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.

Given the growing significance of the metaverse in research, it is crucial to understand its scope, relevance in the tourism industry, and the human-computer interaction it involves. The emerging field of metaverse tourism has a noticeable research gap, limiting a comprehensive understanding of the concept. This article addresses this gap by conducting a hybrid systematic review, including a variable-oriented literature review, to assess the extent and scope of metaverse tourism. A scrutiny on Scopus identified a reduced number of relevant documents. The analysis exposes theoretical and empirical gaps, along with promising opportunities in the metaverse and tourism intersection. These insights contribute to shaping a contemporary research agenda, emphasizing metaverse tourism. While this study offers an overview of current research in metaverse tourism, it is essential to recognize that this field is still in its early stages, marked by the convergence of technology and transformations in tourism. This exploration underscores the challenges and opportunities arising from the evolving narrative of metaverse tourism.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

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.

Interdependence between the United States (U.S.), European Union (EU) and Asia in the semiconductor industry, driven by specialization, can serve as a preventive measure against disruptions in the global semiconductor supply chain. Moreover, with rising geopolitical tensions, the cost-intensive nature of the semiconductor industry and a slowdown in demand, interdependence and partnership provide countries with opportunities and benefits. Specifically, by analyzing global trade patterns, developing the Interdependence Index within the semiconductor market, and applying the Grubel-Lloyd Index to the U.S., the EU, and Asian countries from 2011 to 2022, our findings reveal that interdependence enhances regional semiconductor supply chains, such as the establishment of semiconductor foundries in the U.S., Japan, and the EU; reduces dependence on a single supplier, such as the U.S. distancing from China; and increases market share in different semiconductor segments, as demonstrated by Taiwan in automobile chips. The evidence indicates that China heavily depends on foreign sources to meet its semiconductor demand, while Taiwan and South Korea specialize as foundry service providers with lower Interdependence Index values. The U.S., with a robust presence in semiconductor manufacturing and design, has a moderate dependence on semiconductor imports, whereas the EU demonstrates a higher level of interdependence because it lacks semiconductor foundries. The stage-specific analyses indicate that the U.S. and the EU rely on Asia for semiconductor devices, while China and Taiwan have a higher dependence on American intermediate inputs and European lithography machines.

The melon culture is one of the Brazilian horticultural crops, due to its productive potential and socio-economic role. It is recommended for the State of Goiás and the Federal District for it is easy to plant and having need of zoning of climatic conditions and thus, being able to perform their sowing. The present work used the Sarazon program to perform the water balance of the melon crop, for the 2nd, 4th and 6th five-day sowing dates in August, September and October and in relation to the water reserves in the soil of 50 mm and 75 mm. The data were spatialized using the SPRING 4.3 program. It was observed that the producers are performing in practice what can be demonstrated in theory that the period October 16–20 is the most indicated for sowing in soils of 50 mm of water reserve and October 6–10 the beginning of sowing in soil of 75 mm of water reserve for the cultivation of melon and have adequate profitability.