Military Institute of Science & Technology

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.

In the landscape of natural language processing (NLP) research, the availability of comprehensive datasets plays a pivotal role in advancing various tasks, including paraphrasing. However, for languages such as Bengali, the availability of such datasets remains limited, particularly in specialized domains like health. Recognizing this gap, this study endeavours to address the scarcity of resources by presenting a novel Bengali paraphrasing dataset specifically tailored to the health domain. The dataset construction process involved sourcing sentences from Bengali newspapers, focusing on health-related content. Due to the dearth of existing datasets in Bengali and the specialized nature of paraphrasing, particularly in the health domain, this endeavour necessitated the development of a unique methodology. This methodology included the development of a script for data extraction, pre-processing, translation of Bengali sentences into English, paraphrasing of the English sentences, and subsequent translation back into Bengali to generate paraphrased versions of the original sentences. A user study involving 100 participants was conducted to evaluate 500 sample Bengali paraphrased sentences to identify the most suitable library (VamsiT5 Paws) for creating the paraphrasing dataset. As such, a total of 200,000 sentences were extracted and paraphrased in this process. This dataset holds significant potential to foster advancements in paraphrasing research that facilitate the development of language models; and ultimately contribute to the broader goal of enhancing NLP capabilities in Bengali, particularly in specialized domains like health.

Precision agriculture harnesses data-driven techniques to optimize crop production, resource use, and sustainability. However, low-income countries like Bangladesh face a shortage of localized, high-quality datasets that reflect regional agroclimatic conditions and cropping practices. To address this gap, we present SPAS-Dataset-BD, a robust dataset compiled through a hybrid approach: secondary extraction from the Bangladesh Bureau of Statistics (BBS) 2022 Yearbook and primary on-field surveys of 223 farmers across ten diverse districts. The dataset comprises 4191 records over 73 crop types, with 12 agronomic and environmental features, including underrepresented species. Robustness is demonstrated via threshold-based missing-value handling (<5 % deletion, targeted imputation), hash-based deduplication, and cross-validation against official statistics. We illustrate potential applications, in machine learning (73-class crop classification, yield forecasting) and IoT-driven irrigation scheduling. SPAS-Dataset-BD's scale, methodological transparency, and contextual richness make it a valuable resource for precision agriculture research and policy-making in South Asia.