Technology is offering innovative and powerful tools for educators and learners, Digital transformation in educational sector is revolutionizing the way of learning. Ethiopian higher education faces particular challenges in utilizing digital tools to improve assessment outcomes and challenges that include lack of infrastructure, limited digital literacy and support from educators and students, and related inadequate policies. This Research presents the digital transformation of educational assessment to improve learning outcomes in higher education institutions in developing regions like Ethiopia. Data were gathered through a mixed-methods approach to evaluate existing frameworks by capturing the voices of faculty members and students across multiple institutions in Ethiopian and to identify gaps in the adoption of digital human modeling. findings suggest that digital assessment tools can improve students’ engagement and transparency as well as enhance the teacher’s real-time feedback efficiency. Despite these challenges, a shortage of digital infrastructure, low digital literacy and resistance to change have so far prevented mass adoption of services and products. A strategic framework is proposed in this research for Ethiopian higher education institutions, capacity building initiatives, and for scaling digital solutions. It is then the contribution of the study to the global discourse of digital education to provide insights into local educational needs and international standards in the alignment of digital assessment practices.

Objective: The integration of Artificial Intelligence (AI) into global healthcare necessitates corresponding advancements in health professions education. This study aimed to develop a comprehensive, evidence-based AI literacy curriculum specifically tailored for physiotherapy students in India, where such training is currently absent. Methods: A systematic curriculum development process was employed, guided by Kern’s sixstep framework. This involved a literature review of global AI applications in physiotherapy and healthcare education, a targeted needs assessment identifying gaps in current Indian physiotherapy training, and synthesis and contextualization to adapt international evidence for the Indian healthcare setting. Results: The outcome is a detailed 12-week, 36-contact-hour curriculum titled 'Artificial Intelligence Literacy in Physiotherapy Practice'. The curriculum consists of five sequential modules emphasizing low-cost tools, Indian case studies, ethical reasoning, and hands-on exercises. Conclusion: This paper provides a rigorously developed, ready-to-implement framework to address the critical gap in AI literacy within Indian physiotherapy education. Future research should evaluate its effectiveness in improving student competencies and readiness for AIintegrated healthcare.

Artificial Intelligence (AI) is re-inventing the design, operations, and customer relationships approaches of the global e-commerce system. Customized suggestions and dynamic pricing models, smart logistics, and chatbots are just some examples of AI technologies that have made modern digital retail. This research paper is a hybrid, as it explores the ways AI is transforming e-commerce based on theoretical, empirical, and a simulated dataset, which is a model of consumer behavioral responses to AI-enabled retail interfaces. An abstract map demonstrates the multilateral interplay of AI technologies, efficiency of the operations, the extent of personalization, and consumer trust that further affects the purchase intentions and brand loyalty. Findings of a simulated dataset of 500 hypothetical online shoppers indicate that AI-based personalization, quality of recommendations, and trust in the automated systems have a major predictive power of purchase intention and satisfaction. The paper then ends by providing strategic suggestions that should be adopted by digital retailers, ethical issues, and future of AI-facilitated commerce.

The pandemic-driven move to work remotely has now altered how people handle their offices and mental health at the same time. We explore how behavioral science and ergonomics come together to examine the psychological effects of people working from home over a long period. By using ideas from different fields, the study considers how ergonomics, being isolated from others, blurred work-life balance and digital means of communication impact both psychological health and productivity. Studies have been done through surveys with remote staff, auditing home work areas and doing targeted interviews. There seems to be a strong link between suboptimal ergonomic conditions and both mental tension, stress out and physical injuries in the workplace. In addition, working from home without meeting people in person and mixing tasks from home and work made people feel detached from others and increasingly discontent at their jobs. Even with these issues, quite a few participants found increased independence and choice at work which means that remote work is not always bad but calls for careful design and support. Finally, the study suggests a plan for better remote work, supporting changes in behavior, workspace setting and work rules by the organization.

The necessity to provide the effective and secure data migration processes is growing inexorably because of the fact that cloud computing is being created as the foundation of the modern digital ecosystems. However, the conventional encryption and migration algorithms are challenging in the ability to resist the quantum computing attacks that currently exist and offer security at realtime. In this paper, quantum based hybrid encryption system of safe cloud data transfer has been proposed. The framework embraces the quantum key distribution (QKD) concepts, optimization of chaotic map and generates keys with the help of the genetic algorithm to carry out the high speed and adaptive encryption. The simulated transfers of the data between the nodes of the cloud data have proved that the cloud data encryption solidity and the time lag is 47 and 31 less than the traditional AES and RSA systems respectively. One of the most probable pre-quantum defence technologies which will allow the data to be transferred safely and successfully in the case of the dynamic cloud infrastructures is quantum-inspired computing as it is revealed in the present paper.

Background: Bell's palsy affects approximately 20-30 per 100,000 individuals annually. While conventional facial exercises show benefits (Khan et al., 2022; Teixeira et al., 2011), systematic application of eccentric muscle training principles to facial rehabilitation remains unexplored. Objective: To propose an evidence-informed protocol integrating eccentric facial muscle exercises during early and recovery phases of Bell's palsy, emphasizing accessibility and costeffectiveness. Methods: This protocol synthesizes current facial rehabilitation evidence with established eccentric exercise principles to create a progressive home-based intervention requiring minimal equipment. Results: A 12-week protocol is presented incorporating acute protection (weeks 1-3), active eccentric training (weeks 4-8), and functional integration (weeks 9-12) phases. Total material cost: $3-23 per patient versus $1,600-3,600 for conventional therapy. Conclusion: Eccentric facial muscle exercises represent a promising, cost-effective approach addressing gaps in Bell's palsy rehabilitation. Clinical trials are warranted to establish efficacy.

Life remains possible only through many natural processes, both tiny and large. The work looks at cell functions such as processing food, expressing genes and responding to communications which are important for any life. It explores how each of these functions and changes come together in multicellular organisms. The study examines some current methods used to look at biological systems closely at many levels. The analysis demonstrates that how cells function and the organization of biological systems have major impacts on homeostasis, development and how organisms adapt. Participants discussed the hardships of studying life and what recent work in systems biology and synthetic biology can help achieve.

RTE foods are highly popular due to the reasons of convenience and limited preparation, but continue to serve as a frequent source of foodborne pathogens owing to the large amount of handling, the reliance on cold chaining, and the lack of a terminal heat treatment. The standard regulatory decision regarding the detection of pathogen still relies on conventional microbiological culture and confirmatory molecular techniques; nevertheless, they have a slow turnaround time that slows risk decisions in high-throughput retail and institutional environments. Near-real-time screening of high-risk pathogens such as Salmonella spp., Listeria monocytogenes, pathogenic Escherichia coli (including O157:H7) and Campylobacter spp. can be a promising approach with rapid biosensor technologies, which combines biological recognition factors (e.g., antibodies, aptamers, enzymes, phages) with transduction technologies (electrochemical, optical, piezoelectric, and magnetic). This paper is a synthesis of the existing knowledge on biosensor-based detection of RTE foods, focusing on analytical performance, limitations of sample preparation and applicability to food safety monitoring. An effective methodology framework is suggested to be used in the field-relevant evaluation of the biosensor screening in relation to the reference methods, such as the pre-enrichment combination, management of the matrix effects, and quality assurance controls. It is emphasized in the discussion that although biosensors have the potential to enormously decrease time-to-result and allow decentralized screening performance is highly dependent on the complexity of food matrices, small infectious dose organisms, and the need to discriminate viability. The limitations and future directions are outlined taking into consideration ethical communication of swift results, standardization and adherence to open science and reproducibility standards in food safety diagnostics.

The rise in the amount of population, energy consumption, and climate issues have been issues of concern worldwide, which have given rise to urban sustainability. In the paper, the author explains about a hybrid deep learning system that integrates Cognitive Artificial Intelligence (AI) with urban analytics to predict the sustainability outcomes of the main areas, such as energy efficiency, waste management, transportation, and air quality. The proposed model will make use of Convolutional Neural Network (CNN) to compute the spatial information and Recurrent Neural Network (RNN) to compute the trends in order to realize real-time adaptive predictions. The open urban data was subjected to experimental validation, which demonstrated that the predictive accuracy of the open urban data is 91 percent and the wastefulness of the resource is minimized. It is an AI cognitive model that will help policymakers and urban planners to plan a sustainable city development using data.