Biomaterial Strategies for Immune System Enhancement and Tissue Healing
Qualitative and Quantitative Performance Optimization of Simple Gas Turbine Power Plant using Three Different Types of Fuel
Efficient Shopping: RFID-Powered Cart with Automated Billing System
Medical Drone System for Automated External Defibrillator Shock Delivery for Cardiac Arrest Patients
A Critical Review on Biodiesel Production, Process Parameters, Properties, Comparison and Challenges
Review on Deep Learning Based Image Segmentation for Brain Tumor Detection
Chemistry and Chemical Engineering: Approaches, Observations, and Outlooks
Integration of PMS Software and Decision Matrix Tool Based on Data Acquired from Latest IT Advanced Sensors and 3D CAD Models in Marine Operations Field
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Dynamic Changes in Mangrove Forest and Lu/Lc Variation Analysis over Indian Sundarban Delta in West Bengal (India) Using Multi-Temporal Satellite Data
The Impacts of Climate Change on Water Resources in Hilly Areas of Nepal
An Analysis of Machining Forces On Graphite/Epoxy, Glass/Epoxy and Kevlar/Epoxy Composites Using a Neural Network Approach
Deformation Behaviour of Fe-0.8%C-1.0%Si-0.8%Cu Sintered P/M Steel during Powder Preform Forging
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
Achieving Manufacturing Excelence by Applying LSSF Model – A Lean Six Sigma Framework
Design and Analysis of Piezo- Driven Valve-Less Micropump
The Sultanate of Oman, being a petroleum-producing country, depends on fossil fuels for the majority of its power production. The fossil fuel sources are on the verge of extinction if they are used at the current rate. Globally, buildings are expected to generate the energy they demand using renewable energy sources and, if possible, feed it to the grid. Zero Energy Buildings (ZEBs) and Near Zero Energy Buildings (NZEBs) are put into action for the above purposes. With an average global solar irradiation of 5.56 kWh/m2/day and an average wind speed of 6.58 knots, solar and wind are the primary renewable energy sources that can be yoked in Oman. The solar and wind power plants that are being built in Oman are following the Grid Code Operations as of 2020. Buildings in Oman, newer ones and old ones, are constructed using traditional building materials, leading to poor performance of the building's peripheral enclosure and little or no emphasis on sustainability. This has led to thermally bungling building envelopes in Oman, contributing to high energy consumption for lighting and cooling buildings, which invariably adds to global warming. Extensive use of fossil fuels has also escalated the average global surface temperature. The main aim of this paper is to analyze the design and development of ZEB/NZEB technology to be used in an educational institute in Salalah, Sultanate of Oman, incorporating state-of-the-art techniques in harnessing renewable energy sources with Battery Storage Systems (BSS), construction/modification and orientation of buildings towards energy-efficient buildings, smart and energy-efficient systems and appliances, HVAC approaches, and automated real-time conservation practices using Internet of Energy (IoE).
Nowadays, the corporate world not only prioritizes an individual's skills but also their personality traits, as they play a crucial role in achieving success both professionally and personally. Therefore, recruiters must have knowledge of potential employees' personality traits. However, due to the significant increase in job seekers and the decline in job availability, it is challenging to manually select the most suitable candidate by just reviewing their resume. This analysis aims to explore various machine learning techniques for predicting personality traits effectively by analyzing resumes through Natural Language Processing (NLP) methods. The research demonstrates that the Random Forest algorithm outperforms other approaches such as k-Nearest Neighbors (kNN), Logistic Regression, SVM, and Naive Bayes in terms of accuracy.
The Tesla valve is appealing for drift control and rectification in mini and microfluidic applications due to its passive operation and no-shifting-elements design. The effectiveness of such valves may be increased through their in-series association. In this study, a new Tesla-type valve is effectively designed to improve the amount of heat transfer by facilitating circulation. The Tesla valve is a one-way valve without a moving component that lets the fluid flow effortlessly in a single direction, but presents a very high impedance in the opposite direction. The present study deals with a numerical analysis of the Tesla valve design for the optimal amount of heat transfer. For this purpose, the Taguchi layout optimization approach is considered for the simulation by creating a Design of Experiments (DOE) array. The factors considered are heat flux, velocity, and type of flow, with three levels each. The design is done in SOLIDWORKS, and the simulation is performed in Analysis of Systems (ANSYS). The inlet temperature of the fluid is 22.75 °C, and water is considered the working fluid. The result of the simulation shows that the design with an inlet velocity of 0.25 m/sec and a backward flow type provides the highest heat input of 45000 W/m2 .
This research paper focuses on the on-demand home services industry and proposes enhancements for existing apps on the market. The market analysis reveals the rapid growth of the on-demand services market and identifies the challenges faced by customers and service providers. The user experience of existing apps on the market highlights the need for a more seamless and user-friendly platform. The literature review and related work provide insights into the features, technologies, and business models used in on-demand service apps. The proposed work suggests adding features such as real-time tracking, seamless payment integration, and personalized recommendations to enhance the user experience. The technological aspects of the proposed features are discussed, and the potential social impact of the app is evaluated. The summarized results and future enhancements, including the integration of AI and machine learning algorithms for personalized service recommendations are discussed. Overall, the proposed enhancements could offer a better experience for users and help on-demand home service providers stay competitive in the rapidly growing market.
One of the most significant classes of engineering materials in the modern period is Metal Matrix Composites(MMCs). In recent years, MMCs have attracted a lot of attention. MMCs are displacing traditional metallic materials in the automobile and aerospace industries owing to their numerous advantageous characteristics, which include low weight, high specific strength, good wear resistance, enhanced resistance to creep, etc. MMCs aid in improving the functionality of industrial parts without adding extra weight to the system. Despite the fact that several distinct types of MMCs have been created over the years, aluminum and magnesium MMCs have emerged as the most promising materials in the aerospace and automotive fields due to their improved tribological performance, low weight, and excellent mechanical properties. However, there are a number of factors that are divided into three categories that affect the tribological behavior of these MMCs, including the reinforcement material (volume fraction, reinforcement type, shape), the operating conditions (sliding speed, normal load), and the environmental conditions (relative humidity and temperature). This study seeks to give a thorough review of the history, categorization, materials, and applications in many fields, as well as the distinct tribological and corrosion behavior of the Al/Mg MMCs under various situations. To pave the way for future researchers working in these sectors, it also outlines the methods and properties used in the study.