Despite the near-complete elimination of cholera in many developing countries, numerous lower-income countries continue to face recurring epidemics. Although extensive research has been conducted on cholera transmission dynamics and control, a comprehensive approach to managing outbreaks in resource-limited settings remains elusive. This study introduces a cholera epidemic model incorporating a resource allocation strategy to balance efforts between reducing transmission and enhancing recovery rates. The model is validated using weekly cholera data from the resurgence in Haiti (October 2022-March 2023) to estimate key parameters and the control reproduction number ( R 0 ) . Based on these estimated parameters, the proposed model exhibits rich dynamical behavior, including backward and Hopf bifurcations, highlighting its potential for a multi-wave epidemic pattern. Using a continuous-time Markov chain (CTMC) model and the Gillespie algorithm, we calculate the extinction probability of cholera, comparing it with multitype branching process (MTbp) results, which estimate the analytical form of the probability of cholera extinction and outbreak, showing excellent agreement. Finally, construct a nonlinear programming problem (NLPP) to find the optimal combination of resources to minimize outbreak probability. In solving this NLPP, we find that prioritizing resources to recovery improve interventions is more effective than reducing transmission to minimize the probability of disease outbreak. These insights can guide resource allocation strategies to reduce cholera outbreaks in resource-constrained settings.
Stream sediments, as long-term sinks for potentially toxic elements (PTEs), provide valuable insights into both natural and anthropogenic contamination. This study presents a comprehensive assessment of PTE contamination, ecological risk, and human health implications across Odisha, eastern India- a region characterized by complex Precambrian geology, intensive agriculture, mining, and industrialization. Concentrations of ten PTEs (Cr, Cd, As, Ni, Cu, Zn, Co, Mn, V, and W) from 28,111 locations collected under the Geological Survey of India NGCM program indicate moderate to very high contamination across the state, with pronounced hotspots in mineralized and industrial belts. Multivariate analyses, including principal component analysis and hierarchical clustering, reveal dominant lithogenic control over Cr, Mn, Ni, Co, Cu, Zn, and V, while anthropogenic enrichment of As, Cd, and W is linked to mining, industrial emissions, and agricultural activities. Non-negative matrix factorization corroborates these source apportionment results. Pollution indices, including enrichment factor, geo-accumulation index, pollution load index, and potential ecological risk index, indicate moderate to high ecological risk in several regions. Human health risk assessment shows that 69.05% of locations exhibit high non-carcinogenic risk (HI > 1) for children, whereas adults show non-carcinogenic and carcinogenic risks in < 0.5% and > 65% of locations, respectively, primarily associated with Cr, Cd, and As. Sobol sensitivity analysis demonstrates that concentration variability predominantly governs carcinogenic risk estimates. Additionally, a machine learning-based framework is developed to classify risk and non-risk zones for both adults and children. This integrated approach provides critical insights for public health protection, targeted remediation, and sustainable land-use planning.
Incessant mica mining and improper disposal of mine waste have resulted in widespread arsenic (As) contamination in agricultural soils. Microbially augmented vermicomposting recently emerged as a promising approach to transform toxic mine waste into a nutrient-rich organic amendment, however, its field-scale adoption and validation for arsenic immobilization remain limited. This study evaluated the performance of vermicomposts prepared using mica mine tailings (MMT). Soil physicochemical, microbial, macronutrient, and arsenic content were measured, alongside crop yield and biochemical parameters. Treatment T5 [bacteria-supplemented MMT (1:1) vermicompost (50
Due to increasing sustainability concerns, wood filler-reinforced polymer (WFRP) composite materials have gained prominence as a potential material in various industries such as construction, automotive and consumer products. This real-world application of wood filler-reinforced polymer composites requires well understanding and prediction of important mechanical properties. The conventional experimental methods of material characterization are often resource intensive and time-consuming. Recently, the machine learning (ML) presented a novel and viable avenues for augmenting prediction models, enabling the accurate estimation of mechanical properties with fewer experiments and improved generalization. The current work presents the application of ML techniques for the prediction of tensile properties of WFRP composite. Various models like support vector machine, polynomial regression, and decision trees (DT) are explored for their potential to predict tensile properties based on input variables like filler content, and crosshead speed. These models are very effective and accurate in representing highly intricate and nonlinear interdependencies between material input parameters and its performance. Additionally, the artificial neural network model, in particular, exhibits an excellent capability of predicting tensile strength of composites with the lowest MSE value. The study highlights the efficiency of ML models, demonstrating their potential to enhance material property prediction.
We explore a class of minimal plateau inflationary models constrained by the latest cosmological observations from ACT DR6, Planck 2018, BICEP/Keck 2018, and DESI, collectively referred to as P-ACT-LB-BK18. These models, characterized by a non-polynomial potential, are analyzed using both inflationary and post-inflationary reheating dynamics, and the limits on the viable model parameter space are obtained. Our results show that the minimal model with matter-like post inflationary reheating phase remains consistent with current data at both 1σ and 2σ levels. The inflaton potential’s exponent n and reheating epoch are intertwined in that upon its increase, corresponding to the stiffer reheating equation of state, the viable model parameter space in accordance with ACT shrinks, which is further facilitated by the primordial gravitational waves (PGWs) overproduction. We further explored a supergravity-inspired extension of the model under study with similar results, but with tighter constraints on the model parameters. These results emphasize the importance of jointly analyzing CMB data and reheating physics to test inflationary models.
