Fostering Pro-Environmental Behavior: Pathways to a Sustainable Future
Exergy Destruction Investigation of Complex Gas Turbine Components
CFD Modeling of Blood Flow in Myeloid Sinusoidal Capillaries
Estimation of Ozone Dosage and Residual Ozone for Effective Wastewater Treatment
Brain Tumor Segmentation in 3D MRI Images using W-Net Architecture
Future-Driven Approaches to Municipal Water Quality: Leveraging IoT, AI, and Advanced Purification Technologies for Sustainable Public Health
Leveraging ConvLSTM and Satellite Imagery for Predictive Modeling of Floods, Landslides, and Earthquakes
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
Environmental or pro-environmental behavior is a critical area of interest in psychology, focusing on the factors influencing individuals' interactions with their environment. This study explores a conceptual framework that aids in understanding the diverse determinants of environmental behavior. Additionally, it presents a methodological approach to promoting environmentally responsible actions in practice. Human behaviors, 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 behaviors. However, for academic and practical purposes, pro-environmental behavior is distinguished as intentional actions aimed at minimizing environmental harm and promoting sustainability. By examining the psychological, social, and structural factors that drive such behaviors, this study contributes to the discourse on fostering a more sustainable future.,
In Libya, simple gas turbine power plants are widely used for electrical power generation. However, this study examines complex configurations of the Brayton cycle, including the simple Brayton cycle, with intercooling, regeneration, and reheating, focusing on physical exergy destruction and its impact on specific fuel consumption and net power. The parameters are evaluated under the influence of the overall compression ratio, using selected turbine temperature and standard environmental conditions, with natural gas as the fuel. The results indicate that the combustion process is the primary source of exergy destruction, followed by the expansion stages with reheating, which contribute the next largest amount. In contrast, compression with intercooling results in the lowest exergy destruction across the overall compression ratio.
The bone marrow microcirculatory system's present 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.
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 improve effluent quality. This study explores the estimation method for the optimum ozone dosage and residual ozone for effective wastewater treatment and investigates 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 with a 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%, demonstrating ozone's strong oxidative capability. Ozonation also led to an impressive 98% color removal. The study confirmed 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.
Segmenting the 3D MRI images by the computer to identify the brain tumors is a very critical and challenging task till the invention of the deep learning algorithms. The previous works used some traditional methods like Mathematical Morphological Reconstruction (MMR), superpixel-level features extracted from 3D volumetric MR images, ensemble approaches, CNN, U-Net, etc., to achieve enhanced accuracy in segmenting different tumor regions. This study 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 future of municipal water quality management lies in the integration of cutting-edge technologies such as the 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 paper 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 paper evaluates the effectiveness of these technologies in addressing the challenges of urban water pollution and ensuring safe, clean water for future generations.
This study combines the spatial data from satellite imagery with the temporal learning capabilities of 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 performance of the ConvLSTM network. The architecture is carefully structured to balance spatial and temporal dependencies, ensuring the effective integration of satellite-derived data. The framework is optimized to identify complex relationships within 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 to affected regions.
