Vishwamitra Research Institute

Instabilities and Turing patterns in stochastic spatiotemporal systems in which a fraction of an evolving population, after undergoing a series of dynamic transitions, returns to its original state, remain largely unexplored. Adopting an epidemic model incorporating reinfections as an exemplar of such a system, we present stability and pattern-formation analyses of the stochastic reaction-diffusion equations that represent the model. Saturation effects in epidemic spread lead to nonlinear considerations, while random environmental effects motivate a stochastic term. Turing bifurcation and the emergence of equilibrium patterns are analysed with respect to three fundamental parameters - reinfection, saturation, and noise intensity. Using higher-order stability analysis and stochastic averaging, we find the Turing instability and also uncover self–organized, distinct equilibrium patterns of infection spread. Additionally, results elucidating the effects of stochastic excitation and its intensity, as well as the competing influence of saturation and reinfection on stability and pattern formation, are presented. The results are also expected to be broadly significant beyond epidemic modelling, for studies of noise-induced instabilities and morphogenesis in spatiotemporal nonlinear dynamical systems.

Abstract The investigations on evaluation of 252 landraces rice were undertaken in natural field conditions during Summer 2024, aiming to identify biophysical traits conferring resistance to yellow stem borer (YSB), Scirpophaga incertulas . Genotypes were comprehensively assessed for key morphological and anatomical features, such as plant height, leaf length, width and angle, leaf blade pubescence, stem diameter, number of tillers, peduncle length, and panicle length at various crop stages. Infestation was documented through levels of dead heart, white ear formation, and egg mass deposition at 30, 50, and 90 days after transplanting. The evaluation revealed that resistant rice genotypes consistently exhibited greater plant height, longer and narrower leaves, more erect leaf angles, thinner stems, and higher leaf trichome density, while susceptible types displayed broader leaves, wider leaf angles, thicker stems, and lower pubescence. Third leaf length, flag leaf angle, and tiller number were crucial for resistance in early growth, while flag leaf width and third leaf length dominated in mid-stage, and stem diameter with upper leaf pubescence were critical at the reproductive stage. Integrating these traits into breeding and selection strategies offers a sustainable approach for developing rice varieties with enhanced yellow stem borer resistance, supporting stable yields and reducing dependency on chemical control in important rice-growing regions.

Atomic layer deposition (ALD) is a vapor-phase thin-film deposition technique offering precise atomic-scale control over film thickness and conformality. Widely used across diverse fields including microelectronics, energy storage, and biomaterials, traditional optimization of ALD processes typically relies on trial-and-error experimentation or computational simulations. Recently, artificial intelligence (AI) and machine learning (ML) methods have emerged as powerful tools to significantly accelerate and improve ALD process optimization, material discovery, and fundamental understanding of ALD chemistry. In this review, we highlight recent advancements in the intersection of ALD and AI/ML, examining their roles in process optimization, new material development, reaction mechanism exploration, and the application of advanced algorithms, including large language models. Additionally, we introduce a bibliometric approach to systematically collect and analyze relevant scientific literature. Finally, we outline existing challenges and provide insights into the future potential of integrating AI with ALD to drive further innovation in materials science and engineering.

We assessed the effectiveness of a clinical decision support tool to reduce total cumulative follicle-stimulating hormone dosage used, to eliminate the need for ultrasound exams after day 5 of an individual superovulation cycle, and to improve the number of high-quality embryos obtained. The design we used was a Randomized Control Trial in a private fertility center. This study included 93 women aged 25–45 years undergoing IVF. 48 Test and 45 Control participants included normal and poor responders and patients with polycystic ovarian syndrome. A clinical decision support tool was used to forecast stimulatory hormone dosing for an individual cycle based on follicle size distribution on day 1 and day 5. Cumulative stimulatory hormone doses, oocytes retrieved, number of Mii oocytes, total embryos, high-quality embryos obtained during the cycle, and clinical pregnancy rates was the main outcome measures. Test participants required significantly lower cumulative FSH doses during superovulation cycles (average 1883 IU test, 2530 IU control, p <0.01), with significantly higher numbers of total embryos (average 5.4 test, 3.5 control, p<0.01), and high-quality embryos (average 3.1 test, 1.2 control, p<0.01). Test participants had higher Mii follicles, although the difference was not statistically significant. The clinical pregnancy rate reported was significantly higher for test participants than control participants (52% test, 26% control, p<0.05). The test group had more poor responders and PCOS patients than the control group. In conclusion, the clinical decision support tool eliminated the need for ultrasound exams after day 5, reduced the doses of stimulatory hormone required, yielded significantly higher numbers of high-quality embryos, and resulted in higher clinical pregnancy rates.

The benefit of online systems has been availed by users and cybercrimes alike. Phishing has become a popular cybercrime. Phishing is a significant criminal activity for financial gains. Studies about different machine learning algorithms are a perpetual journey to detect malicious data. There are lots of algorithms proposed for detecting a phishing website. The selection of the best solution for the problem is not an easy task in a phishing domain. In this study, the focus is on experimental study of ensemble learning methods, feature reduction techniques and hybrid approach. In machine learning, for improvement in performance, ensemble learning plays a crucial role. In this study, we do a comparative analysis of bagging, boosting and stacking ensemble learning models and propose a new hybrid model in the phishing domain.

Inordinate consumption of natural resources by humans over the past century and unsustainable growth practices have necessitated a need for enforcing global policies to sustain the ecosystem and prevent irreversible changes. This study utilizes the Generalized Global Sustainability model (GGSM), which focuses on sustainability for the Food-Energy-Water (FEW) Nexus. GGSM is a 15-compartment model with components for the food-web, microeconomic framework, energy, industry and water sectors, and humans. GGSM shows that an increased per capita consumption scenario is unsustainable. In this study, an optimal-control theory based approach is devised to address the unsustainable scenario through policy interventions to evaluate sustainability by employing multiple global indicators and controlling them. Six policy options are employed as control variables to provide global policy recommendations to develop the multi-variate optimal control approach. Seven objectives are proposed to limit the human burden on the environment to ascertain sustainability from a lens of ecological, economic, and social wellbeing. This study observes the performance of the policy options toward seven sustainability indicators: Fisher Information, Green Net Product, Ecological Buffer, Carbon dioxide emissions, Nitrous oxide emissions, and Global Water Stress. The optimal control model assesses these multiple objectives by minimizing the variance in the Fisher Information. One significant result from this study is that optimizing for the Fisher Information based objective is adequate to attain sustainability and manage the other objectives under consideration. Thus, forgoing a multi-objective problem framework. The results show that cross-dimensional policy interventions such as increased vegetarianism and increased penalty on industrial discharge are shown to have a positive impact on scale.

ABSTRACT Harvesting Dactylorhiza umbrosa from natural habitats for salep production (powdered dried tuber) has endangered the existence of this species. Mass production of tubers is possible in vitro. Different osmotic pretreatments for inducing tuber formation in D. umbrosa were investigated. Seeds were germinated in Fast medium and protocorm derived seedlings measuring 2 cm in height with roots were subjected to 2% and 4% w/v of different osmotica including sorbitol, mannitol, and polyethylene glycol 4000 (PEG), increased concentrations of sugars [sucrose 18 g·L−1+fructose 7.5 g·L−1 (sugar 1.5x); sucrose 24 g·L−1+fructose 10 g·L−1 (sugar 2.0x)] and non-pretreated control (sugars 1x; 12 g·L−1 sucrose+5 g·L−1 fructose) for 29 days. Then, the seedlings were transferred to jars containing Fast medium (similar to the control treatment) supplemented with 1 g·L−1 activated charcoal. After 70 days, survival, growth, tuber formation, shoot proline and photosynthetic pigments, and shoot and roots soluble and reserve carbohydrates were determined. PEG and mannitol 2% pretreatments increased root number and photosynthetic pigments. The pretreatments did not affect shoot proline concentration, but induced soluble and reserve sugars accumulation in tubers. According to shoot growth improvement and obtaining the highest tuber number and root carbohydrates content, PEG 2% pretreatment was suggested for D. umbrosa tuberization.

Health, nutrition are the main factors for human beings. Fishes are the source of protein, vitamins, minerals and fats. These are the main source as a food for human beings for their health and nutrition. A proper processing and packaging of fishes helps in maintaining the quality of fish. Today many new technologies are emerging for preservation of fish. This article gives the recent advance techniques in the processing and packaging of fish and their products.