The growing demand for accessible and reliable health information has motivated the adaptation of domain-specific large language models (lLMs). LLMs perform well on general natural language processing (NLP) tasks but require fine-tuning for healthcare applications. In this work, Mistral-7B, a 7.3B parameter Transformer model, is fine-tuned for health text generation and noncritical symptom understanding using three parameterefficient methods-Low-Rank Adaptation (LoRA), Quantized Low-Rank Adaptation (QLoRA), and Rank-Optimized Reliable Adaptation (RoRA). A synthetic dataset comprising medical question answering, symptom descriptions, and home remedies was curated from public sources. Experimental results demonstrate that RoRA achieved the highest BLEU-4 (0.52), ROUGE-L (0.65), and F1-score (0.84), outperforming baselines such as BERT, RoBERTa, and LLaMA7B while maintaining low GPU memory usage. This work supports the use of fine-tuned LLMs for safe and efficient health communication, especially in low-resource settings. It also demonstrates that lightweight adaptation using Parameter Efficient Fine-Tuning (PEFT) can deliver high-quality outputs while minimizing computational demands.

Malnutrition is a persistent challenge in rural India, where generic diet recommendations rarely reflect local food habits or economic realities. Many rural families in India rely on regionally available foods, and generic nutrition models typically overlook these dietary patterns. This paper presents an attention-augmented LSTM encoder-decoder model designed to generate affordable, region-specific meal plans for rural communities in Assam, India. A curated dataset was built using regional recipes, local expert input, and automated web scraping of authentic Bengali and Indian sources. By explicitly modeling cultural and seasonal food patterns, our approach ensures meal suggestions are realistic and easy to adopt in daily village life. The lightweight architecture also enables practical deployment in low-resource healthcare settings without sacrificing performance. On a held-out test set, the proposed model achieved strong results with a BLEU-4 score of 0.42, ROUGE-L of 0.54, F1-score of 0.81, and a BERTScore$F 1$of 0.581, outperforming both transformer and standard LSTM baselines. These results indicate that compact, locally adapted neural models can offer practical nutrition guidance in underserved settings.

The integration of data science and marine science into a single platform has led to a revolution in the understanding of oceanographic processes. Sea Surface Temperature (SST) prediction plays a vital role in various fields, namely marine ecology, climate change studies, and environmental forecasting. This paper delves into the most recent advancements in SST prediction techniques and their impact on oceanographic exploration. Moreover, it presents a novel model aimed at addressing the limitations of previous methodologies. The utilisation of advanced Deep Learning and Machine Learning architectures has significantly improved the accuracy of the SST forecasts, surpassing the less accurate results previously obtained through numerical models. Modern techniques can capture spatial correlations and temporal dependencies in SST data. This enables predicting SST values more reliably and accurately. These cutting-edge discoveries provide valuable insights into oceanographic phenomena, aiding in the enhanced understanding of the ocean and bolstering our capacity to predict and comprehend significant and captivating climate events. This study underscores the importance of leveraging the critical role of harnessing the vast advancements in SST prediction to advance marine science and facilitate informed decision-making across diverse sectors related to the marine realm.

Reversible computing has a remarkable ability to reduce heat dissipation in computing machinery. It can be broadly applied in various fields, which include Digital signal processing, Cryptography, DNA computing, Network congestion, Database transactions, Quantum computing, etc. Fault detection is a complicated and demanding problem in reversible circuits. Fault detection is an essential process in the field of testing to ensure the reliability and integrity of the circuit. This paper proposes a straightforward approach for Single Missing Gate Fault (SMGF), Multiple Missing Gate Fault (MMGF), Repeated Gate Fault (RGF) and Partial Missing Gate Fault (PMGF) under the Missing Gate Fault (MGF) model. The method includes the process of binary to gray code conversion in order to determine the total number of test vectors to detect the respective MGFs. Experimental results are performed on reversible benchmark circuits to evaluate the number of test vectors required to recognize all the MGFs. The comparative analysis of the proposed work with the existing work is also presented.

This article introduces a fault localization method to extract the exact location of single and multiple missing gate faults in reversible \( k \) -CNOT -based circuits. The primary target of the proposed method is to obtain the complete test set for localizing faults in \( k \) -CNOT circuits. We propose a fault localization algorithm to construct a fault localization tree that can be used to find equivalent and non-equivalent faults. For the non-equivalent faults, the test sequences can be obtained from the fault localization tree that uniquely localizes the non-equivalent faults. Finally, this article presents the experimental results and comparative analysis with existing works.

Low-power design is a major concern in the circuit design domain. The reversible circuit is an alternative for moving beyond the conventional ways of computing. For performing the high reliability and the correctness of the circuit, testing is necessary for evaluating the faults. This paper presents the fault detection method for classical fault models like stuck-at faults and bridging faults in reversible circuits using the negative-controlled augmented k-CNOT based circuit. We initially construct the n number of test vectors with n input lines for a given circuit. The constructed test vector sequences successfully attempt as the complete test set on the testable design augmented k-CNOT circuit for detecting faults. The proposed method applies to several benchmark circuits for detecting the stuck-at and bridging faults and also comparative analysis is prepared with some existing works.

This paper introduces a fault diagnosis technique to obtain the exact location of Single Missing Gate Faults (SMGFs) in$k$-CNOT based reversible logic circuits. The proposed fault diagnosis technique establishes that the generated single test vector can identify the exact location of SMGFs. For this purpose, we construct the augmented circuit that behaves as Circuit Under Test (CUT) and the testable augmented circuit is used for detecting the SMGF faults. The parity checking operations are included in the constructed augmented$k$-CNOT circuit to obtain the exact location of SMGF. Finally, this paper presents the experimental results and analysis in order to show the effectiveness of determining the exact location of faults in a$k$-CNOT circuit.

The reversible logic circuit is a prominent research area for its low-power design, and also quantum computing. The development of synthesis and optimization is a well-known problem in the reversible circuits. For ensuring the high reliability and integrity performance of these circuits, the proper testing technique will be required to detect and locate the faults. In this paper, we consider the problem of reversible circuit testing, specifically targeting the fault detection for the missing-gate fault model in the k-CNOT based reversible circuit. It has been shown that n number of test vectors is sufficient for the detection of all single missing-gate faults (SMGFs), repeated-gate faults (RGFs), and partial missing-gate faults (PMGFs) of the proposed fault detection scheme in a reversible circuit with n inputs. Finally, we provide our experimental results based on several benchmark circuits and also show the comparative analysis with existing methods.