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The Covid-19 pandemic has negatively affected every aspect of human life. In these challenging times nursing students, facing academic and psychological issues, are advised to use augmented reality applications in the field of health sciences for increasing their motivations and academic performances. The main motive of the study was to examine the acceptance status of nursing students in implementing augmented reality technology in their education and training. The study is a quantitative research study, and it uses the causal-comparative screening method. The data used in the study was collected online from 419 nursing students. The hybrid method was preferred. First, the hypotheses based on the linear relationships were defined between the variables which were then tested by the method of structural equation modeling. Second, the method of artificial neural networks was used to determine the non-linear relationships between the variables. The results show that the nursing students have a high intention of using augmented reality technology as a way of self-learning. It was also found that the most emphasized motive behind this intention is the expectation that using augmented reality technology will increase their academic performance. They also think that AR technology has many potential benefits to offer in the future. It was observed that a considerable number of students already use augmented reality technology for its usefulness and with a hedonic motivation. In conclusion, nursing students have a high acceptance of using augmented reality technology during their education and training process. Since we live in a world where e-learning and self-learning education/training have become widespread, it is estimated that students will demand augmented reality applications as a part of holistic education, and as an alternative to traditional textbooks.

Comparing each sequencing read in a sample to a reference database is a fundamental step in wide-ranging applications. Results of these comparisons can enable phylogenetic characterization. However, phylogenetic placement is currently only possible at scale for marker genes, a small fraction of the genome. We introduce krepp, an alignment-free k -mer-based method that enables placing reads from anywhere on the genome on an ultra-large reference phylogeny (e.g., 123,853 leaves). We show that krepp is scalable and computes accurate distances that approximate those using alignments, leading to accurate placements. These precise phylogenetic identifications improve our ability to compare and characterize metagenomic samples.

This study develops an innovative framework that utilizes real-time operational data to forecast electrical power output (EPO) in Combined Cycle Power Plants (CCPPs) by employing a temperature segmentation-based modeling approach. Instead of using a single general prediction model, which is commonly seen in the literature, three separate prediction models were created to explicitly capture the nonlinear effect of ambient temperature (AT) on efficiency (AT < 12 °C, 12 °C ≤ AT < 20 °C, AT ≥ 20 °C). Linear Ridge, Medium Tree, Rational Quadratic Gaussian Process Regression (GPR), Support Vector Machine (SVM) Kernel, and Neural Network methods were applied. In the modeling, the variables considered were AT, relative humidity (RH), atmospheric pressure (AP), and condenser vacuum (V). The highest performance was achieved with the Rational Quadratic GPR method. In this approach, the weighted average Mean Absolute Error (MAE) was found to be 2.225 with seasonal segmentation, while it was calculated as 2.417 in the non-segmented model. By applying seasonal prediction models, the hourly EPO prediction error was reduced by 192 kW, achieving a 99.77% average convergence of the predicted power output values to the actual values. This demonstrates the contribution of the proposed approach to enhancing operational efficiency.

Purpose Customer firms and suppliers are valuable knowledge resources that can be used for achieving superior new service development (NSD) performance. This study aims to investigate how supply chain relationship quality (SCRQ) and knowledge sharing promote the success of NSD, and examines service modularity as an important contingency factor that enhances NSD performance in supply chains. Design/methodology/approach Based on service-dominant logic, this study builds a conceptual model to empirically explore the impacts of SCRQ and knowledge sharing on NSD performance, and highlights the moderating effect of service modularity by means of survey methodology of 295 Chinese service firms to test the research hypotheses. Findings Regression analysis results show that SCRQ has significant positive effects on knowledge sharing and NSD performance; knowledge sharing plays a partial intermediary role between SCRQ and NSD performance; and service modularity partially moderates the relationships between SCRQ, knowledge sharing and NSD performance. Research limitations/implications Generalizations here are limited to Chinese service firms. Service modularity in manufacturing firms experimenting with servitization has yet to be examined and provides a good avenue for future research. Originality/value This study contributes to service management literature by providing empirical understanding of how service modularity affects NSD performance in multiprovider contexts. Furthermore, this study offers novel insights on the impacts of inter-firm relationship quality and knowledge sharing in modular collaborative innovation.

New sodium alginate-based hydrogels using activated carbon from banana leaves and organo-modified montmorillonite for water treatment. Activated carbon extracted successfully from banana leaves and montmorillonite clay was surface-modified using cetyltrimethylammonium bromide as a cationic surfactant. Hydrogels were then synthesized using calcium chloride as the cross-linking agent. They were characterized using FTIR, X-ray diffraction, and scanning electronic microscopy. Characterization intimated the incorporation of components successfully. Adsorption performance was determined using pH, adsorbent dosages, initial dye concentration, and contact time. Sodium alginate-based hydrogels demonstrated remarkable efficacy in removing MB and EBT dyes from synthetic solutions, achieving removal efficiencies of up to 80.3% and 84.9% respectively within 90 min at pH 7. The adsorption process corresponded better to the Freundlich isotherm model. The kinetics of EBT dye removal were described by a pseudo-second-order model. Meanwhile, the kinetics of the removal of MB dyes were described by both pseudo-first order and intraparticle diffusion models. We conducted MTT assays to determine the cytotoxicity of our blends. This showed a dose-dependent drop in viability. Sodium alginate-based hydrogels made the cells least cytotoxic. The developed hydrogels can be used as safe and effective agents for water treatment, as indicated by the results.

The increase in consumer demand for high-quality food products has led to growth in the use of new technologies and ingredients. Resistant starch (RS) is a recently recognised source of fibre and has received much attention for its potential health benefits and functional properties. However, knowledge about the fate of RS in modulating complex intestinal communities, the microbial members involved in its degradation, enhancement of microbial metabolites, and its functional role in body physiology is still limited. For this purpose, the current study was designed to ratify the physiological and functional health benefits of enzymatically prepared resistant starch (EM-RSIII) from maize flour. To approve the beneficial health effects as prebiotic, EM-RSIII was supplemented in rat diets. After 21 days of the experiment, EM-RSIII fed rats showed a significant reduction in body weight gain, fecal pH, glycemic response, serum lipid profile, insulin level and reshaping gut microbiota, and enhancing short-chain fatty acid compared to control. The count of butyrate-producing and starch utilizing bacteria, such as Lactobacillus , Enterococcus , and Pediococcus genus in rat’s gut, elevated after the consumption of medium and high doses of EM-RSIII, while the E . coli completely suppressed in high EM-RSIII fed rats. Short-chain fatty acids precisely increased in feces of EM-RSIII feed rats. Correlation analysis demonstrated that the effect of butyrate on functional and physiological alteration on the body had been investigated during the current study. Conclusively, the present study demonstrated the unprecedented effect of utilising EM-RSIII as a diet on body physiology and redesigning gut microorganisms.

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bara. Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO2e, 11,184 tCO2e, and 19,913 tCO2e of greenhouse gases, respectively, would be released into the atmosphere.

This study presents a detailed evaluation of how inlet air filter differential pressure (Filter DP) affects the operational performance of a gas turbine, focusing on its influence on power generation and thermal efficiency. Real operating data combined with machine learning (ML) techniques were used. Following the installation of new filters, the turbine operated for 10,000 h, and 4438 h under base-load conditions were selected for modeling. The impact of Filter DP was examined using Multiple Linear Regression (MLR), Quadratic Support Vector Regression (SVR), Regression Tree, and Artificial Neural Network (ANN) models, allowing both linear and nonlinear behavior to be captured. Results show that each 1 mbar increase in Filter DP leads to roughly a 1.67 MW drop in power output and a 0.094% reduction in thermal efficiency. Additionally, higher Filter DP raises fuel consumption and causes an extra 0.45 kgCO2e of emissions per 1 MWh of electricity produced. These findings underline that even small increases in inlet pressure loss significantly affect economic and environmental performance. Filter fouling increases natural gas demand, CO2e emissions, and overall carbon footprint. The ML-based approach enhances predictive maintenance by enabling early detection of filter degradation and supporting more efficient and sustainable turbine operation.

The angiotensin-converting enzyme (ACE)-related carboxypeptidase, ACE-II, is a type I integral membrane protein of 805 amino acids that contains 1 HEXXH-E zinc binding consensus sequence. ACE-II has been implicated in the regulation of heart function and also as a functional receptor for the coronavirus that causes the severe acute respiratory syndrome (SARS). In this study, the potential of some flavonoids presents in propolis to bind to ACE-II receptors was calculated with in silico. Binding constants of ten flavonoids, caffeic acid, caffeic acid phenethyl ester, chrysin, galangin, myricetin, rutin, hesperetin, pinocembrin, luteolin and quercetin were measured using the AutoDock 4.2 molecular docking program. And also, these binding constants were compared to reference ligand of MLN-4760. The results are shown that rutin has the best inhibition potentials among the studied molecules with high binding energy − 8.04 kcal/mol, and it is followed by myricetin, quercetin, caffeic acid phenethyl ester and hesperetin. However, the reference molecule has binding energy of – 7.24 kcal/mol. In conclusion, the high potential of flavonoids in ethanolic propolis extracts to bind to ACE-II receptors indicates that this natural bee product has high potential for COVID-19 treatment, but this needs to be supported by experimental studies.