ABSTRACT: 

Deltoid muscle pain is a prevalent musculoskeletal issue affecting a significant portion of the adult population that can result from muscle strain or fiber tears, direct trauma like falling or impact to the shoulder, and also improper lifting technique. Neuromuscular electrical stimulation (NMES) has been assumed as a non-invasive method modality designed to facilitate muscle contraction and alleviate pain. This paper focuses on designing and implementing a microcontroller-based Electrical Muscle Stimulation (EMS) device aimed at alleviating Deltoid muscle pain by stimulating the muscle. The proposed NEMS system utilizes an Arduino UNO R3 microcontroller to generate precise electrical impulses. The device generates square waveforms at varying frequencies (1 Hz to 50 Hz) and intensities (10V to 50V), which were validated using an oscilloscope and LCD feedback. Clinical testing on five patients demonstrated the efficacy of the EMS unit in relieving Deltoid muscle pain. The results indicated that a pulse frequency range of 2 to 10 Hz was sufficient to achieve significant pain relief. Factors influencing treatment effectiveness included stimulation intensity, patient age, and individual tolerance levels. Patients reported complete pain resolution with moderate-intensity stimulation, while higher intensities partially relieved severe pain. This study concludes that the developed NEMS device offers a non-invasive, cost-effective, and compact solution for managing Deltoid muscle pain. It provides several advantages over traditional pharmaceutical treatments, including reusability, ease of self-administration, and minimal side effects.

Keywords:  Neuromuscular electrical stimulation (NMES), Electrical Muscle Stimulation, Deltoid Muscle, Non- invasive treatment. 

ABSTRACT: 

The mixing between artificial intelligence (AI) with computational fluid dynamics CFD) in modeling and optimizing nanofluid flow in advanced microfluidic systems is investigated in this study. Focusing on nanofluids including  oxides of aluminum, copper and silicon, the research demonstrates AI’s capability to improve prediction accuracy, simplify the mesh adaption process and expedite design optimization. By means of neural networks with Gaussian process, the models can explicitly characterize complex thermophysical effects—i.e., Brownian motion, thermophoresis, and particle coagulation—pegged in standard solutions. Mesh convergence tests, GPU accelerated calculations, and comparison with experimental measurements show the reliability and efficiency of the AI-improved models. The results are of importance for the design of microchannel geometries, enhancements of thermal transfer performance and for enabling an active control of microfluidic devices for drug delivery, diagnostics, and heat exchangers.

Keywords: Artificial Intelligence, Nanofluids, Microuidics, CFD, Thermal Conductivity, Mesh Optimization, Neural Networks.

ABSTRACT: 

The rapid adoption of electric vehicles (EVs) has intensified the demand for high-performance, safe, and durable battery systems. Lithium-ion batteries, which are predominantly used in EVs, are highly sensitive to operating temperature and temperature non-uniformity. Inadequate thermal management can lead to performance degradation, accelerated aging, reduced driving range, and severe safety risks such as thermal runaway. Conventional battery thermal management systems (BTMS), including air cooling, liquid cooling, and phase change material-based methods, often struggle to meet the growing thermal requirements of modern high-energy-density batteries, particularly under fast-charging and high-discharge conditions. Nanofluid-based cooling has emerged as a promising advanced thermal management approach due to the superior thermo-physical properties of nanofluids, such as enhanced thermal conductivity and convective heat transfer coefficients. Nanofluids, consisting of nanoscale solid particles dispersed in a base fluid, offer improved heat dissipation capabilities compared to traditional coolants. This review provides a comprehensive overview of nanofluid-based battery thermal management systems for electric vehicles. The review discusses the fundamentals of nanofluids, their role in improving battery thermal performance, and the impact of different nanoparticle materials, concentrations, and flow configurations. Additionally, key challenges related to stability, viscosity, cost, and long-term reliability are critically examined. Finally, future research directions are outlined to facilitate the practical implementation of nanofluid-based BTMS in next-generation electric vehicles.

Keywords: Electric vehicles, Battery thermal management, Nanofluids, Heat transfer, Lithium-ion batteries.

ABSTRACT: 

The current study reviews the literature on Artificial Intelligence (AI) and Blockchain (BC) technologies individually, highlighting their conceptual and architectural structure, benefits, applications, and challenges. It also examines the common approach to AI and Blockchain, focusing on applications and challenges. Based on this review, a tentative conceptual framework is proposed that explains the factors that may influence the acceptance and implementation of AI and Blockchain technologies in government services. This framework consists of four dimensions including strategy and governance, technology, people, operations, and five stages of change.

The study proposed a maturity model that supports the successful implementation of artificial intelligence and Blockchain technologies in the public sector in the United Arab Emirates, by providing a comprehensive assessment tool for the effectiveness of the organization. The study results provide a comprehensive understanding of the motivations, applications, and challenges associated with the deployment of these technologies. Based on these insights, a maturity model was formulated to assess organizational readiness for effective implementation. The study provides implementation guidance categorized into four main areas: preparing for the evaluation, conducting the evaluation, setting improvement goals, and tracking progress. The research fills a major gap in the literature by presenting a maturity model to support the public sector in the UAE in successfully adopting and implementing these technologies, which contributes to government digital transformation.

Keywords:  Artificial Intelligence, Blockchain, Public Sector, United Arab Emirates.