Over the last decade, pharmaceutical businesses have battled to standardize product traceability across the supply chain process, enabling counterfeiters to enter the market with counterfeit pharmaceuticals. As a result, an end-to-end product tracking system is crucial for ensuring product safety and eliminating counterfeit products across the pharmaceutical supply chain. In this paper, we introduce PharmaChain, a decentralized hyperledger fabric framework that leverages confidentiality, accountability, and interoperability. This system enables on-chain and off-chain storage for secured, rapid transactions, along with smart contracts establishing data provenance. To demonstrate security, we have provided double signing through the elliptic curve digital signature algorithm, hash data encryption, and 33% node attack. The purpose of this suggested framework is to engage particular governance disciplines to assess its effectiveness in improving drug traceability across the pharmaceutical supply chain to preserve public health by preventing counterfeit pharmaceuticals.

Depression, a prevalent and debilitating mental health disorder, affects millions of individuals worldwide, profoundly impacting their quality of life. Early identification and diagnosis of depression and its intensity are crucial for effective treatment and management. However, many people with depression do not seek professional help, especially in the early stages. In recent years, social media platforms like Twitter have gained popularity as spaces for sharing personal thoughts and emotions, including sensitive signals indicative of serious issues such as self-harm, suicidal thoughts, or illegal activities. These signals may help us to identify depression-related tweets and determine whether an individual is suffering from depression. This research focuses on utilizing artificial intelligence and deep learning (DL) models to categorize tweets related to depression and measure its intensity. The proposed approach combines emotional features, topical events, and behavioral-biometric signals to train the long short-term memory (LSTM)-based DL models. To create a comprehensive dataset, we collaborated with an expert psychologist who followed the clinical assessment procedure outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5-TR) to label 95 322 tweets as “non-depressed” or “depressed,” further categorized into “mild,” “moderate,” and “severe” intensity levels. Through a series of experiments, our proposed method achieved superior performance compared to baseline models, yielding a mean squared error of 0.002336 and the highest R2 value of 0.61. These results highlight the accuracy and potential applications of our approach in automatic depression screening and monitoring on social media platforms.

Mathematical entity recognition is essential for machines to define and illustrate mathematical substance faultlessly and to facilitate sufficient mathematical operations and reasoning. As mathematical entity recognition in the Bangla language is novel, to our best knowledge, there is no available dataset exists in any repository. In this paper, we present state of the art Bangla mathematical entity dataset containing 13,717 observations. Each record has a mathematical statement, mathematical type and mathematical entity. This dataset can be utilized to conduct research involving the recognition of mathematical operators, renowned mathematical terms (such as complex numbers, real numbers, prime numbers, etc.), and operands as numbers. The findings mentioned above, and their combination are also feasible with a modest tweak to the dataset. Furthermore, we have structured this dataset in raw format and made a CSV file, incorporating three columns: text, math entity, and label. As an outcome, researchers may easily handle the data, facilitating a variety of deep learning and machine learning explorations.