Exergy Destruction Investigation of Complex Gas Turbine Components
Leveraging ConvLSTM and Satellite Imagery for Predictive Modeling of Floods, Landslides, and Earthquakes
ESTIMATION OF OZONE DOSAGE AND RESIDUAL OZONE FOR EFFECTIVE WASTEWATER TREATMENT
Future-Driven Approaches to Municipal Water Quality: Leveraging IoT, AI, and Advanced Purification Technologies for Sustainable Public Health
FOSTERING PRO-ENVIRONMENTAL BEHAVIOUR: PATHWAYS TO A SUSTAINABLE FUTURE
Brain Tumor Segmentation in 3D MRI Images Using W-Net Architecture
CFD modeling of blood flow in myeloid sinusoidal capillaries
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
The Impacts of Climate Change on Water Resources in Hilly Areas of Nepal
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
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
Although in Libya simple gas turbine power plants are widely used to produce electrical power. However, in this endeavor the examination of complex configurations of the Brayton cycle including the simple Brayton cycle, with intercooling, regeneration, and reheating from the point of physical exergy destruction and its influence on specific fuel consumption and net power is explored. Therefore, the main task of this paper is directed to investigate the exergy destruction and specific fuel consumption and power under the impact of overall compression ratio at selected turbine temperature and standard environmental conditions of temperature25 OC, pressure 1atm, and 60% relative humidity using natural gas fuel. The paper showed that combustion process presented the most source of exergy destruction beside the stages of expansion with reheating reported the next higher source, while the compression with intercooling indicated the lower one profile along the overall compression ratio .
With the growing frequency and severity of natural disasters, developing reliable predictive models has become essential to minimize their impact. This study combines satellite imagery's spatial data with the temporal learning capabilities of the convolutional long short-term memory (ConvLSTM) networks to improve both prediction accuracy and processing efficiency. By utilizing diverse spectral bands and resolutions, the model captures a wide range of environmental features.Preprocessing steps, such as normalization and noise reduction, are applied to refine the input data and enhance the ConvLSTM network's performance. The architecture is carefully structured to balance spatial and temporal dependencies, ensuring effective integration of satellite-derived data.The framework is optimized to identify complex relationships in the dataset, enabling precise forecasts of upcoming disasters. It has been tested on various natural events, including hurricanes, floods, and wildfires, achieving higher prediction accuracy and shorter lead times compared to traditional techniques.This integration of satellite imagery with ConvLSTM networks aims to strengthen early warning systems, improve disaster preparedness, and reduce economic and social damage for affected regions.
Ozonation has emerged as a promising technology for wastewater treatment due to its potent oxidizing properties, which enable the degradation of recalcitrant organic pollutants and improvement of effluent quality. This study explores the estimation methodology for optimum ozone dosage & residual ozone for effective wastewater treatment, investigate its efficiency in reducing organic pollutants and improving treated effluent quality. The primary focus was on the effects of ozonation on Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), color, and residual ozone concentrations. Ozone was generated using an ozone generator (5% concentration) and applied to wastewater samples for various contact times. The results revealed significant reductions in COD (up to 42.9%) and BOD (up to 44%), indicating ozone's strong oxidative capability. Ozonation also led to an impressive 98% color removal. The study demonstrated that ozonation is highly effective in achieving a superior level of disinfection, proving to be a sustainable technology capable of meeting stringent treated water quality standards. Further optimization of operational parameters can enhance the efficiency and cost-effectiveness of ozonation for large-scale wastewater treatment applications.
The future of municipal water quality management lies in the integration of cutting-edge technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and advanced water purification systems. These technologies have the potential to revolutionize the way water is monitored, treated, and distributed, ensuring its safety, accessibility, and sustainability. This chapter explores the futuristic approaches to municipal water quality by discussing the current state of water quality management, emerging technologies, and their synergistic impact on public health. The focus is on the implementation of IoT and AI in real-time water quality monitoring, predictive analytics, and automated decision-making processes. Advanced water purification technologies, such as membrane filtration, UV treatment, and innovative AI-based systems, are also examined for their potential to improve the quality of municipal water and protect public health. Through a series of experiments, mathematical formulations, and case studies, the chapter evaluates the effectiveness of these technologies in addressing the challenges of urban water pollution and ensuring safe, clean water for future generations./
Environmental or pro-environmental behaviour is a critical area of interest in psychology, focusing on the factors influencing individuals' interactions with their environment. This article explores a conceptual framework that aids in understanding the diverse determinants of environmental behaviour. Additionally, it presents a methodological approach to promoting environmentally responsible actions in practice. Human behaviours, whether minor or significant, have varying degrees of environmental impact—positive or negative. Since individuals are in constant interaction with their surroundings, all human activities can be considered environmental behaviours. However, for academic and practical purposes, pro-environmental behaviour is distinguished as intentional actions aimed at minimizing environmental harm and promoting sustainability. By examining the psychological, social, and structural factors that drive such behaviours, this study contributes to the discourse on fostering a more sustainable future.
This research article presents an innovative 3D brain tumor segmentation method using an extended W-Net architecture, a derivative of U-Net, leveraging deep learning. Python programming on Google Colab facilitated the study, employing MRI scans from the Brats Dataset. The training dataset achieved a remarkable Dice Similarity Coefficient (DSC) and accuracy score of 0.98, showcasing the model's precision in tumor localization. The Matthews Correlation Coefficient (MCC) achieved 0.75, confirming the model's comprehensive segmentation quality. Generalization testing mirrored training outcomes, maintaining DSC and accuracy at 0.98, highlighting the model's robustness. The MCC, at 0.76, strengthened the model's ability to generalize to new data. This approach offers dependable and consistent segmentation outputs for 3-D brain MRI scans with tumor labels.
The bone marrow microcirculatory system presents challenges for experimental investigation due to its complexity and limited accessibility. This study employs Computational Fluid Dynamics (CFD) to model blood flow within the myeloid sinusoidal capillaries of mice femur bone, focusing on velocity profiles, pressure distributions, and wall shear stresses. Using ANSYS Fluent 2023 R2, a detailed computational domain was developed from microscopic focus-stacked images, with a robust unstructured mesh applied for precise flow simulations. Blood was modeled as a multiphase Eulerian mixture, accounting for plasma and red blood cell dynamics. Results were validated against experimental data, showing a good agreement in velocity, volume fractions, and wall shear stress distributions. These findings underline the capability of CFD in providing detailed insights into microvascular blood flow, supporting future studies on hematological disorders and bone marrow mechanics.
