Background Objective Structured Clinical Examinations (OSCEs) are an essential part of the assessment process for medical students. They have traditionally been face-to-face assessments, however, due to the COVID-19 pandemic, medical schools have been forced to attempt to carry them out remotely. OSCEs are difficult to carry out online due to rotation management aspects which make it difficult to synchronize movement of students from one station to another. Methods The authors have developed a dynamic OSCE time management website which aids in solving the movement synchronization issue. This secure website enables participants to view the list of stations they are allowed to enter, and the start and end time of each station. OSCE administrators can control time of entry and monitor progress of the OSCE remotely. Results The authors have used the system to conduct several exams successfully, showing the feasibility and cost effectiveness of this method, as well as user acceptance and satisfaction. In contrast to traditional OSCEs, students are set up in individual virtual rooms for the whole exam while examiners, simulated patients and proctors rotate between them. Conclusions This online OSCE implementation shows feasibility, cost effectiveness and acceptance of this method. The authors found that student outcomes are comparable to traditional OSCEs conducted in the past. There was no significant difference in student marks in one exam compared to last year, while marks were slightly higher in two exams, potentially due to lack of physical exam stations. An unresolved drawback is the inability to assess physical exam stations online, although having students verbally describe what they would do in physical exam situations may be a partial solution.

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A modern pulsar survey generates a large number of pulsar candidates. Filtering these pulsar candidates in a large astronomical dataset is an important step towards discovering new pulsars. In this paper, a novel adaptive boosting algorithm based on deep self normalized neural network (Adaboost-DSNN) is proposed to accurately classify pulsar and non-pulsar signals. To train the proposed method on a highly-imbalanced dataset, the Synthetic Minority Oversampling Technique (SMOTE) was initially employed for balancing the dataset. Then, a deep ensemble network combined with a deep self-normalized neural network and adaptive boosting was developed to train and learn the processed pulsar data. The design of the proposed Adaboost-DSNN method significantly reduced the computational time when dealing with large astronomical datasets, while also improving the classification performance. The scaled exponential liner units (SELU) activation function was used to normalize the data. Considering their neighbor information and the special dropout technique (α-dropout), Adaboost-DSNN displayed good pulsar classification performance, while preserving the data properties across subsequent layers. The proposed Adaboost-DSNN method was tested on the High Time Resolution Universe Survey datasets (HTRU-1 and HTRU-2). According to experimental results, Adaboost-DSNN outperform other state-of-the-art methods with respect to training time and F1-score. The training time of the Adaboost-DSNN model is 10x times faster compared to other models of this kind.

A novel hybrid resonant zero-current-switching (ZCS) three-level DC-DC converter based on a dual transformer with two output filter capacitors is proposed in this paper, which is suitable for application in distributed photovoltaic power generation at medium voltage integrated with a DC distribution network. The proposed converter adds an auxiliary circuit to the traditional neutral point clamped (NPC) three-level (TL) circuit, which contains the basic NPC three-level circuit. Pulse width modulation (PWM) is adopted in the auxiliary circuit portion to realize the power and voltage regulation of the whole converter, and enable the main switches to operate with a fixed duty cycle. This has the advantages of simplified control. By reasonable design of the turn's ratio of the main transformer, the main three-level circuit can deliver most of the power and achieve ZCS within the under full load range that significantly reduced the switching losses. Therefore, the turns ratio of the auxiliary transformer can be optimized to reduce the loss of the converter further and improve the conversion efficiency as discussed in detail, while the parameters' design principles are put forward. Finally, a prototype of 300V-1.5kV/1.5 kW is built to verify the performance of the proposed converter. INDEX TERMS Pulse width modulation, zero-voltage-zero-current-switching, photovoltaic. I. INTRODUCTION

Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model’s good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.

This study presents a hybrid resonant three-level (TL) zero-current-switching (ZCS) converter for distributed photovoltaic power applications accessing medium-voltage DC (MVDC) distribution network. The main constituents of the converter is a neutral point clamped TL circuit, an auxiliary circuit along with two transformers and six switches. The ZCS condition of the TL circuit is reached by operating the auxiliary circuit utilising pulse width modulation technique. It delivers the majority of the power and the switching loss is remarkably reduced. A section on the influence of the turn's ratio of the auxiliary transformer on the current is also presented. The piecewise linear electrical circuit simulation simulation results validate the designed parameter principles. For the sake of comparison, an experimental prototype is developed of 300-1500 V/1500 W to validate the performance of the proposed converter.

The detection of pulsar signals is a highly intensive task. Numerous artificial intelligence (AI) and machine learning techniques (ML) have been proposed to classify pulsar and non-pulsar signals. While existing machine learning techniques improve classification efficiency, these methods are limited when it comes to dealing with large volumes of astronomical data, the extreme problem of class imbalance , and the polarization of high recall and precision. In this paper, to accurately classify pulsar and non-pulsar signals, extreme Gradient Boosting (XgBoost), and Light Gradient Boosting Machine (LightGBM) algorithms based on an asymmetric undersampling method are proposed. Firstly, the proposed model uses an asymmetric undersampling technique that divides the benchmark datasets into 75 subsets for the LOFAR Tied-Array All-Sky Survey (LOTAAS-1) and 9 subsets for the High Time Resolution Universe Pulsar Survey (HTRU2), eluding the class imbalance problem. Finally, XgBoost and LightGBM algorithms have been adopted for each new data subset, with a majority voting classifier being used to integrate the output of proposed models against each subset. Results of the proposed method were compared to state-of-the-art baseline models, which showed that our models significantly performed better than existing methods and gained almost 2% and 2.5% better F-score and precision results, respectively.

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