Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic

Combinations of direct-acting antivirals are needed to minimize drug resistance mutations and stably suppress replication of RNA viruses. Currently, there are limited therapeutic options against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and testing of a number of drug regimens has led to conflicting results. Here, we show that cobicistat, which is an FDA-approved drug booster that blocks the activity of the drug-metabolizing proteins cytochrome P450-3As (CYP3As) and P-glycoprotein (P-gp), inhibits SARS-CoV-2 replication. Two independent cell-to-cell membrane fusion

Background: Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist

Electric vehicles (EVs) can only provide lower carbon emissions than conventional, internal combustion-powered vehicles if they are charged using green energy. They also have the drawback of long charging times to 'refuel' them. To combat these two problems, a solar-powered battery charging and swapping station was developed using centered-human design and systems engineering. The design focuses on electric two-wheeled vehicles (e-scooters) due to the easier handling of their light and low-capacity batteries compared to electric cars. These stations are easy to maintain and manage and can be

With the increasing availability of digital X-ray imaging, artificial intelligence (AI) has emerged as a promising tool for automating the assessment of thoracic diseases. The objective of this study is to systematically review the artificial intelligence (AI) and deep learning methods proposed for the automated assessment of thoracic diseases from chest X-ray images. A thorough search of the relevant literature was conducted, and studies that met the inclusion criteria were critically reviewed. Information on the datasets, model architectures, evaluation metrics, and results was extracted

This presents a hybrid photovoltaic/diesel system simulated as an alternative to the diesel generators required for water pumping systems. This hybrid system is designed to overcome the limitations of the fluctuating price and availability of diesel generators, ensure a reliable supply without interruptions, and improve the overall system efficiency. The system configuration offers an economical benefits over fully renewable hybrid systems or PV only systems. The novelty of the system design lies in minimizing its complexity in terms of design size and control while using a 10 KW photovoltaic

Trajectory tracking of unmanned aerial vehicles (UAVs) has been studied nowadays because it is necessary to design new controllers under different conditions. Severe atmospheric conditions are one of the major problems to overcome according to the path of the UAV. Conditions such as wind speed that can vary according to the weather conditions can affect the flight performance and of course in extreme cases can lead to stability problems. Some kinds of severe conditions are storms, snow, and hurricanes. These conditions are considered as disturbances in the roll, pitch, and yaw orientations of

Polycyclic aromatic hydrocarbons (PAHs) are highly toxic and carcinogenic compounds as they are low water solubility, hardly degradable and may persist in the environment for many years. Therefore, this study was directed to PAHs ‘anthracene and naphthalene’ removal using a combination method between adsorption and degradation using sunlight. Three adsorbent materials, iron oxide (Fe) alone, Sargassum dentifolium (S) alone, and mixture of Iron oxide and Sargassum dentifolium (FeS) were prepared. Afterwards, optimisation process was performed for the three adsorbent forms through some

Despite significant efforts to democratize artificial intelligence (AI), computer vision which is a sub-field of AI, still lags in Africa. A significant factor to this, is the limited access to computing resources, datasets, and collaborations. As a result, Africa's contribution to top-tier publications in this field has only been 0.06% over the past decade. Towards improving the computer vision field and making it more accessible and inclusive, this study analyzes 63,000 Scopus-indexed computer vision publications from Africa. We utilize large language models to automatically parse their

In this work, the effects of adding hydrogen to diesel non-premixed combustion with air in a cylindrical furnace burner are investigated. A numerical model is developed to study the effects of hydrogen addition on combustion flame temperature and exhaust emissions. Different hydrogen addition ratios of 5%, 10%, 15%, and 20% by volume in the burning mixture are considered. The turbulence-chemistry interaction is modelled through the eddy dissipation combustion model to predict the concentration of exhaust emissions and the temperature of the combustion flame. The air-fuel equivalence ratio is