Analysis of bistable behavior and early warning signals of extinction in a class of predator-prey models

In this paper, we develop a method of detecting an early warning signal of catastrophic population collapse in a class of predator-prey models with two species of predators competing for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near {\em{singular Hopf bifurcation}} of a coexistence equilibrium point, we assume that the class of models exhibits bistability between a periodic attractor and a boundary equilibrium point, where the invariant manifolds of the coexistence equilibrium play central roles in organizing the dynamics. To determine whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the point attractor, we reduce the equations to a suitable normal form, which is valid near the singular Hopf bifurcation, and study its geometric structure. A key component of our study includes an analysis of the transient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. As a result of our analysis, we could devise a method for identifying early warning signals, significantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator-prey model considered in [\emph{Discrete and Continuous Dynamical Systems - B} 2021, 26(10), pp. 5251-5279] and we find that our theory is in good agreement with the numerical simulations carried out for this model

Ageing and Cognitive Health: A Preventive Approach

Ageing often leads to a decline in cognitive abilities. Significant cognitive impairment leads to functional impairment, loss of independence and need for long term care. A cognitive reserve is a functional ability that helps to prevent cognitive decline. Identifying the modifiable risk factors for cognitive decline with advancing age is crucial. Research has shown that cognitive exercise and cognitive training in older adults can slow down or resist cognitive decline. Continuous cognitive engagement, adequate and rich cognitive stimulation and complex mental activity can foster neuroplasticity in the brain and, therefore, may be utilized to mitigate age-related changes in cognition. Therefore, adopting a preventive approach to healthy ageing will benefit the ageing population and the community

Visual Documentation of the Project titled Health Communications Strategies and Best Practices in Covid-19 & Amphan Cyclone Affected Communities in South Bengal: A Study of Nadia & South 24 Parganas Districts

Visual Documentation of the Project titled "Health Communications Strategies and Best Practices in Covid-19 & Amphan Cyclone Affected Communities in South Bengal: A Study of Nadia & South 24 Parganas Districts". Project undertaken by MANAS, Nadia, West Bengal, India during 2021-22, and supported by NCSTC-DST, Government of India vide project no: CO/R/FP/G49/2020

Single-pulse analysis and average emission characteristics of PSR J1820-0427 from observations made with the MWA and uGMRT

We have studied the pulse-to-pulse variability in PSR J1820--0427 and its frequency dependence using high-quality, wide-band observations made from the upgraded Giant Metrewave Radio Telescope (uGMRT; 300-750 MHz) and the Murchison Widefield Array ($\sim$170-200 MHz). The low-frequency data reveal a previously unreported feature in the average profile (at 185 MHz) after accounting for the effects of temporal broadening arising from multi-path scattering due to the Interstellar Medium (ISM). We advance a new method for flux density calibration of beamformed data from the uGMRT and use it to measure the single pulse flux densities across the uGMRT band. Combined with previously published measurements, these flux densities are best fit with a power-law spectrum with a low-frequency turnover. We also use calibrated flux densities to explore the relationship between pulse-to-pulse variability and the spectral index of individual pulses. Our analysis reveals a large scatter in the single-pulse spectral indices and a general tendency for brighter pulses to show a steepening of the spectral index. We also examine the frequency-dependence of the pulse-fluence distribution and its relation to the Stochastic Growth Theory.Comment: 13 pages, 9 figures, 2 tables. Accepted for publication in MNRA

Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks

The current COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With more and more people getting affected during the second wave, the hospitals were over-burdened, running out of supplies and oxygen. In this scenario, prediction of the number of COVID-19 cases beforehand might have helped in the better utilization of limited resources and supplies. This manuscript deals with the prediction of new COVID-19 cases, new deaths and total active cases for multiple days in advance. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models that are pre-trained on the data from four different countries (United States of America, Brazil, Spain and Bangladesh) and are fine-tuned or retrained on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then give a multiple days ahead predictions using recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the combination of different models. This method with two countries, Spain and Brazil, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.Comment: 8 pages, 7 figure

Coping the arsenic toxicity in rice plant with magnesium addendum for alluvial soil of indo-gangetic Bengal, India

Arsenic (As3+) is a toxic metalloid found in the earth’s crust, its elevated concentration is a concern for human health because rice is the staple grain in eastern part of India and the waterlogged rice field environment provides opportunity for more As3+ uptake. Magnesium (Mg2+) is an important plant nutrient. Present work is a search for reducing As3+ toxicity in plants through Mg2+ application. The findings are quite impressive, the root to shoot biomass ratio showed more than 1.5 times increase compared to the control. Total protein content increased 2 folds. Carbohydrate and chlorophyll content increased two to three times compared to control. On the other hand, Malondialdehyde content showed a decline with the application of increased Mg2+ dose. The in-silico study shows a better interaction with As3+ in presence of Mg2+ but interestingly without stress symptoms. These findings from the research indicate that Mg2+ application can be effective in reducing As3+ induced stress in plants

Non-Intrusive Uncertainty Quantification for U3Si2 and UO2 Fuels with SiC/SiC Cladding using BISON for Digital Twin-Enabling Technology

U.S. Nuclear Regulatory Committee (NRC) and U.S. Department of Energy (DOE) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the DOE/ NRC. DTs have the potential to transform the nuclear energy sector in the coming years by incorporating risk-informed decision-making into the Accelerated Fuel Qualification (AFQ) process for ATF. A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. However, novel ATF technology suffers from a couple of challenges, such as (i) Data unavailability; (ii) Lack of data, missing data; and (iii) Model uncertainty. These challenges must be resolved to gain the trust in DT framework development. In addition, DT-enabling technologies consist of three major areas: (i) modeling and simulation (M&S), covering uncertainty quantification (UQ), sensitivity analysis (SA), data analytics through ML/AI, physics-based models, and data-informed modeling, (ii) Advanced sensors/instrumentation, and (iii) Data management. UQ and SA are important segments of DT-enabling technologies to ensure trustworthiness, which need to be implemented to meet the DT requirement. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, UQ and SA are paramount to the success of DT framework in terms of multi-criteria and risk-informed decision-making. In this study, the adaptability of polynomial chaos expansion (PCE) based UQ/SA in a non-intrusive method in BISON was investigated to ensure M&S aspects of the AFQ for ATF. This study introduces the ML-based UQ and SA methods while exhibiting actual applications to the finite element-based nuclear fuel performance code

Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction

Computationally efficient and trustworthy machine learning algorithms are necessary for Digital Twin (DT) framework development. Generally speaking, DT-enabling technologies consist of five major components: (i) Machine learning (ML)-driven prediction algorithm, (ii) Temporal synchronization between physics and digital assets utilizing advanced sensors/instrumentation, (iii) uncertainty propagation, and (iv) DT operational framework. Unfortunately, there is still a significant gap in developing those components for nuclear plant operation. In order to address this gap, this study specifically focuses on the "ML-driven prediction algorithms" as a viable component for the nuclear reactor operation while assessing the reliability and efficacy of the proposed model. Therefore, as a DT prediction component, this study develops a multi-stage predictive model consisting of two feedforward Deep Learning using Neural Networks (DNNs) to determine the final steady-state power of a reactor transient for a nuclear reactor/plant. The goal of the multi-stage model architecture is to convert probabilistic classification to continuous output variables to improve reliability and ease of analysis. Four regression models are developed and tested with input from the first stage model to predict a single value representing the reactor power output. The combined model yields 96% classification accuracy for the first stage and 92% absolute prediction accuracy for the second stage. The development procedure is discussed so that the method can be applied generally to similar systems. An analysis of the role similar models would fill in DTs is performed