Despite Cameroon’s immense sand reserves, several enterprises continue to import standardized sands to investigate the properties of concretes and mortars and to guarantee the durability of built structures. The present work not only falls within the scope of import substitution but also aims to characterize and improve the properties of local sand (Sanaga) and compare them with those of imported standardized sand widely used in laboratories. Sanaga sand was treated with HCl and then characterized in the laboratory. The constituent minerals of Sanaga sand are quartz, albite, biotite, and kaolinite. The silica content (SiO₂) of this untreated sand is 93.48 wt.%. After treatment, it rose 97.5 wt.% for 0.5 M and 97.3 wt.% for 1 M HCl concentration. The sand is clean (ES, 97.67%–98.87%), with fineness moduli of 2.45, 2.48, and 2.63 for untreated sand and sand treated with HCl concentrations of 0.5 and 1 M respectively. The mechanical strengths (39.59–42.4 MPa) obtained on mortars made with untreated Sanaga sand are unsatisfactory compared with those obtained on mortars made with standardized sand and with the expected strengths. The HCl treatment used in this study significantly improved these strengths (41.12–52.36 MPa), resulting in strength deficiencies of less than 10% after 28 curing days compared with expected values. Thus, the treatment of Sanaga sand with a 0.5 M HCl concentration offers better results for use as standardized sand.

The main objective of the study was to assess the impact of fiscal management on macroeconomic stability in emerging countries between 2012 and 2022. The study drew on macroeconomic theory, which postulates the importance of responsible fiscal policies for economic stability. Information was taken from ten emerging Latin American countries, and the analysis was carried out through a quantitative approach, using an econometric model. A significant relationship was found between fiscal management and macroeconomic stability, evidencing that effective fiscal policies are crucial for macroeconomic stability in emerging countries. The findings emphasize that balanced fiscal management, which avoids falling into cycles of debt and deficit, is essential for long-term stability. Practices that promote fiscal stability, such as greater efficiency in public spending and effective tax collection, can contribute significantly to economic stability and sustained growth. The results also suggest that fiscal policies should take into account human development conditions and annual particularities in order to formulate effective fiscal policies. It highlights those countries with best fiscal practices, reflected in low debt-to-GDP levels and high fiscal stability, are more likely to achieve macroeconomic stability and sustainable economic growth.

In this research, we explore the psychological factors that SMB owners who are micro-entrepreneurs and use SNS for entrepreneurial purposes rely on to make their self-employment decisions. Research-based on a merger of the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) deals with how perceived ease of use (PEU), perceived usefulness (PU), attitude, subjective norms (SN), perceived behavioral control (PBC), openness to experience (OTE), and dominance contribute to people’s behavioural intention (BI) to use SNS for Data was collected from 342 SMB micro-entrepreneurs in the Delhi/NCR region of India by the means of a standardized questionnaire. Employing PLS-SEM, a partial least squares structural equation modeling was used to analyze the data. The results point out an impact of PU, attitude, and behavioral intention, and unappealing presentations, unacceptance of an explanation, unclear mechanisms, and domination do not make any difference. The research emphasizes how technophobe’s attitude, and the perception of effectiveness would impact micro-entrepreneurs desire to avail SNS for entrepreneurship efforts. Moreover, research shows the psychological understanding based on the SNS adoption by the small business owners, micro-entrepreneurs as well as for the practitioners and policymakers who are working to enhance the capability of the SMB. More investigations should be conducted on the other personality traits and cover more nations as demographic dividends in comparison to acquire more inclusive data.

Countering cyber extremism is a crucial challenge in the digital age. Social media algorithms, if designed and used properly, have the potential to be a powerful tool in this fight, development of technological solutions that can make social networks a safer and healthier space for all users. this study mainly aims to provide a comprehensive view of the role played by the algorithms of social networking sites in countering electronic extremism, and clarifying the expected ease of use by programmers in limiting the dissemination of extremist data. Additionally, to analyzing the intended benefit in controlling and organizing digital content for users from all societal groups. Through the systematic review tool, a variety of previous literature related to the applications of algorithms in the field of online radicalization reduction was evaluated. Algorithms use machine learning and analysis of text and images to detect content that may be harmful, hateful, or call for violence. Posts, comments, photos and videos are analyzed to detect any signs of extremism. Algorithms also contribute to enhancing content that promotes positive values, tolerance and understanding between individuals, which reduces the impact of extremist content. Algorithms are also constantly updated to be able to discover new methods used by extremists to spread their ideas and avoid detection. The results indicate that it is possible to make the most of these algorithms and use them to enhance electronic security and reduce digital threats.