Browse Volumes & Issues

This work presents the state-of-the-art of wireless communication system performance metrics through the reviewing of multi-path propagation model and Singular-Value-Decomposition (SVD) method to enhance the channel capacity in the 4th and 5th generation wireless communication systems. MIMO has shown rapid improvement in data rate by lowering the Signal-to-Noise ratio (SNR). In multi-path propagation, fading is a serious issue that increases the overall average Bit-Error Rate (BER). In this research, an improvement of data rate in terms of enhancing the channel capacity of the MIMO system is summarized by reducing the SNR using SVD. An Empirical model of the MIMO system is studied, and an IID Rayleigh-Fading MIMO system is modeled to compute the channel capacity.

This article focuses on how the behavior of cement matrices is influenced by the water-cement ratio and the percentage of nano clay in concrete mixes. Due to the high costs associated with experimental analysis, an initial simulation is conducted to evaluate the behavior of montmorillonite nano clay (MNC) in cementitious materials. Simulations utilize representative volume element (RVE) techniques, leading to the creation of two regression equations to characterize compressive strength and flexural strength based on the two variables. Ultimately, this study provides valuable insights into MNC-cement composites, aiming to reduce the need for macro reinforcement in construction and to facilitate more efficient and cost-effective concrete designs.

Pneumonia is an infection of the lungs caused by bacteria, viruses, fungi, or parasites, leading to the accumulation of pus in the air sacs, which can affect one or both lungs. This serious illness poses a global health threat, with early diagnosis being a key challenge. Traditionally, it is diagnosed by medical professionals using chest x-rays. In this study, a collection of x-ray and CT-Scan images is employed to enable automated pneumonia detection. As the condition progresses, patients experience increasing difficulty breathing. Machine learning methods offer potential for faster and more accurate diagnosis by applying computer vision techniques for automatic detection in medical imaging.

This paper seeks to explore the recent advancements made in solar power technology in detail. The last couple of decades have seen solar energy rise to popularity as one of the most promising renewable sources which can meet the demands for energy globally in a green way. The review investigates the improvements in photovoltaic cells, advancements in concentrated solar power systems and covering emerging trends such as solar paint and fabrics. It discusses the progress of each modality, how the devices work, factors that can cause efficiency improvement, and the fields where it can be employed. Furthermore, the paper discusses the challenges garnered by these technologies as well the y prospects of such technologies in aiding the transition towards greener energy sources.

Machine Learning (ML) has emerged as a powerful tool for solving complex scientific problems, driving advancements across various fields such as biology, physics, chemistry, and environmental science. This review paper highlights the intersection of ML and scientific research, focusing on key algorithms, popular applications, and unique challenges faced by scientists. With a growing number of researchers leveraging ML to analyze large datasets, make predictions, and uncover hidden patterns, this paper provides an overview of machine learning techniques, their applications in scientific domains, and the challenges scientists face in integrating ML into their workflows

In order to increase productivity and automate processes, the majority of industries today are working to improve their production processes and the necessary technology. These kinds of activities, which are most commonly employed in both small- and large-scale enterprises, include drilling, taping, boring, etc. The majority of industries still rely on manual tapping, drilling, and boring techniques. This traditional approach requires a lot of labor, takes a long time, is less accurate, and ultimately produces less output. Therefore, there is room to design a machine for a variety of operations that would solve every issue the traditional method faces. Therefore, we are going to create a portable auto feed device that requires less human intervention to operate than a hand drill because it runs on compressed air.

Concrete is the third most used substance in the world next to water and air. Production of cement consumes very large amounts of natural resources and 0.9 tons of Carbon Dioxide is produced per ton of cement production which causes about 8% of world Carbon Dioxide emissions. To cope with that industrial and agricultural waste are used in making concrete which will serve two purposes, disposal of waste and conservation of natural resources. The properties of partially substituted concrete is observed in this paper. The behavior of green concrete with rice husk ash (RHA), stone dust (SD) and scrapped rubber as a partial substitution of cement, fine aggregate and coarse aggregate was experimentally explored. In this paper the combine effect of RHA, stone dust and rubber aggregate in different proportions in concrete will be seen. It has been observed that with increase in rubber aggregate compressive strength of concrete decreases but along with that if rice husk ash and stone dust also increases then compressive strength of concrete increases.

The prediction of concrete compressive strength using Non-Destructive Testing (NDT) methods, such as the Rebound Hammer Test, has become increasingly popular in the construction industry due to their efficiency and non-invasive nature for Structural Health Monitoring (SHM) However, the accuracy of these traditional methods remains a concern, due to the higher percentage of error in the prediction of strength of concrete structures. This study proposes the application of Artificial Neural Networks (ANN) to enhance the accuracy of concrete strength predictions based on rebound hammer data. Using a dataset of Rebound Hammer Test samples, which is the rebound number, an ANN model was developed, trained, and validated. The results demonstrate a significant improvement, reducing the Mean Absolute Percentage Error (MAPE) to 8.27%. This research highlights the potential of ANNs in improving the reliability of NDT methods and recommends further exploration of artificial intelligence techniques for enhanced prediction accuracy in structural health assessments.

The term "solar energy" describes the radiant energy that the sun emits, which may be captured and transformed into useful forms of power like heat or electricity. Because of its sustainability and abundance, this renewable energy source is a major priority in the fight against climate change and the decrease in dependency on fossil fuels. Solar energy has emerged as a pivotal player in the global energy landscape, offering sustainable solutions to address both environmental challenges and energy security concerns. This paper provides an in-depth exploration of solar energy, including its technological advancements, economic impacts, policy frameworks, and prospects.

Diabetes Mellitus is a serious condition affecting a large number of people worldwide. Various factors such as age, obesity, lack of exercise, genetic predisposition, lifestyle choices, poor diet, and high blood pressure contribute to the onset of this disease. Individuals with diabetes are at an increased risk for complications like heart disease, kidney disease, stroke, vision issues, and nerve damage. In hospitals, diabetes diagnosis typically involves conducting various tests and providing treatment based on the results. Big Data Analytics has become essential in the healthcare industry, which handles massive volumes of data. By utilizing big data techniques, it is possible to analyze large datasets, uncover hidden patterns, and derive insights to predict outcomes more effectively. However, existing methods for classification and prediction in diabetes diagnosis have limited accuracy. In this paper, we propose an improved diabetes prediction model incorporating additional external factors and standard metrics like glucose levels, BMI, age, and insulin. This new model enhances classification accuracy with an updated dataset and introduces a pipeline framework to further improve prediction accuracy.