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This study aims to investigate the factors that can predict the behavioral intention of students at public universities in Saudi Arabia to use Cryptocurrency. The UTAUT model was enhanced with the incorporation of security and awareness to develop the theoretical model for this investigation. The paper investigates the impact of performance expectancy, effort expectancy, facilitating condition, social influence, security, and awareness on the behavioral intention to use Cryptocurrency. The moderating role of financial literacy was also investigated on the associations between the proposed adoption factors and the behavioral intention to use Cryptocurrency. SmartPLS 3.2.8 software was used to analyse 344 responses collected via an online survey. The Findings showed that performance expectancy, effort expectancy, social influence, security, and awareness positively impact on behavioral intention to use Cryptocurrency. Moreover, financial literacy moderates the associations between performance expectancy, security, social influence, and behavioral intention. The findings offer valuable insights to Cryptocurrency users, Cryptocurrency developers, and the government of the KSA.

Background Animal models of COVID-19 have been rapidly reported after the start of the pandemic. We aimed to assess whether the newly created models reproduce the full spectrum of human COVID-19. Methods We searched the MEDLINE, as well as BioRxiv and MedRxiv preprint servers for original research published in English from January 1 to May 20, 2020. We used the search terms (COVID-19) OR (SARS-CoV-2) AND (animal models), (hamsters), (nonhuman primates), (macaques), (rodent), (mice), (rats), (ferrets), (rabbits), (cats), and (dogs). Inclusion criteria were the establishment of animal models of COVID-19 as an endpoint. Other inclusion criteria were assessment of prophylaxis, therapies, or vaccines, using animal models of COVID-19. Result Thirteen peer-reviewed studies and 14 preprints met the inclusion criteria. The animals used were nonhuman primates ( n  = 13), mice ( n  = 7), ferrets ( n  = 4), hamsters ( n  = 4), and cats ( n  = 1). All animals supported high viral replication in the upper and lower respiratory tract associated with mild clinical manifestations, lung pathology, and full recovery. Older animals displayed relatively more severe illness than the younger ones. No animal models developed hypoxemic respiratory failure, multiple organ dysfunction, culminating in death. All species elicited a specific IgG antibodies response to the spike proteins, which were protective against a second exposure. Transient systemic inflammation was observed occasionally in nonhuman primates, hamsters, and mice. Notably, none of the animals unveiled a cytokine storm or coagulopathy. Conclusions Most of the animal models of COVID-19 recapitulated mild pattern of human COVID-19 with full recovery phenotype. No severe illness associated with mortality was observed, suggesting a wide gap between COVID-19 in humans and animal models.

Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

The ability to make accurate energy predictions while considering all related energy factors allows production plants, regulatory bodies, and governments to meet energy demand and assess the effects of energy-saving initiatives. When energy consumption falls within normal parameters, it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy consumption. The objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily interpretable. The proposed model is an implementation of the stacked Long Short-Term Memory (LSTM) snapshot ensemble combined with the Fast Fourier Transform (FFT) and meta-learner. Hebrail and Berard’s Individual Household Electric-Power Consumption (IHEPC) dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy consumption. The model is trained, and the results measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) metrics are 0.020, 0.013, 0.017, and 0.999, respectively. The stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized errors. The results of this study show that prediction accuracy is high, and the model’s stability is high as well. The model shows that high levels of accuracy prove accurate predictive ability, and together with high levels of stability, the model has good interpretability, which is not typically accounted for in models. However, this study shows that it can be inferred.

This study aims to characterize the statistical durability under random strain loads for fatigue reliability prediction of a heavy leaf spring. Characterizing random data involving a probabilistic approach needs to be addressed in terms of defining applicable distribution as the strain data are random cyclic loading that leads to fatigue damage. In this study, random strain loads were extracted repetitively at a sampling rate of 500 Hz for 300 s per block of various road load profiles to obtain the probabilistic features (i.e., the parameters of location and scale, kurtosis, and root-mean-square values). The rainflow cycle counting technique was used to determine the strain-based fatigue life based on the linear damage rule. The load sequence effects computed the highest fatigue life with an estimated range of 1.66×10 4 –3.16×10 4 cycles/block. The Akaike information criterion proposed that the Gumbel distribution is the most appropriate distribution to be used to model the durability of the leaf spring based on the captured strain signals. From the Gumbel distribution plot, the highest probability of fatigue failure was identified for the highway data in the range of 1.06×10 6 –1.71×10 7 cycles/block with the probability of 0.72–0.99 since a smooth road surface produced low amplitudes in the strain data. In the reliability assessment, a mean cycle to failure was obtained in the range of 3.07×10 7 –3.57×10 7 cycles/block, which is the highest value among other data. Hence, the probabilistic approach using the Gumbel probability plot for analyzing fatigue failure under random strain loads provides better statistical characterization in terms of the mean cycle to failure in reliability assessment.

The interaction between the tumor suppressor protein p53 and its negative regulator, the MDM2 oncogenic protein, has gained significant attention in cancer drug discovery. In this study, 120 lignans reported from Ferula sinkiangensis and Justicia procumbens were assessed for docking simulations on the active pocket of the MDM2 crystal structure bound to Nutlin-3a. The docking analysis identified nine compounds with higher docking scores than the co-crystallized reference. Subsequent AMDET profiling revealed satisfactory pharmacokinetic and safety parameters for these natural products. Three compounds, namely, justin A, 6-hydroxy justicidin A, and 6′-hydroxy justicidin B, were selected for further investigation due to their strong binding affinities of −7.526 kcal/mol, −7.438 kcal/mol, and −7.240 kcal/mol, respectively, which surpassed the binding affinity of the reference inhibitor Nutlin-3a (−6.830 kcal/mol). To assess the stability and reliability of the binding of the candidate hits, a molecular dynamics simulation was performed over a duration of 100 ns. Remarkably, the thorough analysis demonstrated that all the hits exhibited stable molecular dynamics profiles. Based on their effective binding to MDM2, favorable pharmacokinetic properties, and molecular dynamics behavior, these compounds represent a promising starting point for further refinement. Nevertheless, it is essential to synthesize the suggested compounds and evaluate their activity through in vitro and in vivo experiments.

Jatropha oil-based polyurethane acylate gel polymer electrolyte was mixed with different concentrations of tetrabutylammonium iodide salt (TBAI). The temperature dependences of ionic conductivity, dielectric modulus and relaxation time were studied in the range of 298 to 393 K. The highest ionic conductivity of (1.88 ± 0.020) × 10 –4  Scm −1 at 298 K was achieved when the gel contained 30 wt% of TBAI and 2.06 wt% of I 2 . Furthermore, the study found that conductivity-temperature dependence followed the Vogel-Tammann Fulcher equation. From that, it could be clearly observed that 30 wt% TBAI indicated the lowest activation energy of 6.947 kJ mol −1 . By using the fitting method on the Nyquist plot, the number density, mobility and diffusion coefficient of the charge carrier were determined. The charge properties were analysed using the dielectric permittivity, modulus and dissipation factor. Apart from this, the stoke drag and capacitance were determined.

Cognitive fatigue is a psychological condition characterized by opinions of fatigue and weakened cognitive functioning owing to constant stress. Cognitive fatigue is a critical condition that can significantly impair attention and performance, among other cognitive abilities. Monitoring this condition in real-world settings is crucial for detecting and managing adequate break periods. Bridging this research gap is significant, as it has substantial implications for developing more effectual and less intrusive wearable devices to track cognitive fatigue. Many models consider intricate biosignals, like electrooculogram (EOG), electroencephalogram (EEG), or detection of basic heart rate inconstancy parameters. Artificial Intelligence (AI)-driven methods aid in handling and categorizing these biosignals, recognizing fatigue-related patterns with higher accuracy. This technique is essential in high-demand surroundings such as education, healthcare, and workplaces or where cognitive fatigue may affect decision-making and performance. Therefore, the study presents an Exploratory Analysis of Longitudinal Artificial Intelligence for Cognitive Fatigue Detection Using Neurophysiological Based Biosignal Data (EALAI-CFDNBD) approach. The main aim of the EALAI-CFDNBD model is to detect cognitive fatigue using neurophysiological-based biosignal data. Primarily, the EALAI-CFDNBD model utilized the linear scaling normalization (LSN) model to ensure that the input features were appropriately scaled for subsequent analysis. Furthermore, the binary olympiad optimization algorithm (BOOA)-based feature selection is utilized to extract the most informative features, reducing the data dimensionality. The graph convolutional autoencoder (GCA) classifier is employed to classify cognitive fatigue detection. Finally, the multi-objective hippopotamus optimization (MOHO) method is utilized for parameter tuning, optimizing the model’s hyperparameters to enhance overall detection accuracy. An extensive range of simulations is accomplished using the MEFAR dataset to establish a good classification outcome of the EALAI-CFDNBD method. The experimental validation of the EALAI-CFDNBD technique portrayed a superior accuracy value of 97.59% over the recent methods.

Background Malnutrition in older persons is a public health concern. This study aimed to estimate the prevalence of malnutrition and its associated factors among community-dwelling older persons in Sri Lanka. Methods A cross-sectional study was conducted in the Kandy district, Sri Lanka. The nutritional status of older persons was assessed using the Mini Nutritional Assessment –Short Form (MNA­SF). A standardised questionnaire was used to record factors associated with malnutrition: demographic characteristics, financial characteristics, food and appetite, lifestyle, psychological characteristics, physical characteristics, disease and care, oral health, and social factors. Complex sample multinomial logistic regression analysis was performed. Results Among the 999 participants included in the study, 748 (69.3%) were females and 251 (25.1%) were males. The mean age was 70.80 years (95% CI: 70.13, 71.47). The prevalence of malnutrition, risk of malnutrition and well-nutrition was 12.5%, 52.4% and 35.1% respectively. In the multivariate model, hypertension (adjusted OR = 1.71; 95% CI: 1.02, 2.89), alcohol consumption (aOR = 4.06; 95% CI: 1.17, 14.07), and increased age (aOR = 1.06; 95% CI: 1.01, 1.11) were positively associated with malnutrition. An increased number of people living with the older person (aOR: 0.91; 95% CI: 0.85, 0.97) was a protective factor among those at risk for malnutrition. Conclusion Both the prevalence of malnutrition and risk of malnutrition were commonly observed among community-dwelling older persons in Sri Lanka. The associated factors identified in this study might help public health professionals to implement necessary interventions that improve the nutritional status of this population.

An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms.