Engineering Commons^™

The Effect Of Walnut Shell Ash On The Properties Of Cement Paste And Mortar: A Study On Partial Replacement Of Cement, Ali Shebli, Jamal Khatib, Adel Elkordi

BAU Journal - Science and Technology

In the pursuit of a more sustainable future, the utilization of agricultural waste in cementitious products has emerged as a popular and effective solution. The current study explores the feasibility of incorporating ash derived from walnut nutshells, considered agro residues, into eco-friendly blended cement paste and mortars to examine its impact on their properties. A range of blended cement mixtures was formulated by incorporating walnut shell ash (WSA) in three varying proportions of 5%, 10%, and 15% relative to the weight of the cement. Tests were conducted to investigate the properties of the blended cement paste and mortar incorporating WSA, …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Study On Image Processing Techniques And Deep Learning Techniques For Insect Identification, Vinita Abhishek Gupta, M.V. Padmavati, Ravi R. Saxena, Pawan Kumar Patnaik, Raunak Kumar Tamrakar

Karbala International Journal of Modern Science

Automatic identification of insects and diseases has attracted researchers for the last few years. Researchers have suggested several algorithms to get around the problems of manually identifying insects and pests. Image processing techniques and deep convolution neural networks can overcome the challenges of manual insect identification and classification. This work focused on optimizing and assessing deep convolutional neural networks for insect identification. AlexNet, MobileNetv2, ResNet-50, ResNet-101, GoogleNet, InceptionV3, SqueezeNet, ShuffleNet, DenseNet201, VGG-16 and VGG-19 are the architectures evaluated on three different datasets. In our experiments, DenseNet 201 performed well with the highest test accuracy. Regarding training time, AlexNet performed well, …

A Novel Insect And Pest Identification Model Based On A Weighted Multipath Convolutional Neural Network And Generative Adversarial Network, Vinita Abhishek Gupta, M.V. Padmavati, Ravi R. Saxena, Raunak Kumar Tamrakar

Karbala International Journal of Modern Science

Timely identification of insects and their management play a significant role in sustainable agriculture development. The proposed hybrid model integrates a weighted multipath convolutional neural network and generative adversarial network to identify insects efficiently. To address the shortcomings of single-path networks, this novel model takes input from numerous iterations of the same image to learn more specific features. To avoid redundancy produced due to multipath, weights have been assigned to each path. For Xie2 dataset, the model shows 3.75%, 2.74%, 1.54%, 1.76%, 1.76%, 2.74 %, and 2.14% performance improvement from AlexNet, ResNet50, ResNet101, GoogleNet, VGG-16, VGG-19, and simple CNN respectively. …