The aim of the study is to identify the requirements for qualifying administrative leaders and the challenges they face at King Khalid University, in light of the general framework of the Human Capacities Development Program, which includes four dimensions (values and behaviours-basic skills-future skills-knowledge). A descriptive approach was used, and the study population consisted of academic leaders at King Khalid University, totalling (107). A questionnaire was used as a research tool, comprising three axes and (53) statements to collect data after ensuring their validity and reliability. The results showed the agreement of the study population on the axis of requirements for qualifying administrative leaders at King Khalid University to a very high degree, with an average score of (4.44), and their agreement on the challenges facing the qualification of administrative leaders at King Khalid University to a very high degree, with an average score of (4.11), and their agreement on the mechanisms for qualifying administrative leaders at King Khalid University to a very high degree, with an average score of (4.29). The results also showed no statistically significant differences at the significance level (0.05) between the means of responses of the study population on the requirements, challenges, and mechanisms for qualifying administrative leaders according to variables (gender-academic qualification—experience in the current job). In light of the study results, a proposed strategy was developed, and recommendations were made, including adopting the proposed strategy and governing the programs for qualifying administrative leaders at King Khalid University to ensure transparency, fairness, and accountability at all stages from nomination, preparation, and evaluation, in addition to considering the university’s strategic plan when designing programs for qualifying administrative leaders to adopt the values embraced by the administration and build leaders who contribute to achieving its vision and mission in the long term.

In Nigeria, deforestation has led to an unimaginable loss of genetic variation within tree populations. Regrettably, little is known about the genetic variation of many important indigenous timber species in Nigeria. More so, the specific tools to evaluate the genetic diversity of these timber species are scarce. Therefore, this study developed species-specific markers for Pterygota macrocarpa using state-of-the-art equipment. Leaf samples were collected from Akure Forest Reserve, Ondo State, Nigeria. DNA isolation, quantification, PCR amplification, gel electrophoresis, post-PCR purification, and sequencing were done following a standardized protocol. The melting temperatures (TM) of the DNA fragments range from 57.5 ℃to 60.1 ℃ for primers developed from the MatK gene and 58.7 ℃ to 60.5 ℃ for primers developed from the RuBisCo gene. The characteristics of the ten primers developed are within the range appropriate for genetic diversity assessment. These species-specific primers are therefore recommended for population evaluation of Pterygota macrocarpa in Nigeria.

The aim of this paper is to develop a methodology for determining the size of the unified land tax in agriculture based on the results of the economic assessment of agricultural land to form the foundation of a new effective system of macroeconomic instruments for state regulation of the innovative development of the agro-industrial complex of the Republic of Kazakhstan. There were used gatherings of facts and summaries, induction and deduction, analysis and synthesis, historical and logical, normative, comparison, index and modeling methods in the research. The article provides an overview of various scholarly perspectives on the challenges and strategies for improving the tax system. The base rates of the unified land tax per hectare of arable land have been calculated to establish equal conditions for all land users. This unified land tax rate is expected to encourage the efficient utilization of land resources and enable the optimization of production structure. The article addresses avenues for improving water management relations in agriculture, aimed at fostering a shared interest and creating incentives for adopting innovative technologies in both agriculture and the water management sector. An essential condition for achieving the effective functioning of Kazakhstan’s agro-industrial complex is its transformation to an innovative development model. This necessitates the development and application of a new system of macroeconomic tools for its implementation, aimed at creating a favorable environment for entrepreneurial development.

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 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.