Banana macropropagation in a thermal chamber is an economical technology, effective as a phytosanitary cleaning method, and efficient to enhance seedling production. The objective of this work was to evaluate the effects of corm size (CS) and benzylaminopurine (BAP) on plantain cv. Barraganete seedling proliferation in two propagation environments (PE). The treatments consisted of two levels of BAP (with and without BAP), three CS (2 ± 0.5, 4 ± 0.5 and 6 ± 0.5 kg) and two PE (thermal chamber and raised bed). The variables evaluated were sprouting time (days), multiplication rate (MT) per unit (seedlings per corm) and area (seedlings per m²). Sprouting time was significantly influenced (p < 0.05) by the PE, where the thermal chamber advanced shoot emergence by 12 days, with respect to the raised bed. MT of seedlings per corm and m², were significantly influenced (p < 0.05) by BAP × AP and TC × AP interactions, where the highest seedling production per corm occurred inside thermal chamber with BAP and 6 ± 0.5 kg corms, while seedling production per m² was higher with 2 ± 0.5 kg corms under the same thermal chamber conditions and with BAP. The main effects results reported that with BAP there were 30 and 31% increases in MT per corm and per m², respectively, relative to the treatment without BAP. Within the thermal chamber the MT per corm and per m² increased by 44% relative to the raised bed. Regarding the effect of CS, larger corms achieved higher individual MT, while smaller corms achieved higher MT per area. The use of a thermal chamber and BAP is recommended for mass production of banana seedlings through macropropagation.

This research was conducted using a survey research method to investigate the influence of Artificial Intelligence (AI) on Nigerian students’ academic performances in tertiary institutions. Nigerian tertiary institutions have an estimated population of about 2.5 million students across the universities, polytechnics, monotechnics, and colleges of education. A sample size of 509 was used. The researchers adopted an online questionnaire (Google Form) to administer questions to respondents across Nigeria to elicit responses from the respondents bordering on their awareness and the use of AI and its attendant impacts on their academic performance. Five research objectives were raised for the proper investigation of this study. From the findings of the study, the researchers found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students. It was also found that Nigerian students have training on the use of AI for educational purposes and that they are more familiar with Snapchat AI and ChatGPT. Conclusively, AI is useful to students in the sense that it enhances their knowledge of their courses, improves their learning and speaking skills, and helps them to have a quick understanding of their course by way of simplifying technical aspects of their courses. The researchers therefore recommend as follows: Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes so that they can understand the ethical implications of the use of AI. Using AI for writing could be interpreted to mean examination malpractice, and this should not be condoned in the educational sector; however, at the moment, a small number of students used AI for examinations. Albeit, the appropriate use of AI should be fully integrated into Nigerian tertiary institutions’ curricula.

Adult obesity is a significant health problem, with nearly a quarter of Hungarian citizens aged 15 years and older being obese in 2019 (KSH, 2019a). The use of mobile devices for health purposes is increasing, and many m-health apps target weight-related behaviours. This study uniquely examines the effectiveness and user satisfaction of health-oriented apps among Hungarian adults, with a focus on health improvement. Using a mixed-methods approach, the study identifies six key determinants of health improvement and refines measurement tools by modifying existing parameters and introducing new constructs. The principal objective was to develop a measurement instrument for the usability of nutrition, relaxation and health promotion applications. The research comprised three phases: (1) qualitative content analysis of 13 app reviews conducted in June 2022; (2) focus group interviews involving 32 students from the fields of business, economics and health management; and (3) an online survey (n = 348 users) conducted in December 2023 that included Strava (105 users), Yazio (109 users) and Calm (134 users). Six factors were identified as determinants of health improvement: physical activity, diet, weight loss, general well-being, progress, and body knowledge. The LAUQ (Lifestyle Application Usability Questionnaire) scale was validated, including ‘ease of use’ (5 items), ‘interface and satisfaction’ (7 items) and ‘modified usefulness and effectiveness’ (9 items), with modifications based on qualitative findings. This research offers valuable insights into the factors influencing health improvement and user satisfaction with healthy lifestyle-oriented applications. It also contributes to the refinement of measurement tools such as the LAUQ, which will inform future studies in health psychology, digital health, and behavioural economics.

Ensuring access to quality education and career training is a crucial challenge, especially in developing nations. Vocational, scientific, technological, and engineering education are essential for active participation in any community and play a significant role in shaping life perspectives. The ability to sustain competitiveness depends on receiving high-quality vocational, scientific, technological, or engineering education and professional growth. These factors are vital for the long-term growth of prosperous economies and nation-building. Hence, this perspective review attempts to provide information on some contemporary pedagogies in science, technology, engineering, and mathematics (STEM) and science, technology, engineering, arts, and mathematics (STEAM) vis-à-vis scientific and engineering education in Nigeria. The study zooms into the challenges and possible solutions that will promote and enhance pedagogies in scientific and engineering education in Nigeria. The study adopted a perspective review approach in overviewing prior accessible studies (literatures) as well as a methodological framework. It is believed that this perspective review study will serve as a way forward for other developing nations.

Local community members play a critical role in the success of conservation projects, which in turn have the potential to influence the perceptions of local people. Relationships matter when it comes to sustainable long-term conservation and community well-being. The study aims to establish the relationship between local communities and wildlife conservation organizations in the context of Phinda Private Game Reserve and the Mnqobokazi community, located in South Africa. Data was collected using the qualitative methods of interviews and focus group discussions. The findings show that a symbiotic relationship between conservation organizations and local community members is critical in conserving the environment. The research indicates that both participation and benefits result in improved perceptions towards the protected area and a strong positive relationship. The accrual of benefits also appears to result in pro-environmental consciousness amongst community members. Several existing studies examine participation or benefit-sharing in community-based tourism in developing nations. However, less is known about the relationships between local communities and conservation organizations and the effect of participation and beneficiation on these relationships. This research narrows this gap in the body of knowledge by qualitatively examining a single case study. The findings add value to global collaborative efforts aimed at achieving positive relationships between communities and conservation management.

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.