Understanding the emergence of tipping points is essential for safeguarding populations and maintaining ecosystem stability. This study investigates the influence of environmental stochasticity on tipping phenomena within a planktonic system. Plankton populations in aquatic ecosystems are highly sensitive to environmental fluctuations, driven by factors such as the bioavailability of essential nutrients, acid content, and aquatic thermal conditions. To explore these dynamics, we formulate and analyze a two-dimensional stochastic model involving toxin-producing phytoplankton and zooplankton. In the absence of noise, the deterministic system exhibits a rich array of bifurcations, including saddle-node, transcritical, Hopf, and BT bifurcations. A persistence and extinction analysis is conducted to assess species viability under varying ecological conditions. Bistability emerges between coexistence states and a zooplankton-free equilibrium, governed by toxin liberation rates. Furthermore, we identify three distinct types of noise-induced tipping: transitions between two coexistence states and shifts between coexistence and zooplankton extinction states. Our results underscore the critical role of initial species abundance in triggering these tipping events. To quantify the likelihood of state switching, we construct confidence ellipses and estimate threshold noise intensities using numerical simulations informed by stochastic sensitivity analysis. Overall, this study offers novel insights into the mechanisms underlying ecological tipping points and provides a foundation for identifying potential control strategies within the framework of plankton ecology.
In this research work, the authors have developed a highly efficient optical switch based on exotic GaN/Al0.45Ga0.55N/GaN/GaN p+-n-n−-n+ High Electron Mobility Avalanche Photodiode (HEM-APD) in 200 nm to 500 nm wavelength. The optical characteristics of a designed optical switch are analysed by developing a Quantum Modified Carrier Transport Model (QMCTM) coupled with the Monte Carlo simulation technique in terms of absorption coefficient, extinction coefficient, transmission, and reflection spectrum, gain and excess noise factor. Due to the incorporation of a small mole fraction of Al into the GAN, the 2-D Electron Gas (2-DEG) is formed at the interface between the GaN and AlGaN. The 2-DEG significantly enhances electron mobility in the active region of the device at low temperatures. Hence, the electron can travel quickly without restraint through the active region of the device. This phenomenon leads to quicker operation of the device. Additionally, the performance of the designed HEM-APD based optical switch is compared with its flat AlGaN/GaN counterpart. It is observed that the performance of the designed HEM-APD based optical switch is significantly higher compared to its counterpart. The authors have also validated the developed Quantum Modified Carrier Transport Model (QMCTM) coupled with the Monte Carlo simulation technique through experimental verification. This is the first report on development of highly efficient optical switch based on an exotic GaN/Al0.45Ga0.55N/GaN/GaN type p+-n-n–n+ High Electron Mobility Avalanche Photodiode (HEM-APD) in 200 nm to 500 nm wavelength.
Lung cancer accounts for the highest number of deaths due to cancer in the world. Precise and prompt identification on the basis of histopathology pictures is vital in enhancing survival outcomes. This study proposes to create a fast and intelligible CNN with high accuracy and an easy-to-use CBQA model that can be deployed in the clinical setting. To do this, we created a bespoke model of CNN with four convolutional layers, group normalisation, dropout and SoftMax output. A small balanced dataset of 15,000 histopathology images (5000 adenocarcinoma, 5000 squamous cell carcinoma, 5000 benign) out of Kaggle dataset of Lung Cancer Histopathological Images was adopted with a 70–15–15
Rice (Oryza sativa L.) is fundamental to global food security, yet yield stagnation under changing climatic conditions necessitates improved utilization of phenotypic diversity in breeding programs. This study evaluated 62 rice genotypes, including traditional landraces from West Bengal and Tripura and commonly grown improved varieties, for 13 quantitative traits across two cropping seasons. The panel captured wide variation in grain type, maturity duration, and plant architecture typical of eastern India. Multivariate analyses revealed that reproductive sink-related traits—panicle weight, slpikelet number, test weight, and grain yield per plant—were the primary contributors to phenotypic variation, while tillering and panicle number defined a secondary axis of diversity. K-means clustering, validated by silhouette analysis, grouped genotypes into four distinct classes based on yield potential, phenology, and tillering behaviour. Nonlinear 3D t-SNE visualization further resolved fine-scale patterns of genotypic similarity beyond linear methods. Outlier detection identified grain breadth, panicle weight, and grain length-to-breadth ratio as key drivers of phenotypic divergence, while correlation analysis demonstrated that yield was predominantly influenced by sink-related traits, with early flowering and maturity showing negative associations with productivity. By integrating linear, nonlinear, and statistical association approaches, this study presents a novel and robust framework for deciphering complex phenotypic variation and provides actionable insights for parental selection, ideotype breeding, and the development of stable, high-yielding rice cultivars.
This paper presents a new approach to analyzing the controllability of fractional Volterra-Fredholm integro-differential equations with state-dependent delay, characterized by the Caputo fractional derivative and governed by a semigroup of compact and analytic operators. Controllability results are derived using Schauder's fixed point theorem, addressing the challenges of fractional dynamics and state-dependent delays. A key innovation in this study is the integration of theoretical analysis with advanced computational methods, specifically Physics-Informed neural networks, to approximate solutions and verify controllability conditions. To validate the theoretical findings, a detailed example is provided, along with numerical simulations that confirm the convergence of solutions. Graphical representations offer additional insights into the solution dynamics, enhancing the understanding of the system's behavior. By combining mathematical rigor with machine learning techniques, this work establishes a computational framework for tackling complex fractional systems, paving the way for further exploration of their controllability.
