Military Institute of Science & Technology

This manuscript presents a comprehensive, expert-annotated dataset comprising 19,000 rice leaf images, including 2,753 original images and 16,247 augmented images, sourced from the Bangladesh Rice Research Institute (BRRI). The dataset includes seven disease classes: Healthy (603 original images), Rice Blast (696 original images), Scald (421 original images), Leaf-folder Injury (247 original images), Insect Infestation (281 original images), Rice Stripes (266 original images), and Tungro Disease (239 original images). These images, captured under varying environmental conditions using smartphone cameras, accurately reflect real-world conditions. The images have been meticulously annotated by agronomy experts for reliable disease labeling. To enhance dataset diversity, data augmentation methods such as rotation, scaling, brightness adjustment, and horizontal flipping were systematically applied, expanding the dataset by creating additional variants from the original images. The dataset serves as a rich resource for developing machine learning models for the automatic detection of rice diseases. This initiative aims to enable early disease detection, promote sustainable farming practices, and improve food security, particularly in rice-dependent developing countries.

This study investigates the potential of three waste-derived supplementary cementitious materials (SCMs)—volcanic ash (VA), eggshell (ES), and rice husk ash (RHA)—as partial replacements for ordinary Portland cement (OPC) in mortar. Mortar mixes with 0%, 5%, 10%, 15%, and 20% replacement levels were evaluated in terms of fresh, mechanical, and durability properties, along with high-temperature performance, failure modes, microstructural characteristics, and cost analysis. The results demonstrated that a 5% replacement of OPC with VA and RHA enhanced compressive strength by 8.2% and 7%, respectively, while ES achieved a 16% increase at a 10% replacement. However, exposure to elevated temperatures led to compressive strength reductions of up to 87%. Flexural strength improvements were observed, with increases of 15% for VA, 43% for ES, and 21% for RHA. Economically, incorporating 20% VA, ES, and RHA led to cost reductions of 1.2%, 7.4%, and 2.5%, respectively. Additionally, the strength-to-CO2 ratio increased up to 11.4%, 22.0%, and 8.3% for VA, ES, and RHA-based mortars, respectively, compared to conventional OPC mortar. All SCMs met ASTM C618-22 criteria for natural pozzolans, with optimal replacement levels determined as 5% for VA, 10% for ES, and 5% for RHA. This study underscores the eco-friendly potential of waste-derived SCMs in producing sustainable mortar while reducing cement consumption, costs, and environmental impact.

Conductive Polymer Composites (CPCs) have emerged as promising materials with applications in soft robotics, flexible sensors, and energy storage.This review paper begins with a brief introduction to the available polymers and additives in context of CPCs, followed by a classification of the resulting composites, setting the stage for further discussions.Then we sequentially analyze the fabrication techniques and practical applications of CPC in the aforementioned sectors highlighting their performance.Furthermore, the review delves into critical aspects such as failure modes, including interfacial debonding and fatigue, the environmental impact and sustainability of CPCs, considering the source and biodegradability of polymers and the environmental implications of additives.Limitations such as particle leakage in metal-based flexible devices, nanoparticle agglomeration and high cost of modern fabrication techniques have been discussed.Future research directions, including development of cost-effective manufacturing methods and proper encapsulation techniques for flexible devices have been highlighted.