A large number of publications devoted to a new class of materials - high-entropy alloys (HEA), is associated with their unique chemical, physical and mechanical properties both in cast materials and in various classes of coatings and refractory compounds. As a result of the research, the features of solid-soluble high-entropy alloys based on BCC and FCC phases have been revealed. These include the role of the most refractory element in the formation of the lattice parameter, the relationship of distortion with elastic deformation, and the contribution of the enthalpy of mixing to the strength and modulus of elasticity. This made it possible, on the basis of Hooke's law, to propose a formula for determining the hardness of the HEA based on the BCC and FCC phases. Based on the fact that with an increase in temperature in high-entropy alloys, the values of the modulus of elasticity, distortion and enthalpy of mixing will obey the same laws, a formula is proposed for determining the yield strength depending on the test temperature of solid-soluble HEA based on BCC and FCC phases. A formula based on the role of the most fusible metal in the alloy is proposed to calculate the melting point of solid-soluble materials.

This research investigates the dynamic landscape of succession planning (SP) strategies in higher education, with a focus on synthesizing existing literature to guide improvements in presidential succession practices. The intense global competition in higher education has led to imbalances in the quantity and composition of potential successors, hindering institutions’ rapid advancement and affecting their competitiveness on the global stage. The study addresses critical challenges such as attracting, retaining, and nurturing successors in key positions beyond material incentives. Employing a literature analysis methodology, the research comprehensively examines the existing body of literature related to succession planning, offering recommendations to promote stability in leadership, foster continuous talent development, and mitigate talent crises. The study evaluates the current state of succession planning in higher education, identifying issues and their root causes. It provides a summary and analysis of ongoing research efforts related to successor quality, team formation, and cultivation models. Despite advancements through national talent cultivation policies, persistent challenges like talent scarcity, the absence of gender-inclusive succession plans, a lack of originality, and inconsistent staff flow hinder progress. The research attributes these challenges to traditional personnel systems and university administrators. Proactive measures are proposed, including creating awareness of succession planning, advocating for personnel mechanism reform, establishing a comprehensive training system, and developing a scientifically-grounded succession plan. Though the study aims to contribute to leadership development and address pressing issues faced by higher education institutions, with only a limited number utilizing mixed techniques, it restricted the comprehensive inclusion of social context knowledge and evidence regarding the motivations, beliefs, and experiences of individuals in this investigation.

This article aims to explain the principles of the leadership styles of madrasah heads in enhancing the quality of Islamic education in Lhokseumawe City. It turns out that the leadership of madrasah heads has a significant impact on the functioning of the madrasah leadership. The madrasah head plays a direct role in developing Islamic educational institutions through leadership characteristics, leader types, leader functions, and leader activities during their tenure as madrasah heads. A quantitative research method with a phenomenological approach is considered capable of addressing various issues in the research problem formulation. It simultaneously analyzes data obtained from observations, interviews, and in-depth documentation to find answers to the research problem. The research findings reveal that the leadership styles of madrasah heads can be divided into two categories: democratic leadership style and autocratic leadership style. The first principle of leadership style involves giving responsibilities and authority to all parties, encouraging active involvement in the organization. Members are given opportunities to provide suggestions, recommendations, and criticisms for the progress of the organization. The second principle, the autocratic leadership style, positions a leader as the source of policies.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.