CONFERENCE ON RECENT RESEARCH IN SCIENCE AND TECHNOLOGY | Year:2023 | دوره:24 |Papers Count:4

Using computational fluid dynamics is of utmost importance in simulating phenomena where using of conventional methods such as analytical methods for solving the PDE are impossible in some cases, therefore of no use. Using CFD can expedite the procedure of simulating these phenomena and can lead to very interesting results if done properly. In this here project movement of a shark in water is simulated using X-FLOW and ANSYS fluent software in order to analyze the Fields of flow, Force, Pressure and Velocity around the body and also drag and lift forces acting on the body of the fish. Simulation is carried assuming incompressible Flow and steady state Condition and lift and drag Force has been calculated.

The purpose of this study is to determine the effective components and their role and effect in explaining the philosophical foundations of moral values in the formation of organizational behavior and its method in terms of applied purpose, which has been done with an approach mixed with exploratory design. In the qualitative approach, the Delphi method and in the quantitative approach, the current situation has been studied and the descriptive survey method has been used. In the first stage, a "qualitative study" was performed to obtain the dimensions, basic components and items of philosophical, educational, moral values and organizational behavior. In the second stage, the basic dimensions and components obtained in the qualitative stage were exposed to the judgment of the statistical community and its importance was quantified through factor loading. In the third stage, using the obtained information, the model was presented on the content and structural dimensions of education staff. The statistical population in the qualitative part is 14 knowledgeable and specialized employees of the Kerman Education Organization and in the quantitative part includes the heads, educational deputies, managers and education staff of Kerman province, which was selected by purposeful census by 135 people. Were. Conclusion: Based on the research findings, the proposed model in the present study has a very good fit in every way and it can be said that it has a high degree of appropriateness of this proposed model.

Alzheimer's disease (AD) is a brain disorder that slowly destroys memory and the ability to think, and symptoms eventually grow severe enough to interfere with daily tasks. Alzheimer's is a progressive disease, where dementia symptoms gradually worsen over a number of years. Thus it is very helpful to diagnose this disease in its early stage. There are several methods and tools to determine Alzheimer’, s. However, diagnosing AD base on machine learning and brain imaging such as CT, MRI, PET and etc is the most reliable way to detect this disease in the early stage. Machine learning (ML) has become a major part of medical imaging research. It can completely change the working layout of the healthcare facilities while detecting and predicting diseases with a superior level of care and satisfaction. This paper aims to review the impact of Machine Learning systems for detecting early-stage Alzheimer’, s Disease using imaging techniques.

During the last two decades, with the progress of industry, people have witnessed various environmental issues in society. One of these effects is pollution. Wastewater from a large number of industries, such as electroplating, contains a significant amount of heavy metals. Common methods of heavy metal pollution removal are expensive, energy-intensive and often associated with toxic by-products, but the absorption method with natural materials has been investigated as a cost-effective method to remove heavy metals from wastewater. In the present study, various low-cost adsorbents have been used to remove heavy metal pollution from industrial wastewater. This adsorbent includes natural materials such as zeolite, clay, peat moss and chitin as an effective agent for the removal of heavy toxic metals. Apart from these cases, various agricultural wastes such as rice bran, walnut shell, black mung beans, and tea waste are used as low-cost absorbents. Also, these adsorbents are considered as a strong adsorbent to remove heavy metals, even low-cost industrial by-products such as ash, blast furnace sludge, red iron, sugar beet pomace and grape stalks to remove toxic heavy metals from polluted water. They can be used for this method.