European Journal of Engineering Science and Technology

Unified Perspectives in Urban Transport Sustainability: An AHP-Based Comparative Analysis of National and Local Indicator Weights

2024-07-11T12:23:13+00:00

This study investigates the differences in urban transport sustainability indicators from national and local perspectives using Amman, Jordan as a case study. Employing the Analytic Hierarchy Process (AHP) method, we aim to understand the weighted importance of various sustainability indicators as viewed by experts at different administrative levels. A structured survey was conducted with 30 national and 30 local experts gathering their insights through pairwise comparisons of indicators. The results reveal significant disparities between national and local priorities, highlighting the need for integrated approaches in urban transport planning. National experts prioritize public transport coverage and air quality, reflecting broader strategic goals, while local experts emphasize pedestrian infrastructure and accessibility to green spaces, focusing on community-specific needs. This comparative analysis underscores the importance of balancing both perspectives to enhance urban transport sustainability effectively. The findings suggest that integrating national and local views can lead to more comprehensive and inclusive transport policies. This research contributes to the existing literature by addressing the often-overlooked social dimension of sustainability and providing a practical framework for evaluating and balancing national and local priorities in urban transport planning.

Classification of Cardiac Arrhythmias Based on Electrocardiogram Data Using a Convolutional Neural Network Model

2024-09-03T07:13:30+00:00

Cardiac arrhythmias are a disease with considerable incidence and prevalence worldwide, and their diagnosis can be complex due to the existence of different types of arrhythmias that share similar characteristics and make an accurate diagnosis difficult. Making a correct diagnosis of the kind of arrhythmia that affects an individual is important to define the most appropriate type of treatment for the case. Machine Learning and Deep Learning techniques have been proposed to automate the diagnosis of arrhythmias to assist healthcare professionals in decision-making. This study proposes a Convolutional Neural Network model for classifying cardiac arrhythmias using electrocardiogram data. The objective is to present a model that achieves high accuracy rates in identifying types of arrhythmias and presents an adequate balance between performance and computational costs. The model was trained with a dataset composed of electrocardiogram exams with 32 types of arrhythmias. In the pre-processing phase, the dataset was restructured to allow the data to be treated as a time series to explore the potential of Convolutional Neural Networks in dealing with data organized in this way. Training was carried out using a state-of-the-art Deep Learning model and the model achieved an accuracy rate of 98.37% in its predictions. This excellent performance confirms the ability of Convolutional Neural Networks to efficiently deal with pattern learning in time series. The results obtained demonstrate the potential of Deep Learning techniques as aiding tools to provide improvements in medical processes.

Creation of a Laboratory Fog Chamber for Testing Optical Sensors

2024-12-31T10:57:00+00:00

LIDAR sensors and systems are gaining popularity, but their effectiveness can be compromised by light reflections in foggy conditions. To address the need for small, laboratory-scale testing systems, a fog chamber was developed and tested to have a laboratory environment. Two different methods to produce fog were evaluated: hot steam and ultrasonic nebulizers. The cold fog from ultrasonic nebulizers was more prone to stratification, while the hot fog from hot steam produced rather disturbing condensation for the measurement. All this was solved by implementing improving aids such as a ventilation and windscreen wiper system. First to mitigate the fog stratification with help of circulation and second to prevent condensation with help of wiping the windows in the measurement path. Ultimately, the ultrasonic nebulizers showed their strengths in the experiment due to the lower influence of the enhancement aids on the signal quality, which is why they are recommended as a fog source.