select stocks so as to produce optimal portfolio performance.

The effect of the chemical composition of geopolymer pastes on compressive strength was investigated in high-calcium fly ash and copper slag blends. In synthesizing the pastes, soda ash at activator to binder ratio of 0.4 was used. The characterization of material samples and the hardened fly ash-copper slag pastes was conducted through X-ray fluorescence (XRF), and X-ray diffraction (XRD) for the major oxide and phase composition. The hardened paste cubes which were cured at 80 °C were tested for compressive strength at ages 3, 7, and 28 days to obtain the mechanical performance property of each respective mix. The findings establish the impact of variation in the individual material and paste composition on the compressive strength of fly ash-copper slag geopolymer. The result shows that increase in the SiO₂/Al₂O₃ and Na₂O/Al₂O₃ ratios of paste products of samples corresponded to an increase in compressive strength. Whilst Fe₂O₃ wt.% increase in products from slag addition and positively influenced the compressive strength as fillers. However, CaO had no positive influence on the matrix of the activated product. The optimal blend mix design was 40 wt.% of copper slag which achieved a 28-day compressive strength of 24.66 MPa.

This study investigates the dynamic relationship between a set of banking sector development indicators and interest rate volatility for 12 emerging market countries during the period of 1980-2019. For this purpose, the bounds testing within autoregressive distributed lag (ARDL) methodology is employed. The empirical results reveal that the interest rate volatility has negative impacts on the majority of the banking sector development indicators which also play a significant role in dampening the banking sector development path in the long-run. These findings suggest that the banking sectors of emerging countries are vulnerable to interest rate risks. Thus, the results have important implications for policymakers to improve the banking system and to promote economic growth of emerging economies.

This study analyzes the vulnerability of the power relations between the Indonesian National Armed Forces and the Indonesian National Police (TNI-Polri power relations) post-1998 Reform. This article employed exploratory sequential mixed methods in answering the research problem. Analytical Hierarchy Process (AHP) and System Dynamics methods were utilized in the study. Based on the research results, the variables of Socio-Economic (SE) Vulnerability and Adaptive Capacity (AC) have the highest weight value of 0.329. Meanwhile, the variable of Institutional Vulnerability has the lowest weight, 0.142. Overall, the vulnerability value of TNI-Polri power relations post-1998 Reform was still in the Low Vulnerability category with a value of 1,699 (33.97%). The vulnerability value of TNI-Polri power relations in the next five years will increase from a score of 1.66 in 2022 to 1.74 in 2027 so that it will increase by 5% with the same category level, namely Low Vulnerability. This study is expected to strengthen TNI-Polri power relations in maintaining national political stability.

This paper considers a practical truncated traveling salesman problem (TTSP), in which the salesman is only required to cover a subset of out of given cities (rather than covering all the given cities as in conventional travelling salesman problem (TSP)) with minimal traversal distance. Thus, every feasible solution tour contains exactly cities including the starting city. However, extensive research on TSP has been received and various efficient solution techniques including exact, heuristic, and metaheuristic algorithms are devoted, a very limited attention has been given to TTSP models because of its solution structure. The TTSP model comprises two types of problems including city selection i.e. as a salesman's trip need not include all the cities, the challenge is to identify which combination of cities are to be visited and which sequence of cities will constitute minimal traversal distance. A hybrid genetic algorithm (GA) comprising sophisticated mutation operators is developed to tackle this problem efficiently. Comparative computational findings suggest that the proposed GA has capability to outperform existing approaches in terms of TTSP results. In addition, the proposed GA report improved results and will serve as a basis for forthcoming TTSP studies.

This paper proposes the cuckoo search (CS), central force optimization (CFO), chemical reaction optimization (CRO) and 3-Opt for solving the capacitated vehicle routing problem (CVRP). HCSCROCFO-3Opt, which is the parallel hybrid algorithm that is proposed, is a form of augmented HCSCROCFO with a local search process founded on CS that utilizes positive aspects of the other optimization approaches including CRO and CFO in order to enhance quality of initial population and improve local search, correspondingly. The work is motivated by the need to enhance the computational effectiveness through attainment of improved outcomes compared to previous popular solutions, to explore the features of different parameters of to seek some ideal solutions. The first stage entails solving of CVRP through setting a variety of values to tune parameters for the HCSCROCFO-3Opt proposed. Then initialization of algorithm CS, CRO, CFO parameters are accomplished through tuning parameters within a tuning cycle. Subsequently, a novel solution is swapped in a random manner through a levy flight within the central loop, followed by execution of the hybrid solution as well as new CRO, CFO and CS algorithm solutions, whose implementation is supposed to enhance results for the local 3-Opt. Ultimately, the most ideal solution for general hybrid model’s solution space is identified, after which the solution that is best-suited for the CVRP purposes is presented. Within the standard CVRP cases, reported computational tests in large scale in the literature demonstrate the efficiency of presented approach.

The property is a unique product that cannot be contrasted with other commercial products due to pricing conditions. Property price determination is one of the crucial aspects of property development activities because of the profit margin made by the developer and the purchasing preferences. This study attempts to extend the literature that has largely focused on factors of housing prices in developed markets and provided recent evidence of housing price determinants in two countries (i.e., Indonesia and Malaysia). Thus, this study examines the factors affecting housing prices in Jakarta Metropolitan Region and Greater Kuala Lumpur. A quantitative approach was used involving two countries, namely Indonesia and Malaysia. The data was collected using a survey questionnaire through purposive sampling. A total of 100 respondents (Indonesia) and 134 respondents (Malaysia) participated in this study. The data was analyzed using descriptive (frequency) and inferential statistics (chi-square test and multinomial regression). The results indicated that housing location, property funding, and health have a significant effect on residential property prices in Indonesia. Besides that, the results displayed that housing physical design, home design and construction, developer and real estate products, development concepts, housing location, property funding, social status, health, law provisions, and external factors do not affect residential property price in Malaysia. Despite being neighbors, Indonesia and Malaysia have distinct economic and landscape characteristics. Furthermore, considering Indonesia has a higher number of Covid-19 cases than Malaysia, significant information on how the pandemic has affected the demand, cost, and pricing of residential housing in Jakarta and Kuala Lumpur will be provided. The findings of this study will provide recommendations to investors, buyers, and policy about the residential housing industry's prospects for growth in emerging nations following the pandemic.