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Virtualisation is a major technology in cloud computing for optimising the cloud data centre’s power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center’s size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab’s real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Purpose The increase in plug-in electric vehicles (PEVs) is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior. To address this problem, the present work mainly focuses on optimal integration of distributed generators (DG) into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily (24 h) power loss and voltage improvement of the system as objectives for better system performance. Design/methodology/approach To achieve the desired outcomes, an efficient weighted factor multi-objective function is modeled. Particle Swarm Optimization (PSO) and Butterfly Optimization (BO) algorithms are selected and implemented to minimize the objectives of the system. A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system. The simulations are carried out using MATLAB software. Findings The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects. Among PSO and BO, BO is comparatively successful in achieving the desired objectives. Originality/value The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential, industrial and commercial loads. Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

In this study, the effect of ultrasonic vibration during Friction Stir Vibration Processing (FSVP) on the microstructure and mechanical behaviour of AZ31/TiC surface composites was investigated. Specifically, Titanium Carbide (TiC) particles were introduced as a reinforcement (15 vol%) into the magnesium alloy AZ31 using both Friction Stir Processing (FSP) and FSVP. Comprehensive examinations were carried out to analyse the microstructure, hardness, and tensile behaviour of the resulting composites. The study revealed significant improvements in mechanical properties due to the application of ultrasonic vibration during FSP. Firstly, the stir zone region was found to be free from voids, enhancing material flow and promoting even dispersion of TiC powders within the matrix. Secondly, refinement of grains was observed due to dynamic recrystallization and the pinning effect imposed by TiC particles, leading to the formation of more dislocations in the composite and indicating a considerable alteration in the material’s structure. Importantly, the vibration during FSP introduced an auxiliary energy source, resulting in a remarkable enhancement in both hardness and tensile strength. Compared to the AZ31/15 vol% TiC FSP composite, the composites produced via FSVP exhibited a grain size reduction of about 64% and improvements in hardness and ultimate tensile strength (UTS) of about 55% and 21%, respectively. Notably, these improvements were achieved without compromising the ductility of the composite, which remained at appreciable levels.

Ground-glass opacity (GGO)-a hazy, gray appearing density on computed tomography (CT) of lungs-is one of the hallmark features of SARS-CoV-2 in COVID-19 patients. This AI-driven study is focused on segmentation, morphology, and distribution patterns of GGOs. We use an AI-driven unsupervised machine learning approach called PointNet++ to detect and quantify GGOs in CT scans of COVID-19 patients and to assess the severity of the disease. We have conducted our study on the \"MosMedData\", which contains CT lung scans of 1110 patients with or without COVID-19 infections. We quantify the morphologies of GGOs using Minkowski tensors and compute the abnormality score of individual regions of segmented lung and GGOs. PointNet++ detects GGOs with the highest evaluation accuracy (98%), average class accuracy (95%), and intersection over union (92%) using only a fraction of 3D data. On average, the shapes of GGOs in the COVID-19 datasets deviate from sphericity by 15% and anisotropies in GGOs are dominated by dipole and hexapole components. These anisotropies may help to quantitatively delineate GGOs of COVID-19 from other lung diseases. The PointNet++ and the Minkowski tensor based morphological approach together with abnormality analysis will provide radiologists and clinicians with a valuable set of tools when interpreting CT lung scans of COVID-19 patients. Implementation would be particularly useful in countries severely devastated by COVID-19 such as India, where the number of cases has outstripped available resources creating delays or even breakdowns in patient care. This AI-driven approach synthesizes both the unique GGO distribution pattern and severity of the disease to allow for more efficient diagnosis, triaging and conservation of limited resources.

5-Fluorouracil is used in the treatment of colorectal cancer along with oxaliplatin as first line treatment, but it is having lack of site specificity and poor therapeutic effect. Also toxic effects to healthy cells and unavailability of major proportion of drug at the colon region remain as limitations. Toxic effects prevention and drug localization at colon area was achieved by preparing enteric-coated chitosan polymeric nanoparticles as it can be delivered directly to large bowel. Enteric coating helps in preventing the drug degradation at gastric pH. So the main objective was to prepare chitosan polymeric nanoparticles by solvent evaporation emulsification method by using different ratios of polymer (1:1, 1:2, 1:3, 1:4). Optimized polymer ratio was characterized by differential scanning calorimetry (DSC), X-ray diffraction (XRD), entrapment efficiency and particle size and further subjected to enteric coating. In vitro drug release studies were done using dialysis bag technique using simulated fluids at various pH (1.2, 4.5, 7.5, 7.0) to mimic the GIT tract. 5-FU nanoparticles with drug: polymer ratio of 1:2 and 1:3 has shown better particle size (149 ± 1.28 nm and 138 ± 1.01 nm respectively), entrapment efficiency (48.12 ± 0.08% and 69.18 ± 1.89 respectively). 5-FU E1 has shown better drug release after 4 h and has shown 82% drug release till 24 h in a sustained manner comparable to the non-enteric coated tablets, which released more than 50% of the drug before entering the colon region. So we can conclude that nanoparticles prepared by this method using the same polymer with the optimized ratio can represent as potential drug delivery approach for effective delivery of the active pharmaceutical ingredient to the colorectal tumors.

East Kolkata Wetlands (EKW) is an important site for fish culture in sewage-fed areas, which are major receivers of pollutants and wastages from Kolkata. EKW is internationally important as the Ramsar site was declared on Aug 2002 with an area of 125 km 2 . EKW is a natural water body where wastewater-fed natural aquaculture has been practiced for more than 70 years. It is ecologically vulnerable due to the discharge of toxic waste through sewage canals from cities. Assessing the EKW to understand the inflow and load of the toxic metal (s) in fish, water, and sediments samples is essential. The field (samples collection from 13 sites) and lab (determination of toxic level of metals) based research were carried out to assess metal toxicity and health risk assessment in EKW. The levels of eighteen metals (18), namely Chromium, Vanadium, Cobalt, Manganese, Copper, Nickel, Zinc, Silver, Molybdenum, Arsenic, Selenium, Tin, Gallium, Germanium, Strontium, Cadmium, Mercury, and Lead, were determined using Inductively coupled plasma mass spectrometry (ICP-MS) in five fish tissues viz. muscle, liver, kidney, gill and brain, along with the water samples and soil sediments in 13 sampling sites. The bioaccumulation and concentration of metals in fish tissues, soil sediments, and water samples were well within the safe level concerning the recommendation of different national and international agencies except for a few metals in a few sampling sites like Cd, As, and Pb. The geoaccumulation index (Igeo) was also determined in the soil sediments, indicating moderate arsenic, selenium, and mercury contamination in a few sites. The contamination index in water was also determined in 13 sampling sites. The estimated daily intake (EDI), reference dose (RfD), target hazard quotient (THQ), slope factor and cancer risk of Cr, Mn, Co, Ni, Cu, Zn, As, Se, Cd, Pb and Hg from fish muscle were determined. Based on the results of the present investigation, it is concluded that fish consumption in the East Kolkata Wetland (EKW) is safe. The effects of bioaccumulation of metals in muscle tissue were well within the safe level for consumption as recommended by WHO/FAO.

The COVID-19 pandemic has brought to light the lacunae in the preparedness of healthcare systems across the globe. This preparedness also includes the safety of healthcare providers (HCPs) at various levels. Sudden spread of COVID-19 infection has created threatening and vulnerable conditions for the HCPs. The current pandemic situation has not only affected physical health of HCPs but also their mental health. This study aims to understand the prevalence and severity of secondary traumatic stress, optimism parameters, along with states of mood experienced by the HCPs, viz., doctors, nurses and allied healthcare professionals (including Physiotherapist, Lab technicians, Phlebotomist, dieticians, administrative staff and clinical pharmacist), during the COVID-19 lockdown in India. The assessment of level of secondary traumatic stress (STS), optimism/pessimism (via Life Orientation Test-Revised) and current mood states experienced by Indian HCPs in the present COVID-19 pandemic situation was done using a primary data of 2,008 HCPs from India during the first lockdown during April-May 2020. Data was collected through snow-ball sampling technique, reaching out to various medical health care professionals through social media platforms. Amongst the study sample 88.2% of doctors, 79.2 of nurses and 58.6% of allied HCPs were found to have STS in varying severity. There was a female preponderance in the category of Severe STS. Higher optimism on the LOTR scale was observed among doctors at 39.3% followed by nurses at 26.7% and allied health care professionals 22.8%. The mood visual analogue scale which measures the \"mood\" during the survey indicated moderate mood states without any gender bias in the study sample. The current investigation sheds light on the magnitude of the STSS experienced by the HCPs in the Indian Subcontinent during the pandemic. This hitherto undiagnosed and unaddressed issue, calls for a dire need of creating better and accessible mental health programmes and facilities for the health care providers in India.