The advancement in technology urges the need for storage and pay-as-you-go service due to which the Cloud Computing(CC) came into existence. The emergence of Cloud in technology increases the number of users due to which the energy consumption of the cloud data centers increases. To overcome this issue, in this paper we have proposed a novel Virtual Machine Migration(VMM) technique performed using a quantum nature-inspired based hybrid approach i.e. Quantum-Inspired Genetic and Multi-objective Ant Colony based Energy-Aware Scheduler(QIGMAC-EAS) which leverages the property of Quantum-inspired Genetic Algorithm(QIGA), Shortest Job First(SJF), & Multi-Objective Ant Colony Systems(MO-ACS). The proposed approach is a three layer architecture: the first layer is user sending request to, the second layer consists of mapping of cloudlets to virtual machines(VMs) and the third layer consists of migration of VMs to physical machines(PMs). The experimentation was performed using the Qiskit environment over Bitbrains dataset. The comparison was made with five different approaches i.e. Quantum-inspired binary chaotic salp swarm algorithm (QBCSSA), Quantum Reinforcement Learning (QRL), Q-learning with Arithmetic Optimization Algorithm(QQLAOA), Energy Aware VM Scheduling(EAVMS), Energy-Aware Cosine Similarity Learning Network (ECSLN), FCFS-Greedy-Threshold-based Migration(FGTM). For validating the performance of the proposed approach the ANOVA test is also performed. Hence, improving the efficiency and quality of service. The proposed approach is able to reduce the energy consumption by 99.80% whereas the comparative algorithm reduces it by 71.6%, 62.2%, 58.99%, 63.29%, 50.00%, 49.30% respectively.
In this paper, a numerical scheme for solving coupled systems of second-order Emden–Fowler equations using the higher-order Haar wavelet method is developed. Coupled Emden–Fowler systems are most commonly found in astrophysics, nuclear physics, and fluid mechanics. These equations play an important role in mathematical biology and combustion theory. The proposed method uses a Haar wavelet to approximate the fourth-order derivative of unknown functions from the system of differential equations and convert them into an algebraic system. Subsequently, two solvers, namely the Newton–Raphson and Newton–Krylov, are used to solve the resulting nonlinear algebraic system. The Emden–Fowler equations are difficult to solve because of their strong nonlinearity and singularity. Therefore, several benchmark problems have been solved using the proposed scheme to show the effectiveness of the method. While this problem has been solved using other existing methods like the Haar wavelet collocation method and the artificial neural networks, it is to be noted that the results from the higher order Haar wavelet method are in excellent agreement and superior to the existing methods.
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.
