This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.

The initiation of tapering, sparked by heightened inflation in the United States, reverberates across global markets, with notable implications for Indonesia. This study delved into the nuanced impact of tapering on Sharia-compliant stocks in both Indonesia and Malaysia. The rationale behind selecting Sharia stocks for analysis lies in their composition, featuring companies boasting low debt-to-asset and equity ratios, thereby positing robust resilience in the face of the Federal Reserve’s implementation of tapering. Employing a time series dataset with a weekly sampling period spanning from January to September 2022, the analysis adopted the Error Correction Model (ECM) within a multiple regression framework to circumvent potential spurious regression pitfalls. The results of this study indicate that the impact of tapering off policy in Indonesia has a positive impact in the short term and long term, while in Malaysia it tends to be insignificant in the short term and has a positive impact from the US 10-year bond yield variable and a negative impact from US 1-Year Treasury Bills. This result is interesting because it differs from the general theory. The causal factors include the agility of the Indonesian central bank in maintaining the benchmark interest rate spread with the Fed, the economic stability of both countries, and the increasing trend of coal, with Indonesia being one of the largest producers of the commodity. Investors, in navigating these intricate dynamics, may find strategic insights derived from this research invaluable for shaping their investment decisions. while government policymakers may use them as a reference for shaping policies related to Sharia stock investments, including the incorporation of artificial intelligence.

The affectations caused by extreme events of natural origin such as droughts and floods in traditional homes in the province of Gran Chaco, in Bolivia, are frequent. These aspects compromise the habitat of the populations that occupy them, as is the case of the original Weenhayek people, as an alternative for the improvement of the human habitat of this town. Through theoretical and empirical methods, five variables used for the development of the adaptation model were determined, from the bases of planned adaptation as a component of urban-territorial resilience, in search of an improvement of socio-environmental systems in the face of the effects of climate change, exemplified in the Weenhayek native people. The model establishes the improvements of traditional dwellings, from a current trend of deterioration to one of preservation, conservation and growth in the Weenhayek culture, through various features, such as: Respects the cultural design of the house that integrates local patterns of the environment, ecosystem and contemporary construction elements without affecting its image, the materials and construction techniques used are of a traditional nature, but with contemporary elements that improve their application, durability, stability, as an articulated construction system, commits governments in all instances to the technical-constructive study of the rural areas of the human settlements of the Weenhayek people, and establishes a starting point towards new studies focused on native peoples.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

Many financial crises have occurred in recent decades, such as the International Debt Crisis of 1982, the East Asian Economic Crisis of 1997–2001, the Russian economic crisis of 1992–1997, the Latin American debt Crisis of 1994–2002, the Global Economic Recession of 2007–2009, which had a strong impact on international relations. The aim of this article is to create an econometric model of the indicator for identifying crisis situations arising in stock markets. The approach under consideration includes data for preprocessing and assessing the stability of the trend of time series using higher-order moments. The results obtained are compared with specific practical situations. To test the proposed indicator, real data of the stock indices of the USA, Germany and Hong Kong in the period World Financial Crisis are used. The scientific novelty of the results of the article consists in the analysis of the initial and given initial moments of high order, as well as the central and reduced central moments of high order. The econometric model of the indicator for identifying crisis situations arising considered in the work, based on high-order moments plays a pivotal role in crisis detection in stock markets, influencing financial innovations in managing the national economy. The findings contribute to the resilience and adaptability of the financial system, ultimately shaping the trajectory of the national economy. By facilitating timely crisis detection, the model supports efforts to maintain economic stability, thereby fostering sustainable growth and resilience in the face of financial disruptions. The model's insights can shape the national innovation ecosystem by guiding the development and adoption of monetary and financial innovations that are aligned with the economy's specific needs and challenges.