Learning approaches and performance of medical students

Objective: To identify the best assessment method for medical students with different learning approaches.Methods: The cross-sectional questionnaire-based study was conducted at Bahria University Medical and Dental College, Karachi, from March 2010 to April 2011, and comprised first year medical students. The questionnaire was tailored from the Approaches and Study Skills Inventory for Students on a five-point scale Deep approach, Surface apathetic approach and Strategic approach were assessed through relevant sub-scales. Response to questions was summed for the subscales and main scales for a learning approach. Mean scores for aggregate marks obtained by multiple choice questions, short answer questions, problem-based learning and objective structured physical examination were derived. Coefficient of variation was estimated to find the most reliable assessment method.Results: Of the 100 students enrolled, 98(98%) completed the study. Of them, 51(52%) were girls and 47(48%) were boys. Overall, 70(71.4%) students displayed Strategic approach, and 13(13.3%) showed Surface apathetic approach. Objective structured physical examination had the least variation (12.27) for all approaches whereas maximum variation (14.92) was observed by problem-based learning scores.Conclusions: Assessment by problem-based learning scores was able to demarcate deep learners whereas consistent scores were obtained by objective structured physical examination which failed to discriminate variance in performance by different learners

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.publishedVersio

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

A blockchain based privacy-preserving system for electric vehicles through local communication

In this study, we propose a privacy preservation and efficient distributed searching and matching of Electric Vehicles (EVs) charging demander with suppliers based on reputation. Partially homomorphic encryption-based on reputation computation using local communication is used in the implementation, while hiding EVs users' location. A private blockchain is incorporated in the system to verify and permit secure trading of energy among the EVs' demander and suppliers. The results of the simulation show that the proposed privacy preserved algorithm converges more faster as compared to Bichromatic Mutual Nearest Neighbor (BMNN) algorithm. © 2020 IEEE

A blockchain-based privacy-preserving mechanism with aggregator as common communication point

The high penetration of renewable energy resources into the distributed system and their intermittent behavior of the non-dispatchable generation causes issues of demand supply mismatch and serious security and privacy concerned in the system. It is believed that incorporating blockchain will reduce costs, enhance data security, and improve the system efficiency. However, privacy issues are not completely eliminated and can hinder the wide applications of blockchain. In the study, we present a Reputation Based Starvation Free Energy Allocation Policy (Reputation-SFEAP) in a decentralized and distributed blockchain-based energy trading; while keeping Aggregator as Common Communication Point. In addition, Identity-Based encryption (ID-Based encryption) technique is added that improves transactional information privacy. According to the research analysis, it is observed that the proposed system model has optimal and fair energy allocation algorithms, which prevent all the energy users from energy starvation and share the available energy accordingly. Moreover, the incorporated encryption system has greater security-privacy level, which protects passive attacker and disguises attacker from penetration. © 2020 IEEE

DE-RUSBoost : an efficient electricity theft detection scheme with additive communication layer

Modern power grids depend on the Advanced Metering Infrastructure (AMI) for consumption monitoring, energy management and billing. However, AMIs are vulnerable to electricity theft cyber attacks due to addition of communication layer. Electricity theft is one of the major Non-Technical Losses (NTLs) in the electricity distribution systems that has become a global concern, recently. Although the machine learning techniques are widely used for Electricity Theft Detection (ETD) in literature, some significant challenges need to be address. (i) The consumption data is usually unlabeled, there should be proper method to label the data. (ii) The fair consumers significantly outnumber the fraudulent consumers, which negatively impacts the performance of classification algorithm. (iii) The performance of classifier must be validated using proper performance evaluation measures. In this paper, an enhanced ETD model is proposed that is an optimized classifier Differential Evaluation Random Under Sampling Boosting (DE-RUSBoost) is used for classification. Proposed classifier DE-RUSBoost is optimized using a metaheuristic optimization algorithm named Differential Evaluation (DE). The proposed method is evaluated on a real-world dataset, i.e., State Grid Corporation of China (SGCC) datasets. DE-RUSBoost achieves the highest accuracy of 96% and low false detection rate of 0.004. The proposed method outperforms its counterparts in terms of accuracy and false detection rate. © 2020 IEEE

A blockchain-based decentralized energy management in a P2P trading system

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce energy consumption cost, emission of harmful gases (as renewable energy sources (RESs) are used to generate energy at user's premises) and increase smart grid resilience. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based solution is proposed. A blockchain-based system is fully decentralized and it allows the market members to interact with each other and trade energy without involving any third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with main smart contract which requests P2P smart contract and prosumer to grid (P2G) smart contract for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio (PAR) of electricity. © 2020 IEEE

Awareness of dental practitioners regarding oral radiology

Background: The damaging radiations exposure can causes harm to body cells and DNA. It also leads to shortening of life expectancy. Many Dental techniques and equipment made to lower the radiation dose for staff and patients. Objective: In this study the knowledge, attitude and practice based questions were asked to analyze the awareness level of house officers regarding oral radiology. Study design: It is a questionnaire-based study conducted for the duration of the six months from Feb 2022 to July 2022. Material and Methods: The study comprised of 105 house officers that were practicing dentistry after completion of their degrees. They were fully aware of the objective of the study. They were practicing in institutes like Liaqat College of medicine and dentistry, Fatima Jinnah dental College and Bahria university medical and dental College. Results: All the subjects had taken the radiographs and 38 subjects reported about using both conventional and digital method during practice. The average age of the subjects was 25 years. There were 32 subjects that prefer using conventional method and among them 27 reported that they had only one equipment that’s why they have to use it

A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN