In light of the fact that sustainable development and financial stability are now essential to global social and economic progress. Thus, the current study looks into how risk management affects Ethiopian banks’ financial stability between 2017 and 2024 using the generalized moment (GMM) model. The study used financial stability as the dependent variable and liquidity risk, credit risk and operational risk as the independent variables. Moreover, the study used inflation GDP and exchange rate as the control variable. In light of this, the study found that the financial stability of Ethiopia’s commercial banks is significantly impacted negatively by credit risk, inflation, and exchange rates. Whereas GDP and liquidity risk have a significant and positive impact on the financial stability of Ethiopia’s banking industry, apart from them, operational risk doesn’t affect the financial stability of banks in Ethiopia. Thus, the study concluded that macroeconomic features (GDP, inflation, and exchange rate) and risk management factors (credit risk and liquidity risk) significantly affected the financial stability of banks in Ethiopia. Therefore, bank management should establish effective risk management mechanisms like ensuring secure loan granting and, improving the quality of credit appraisal and analysis and, collecting and updating information about customers in a timely and accurate manner, analyzing the risks of the loan plan to provide the expected risks and the bank’s controllability.

Mobile Edge computing is one of the emerging fields in cloud environments where numerous user applications leverage a wide range of strong and powerful resources. To ensure optimal utilization, cloud computing resources such as storage, applications, and other services require effective management and scheduling. Managing resources is particularly challenging in scientific workflows, which involve extensive computations and interdependent operations. Task scheduling is the crucial challenge in this setup since the edge setup is migrated near to the user’s environment most of the computation is going to be handled by the edge server. Various algorithms and techniques have been proposed to address this issue. This paper explores a novel scheduling method for tasks offloaded by different users in a multi-user access computing paradigm. Also, the priority of the task is being considered while the tasks from mobile users are assigned to the data center. Considering the priority of the task, the tasks are being scheduled parallelly to the data centers. The completion time and the CPU utilization are extremely enhanced by using the proposed PBPPMUMTSA- Priority Based Parallel Processing Multi User Multi Task Scheduling Algorithm.

The pursuit of high-quality education is a global priority, and the impact of teachers is fundamental to defining the quality of education systems internationally. Teachers’ professional development is integral to enhancing their teaching capabilities, improving student outcomes, and contributing to the broader societal and national development. Continuing professional development (CPD) is essential for fostering lifelong learning, enhancing teachers’ competencies, and promoting effective teaching practices. However, in India, CPD has traditionally been equated with in-service teacher education and training (INSET), which often consists of short-term, isolated training events. While CPD is recognized as a lifelong, multifaceted process of growth, previous educational policies have primarily focused on INSET, limiting the scope of professional development opportunities for teachers. A substantial policy shift is evident with the introduction of NEP 2020 by broadening the scope of CPD, emphasizing its importance across all educational levels and recommending strategies for comprehensive and ongoing teacher development. This paper examines the evolution of CPD in Indian educational policy, the limitations of earlier frameworks, and the changes proposed by NEP 2020. By exploring the broader concept of CPD, this study highlights its potential to enhance both individual teachers’ professional growth and the overall quality of the education system.

Globally, we are fronting environmental pollution with augmented greenhouse gases which are the foremost ground of climate variation. Abiotic stresses i.e., drought, flood, heat, cold, salinity, ozone, heavy metal, and high irradiance are responsible for their detrimental influences on crop plants. A significant impact on plant distribution, growth and development, and agricultural productivity is imposed by abiotic stresses. As plants are sessile, they must deal with abiotic challenges as plants withstand these abiotic stresses by modifying morphological, physiological, and biochemical retorts. Addressing and mitigating these stresses are essential for ensuring sustainable agriculture. This review highlights the advancements in strategies to enhance crop adaptation and mitigation under abiotic stress conditions. Prominent approaches include developing stress-resilient crop varieties through traditional breeding methods and genetic engineering, employing bio-stimulants, phytochelatins, plant growth regulators, adopting innovative irrigation and soil management techniques. Emphasis is placed on molecular-level adaptations, such as modulating stress-responsive genes and strengthening antioxidant defense systems, for their role in boosting crop resilience. By consolidating recent progress and identifying research gaps, this review aims to establish a comprehensive framework for enhancing crop productivity amidst escalating environmental challenges.

Medium-sized enterprises currently encounter significant challenges in effectively managing their operations, often struggling to sustain long-term viability. Financial literacy equips business owners with the knowledge necessary to make informed and strategic decisions regarding financial matters, thereby fostering a robust business environment. However, the impact of financial literacy on the performance of medium-sized enterprises has not been thoroughly investigated. Consequently, this study aims to evaluate the influence of financial literacy on the performance of medium-sized enterprises in the Bench Sheko zone. An explanatory research design combined with a descriptive survey approach was utilized for this investigation. Out of a total of 453 medium-sized enterprises, 212 were selected using a stratified random sampling method. Financial literacy was treated as the independent variable, assessed through budgeting literacy, debt management literacy, accounting literacy, and saving literacy. In contrast, enterprise performance was measured as the dependent variable through return on assets. The findings indicated that budgeting literacy, debt management literacy, accounting literacy, and saving literacy positively and significantly affect the performance of medium-sized enterprises. Therefore, it is recommended that the government enhance the financial literacy of medium-sized enterprise owners through training programs and by integrating financial literacy topics into formal education, thereby improving the performance of these enterprises.

This paper’s goals center on understanding the physiological behavior of diabetes, specifically type 2 diabetes, through mathematical modeling and in order to assess the health of diabetic patients and identify the most effective and practical blood glucose control strategies. Additionally, research on diabetes patients, both those with and without complications, is the main objective. Either a new model can be built or an existing model can be improved in order to develop a mathematical model for diabetes mellitus.

The significance of the efforts by local governments in achieving Sustainable Development Goals (SDGs) is paramount. However, local governments in India face several obstacles in achieving the SDGs, bottlenecks in the free flow of funds being one of them. The 73rd and 74th Constitutional Amendments, which constitutionalized rural and urban local governments in 1993, also mandated the periodic constitution of the State Finance Commission (SFC) as a constitutional body in India. The design of the SFCs aimed to replicate the Union Finance Commission (UFC) at the provincial level, promoting democratic decentralization from states to local bodies. These amendments introduced Articles 243I and 243Y, which mandated periodic institutions of SFCs to supervise the transfer of funds to local governments. By now, all states should have progressed to their seventh-generation SFCs. Several challenges, such as delay in the constitution of SFCs, non-synchronization of SFCs with UFC period, and delay in the submission of reports, have impeded the functioning of the SFCs as well as the UFCs. The 15th UFC has recommended using the SFC reports as a precondition for releasing grants to local bodies after March 2024. Even though the 16th UFC has already been constituted, only nine states have managed to submit the report of their 6th SFCs. Against this backdrop, this paper delves into the institution of SFCs in India, exploring their significance, analyzing the challenges they face, and proposing potential solutions.

The rapid adoption of cloud computing has transformed IT resource management by providing scalable, flexible, and cost-effective solutions. Despite these benefits, cloud computing presents critical security challenges, particularly in protecting sensitive data during transmission and storage. This paper introduces the Enhanced Cloud Data Security (ECDS) technique, a new approach aimed at strengthening data protection within cloud infrastructures. ECDS incorporates substitution and permutation methods to secure data and utilizes a combination of encryption strategies to ensure that encrypted data remains inaccessible to unauthorized users. ECDS is a symmetric cryptographic system that uses the same key for encryption and decryption. It is 256-bit block cipher encryption and it uses 312-but keys. The ECDS is implemented in Python and compared against DES and Blowfish Encryption techniques. Extensive testing and performance analysis reveal that ECDS significantly enhances security and efficiency compared to traditional encryption methods. This paper contributes to the ongoing efforts to secure cloud computing environments for safeguarding sensitive data in the cloud.

Combining energy-efficient routing with secure data management for LAWSNs represents a challenging research domain and an emerging scientific paradigm. This approach includes various elements, for example, data collection, asymmetrical clustering and hybrid optimization as well as a model of the combined encryption Wireless Sensor Networks (WSNs). In the process of data collection, sensor nodes will sense environmental information (temperature, humidity, etc.), sample regularly and fuse to detect redundancy information for power saving. Geospatial clustering with IFUC is important in location-aware routing. This approach overcomes the hot spot problem and provides energy-efficient management of traffic within and between clusters by optimizing cluster sizes based on their access to the base station. Here, a hybrid encryption model is presented to combine asymmetric RSA with symmetric data encryption standard (3DES) encryption in order to carry out absolute encrypted data transmission, minimizing the probability of unauthorized accessible information. All the keys are systematically managed, periodically renewed, and handpicked through the phoenix fusion optimiser (PFO). The data transfer process is about the encryption of data by hardcoded keys and how this encrypted information is sent on the best route possible. The cluster heads (CHs) fuse data in clusters to minimize the redundant transmission of data. This data is then shared along the inter-cluster pathways to become a base station. A holistic energy-efficient, secure and optimized data handling solution tailored for location-aware wireless sensor networks (WSN) is thus proposed, extending the lifespan and enhancing the efficiency of the network using an integrated strategy.

The rapid evolution of machine learning (ML) and big data analytics has modernized medical decision-making procedure, offering promising path for improving classification precision and ultimately, patient outcomes. This research inspects methodologies for enhancing the classification accuracy of medical decision systems by leveraging ML algorithms and big data analytics procedure. In this study, a broad evaluation of existing literature on ML applications in healthcare and medical decision-making is carried out to discover current challenges and potential areas for improvement. The research explores the integration of diverse data sources, including electronic health records (EHRs), medical imaging, genomic data, and patient-generated data, to build robust predictive models. Moreover, the research emphasizes the importance of interpretability and transparency in ML models for medical decision-making, particularly in critical healthcare settings where the rationale behind predictions is crucial. Techniques for model explainability, such as feature importance analysis and model-agnostic interpretability methods, are explored to enhance trust and adoption of ML-driven decision systems by healthcare professionals. Furthermore, the study investigates advanced ML algorithms such as deep learning, ensemble methods, and feature engineering techniques to extract meaningful patterns from large and complex medical datasets. Through experimentation with real-world medical datasets, the efficacy of these algorithms in improving classification accuracy is evaluated and compared against traditional methods. The result of this research contributes to the advancement of ML-driven medical decision systems by providing insights into strategies for improving classification accuracy, thereby facilitating more exact diagnosis, prognosis, and treatment recommendations. Ultimately, the integration of ML and big data analytics holds immense potential for revolutionizing healthcare delivery and improving patient outcomes.