Climate change is gradually affecting the quality, reliability and safety of potable water throughout the world. Rising temperatures, excess rainfall and changing hydrological cycles are altering the physical, chemical and biological characteristics of water sources,. This in turn poses serious challenges to drinking water facilities and water distribution systems. Artificial intelligence (AI), machine learning (ML) and internet of things (IoT) have emerged as powerful tools for better water quality (WQ) monitoring, optimisation of treatment and management of infrastructure. This review is a critical assessment of the recent developments of AI-based approaches for the evaluation and remediation of drinking water quality under changing climatic conditions. It involves management of chlorination, adsorption, membrane filtration and the risk foreseen for the water distribution network in a city. The review addresses a critical gap by synthesising an integrated AI‑driven framework to mitigate climate‑induced WQ variability and support resilient potable water systems. It also examines the integration of AI with IoT-enabled sensor systems for real-time monitoring and predictive risk management. Furthermore, the review discusses current limitations, operational challenges, and sustainability implications of AI-based water management systems. The outcome demonstrates a growing potential of data-driven technologies to facilitate adaptive decision-making, enhance the efficiency of water treatment systems. It also portrays the resiliency of drinking water systems under climate change pressures.
Returning to work after maternity leave requires a great management of time and resources and could be a risk factor for women’s mental health. In addition, rigid maternal beliefs could create a cycle of negative thinking that undermines mothers’ resources. This paper aims to test the role of rigid maternal beliefs in the relationship between spirit at work, resilience, and poor mental health. A self-report questionnaire was administered to 200 Italian working mothers. A structural equation model with observed variables was implemented to investigate the relationship between spirit at work and poor mental health and test the mediator role of resilience and the moderator role of rigid maternal beliefs. Results show a significant negative effect of spirit at work on poor mental health mediated by resilience. Rigid maternal beliefs moderate the relationship between spirit at work and resilience, and the mediated effect decreases to non-significance with the increase of rigid maternal beliefs. Findings suggest that action can be taken on multiple fronts to improve working mothers’ mental health. Organizations can promote interventions to strengthen employees’ resources (e.g., spirit at work, resilience) and improve the management of rigid maternal beliefs. Integrating different psychological strategies can help women better manage adversity while supporting them in coping with working mothers’ challenges.
Precise and up-to-date cross-sections with uncertainty propagation data of materials used in control rods are essential for the smooth functioning of nuclear reactors, since fast neutrons interact with control rods that regulate chain reactions by absorbing excess neutrons. The present study aims to measure the neutron-induced reaction cross-section values for the isotopes of silver (Ag) and indium (In). Deuterium–tritium (D–T) fusion neutrons with an energy of 14.96 ± 0.22 MeV were produced and used to irradiate natural samples of Ag and In targets, inducing measurable activation for the reactions 109Ag(n,2n)108Agg, 109Ag(n,p)109Pdm, 107Ag(n,2n)106Agm, 115In(n,p)115Cdg, and 115In(n,α)112Ag at the Neutron and Ion Irradiation Facility, Institute for Plasma Research (NIIF-IPR), Gujarat, India. The radioactive samples were subsequently taken for offline γ-ray counting in a high purity germanium detector (HPGe), with a fine resolution of 2.1 keV at 1.33 MeV γ-ray energy of 60Co connected with GENIE software. Two well-known standard reactions of Aluminium, 27Al(n,p)27 Mg and 27Al(n,α)24Na were employed for neutron flux measurements as per the irradiation period. Appropriate correction factors were applied in cross-section assessment, and uncertainties from all input parameters were rigorously propagated to the final values through covariance analysis. The experimental results were also validated by predictions from the nuclear code TALYS-2.0. Furthermore, prior reported studies from EXFOR and evaluated libraries from ENDF were compared with the presently measured results.
The present work investigates electrical energy production utilizing a quasi-solid-state device including freshwater live green macroalgae. The bio-photovoltaic device has been developed utilizing filamentous macroalgae as the primary photoactive component. These filamentous algae, which are part of the green algae family, typically thrive at the bottom of water bodies or present at the surface forming a dense layer on aquatic environments. These algae have been harvested from a nearby pond and transformed to create a biofilm. The bio-photovoltaic device was assembled by incorporating this algal biofilm in between counter electrode platinum (Pt), and photoanode consists of titanium oxide (TiO2) layer which was drop casted over fluorine-doped tin oxide (FTO) glass substrates. This fabricated device demonstrated significant generation of photo-generated current and voltage when exposed to solar illumination, indicating its potential for harnessing solar energy effectively. The optimized device (area: 1 cm × 1 cm) exhibit an open-circuit photovoltage of 0.82 V on the irradiance of white light (100 mW/cm2). The device studies were also performed in the different wavelength of light source. At zero bias voltage, the device showcases a highest photo-current of 53 µA at 365 nm wavelength of incident light.
This study investigates a diffusion-driven SIHTRM epidemic model that integrates a Holling type II incidence rate to assess the effective utilization of healthcare facilities and to investigate the impact of psychological fear in disease prevention and control. The model captures the role of both permanent and temporary hospital bed capacity in mitigating the transmission. We establish positivity, boundedness, and biological feasibility of equilibria, analyze their stability, and compute the basic reproduction number as the threshold governing disease persistence or eradication. Utilizing Mpox data from the Democratic Republic of the Congo, the proposed model is validated over the period from March 2024 to May 2025. The essential epidemiological parameters are subsequently determined by using least squares method, which enhances prediction reliability. Partial rank correlation coefficient (PRCC) analysis is employed to identify influential parameters that significantly contribute to shaping the dynamics of the epidemic. The transcritical bifurcation of the model is also examined, highlighting threshold phenomena and the potential for complex and widespread dynamics. Basic reproduction number corresponding to forward bifurcation, as well as the dynamics of backward bifurcation, are exhibited. The saddle-node bifurcation occurs in the system at the point of backward direction. Finally, numerical simulations are carried out to demonstrate how variations in system parameters influence the dynamic behavior of the model. We observe that the system undergoes subcritical Hopf bifurcations. Subsequently, we further examine the spatially explicit system. Additionally, we investigate the spread of infectious disease and its transmission as the diffusion coefficient of vulnerable individuals is increased through a reaction-diffusion model. Public movement can enhance disease transmission, as frequent interactions facilitate the rapid spread of infection and create localized hotspots within the region. The spatiotemporal system further exhibits the formation of Turing patterns. While traditional numerical approaches are commonly used to evaluate epidemiological systems, here we adopt an innovative Physics-Informed Neural Networks (PINNs) framework to solve the system of nonlinear differential equations. By integrating data-driven learning with fundamental biological principles, the PINN approach enhances the efficiency and accuracy of estimating system dynamics. These insights derived from these simulations can enhance epidemic preparedness and facilitate more effective mitigation strategies, ultimately ensuring improved treatments for patients while reducing the load on healthcare system. Established in large public venues such as stadiums and exhibition centers, these facilities effectively mitigate hospital bed shortages during future pandemic situations by accommodating moderately ill patients, thereby reducing disease progression and alleviating pressure on conventional healthcare infrastructure.
