Krishna Chandra College

Understanding how interacting ecological processes influence prey-predator dynamics is crucial for predicting population persistence, anticipating multistability, identifying critical transitions, predicting tipping points, and guiding conservation strategies under environmental fluctuations. We examine a prey-predator model that integrates the Allee effect in the predator population and habitat complexity, which may operate simultaneously. To incorporate the environmental stochasticity, we extend the deterministic framework by introducing multiplicative white noise into the growth rate of the prey and the death rate of the predator. The deterministic model exhibits rich dynamics, including bistability, tristability, and both local and global bifurcations, such as saddle-node, Hopf, Bogdanov-Takens, homoclinic, generalised Hopf, and saddle-node bifurcations of limit cycles. Varying the strengths of the Allee effect and habitat complexity partition the parameter space into six subregions, each with distinct stability properties, including the extinction risk of the predator population. Most importantly, we critically examine how environmental stochasticity drives tipping between two alternative stable states in the system. By analysing the relationships among noise intensity, tipping probability, and tipping time, we also quantify predator extinction risk and assign extinction warning levels for different initial population sizes. For three cases of multistability, the probability of tipping to predator extinction from coexistence generally becomes more likely and faster with increasing noise, while the tipping probability from predator extinction to the coexistence state peaks at intermediate noise levels. Prey noise dominates tipping towards coexistence, whereas predator noise delays the transition. Warning levels vary most near the basin boundary with low noise intensities, although strong prey-dominated noise can alter risks even far from the basin boundary. Extensive numerical simulations further elucidate the influence of the Allee effect strength, habitat complexity, and noise intensity on system dynamics. These results highlight the critical roles of the Allee effect and habitat complexity in guiding conservation strategies to maintain biodiversity and prevent undesirable noise-induced critical transitions in stochastic ecological systems.

Microplastic pollution has posed significant threats to the biosphere. They have been detected in human samples and are known to modulate endogenous antioxidants. However, the mechanism of action remains unclear. Hence, the present study aims to determine the docking affinities of microplastic-associated compounds, such as bisphenol A (BPA), methyl methacrylate (MMC), vinyl chloride (VC), and polyethylene terephthalate (PET), for catalase (CAT) and superoxide dismutase-1 (SOD1). Binding affinity and SwissADME analyses were performed. The tested compounds exhibited binding affinities for CAT in order of VC (-3.0kcal/mol) < MMC (-4.4kcal/mol) < PET (-6.8kcal/mol) < BPA (-8.3kcal/mol). Similarly, compounds showed binding affinity for SOD1 in order of VC (-2.2kcal/mol) < MMC (-4.2kcal/mol) < PET (-6.3kcal/mol) < BPA (-7.2kcal/mol). Hydrogen bonds, van der Waals forces, and hydrophobic interactions were observed. Compounds, except VC, were permeable through the gastrointestinal tract and the blood-brain barrier. Moreover, they followed Lipinski’s and Veber’s rule, indicating bioabsorption and distribution potentials. Therefore, results suggest that the tested microplastic-associated compounds can interact with CAT and SOD1 to modulate their functions and fuel oxidative stress.

Cooperative predation and Allee-effect-driven prey growth are widespread ecological processes that can strongly influence population dynamics, but their behaviour in fragmented habitats with ecological corridors remains poorly understood. The pervasive fragmentation of natural ecosystems requires a comprehensive understanding of the mechanisms through which the design of the corridor influences prey–predator interactions. We develop a modified Leslie–Gower model that incorporates cooperative hunting, Allee-driven prey growth, and self- and cross-diffusion, implemented in a fragmented twin-trapezoidal habitat with single and multiple corridors. Prey–predator interactions follow a modified Holling type IV response, allowing quantitative variation in cooperative attack strength. We derive conditions for the existence of at least one non-constant positive steady state to explain the emergence of stationary patterns and spatially heterogeneous structures. We present analytical results on diffusion-induced Turing instability and show how cross-diffusion can generate two contrasting outcomes: inducing instability or restoring stability in the system. Our analysis shows that the interplay among the Allee effect, supplementary resources, cooperative predation, habitat heterogeneity, and corridor geometry critically structures system resilience and pattern formation. Importantly, our model shows that multiple corridors enhance connectivity and accelerate population spread, promoting a faster transition from spiral waves to spatiotemporal chaos and generating target-like transients, invasion-wave transients in chaotic regimes, and ghost-attractor-driven spiral oscillations. Our extensive numerical simulations advance the understanding of diffusion-driven prey–predator interactions and reveal rich spatial dynamics that emerge in a complex, corridor-mediated habitat, whose geometric intricacy makes numerical implementation substantially more challenging than in traditional rectangular domains.

West Bengal, once a leading educational hub in India, has experienced significant stagnation in its school education system over the past two decades, with progressively declining enrolment rates. To address this issue, the state government introduced the Kanyashree Prakalpa in 2013, a Conditional Cash Transfer (CCT) scheme aimed at reducing dropout rates among girls, increasing enrolment in secondary and higher secondary education, preventing child marriage, and empowering women. This study evaluates the scheme’s impact using a time-series analysis, comparing enrolment trends before and after its implementation. The findings reveal mixed outcomes, with government institutions witnessing marginal improvements in female enrolment, while private institutions consistently show higher growth rates. The analysis underscores the need for structural reforms to enhance the quality of education in government schools and ensure the scheme’s long-term success in promoting educational equity and gender empowerment.

The present study investigates the role of channel bottlenecking and anthropogenic interventions in shaping flood dynamics and influencing floodplain wetland sustainability in the moribund deltaic Ganges floodplain, India. The primary objective is to evaluate how channel bottlenecking, embankment construction, damming, source closure of avulsed channels, and urban-induced channel constriction affect flood magnitude, frequency, duration, and wetland transformation. To achieve this, extensive field-based measurements of channel morphology and flood characteristics were conducted, complemented by advanced machine learning (ML) techniques for flood susceptibility mapping. The sensitivity of spatial flood susceptibility to bottlenecking and embankment parameters was quantified, while the effects of damming were examined using downstream water level data. Results show that the convergence of multiple rivers (Mayurakshi, Kuya, Mor, Banki, and Dwarka) within a small low-lying area has created a highly flood-prone setting. Among the applied ML algorithms, the Random Forest (RF) model has demonstrated the highest predictive performance for flood susceptibility mapping. Channel bottlenecking in the confluence segment has substantially increased flood magnitude, expanding the area of extreme flood susceptibility from 7.22 to 84.35 km2. Conversely, embankment installation has enhanced the river’s carrying capacity from 53 to 88