Journal of Contributions of Science and Technology for Engineering
Volume:2 Issue: 1, Winter 2025

The recent Covid-19 pandemic raises unprecedented questions regarding the spread of SARSCoV-2 virus or any other contagion disease. How long viruses remain in a room? How safe am I in a crowded room equipped with a poor ventilation system? Will an open window decrease the risk of virus transmission in public places? A deeper understanding of the drop propagation laden with viruses in confined environments is essential for controlling the recent Covid-19 pandemic and preventing the next epidemic waves. For addressing above questions, a numerical simulation of cough and propagation of the respiratory droplets is performed in this paper. Effects of air conditioner and ventilation on spreading the viruses are studied and suspension time of the aerosols and their size will be provided. In this paper study of cough flow and the spreading of the cough droplets has been investiga ted. First the capability of the flow solver is validated by comparing the velocity field with experimental data. Then the propagation of the drops is studied.

Grounding systems play a crucial role in ensuring the safety and reliability of high-voltage substations. However, variations in soil composition, particularly the presence of heterogeneous layers such as rock, can significantly impact grounding effectiveness. This study investigates the effects of non-homogeneous soil on step and touch voltage patterns using the Finite Element Method (FEM) in COMSOL Multiphysics. The simulation compares grounding performance in homogeneous and heterogeneous soils, analyzing electric potential and field distribution. A sensitivity analysis is conducted to assess the impact of varying soil resistivity on grounding safety. Additionally, different grounding techniques are compared to identify optimal solutions for mitigating potential hazards. The results indicate that heterogeneous soils increase step voltage and potential gradients, posing higher risks for personnel and equipment. The findings provide essential insights for improving grounding system designs in complex soil environments and expands new technical horizons to accurate investigation of new enhanced grounding grid structures.

The Internet of Things is one of the most transformative technologies of the modern era, enabling seamless connectivity and data exchange across a wide range of applications, including smart cities, healthcare, agriculture, and industrial automation. However, the rapid growth of IoT has introduced significant challenges, particularly in terms of security. Among these challenges, securing routing protocols in low-power and lossy networks is critical, as they are vulnerable to various attacks, such as rank spoofing and version number attacks, which can disrupt network topology and compromise data integrity. In this paper, we propose a novel identity-based routing protocol for IoT, built on the RPL (Routing Protocol for Low-Power and Lossy Networks) framework. Our approach utilizes Identity-Based Signature cryptography to enhance the security of RPL against rank and version number attacks. By utilizing a lightweight digital signature scheme, our protocol ensures that only legitimate nodes can modify the network topology, thereby preventing malicious actors from forging rankings or version numbers. The proposed scheme is designed to be computationally efficient, making it suitable for resource-constrained IoT devices. We provide a comprehensive security analysis, demonstrating that our protocol offers robust resistance to forging attacks. Additionally, we evaluate the performance of the scheme in terms of time and energy consumption, showing that it is both efficient and scalable for large-scale IoT deployments. Our results indicate that the proposed identity-based routing protocol not only enhances the security of RPL but also maintains low overhead, making it a practical solution for securing IoT networks in real-world applications.

Alzheimer's disease (AD) is a common neurodegenerative disorder that requires early diagnosis for effective treatment. MRI data provides valuable insights into brain structure, which can assist in diagnosing the disease. However, traditional diagnostic methods often face errors due to expert limitations and data uncertainty. To address this issue, we propose a Computer-Aided Diagnosis (CAD) system that can automatically identify the disease. Moreover, since MRI images inherently contain uncertainty, the proposed method offers a solution to minimize this uncertainty. In the proposed method, after performing preprocessing, feature extraction, and selection steps, the information obtained from brain white matter (WM) and gray matter (GM) is combined. This combination is achieved using the Evidence Theory and Dempster-Shafer Theory (DST). In this theory, mass functions are employed instead of probability functions. Subsequently, three different classifiers are applied separately in the final stage to the combined data. Experimental results demonstrate that combining GM and WM data using DST achieves higher accuracy compared to using either data type alone. This fusion-based method presents a reliable and effective approach for improving Alzheimer's diagnosis. Our proposed method achieved 91% accuracy in three different binary classification cases using the LDA classifier when distinguishing between AD and Normal Control (NC) groups. This result, obtained by combining WM and GM data, demonstrated a significant improvement compared to using each data type independently.

Rapid urbanization has created complex challenges affecting the well-being of city inhabitants. This study reevaluates historical urban design paradigms within the context of today's rapid urban growth, emphasizing the need for vibrant and sustainable environments. It explores innovative urban design and planning strategies, particularly regenerative approaches. Over the past two decades, urban design discourse has increasingly prioritized mental well-being and enriching user experiences. This shift has led to the emergence of Neuro Urbanism, an interdisciplinary field combining urban design, neuroscience, and psychology.Using a rigorous methodological framework, this study integrates human experience sensing, remote sensing, and environmental atmospheric sensing to analyze urban areas. The goal is to facilitate the reconstruction and reorganization of these spaces. Central to this framework are machine learning algorithms that discern emotional dimensions in user experiences, data-loggers that capture climatic data, and voxel-based assessment techniques that quantify physical and architectural attributes.Empirical findings highlight significant correlations between climatic variables and user experiences across various urban contexts. This study sheds light on the intricate relationships between environmental factors and human well-being. It contributes valuable insights into fostering inclusive, resilient, and psychologically enriching urban landscapes. The research aims to inform evidence-based urban design interventions to create sustainable.

The study of groundwater levels is of paramount importance due to its critical role in water resource management, agriculture, and ecosystem sustainability. This study focuses on predicting groundwater levels in observation wells across Tehran using machine learning algorithms. A range of input parameters, including satellite-derived data from GRACE, GLDAS, and ERA5, were employed to train models for estimating groundwater level fluctuations. The primary aim was to evaluate and compare the performance of 12 different machine learning models, including Random Forest, AdaBoost, Support Vector Machine, and Artificial Neural Networks, among others, in terms of their prediction accuracy. The results indicated that ensemble-based models generally outperformed individual algorithms, achieving the highest coefficients of determination (R²) and the lowest error metrics. Spatial analysis of the errors revealed that the northern part of the study area experienced higher prediction errors than the southern region, likely due to more significant groundwater level fluctuations, influenced by regional climatic conditions and topography. Furthermore, the study demonstrated that combining various input parameters, such as terrestrial water storage, total soil moisture, and precipitation, improved the accuracy of the groundwater level predictions. The models were evaluated using standard error metrics, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Pearson Correlation Coefficient (R), with results showing strong agreement between predicted and observed data. The findings suggest that machine learning models, especially those leveraging high-resolution satellite and reanalysis data, can be highly effective for groundwater level prediction and management in regions with limited in-situ measurement data.