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The importance of green energy in the current times cannot be understated. The ill effects of traditional forms of energy generation, make solar energy one of the most environmentally friendly alternatives. SAI MITHRA – a roof top multi building, multi-capacity solar energy generation system, executed by Sri Sathya Sai Central Trust at Prasanthinilayam in South India is a prime example for promotion of green energy. Using a regression model, this study attempts to understand the impact of weather variables on solar energy production across different production capacities using high frequency daily data. In order to provide predictive insights, the impact of weather variables with t-1 and t-2 days lags on solar energy generation have also been studied. The study identifies the three important weather variables that have an impact on solar energy production – atmospheric pressure, relative humidity and dew point temperature. The insights from the paper are relevant for multi-capacity solar energy systems for improving operational efficiencies and promoting green energy ecosystems.

Copper nanoparticles (CuNPs) have garnered considerable attention owed to their straightforward and cost-effective synthesis techniques, making them suitable for various applications across several fields. This paper describes a straightforward method for synthesizing CuNPs using the aqueous chemical reduction approach. A unique copper nanozyme (CNZ) is also prepared to degrade dyes. The nanoparticles were analyzed using various techniques to examine their morphological structure, optical properties, functional groups, and crystallite size via SEM, UV–visible spectrophotometry, FTIR, XRD, and zeta potential with particle size analyzer. SEM analysis showed that the CuNPs have a cubical form, with particle size fluctuating from 250 to 300 nm. The CNZ has a flower-like structure with a typical 100 to 150 nm size. The zeta potential of CuNPs is measured to be + 23.4 mV, indicating a high level of stability for the nanoparticles. The XRD evaluation showed that the CuNPs displayed a prominent peak at an angle of 36.64°, indicating a crystallite size of 26.97 nm. The CNZ displayed a peak at 31.18 nm, corresponding to a crystallite size of 44.84 nm. The capacity of CuNPs and CNZ to degrade a combination of dyes (Methyl orange, Methyl red, Congo red, Tropaeolin-O, and Tartrazine) was investigated. The innovative approach utilizing CNZ and CuNPs resulted in a degradation percentage of 84.61 % for mixed colors. Experimental findings have demonstrated that the combined effect of CuNPs and CNZ is remarkably effective in catalytically degrading azo dyes, making it a highly efficient method for treating effluents from the textile sector and wastewater treatment.

The implementation of hard-facing alloy on the existing materials caters the need for high-performance surfaces in terms of wear and high temperatures. The present research explore the effect of Plasma Transferred Arc Welding (PTAW) parameters and powder composition on dilution, microstructure and hardness of the commonly used hard-facing alloy Ni–Cr–Si–B powder. The hard-facing alloy was deposited with three weight proportions of boron (2.5 %, 3 % and 3.5 %). The statistical-based Grey Relational Analysis (GRA) followed by a Machine Learning Algorithm (MLA) was implemented to identify the ideal parameters and degree of significance of each parameter and for the prediction of the responses. The dilution percentage, microstructure analysis, and phase detection were estimated through elemental analysis, Scanning electron Microscopy (SEM) and X-ray Diffraction Analysis (XRD) respectively. The experimental and modelling results revealed that 400 mm/min of scanning speed, 8 gm/min of powder delivery, 14 mm of stand-off distance, and 120 A of current were the optimal parameters along with 3.5 wt% of boron powder composition to yield a better dilution, microstructure and hardness.

A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency.

Plasmablastic lymphoma (PBL) is a rare AIDS associated non-Hodgkin’s lymphoma with the oral cavity as the common known site for its occurrence. Herein, we present a case of 10-year-old HIV-positive patient who presented with a benign-looking gingival lesion in the oral cavity. On microscopic examination, lesional tissue showed sea of small and large lymphoid cells. Tumor cells were displaying plasmablastic differentiation, showed large oval to round-shaped cells with eccentrically placed nuclei and abundant cytoplasm intermixed with few mature plasma cells. On immunohistochemical examination, tumor cells were showing immunophenotype of plasma cells and very weak/negative expression for B-cell And T-cell antigen. Ki-67 proliferative index was 25–30 %. Considering the immunocompromised status, clinical and microscopic findings the final diagnosis of oral PBL was rendered.

Zika virus (ZIKV) remains a public health concern, with epidemics in endemic regions and sporadic outbreaks in new areas posing significant threats. Several mosquito-borne flaviviruses that can cause human illness, including West Nile, Usutu, and St. Louis encephalitis, have associations with birds. However, the susceptibility of chickens to ZIKV and their role in viral epidemiology is not currently known. We investigated the susceptibility of chickens to experimental ZIKV infection using chickens ranging from 1-day-old chicks to 6-week-old birds. ZIKV caused no clinical signs in chickens of all age groups tested. Viral RNA was detected in the blood and tissues during the first 5 days post-inoculation in 1-day and 4-day-old chicks inoculated with a high viral dose, but ZIKV was undetectable in 6-week-old birds at all timepoints. Minimal antibody responses were observed in 6-week-old birds, and while present in younger chicks, they waned by 28 days post-infection. Innate immune responses varied significantly between age groups. Robust type I interferon and inflammasome responses were measured in older chickens, while limited innate immune activation was observed in younger chicks. Signal transducer and activator of transcription 2 (STAT2) is a major driver of host restriction to ZIKV, and chicken STAT2 is distinct from human STAT2, potentially contributing to the observed resistance to ZIKV infection. The rapid clearance of the virus in older chickens coincided with an effective innate immune response, highlighting age-dependent susceptibility. Our study indicates that chickens are not susceptible to productive ZIKV infection and are unlikely to play a role in the ZIKV epidemiology.

Sustainable energy sources are valuable energy sources.Renewable energy production boosts the economic status of a country. Wind energy is one of the most abundant renewable energy sources, and as a result, the technology to harvest energy from the wind is growing rapidly around the world. As load centers are far away from renewable energy sources, electricity must be transferred over long distances. The most common problems with power fluctuations are caused by long-range voltage sag riding (VSRT). DFIG (Doubly Fed Induction Generator) is very popular in wind energy conversion systems due to its variable speed, high energy collection, efficiency, excellent design and unique control of phase side converters and rotor side converters. Wind speed monitoring is done with the help of Internet of Things (IOT). This paper described the use of training networks in developing adjustment algorithms for direct reference model adaptive IMC for DFIG wind farms. Here, a novel training-based neural network MIT (NNMIT) adjustment mechanism using neural network method is developed and implemented in direct reference model adaptive IMC to improve the performance of the controller during voltage sag. Direct and quadrature axis rotor current controllers are developed and the resulting DFIG is balanced with the FuzzyMIT correction mechanism in the sag ride through the conditions in the wind farm. Improvements across the voltage sag are identified and presented using NNMIT. The proposed NNMIT attain 0.15% torque ripple and 1 ms response time better than the existing FuzzyMIT method. The proposed method preserves high accuracy ranges of 97.88% than the existing method. This approach gives better performance than other control design methods which assume that the flux in the stator is constant in amplitude.

In a Wireless Sensor Network (WSN), one of the most important issue is to minimize the energy consumption without losing accuracy during faster data transmission. During information broadcast, message communication is to be sent in an optimized way to increase energy efficiency in the networks. By applying various techniques and methodology in cluster WSN the network lifetime is increased and delay is minimized with the load balanced network. To accomplish load balance, Adelson-Velskii and Landis (AVL) tree rotation clustering algorithm is simulated considering the cluster sensor node. A single large area network is divided into multiple clusters using modified K-means clustering algorithm. Computational complexity is reduced through the construction of Minimum Connected Dominating Set with Multi-hop Information (MCDS-MI) and Bi-Partite Graph (BG) technique. Cluster Head (CH) assortment mechanism is implemented to find maximum cover set count of the sensor nodes. In addition, the enactment of the anticipated design is established through simulations during scalable data transmission in a WSN. Hypothetical investigation and experimental simulations are studied by measuring various performance evaluation metrics namely Virtual Dominators, Size Reduction, Network Lifetime and Residual Energy. The results shows that the proposed MSDS-MI system has maximum reduction in network size of 50%, maximum increase in network lifetime of 60% and saved maximum residual energy consumption of 47.76%. The results are encouraging and our proposed method is found to be more efficient than Connected Dominating Sets (CDS), Pseudo Dominating Sets (PDS), Dynamic Cluster Head Genetic Algorithm (DCH-GA) and Distributed Self-Healing Approach (DSHA).

In the construction field, supplementary cementitious materials (SCMs) have brought about a technological revolution in the manufacturing of concrete as a partial replacement or addition to conventional binder mass. Keeping this in mind, this paper aims to summarise and discuss the reported findings on the mechanical and durability properties of alccofine-1203 based concretes. It is also aimed to give a better understanding of the behaviour and effect of alccofine-1203 as an SCM in various types of concretes. The alccofine-1203 has ultra-fine particles with a unique chemical composition that improves the hydration process and pozzolanic reaction. Therefore, its incorporation in concrete has resulted in good workability, reduction in segregation, reduction in heat of hydration, and reduction in permeability to concrete, and increased the rate of hydration process and improved the pozzolanic reaction to achieve high strength to concrete at the early curing stage. The presence of calcium (CaO) and silica (SiO2) in alccofine-1203 improved the mechanical and durability properties of concrete better than the other SCMs. From the literature review, the optimum dosage of alccofine-1203 is obtained between 8% to 12%, and at these percentages, the improvement in mechanical and durability properties of the concrete is highest.

This paper presents a modeling and performance evaluation of a five-phase PMBLDC motor with different controllers to analyze its transients and dynamic response with time domain specifications. Four different types of speed controllers, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS), an Adaptive Hybrid fuzzy-PI, a Proportional Integral (PI) control and a Fuzzy Logic Control (FLC), were considered and compared for this purpose. The mathematical model of the five-phase Permanent Magnet Brushless Direct Current (PMBLDC) motor was developed and simulated using MATLAB/Simulink to analyze its performance. The simulation results for all controllers with step and linear load fluctuations were evaluated. It was evident that the ANFIS controller provided a better dynamic time domain response than the other controllers. It gave eight times the peak torque at starting, and settled at 92.6% of the rated speed, with negligible overshoot, a very low rise time and a quick settling time compared to other controllers, which are ideally suited for an electric vehicle (EV). A real-time experimental setup was also developed, and experiments were carried out to validate the simulation results.

Background: The limited representation from developing countries in the original COME TOGETHER survey gave us an impetus to conduct this survey in the Indian subcontinent. Methods: This cross-sectional online survey was conducted from August through September 2022. Participants were health care physicians caring for patients with coma and disorders of consciousness. Fischer's exact test or the Mann–Whitney U-test was used to compare respondents who agreed or disagreed with the preestablished coma definition. Fleiss κ values were calculated to assess agreement among respondents. A p value less than 0.05 was considered statistically significant. Results: The survey was completed by 130 physicians. We found substantial interrater agreement on absence of wakefulness (71.54%; κ = 0.71), Glasgow Coma Score ≤ 8 (78.46%; κ = 0.78), and failure to respond purposefully to visual, verbal, or tactile stimuli (66.15%; κ = 0.66). Reported common etiologies of coma included traumatic brain injury (50.76%), ischemic stroke (30%), and intracerebral hemorrhage (29.23%). The most common clinical assessment tools used for coma included the Glasgow Coma Score (92.3%) and neurological examination (60.8%). Neurological examination was the most common diagnostic tool used (100%), followed by magnetic resonance imaging (89.2%), basic laboratory studies (88.5%), and head computed tomography/angiography (86.9%). Pharmacological interventions used to stimulate arousal in patients with coma were sedation vacation (91.5%), electrolyte/endocrine correction (65.4%), osmotic therapy with mannitol (60%), hypertonic saline (54.6%), modafinil (46.9%), and antidote for drugs (45.4%). Among the nonpharmacological interventions, sensory stimulation (57.7%) was the most commonly used modality. The most common discharge disposition for comatose patients who survived hospitalization were home with or without services (70.0%). Conclusions: Differences from the global survey were noted regarding the following: traumatic brain injury being the most common etiology of coma in India, more frequent practice of sedation interruption, less frequent use of electroencephalography in India, rare use of pharmacological neurostimulants, and home being the most common discharge disposition in India. [ABSTRACT FROM AUTHOR]

Cellulase shows great interest in the field of organic acids, and biotechnology industries. Cellulase-producing thermophilic bacteria was isolated from West Coast hot spring (Rajapur, Ratnagiri District of Maharashtra (Lat. 16°38′42″N; long. 73°31′53″E) and identified as Bacillus licheniformis. Out of seven variables in the cellulase production medium, four variables such as carboxy methyl cellulose (CMC), calcium chloride, Tween-20, and temperature were screened through Plackett–Burman design and further optimized through response surface methodology (RSM) for higher cellulase production. The optimal conditions were found to be CMC, 19.21 g/L, CaCl2·6H2O, 25.06 mg/L, Tween-20, 2.96 mL/L and temperature 43.35 °C. The high cellulase production 42.99 IU/mL was achieved in a 7 L scale bio fermenter using optimal conditions. A 3 fold increase in cellulase production was achieved by RSM model. These methods have proven suitable to optimize cellulase production by a thermophilic Bacillus.

The simultaneous interaction of viscous dissipative and thermal radiation in MHD two dimensional flows of ferro-liquid over a nonlinear moving surface is analyzed here. The slip on velocity and convective boundary condition on temperature are imposed on stretching surface. We used water as conventional base liquid which have magnetite (Fe3O4) and alumina (Al2O3) as nanoparticles. The governing mathematical expressions are converted into non-dimensional form via nonlinear type similarity variables. The resulting mathematical model is numerically solved with the help of MATLAB solver bvp4c. The roles of non-dimensional constraints on velocity and temperature are elaborated through plots. The numerical data of skin-friction coefficient and Nusselt number is presented and visualized. The validity of computed results is analyzed through comparative benchmark. Keywords: Ferro fluid, Velocity slip condition, Convective boundary condition, Viscous dissipation, Radiation

Incorporating changes into the logics of composed services dynamically and successfully is a challenge for sustaining a business’ image and profit in the society, especially when the change is expected to be made immediately at low cost. In this paper, we address this challenge by proposing a change impact analysis framework for long term composed services (LCS) which: (i) enables the business people to implement the changes by themselves through their analysts, (ii) reduces cost and time by eliminating the dependence on IT developers once the application services are developed and delivered, (iii) ensures effective incorporation of the changes made by using standard methodologies for evaluation – finite state automaton for verifying the runtime compatibilities and change evaluation and probabilistic cellular automaton for impact analysis and prediction. Through the evaluated probability measures and effective incident matching, the knowledge gained by the analyst over his service logics and the efficiency of incorporating changes are increased.