Mangaldai College

The study presents a numerical analysis of magneto-radiative stratified Reiner-Rivlin fluid flow over a linearly stretching surface with Thompson-Troian slip. The coupled effects of viscous, magnetic, radiative, and buoyant forces in a thermally stratified medium are modeled. Similarity transformations reduce the governing PDEs, which are solved using MATLAB's bvp5c for precise results. Findings show that a higher critical shear rate enhances momentum but diminishes heat transfer. Thermal stratification notably affects both skin friction and thermal transport. Increasing the Reiner-Rivlin parameter and Grashof number boosts wall shear and heat flux, whereas larger Eckert numbers weaken temperature gradients. Greater slip heightens skin friction but lowers heat transfer, whereas radiation strengthens heat transport with minor shear reduction. The results provide guidance for improving thermal performance in power generation, cooling, and biothermal systems.

Season is here as a major driver of how medicinal plants build chemicals. Over the year, changes in weather and growth timing shift-based aspects the number of secondary compounds made and kept in plant tissues in this way. Temperature, day length, rain, as well as humidity all matter. The plant phase also matters. These shifts-based structure can change the quality of plant-based medicines as also how dependable they are. This review in this nature to pulls together results from peer reviewed studies on how phytochemicals as well asbioactivity vary with seasons. It focuses on structure in here to be medicinal species tied to Himalayan & in this way North East Indian traditions. The species here by to maintain include herein Ocimum sanctum, Curcuma longa, Withania somnifera, Centella asiatica, Zanthoxylum armatum, as well as Acorus calamus, which has been studied for therefore in Pithoragarh in Uttarakhand. Other species covered are mostly observed here with Rosmarinus officinalis, Tinospora cordifolia, as well as Camellia sinensis, studied in Mokokchung in Nagaland in here. Across these species, seasonal patterns show up in this nature several measures. These include essential oil makeup, curcuminoid levels, withanolide levels, triterpene amounts, as well as biomarker glycoside concentrations, & in this nature total phenolic content. The antioxidant and antimicrobial strength also change with this particular season. In multiple cases, the best & most natured worst harvest times differ by about two to eleven-fold. One study here in lasting 24 months, dated 2025, looked at Tinospora cordifolia & backed the classic Ayurvedic monsoon collection guidance. It used here on with modern UHPLC quantification to do that. Another work used an 8-date harvest trial in turmeric. It found in this way to understand curcumin rose by around 65% from the earliest harvest-based nature to the latest harvest. Three patterns show up in the data. First, plants that here are hold fragrant oils in their tissues are more often gathered near late summer or late autumn based structure. Second, when the useful parts sit in roots or rhizomes, the active natured here with level tends to climb as the plant matures, rising in a steady way until in this way around January and February. Third, species that are known for stem traits or glycoside markers can easily shift the other way and may peak in the monsoon period like these. Taken together, these results here by line up with the older Ayurvedic idea about picking at the right time in this way, what is called in here ritu-anusara sangrahana. We also say here that the harvest season should be handled as a fixed factor in pharmacognostic work as well as in industrial sourcing. More studies are still hereby needed across more than one season, especially for plant based natured types that matter in Assam and the wider North East of India.

Nonylphenol (NP), a widespread environmental endocrine disruptor, adversely affects male reproductive health through oxidative stress and impaired steroidogenesis. Although natural products have shown protective effects against reproductive toxicity, the bioactive compounds responsible for these effects and their molecular mechanisms remain inadequately characterized. This study investigated the protective potential of Cucurbita maxima seed extract (CMSE) against NP-induced reproductive toxicity in adult male albino mice and identified key phytochemicals associated with its activity. Male mice were divided into six groups: Control, NP-treated, CMSE (HD), NP+CMSE (LD), NP+CMSE (HD), and NP + CC, and treated for 35 days. LC-MS analysis identified eleven bioactive compounds in CMSE, which were subsequently evaluated through molecular docking and molecular dynamics simulations against key steroidogenic enzymes. Among the identified constituents, Ganoderic acid D exhibited the highest binding affinity and the most stable interactions with steroidogenic targets, suggesting a potential role in regulating testosterone biosynthesis and reproductive function. In vivo analyses demonstrated that CMSE significantly attenuated NP-induced reproductive toxicity. Treatment improved gonadosomatic index, restored antioxidant defenses (GSH, SOD, and CAT), reduced lipid peroxidation (MDA), normalized testicular protein and cholesterol levels, enhanced 3β-HSD and 17β-HSD activities, and restored serum testosterone concentrations. Histopathological examination further confirmed substantial recovery of testicular architecture following CMSE administration. The integrated in silico and in vivo evidence provides novel mechanistic insights into the reproductive protective effects of Cucurbita maxima and highlights Ganoderic acid D as a promising natural therapeutic candidate against NP-induced male reproductive dysfunction.

This study presents an LSTM-based speech recognition system for Assamese, a low-resource language with limited computational linguistic resources. A self-curated dataset comprising 1760 vowel samples collected from multiple speakers is used to capture variations in pronunciation. The proposed approach integrates Mel Frequency Cepstral Coefficients (MFCC), Short-Term Energy (STE), and Zero Crossing Rate (ZCR) features to enhance speech representation. An LSTM model is employed to effectively learn temporal dependencies in speech signals. The model achieves an average classification accuracy of 95% with a Word Error Rate (WER) of 18.60%. Comparative evaluation with existing models demonstrates competitive performance of the proposed system. However, the results indicate that phonetic similarities among certain Assamese vowels lead to higher misclassification rates. This work highlights the challenges of speech recognition in low-resource languages and provides a foundation for further improvements using larger datasets and advanced deep learning architectures.

This study explores the analysis of the magnetohydrodynamic stagnation point flow of the Cattaneo-Christov thermal flux model of Carreau nanofluid over a stretched sheet incorporating gyrotactic microorganisms and Joule heating. Moreover, integrating the influences of activation energy, chemical reactions, heat source, Brownian motion, and thermophoresis provides the distinctiveness of this investigation. Numerical outcomes are attained using a Runge-Kutta-based shooting technique implemented via the MATLAB BVP5C approach. The influences of several dimensionless parameters on velocity, thermal, and concentration profiles are represented graphically. Notable findings indicate that the thermal distribution is enhanced for augmenting the heat source and thermal radiation.