The present study aimed to determine the dynamic relationship between good governance, fiscal policy, and economic growth in Oman. In the context of the current study, researchers chose a quantitative approach to answer the research questions, utilizing the latest 2023 data from the World Bank and The Global Economy databases. The data for the current study was carefully selected using variables that represent aspects of governance, fiscal policies, and economic performance. Our analysis uses Ordinary Least Squares (OLS) regression and the Autoregressive Distributed Lag (ARDL) Model. These methods help us understand these factors’ immediate and long-term impacts on Oman’s economy. The results we obtained offer fascinating insights into the country’s economic dynamics. We observe bidirectional causal relationships between the Good Governance Index (GGI) and the Regulatory Quality Index (RQI) and economic growth, while Fiscal Policy Effectiveness (FPE), Government Efficiency Index (GEI), and the Rule of Law Index (RLI) exhibit unidirectional causality towards GDP. Budget Balance (BB) shows no causal relationship with GDP, implying external factors influence it. Additionally, moderation analysis underscores the significance of digital financial inclusion in amplifying the effects of governance and fiscal policies on economic growth. These findings hold practical implications for policymakers and stakeholders in Oman. Specifically, they highlight the importance of governance, regulatory quality, and effective fiscal policies in shaping the economic landscape. To foster sustainable economic development, efforts should improve governance, enhance fiscal policy effectiveness, and promote digital financial inclusion.

In an era characterized by technological advancement and innovation, the emergence of Electronic Government (e-Government) and Mobile Government (m-Government) represents significant developments. Previous studies have explored acceptance models in this domain. This research presents a novel acceptance model tailored to the context of m-Government adoption in Jordan, integrating the Information System (IS) Success Factor Model, Hofstede’s Cultural Dimensions Theory, and considerations for law enforcement factors. The primary objective of this study is to investigate the strategies for promoting and enhancing the adoption of m-Government applications within Jordanian society. Data collection involved the distribution of 203 electronic questionnaires, with subsequent analysis conducted using SPSS. The findings reveal the acceptance and significance of three hypotheses: Information Quality, Service Quality, and Power Distance. Additionally, the study incorporates the influence of Law Enforcement factors, contributing to a comprehensive understanding of the multifaceted determinants shaping the adoption of m-Government services in Jordan.

This study rigorously investigates the Starlink Project’s impact on Thailand’s legal frameworks, regulatory policies, and national security concerns. Utilising a well-structured online questionnaire, we collected responses from 1378 Thai participants, meticulously selected to represent diverse demographics, technology usage patterns, and social media interactions. Our analytical approach integrated binary regression analysis to dissect the intricate relationships between various predictor variables and the project’s potential effects. Notably, the study unveils critical insights into how factors such as age, gender, education level, income, as well as specific technology and social media usage (including laptop, smartphone, tablet, home and mobile Internet, and TikTok), influence perceptions of Starlink’s impact. Intriguingly, certain variables like Twitter and YouTube usage emerged as non-significant. These nuanced findings offer a robust empirical basis for stakeholders to forge targeted strategies and policies, ensuring that the advent of the Starlink Project aligns with Thailand’s national security, legal, and regulatory harmony.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.