EJAS Journal of Science and Technology

EQUATORIAL IONOSPHERIC DYNAMICS OF F-REGION

2024-01-31T07:52:08+00:00

This study aimed to explore and comprehend the dynamics of the ionosphere's F-region, particularly in equatorial regions. A model that was used to solve the dynamics would likely be developed by integrating the satellite data from the communication/navigation outage forecasting system (CNOFS) with other relevant features like solar activity, geomagnetic disturbances, and atmospheric variables. Ion Velocity Meter (IVM) instruments onboard the satellite are used to acquire insights into both particle density and vertical riding speed. During specific days around equal day-night throughout the cycle periods of the 2010 and 2011 years, we have included observation periods for plasma densities and upward drift velocities at different local hours along predetermined courses walked by the spacecraft. The CNOFS satellite signals indicate that daily variations in plasma density between 109 and 1012 m-3 take place between six in the morning and six in the evening. The night mode lasts till dawn, occurring within a geographical latitude range that is only slightly larger than -13.070 south, and hence covers altitude variations ranging from around 400 to 836 kilometers. This daily change highlights the complex and dynamic nature of the equatorial F-region and the ionospheric response to variations in solar illumination and geomagnetic circumstances. These results suggest that further study has to be done to look at factors that could affect ionospheric dynamics, such as solar activity, geomagnetic disturbances, and atmospheric conditions.

Constructing a Safer Future: Unveiling Health and Safety Challenges in Building Construction Projects in Ethiopia

2024-01-24T12:55:06+00:00

Abstract:

The construction industry is one of the most dangerous work sites in the world, having a high rate of accidents, illnesses, and fatalities, construction personnel workplace health and safety are major challenges. The building construction sector in Ethiopia has seen remarkable growth. However, this growth has led to increased health and safety concerns in building construction projects. This study seeks to identify and address health and safety challenges of Ethiopian building construction projects, aiming to enhance the well-being of workers and promote a safer construction environment. This study employed a descriptive study, once reviewing sufficient literature; questionnaires used to collect primary data from the respondent. Based on the findings of the research, the most health and safety challenges are insufficient regulatory enforcement (Mean=4.90), corruption (4.78), inadequate training and awareness (4.24), limited investment in safety equipment (4.11), lack of management commitment (4.09) and resource constraints (3.98). And the research proposed solutions to alleviate the issues these are strengthening regulatory enforcement (Mean=4.37), adopt anti-corruption strategies (4.27), and comprehensive health and safety training programs (4.18) are crucial. These solutions should aim to improve health and safety practices within the building construction projects in the country, contributing to a safer future. In general, the comprehensive insights offer valuable guidance to organizations, policymakers, and regulatory bodies to effectively address health and safety challenges of workers, ultimately fostering a safer construction environment in building construction projects and contributing to sustainable urban growth in Ethiopia.

Keywords: construction industry, building construction projects, health and safety challenges, solutions

Partial Replacement of Cement with Calcined Clay and Cow Dung Ash for Third Coat Wall Plastering Works in Low-Cost House Buildings

2024-03-05T13:28:56+00:00

Abstract

Due to cement is a costly material, people have been using clay and cow dung plaster alone or reinforced with grasses for on-wall plastering, with no mix composition. Having this point to mind, the purpose of this study was, to investigate partial replacement of cement with calcined clay and cow dung ash for third coat wall plastering works in low - cost house buildings.Pre-conditional test of: natural moisture content, gradation, atterberg limit, specific gravity, free swell, silt content, water absorption, moisture content, and unit weight were took place in order to identify the individual properties of the materials used in mortar production. Chemical compositions of the materials were identified: to compare the mineral constituent of the materials replacement with cement. Post ˗ conditional testes such as: consistency and setting time of cement and cement replacement and some cube samples of mortar with:0, 10, 20, 30 and 40 percentage of cow dung ash and calcined brown clay were prepared and subjected to:7,14 and 28 days of period before tested for water absorption, shrinkage, compressive strength and soundness test.The test showed that the compressive strength was decreased as an amount of calcined brown clay and cow dung ash increased in mortar and increased with the curing days. Finally, durability test were performed using visual observations by referring to ASTM specification code. For all, the best test result showed that, for tested properties with 10% proportion and 1:9 equal amount of calcined brown clay and cow dung ash (1CBC:1CDA) to cement mix ratio. Thus, it was low cost alternative materials for third coat wall plastering and under normal weather conditions, it was used as low ˗ cost house buildings, in case of non-structural members.

Keywords: Clay, cow dung ash, durability, free swell, liquid limit, and mortar.

Assessment of Radio Frequency Radiation from Smartphones: Implications for Human Health

2024-01-31T07:24:05+00:00

The thermal behavior of biological tissues and the radio frequency (RF) radiation emissions from well-known smartphone models are both examined in this work. The study of the thermal dynamics of human facial tissues is conducted using computational simulations and theoretical models, taking various characteristics such as density, specific heat capacity, and thermal diffusivity. The complex thermal dynamics are explained by the contour plots that illustrate the temperature differences throughout the face, which range from 37.0 to 39.277 degrees Celsius. Additionally, the study evaluates the specific absorption rate (SAR) values (W/kg) of radiofrequency radiation emissions from smartphones, such as the Samsung Galaxy S20, Google Pixel 5, OnePlus 8 Pro, Huawei P40 Pro, and Samsung Galaxy A series (A70, A71, A72, and A73). The Samsung Galaxy A series has the lowest SAR values, ranging from 1.20 to 1.26 W/kg, while the iPhone 12 has the highest SAR of 1.23 W/kg. When compared to other models, the iPhone 12 has higher sound output levels; the Samsung Galaxy A series has the lowest sound output levels. These results underline the significance of additional research into their possible health consequences by providing insights into the thermal dynamics in biological tissues as well as the exposure to radiofrequency radiation from cell phones.

The Automatic Tea Leaf Classification Using Deep Learning Models

2023-12-11T18:05:29+00:00

Abstract: Tea leaf classification plays a crucial role in quality control and grading within the tea industry. However, the manual classification process is time-consuming and subjective. This study aims to address these challenges by developing an automatic leaf quality detection technique. Three deep learning models, namely convolutional neural networks (CNN), Inception v3, and EfficientNet B0, were employed to classify tea leaves based on their visual features. A dataset of tea leaf images was collected and split into training, validation, and testing sets. The models were trained on the training set, with hyperparameter tuning performed using the validation set. The performance of the models was evaluated on the testing set. The results revealed that both convolutional neural networks (CNN) and EfficientNet B0 outperformed Inception v3, achieving accuracy rates of 96.9% and 97.4%, respectively, compared to Inception v3's accuracy rate of 95.9%. These findings demonstrate the effectiveness of deep learning algorithms in tea leaf classification and suggest their potential for further improvement and application in the tea industry.