This study investigates the role of agricultural exports as a potential engine of economic growth in South Africa, employing a cointegration and error correction model (ECM) framework on time series data from 1980 to 2023. The results confirm a long-run equilibrium relationship between agricultural exports and economic growth, with lagged total exports and employment significantly influencing GDP growth in the short run. However, other factors like foreign direct investment, gross capital formation, and population growth did not exhibit a statistically significant impact. These findings underscore the importance of agricultural exports in driving South Africa’s economic growth. To further enhance this potential, the study recommends establishing a consistent and transparent policy environment to foster investor confidence and long-term planning in the agricultural sector, expanding the range of agricultural exports to reduce vulnerability to external shocks and enhance overall economic resilience and streamlining customs procedures, reducing trade barriers, and improving logistics to enhance the competitiveness of South African agricultural exports in the global market. These policy recommendations, grounded in empirical evidence, offer a roadmap for harnessing the full potential of agricultural exports to drive sustainable economic growth in South Africa.

This study sought an innovative quality management framework for Chinese Prefabricated Buildings (PB) projects. The framework combines TQM, QSP, Reconstruction Engineering, Six Sigma (6Σ), Quality Cost Management, and Quality Diagnosis Theories. A quantitative assessment of a representative sample of Chinese PB projects and advanced statistical analysis using Structural Equation Modeling supported the framework, indicating an excellent model fit (CFI = 0.92, TLI = 0.90, RMSEA = 0.06). The study significantly advances quality management and industrialized building techniques, but it also emphasizes the necessity for ongoing research, innovation, and information exchange to address the changing problems and opportunities in this dynamic area. In addition, this study’s findings and recommendations can help construction stakeholders improve quality performance, reduce construction workload and cost, minimize defects, boost customer satisfaction, boost productivity and efficiency in PB projects, and boost the Chinese construction industry’s growth and competitiveness.

The scientific discourse on university towns (UT) has progressed for a long time, with a surge of interest in recent years. However, a global overview of the research conducted on this topic have yet to exist. This paper aims to re-examine the relationship between UT and urbanization in literature. Built environment and people are often the most talked aspects in UT literatures. The variety of definitions remains largely uncharted. Policies behind UT development are also rarely studied. This article used an R studio-based bibliometric literature review to synthesize findings from various scientific literature. Keywords related to university towns and urban were used in digital search engines to examine and analyse the literature. Results revealed a significant gap in scientific research on critical theoretical concepts that planners can use as a guide in creating, formulating, and evaluating UT, especially in developing countries. This study promotes simplification of existing literature by examining the impact of UT on the stakeholders involved.

The present research focuses on researching the impact of the diverse communication media that facilitate or develop Student Motivation and Engagement in the educational systems of the states in the Gulf, especially Oman. The main goal of this work is to determine which type of method is most effective in encouraging students in view of cultural and technological factors present in the region. Comparisons using hypothesis testing and structural models which provided higher T value for Technology-Based Communication Methods (TBCM) and Human Face-to-Face Communication Methods (HFtFCM). Next, the research hypothesis H2 that TBCM has a direct positive relationship with SMaE was supported by the following regression coefficients: β = 0.177, t = 4.493; p = 0.000. On the other hand, there was no effect of HFtFCM on SMaE as indicated by a regression coefficient of 0.056 (p < 0.124) for this hypothesis and therefore, this hypothesis was rejected. The analysis using the mediator of Student Perception of Communication Effectiveness (SPoCE) only partly mediates TBCM and SMaE (β = 0.047, t = 3.737, p = 0.000). However, SPoCE was found not to moderate the relationship between HFtFCM and SMaE (β = −0.01, t = 1.125, p = 0.005). The present study underlines the efficiency of TBCM in the area of student engagement, while face-to-face conversation does not play significant part in this process. The obtain results conclude that, the traditional and technological evolution in the Gulf region supports the adoption of TBCM in educational systems. Such approaches support with the technological learning and likings of students, offering greater flexibility and engagement. Educational systems must highlight TBCM to better meet the growing needs of their student, while identifying that face-to-face remains important, though secondary, in energetic motivation.

The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.

The COVID-19 pandemic has significantly restricted household resilience, particularly in developing countries. The study investigates the correlation between livelihood capital and household resilience amid uncertainties due to the COVID-19 pandemic, specifically in Bekasi Regency, West Java Province, Indonesia. Livelihood capital encompasses social, human, natural, physical, and financial, which are crucial in shaping household resilience. This study used the SEM-PLS method and utilized a survey of 120 respondents (household heads) from four villages in two districts (Muaragembong and South Tambun) in Bekasi Regency to identify critical factors that either enhance or impede rural household resilience during and after the pandemic. Findings reveal that households possessing human capital, financial capital, and empowerment are more adept at navigating socioeconomic difficulties during and after the pandemic. However, this research stated that trust and social networks enhance household resilience during the pandemic, whereas social norms are crucial for rebuilding household resilience in the post-pandemic phase. The finding revealed that social cohesion adversely affected household resilience during and after the pandemic, while trust diminished household resilience in the post-pandemic COVID-19 phase. These findings offer insight to policymakers, scholars, and other stakeholders aiming to foster household resilience during and in recovery efforts after the pandemic.

What is “truth”? This is the main philosophical question that many of the contemporary philosophical theories (e.g., consistency theory, correspondence theory, semiotics, and pragmatism) tried to investigate over the past decades. However, these theories mostly approached “truth” from logical and epistemological perspectives. On the other hand, Santayana’s theory of truth embarks in a different direction. His perspective was laid out in his book “The Realm of Truth”, which is considered one of the parts of his seminal work “The Realms of Being”. Santayana's theory of truth founded on the “critical realism” to which he belongs, and thus his approach was “realistic” or “ontological”. The novelty of Santayana's theory of truth is that it brings the “theory of truth” out of the fields of logic, epistemology, and philosophies of language, and into the field of being, ontology, or the realm of lived experience. In this paper we introduce an analytical and critical account of Santayana's theory of truth, and its moving from logic to realism.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.