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
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
A Series of Tool-Life Studies on Aluminium Matrix Hybrid Composites
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
Recent biomaterials like nanoparticles, graphene, and functional hydrogels are advancing tissue engineering and disease therapy through immunomodulation, tissue regeneration, and cancer therapy. This study explores the role of biomaterials in enhancing immune responses and promoting tissue regeneration. Implantable biomaterials offer innovative therapeutic effects in various disease situations. Understanding the interactions between biomaterials and host cells is crucial for creating therapeutic biomaterials that facilitate tissue integration and mitigate foreign body reactions. This study emphasizes how biomaterial properties, like size, shape, surface composition, and mechanical characteristics, influence immune cell responses, particularly macrophage polarization, which is crucial for minimizing inflammation and supporting tissue repair. The findings underscore the importance of tailored biomaterial design to mitigate foreign body reactions, improve biocompatibility, and ultimately enhance patient outcomes.
Gas turbine power plants are the prime mover of electrical energy in addition to steam power plants in Libya. All of them are using natural gas fuel or liquid diesel when natural gas is unabundant. The purpose of this paper is to optimize the thermodynamic performance of a simple gas turbine power plant using hydrocarbon fuels (natural gas), diesel Heavy Fuel Oil (HFO), and the alternative promising hydrogen fuel. Qualitative and quantitative approaches are adopted for optimization analysis. The objective is to assess the potential advantages associated with hydrogen as a carbon-neutral fuel instead of natural gas or diesel for gas turbine power plants producing electricity. The compression ratio within the range of (2 ≤ Pr ≤ 30) is chosen as a decision variable. The environmental conditions are chosen at restricted dead state: T0 = 298.15K, P0 = 101.325 kPa, and relative humidity of 60% in the summer season. Two turbine inlet temperatures are chosen for this study: TIT = 1200K and 1400K. The thermodynamics objective functions are Specific Fuel Consumption (SFC), thermal efficiency ηth, exergy efficiency ε, and electrical power We. The specific exergy destruction is taken as a measure of process satisfaction. It is found that the thermodynamic objective functions are strongly affected by the compression ratio for the turbine inlet temperatures at the same environmental conditions. Furthermore, the hydrogen fuel indicated minimum (SFC) and maximum exergy and energy efficiencies and a maximum W e at the specified mass flow rate of air and fuel. However exergy destruction has its allies a tendency to rise along the changing of compression ratio.
In traditional retail systems, manual billing systems lead to long queues and customer dissatisfaction. This study presents a solution to streamline the billing process and enhance the shopping experience using RFID technology and Arduino microcontrollers. The Smart Shopping Cart system integrates EM-18 RFID modules with Arduino to automate the billing process in supermarkets. Each product is tagged with an RFID tag, and carts are equipped with RFID readers. As customers scan items, prices are automatically added to the billing system, displayed on an LCD screen. This study aims to reduce wait times, errors, and operational costs associated with manual billing. This study includes design considerations such as circuit optimization, simulation using Proteus software, physical testing on breadboards, and calibration for accuracy. The use of rechargeable batteries ensures portability and minimizes clutter, enhancing the system's practicality. By embracing automation and RFID technology, this paper contributes to improving retail efficiency and customer satisfaction in modern supermarkets.
Fibrillation, a disorder characterized by an irregular and frequently abnormally fast heart rhythm, is one of the leading causes of heart attacks. There is solid evidence that the survival rate of sudden cardiac arrest patients who are treated with Cardiopulmonary Resuscitation (CPR) plus an Automated External Defibrillator (AED) is much higher. Despite the recommendation that automated external defibrillators (AEDs) be provided in the workplace, along with a proper management system and employee training on how to use the device, less than 70% of non-residential locations have one installed. The situation is significantly worse in residential settings, where less than 30% have an AED fitted. This paper focuses on the development of a medical drone management system that can supply. In the event of a heart attack, the patient or accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient's location. An app that can be installed on the patient's or accompanying person's mobile phone and tablet has been developed.
Over the years, increased industrialization and population have been identified as key factors in the need for energy. Higher pollution levels and rising fuel prices coincide with the growing demand for renewable energy. Biodiesel, a renewable, biodegradable, and sustainable alternative to fossil fuels, has gained significant attention. Biodiesel offers a promising alternative to traditional biofuel, with benefits including renewability, sustainability, emitting fewer air pollutants, low sulphur content, energy security, being easier to treat, stronger lubricity, and lower levels of exhaust emissions than petroleum diesel fuel. However, challenges remain, including high production costs and land use concerns. Accordingly, in this study, apart from the scope and need of biodiesel, an attempt has been made to address the number of process parameters that influence the production process of biodiesel, the sources of biodiesel production, and the obstacles and possibilities that will shape its future trajectory. This study adds to a greater knowledge of biodiesel as a renewable energy source by delivering crucial insights and suggestions. It also gives advice for future research, policy development, and industry actions targeted at supporting its sustainable expansion and acceptance.
The use of machine learning in the detection of brain tumors increases the outcome and result of diagnosing from medical images, particularly MRI. This study also points out the actualization of deep learning models like CNN with an ability to determine and categorize tumors accurately. Reduction in the reliance on ML-sourced interpretation increases cutting-edge tumor detection and differentiation and, thus, might result in benefits for patients. This study highlights the effectiveness of hybrid models, transfer learning, and preprocessing techniques in improving image quality and segmentation precision. Key challenges, including data scarcity and model interpretability, are discussed, as are future directions for refining models and expanding clinical application. Hence, the findings affirm that critical impediments like data availability and model interpretability do not inhibit ML from enhancing the discovery of brain tumors in principle, thus preparing the ground for implementing and deploying ML in actual clinical practice. This study suggests that deep learning can revolutionize brain tumor diagnosis, supporting early detection and optimized treatment planning.
