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The teaching and learning of anatomy has experienced a significant paradigm shift. The present study assessed the level of knowledge in anatomy in medical postgraduate students and explored the impact of interventions in the form of anatomical videos on knowledge obtained. An awareness of the importance of human anatomy for clinical skills was created to ensure a certain level of competence be achieved by the end of the anatomy course.

Postgraduate medical students were recruited from various specialties on voluntary basis. The first step was to conduct a preliminary screening exam to determine the level of anatomical knowledge. The students were then divided into two groups at random, one of which received no intervention (the control group), and the other of which watched the videos with content that was pertinent to the practical demonstrations (intervention). To assess the effects of the video intervention, a post-test was administered to all students.

Both spot tests (SPOTs) and short answer question (SAQ) components for scores of all the regions from the intervention groups were comparable to the scores obtained by the post-test control group, although the findings were not significant (p > 0.05). However, the intervention group from the abdomen (ABD) region did perform significantly better (p < 0.05) than the screening test score.

The results of the research study imply that interventions like anatomical videos can bridge the postgraduate trainee’s anatomy knowledge gap in a practical method which will immensely help in increasing their knowledge.

A finite element computational methodology on a curved boundary using an efficient subparametric point transformation is presented. The proposed collocation method uses one-side curved and two-side straight triangular elements to derive exact subparametric shape functions.

Our proposed method builds upon the domain discretization into linear, quadratic and cubic-order elements using subparametric spaces and such a discretization greatly reduces the computational complexity. A unique subparametric transformation for each triangle is derived from the unique parabolic arcs via a one-of-a-kind relationship between the nodal points.

The novel transformation derived in this paper is shown to increase the accuracy of the finite element approximation of the boundary value problem (BVP). Our overall strategy is shown to perform well for the BVP considered in this work. The accuracy of the finite element approximate solution increases with higher-order parabolic arcs.

The proposed collocation method uses one-side curved and two-side straight triangular elements to derive exact subparametric shape functions.

This research aims to investigate how Chinese leisure travelers value hotel amenities when they book hotel rooms in Hong Kong.

The research method was based on a conjoint analysis approach. Conjoint models were developed to determine how people make decisions and what they really value in products or services.

Price had the highest average importance value, followed by airport/local area shuttles, wireless internet, breakfast and quality of coffee/tea. Price, airport/local area shuttles and wireless internet were rated as being relatively more important than breakfast and quality of coffee/tea.

This research has some limitations in terms of the generalizability of its findings to all hotels and travelers. First, only four hotel amenities were considered. Second, the research focused on Chinese leisure travelers staying in hotels in Hong Kong. Finally, the sample only consisted of leisure travelers.

This research shows that providing complimentary breakfast and free access to quality coffee/tea when a hotel already provides a shuttle service and free wireless internet does not add much value to the overall hotel product from the customer’s point of view. Moreover, it provides insights into how hotel professionals can customize and select the amenities they provide to impress their customers.

This research has significant implications for hotel managers’ efforts to formulate and implement strategies or tactics in their daily operations or long-term plans through the selection of hotel amenities.

This study explores the service quality dimensions in Internet banking and their impact on e-customer’s satisfaction and e-customer’s loyalty. This study tries to inspect the structural association between Internet banking service quality, electronic customer satisfaction and electronic customer loyalty based on separate constructs.

In this present research, quantitative approach is applied. The data is gathered from 500 bank clients in Pakistan by using structured questionnaires, and the theoretical model is tested by partial least square structured equation modeling (PLS-SEM). Moreover, convergent validity and discriminant validity were assessed.

Results show that all the dimensions are found to have a positive and significant influence on customer satisfaction while customer’s satisfaction has a significant and positive impact on customer’s loyalty. Findings indicate that service quality plays a very important role in every society, as it has become the basis for how customers interpret online banking and, in the end, how it interacts and operates with online services.

This research adds up considerably to the literature of bank marketing, and it is also fruitful for the academicians since it demonstrates the way Internet banking service quality determinants predict e-satisfaction of clients which ultimately raises the e-loyalty of clients. This study is useful for those E-retailers and managers who want to grab e-retailing market.

This research suggests a model which ultimately enhances customer loyalty towards Internet banking service quality through customer satisfaction in Pakistan. It involves modified model of E-SERVQUAL (user friendliness, efficiency of websites, personal need, and site organization) which connects it to electronic customer satisfaction and electronic customer loyalty. Therefore, it will assist the Internet banking sector in building effective marketing tactics, establishing long lasting relationships with clients and acquiring the competitive edge in the market.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.