Personalization, Cognition, and Gamification-based Programming Language Learning: A State-of-the-Art Systematic Literature Review

Programming courses in computing science are important because they are often the first introduction to computer programming for many students. Many university students are overwhelmed with the information they must learn for an introductory course. The current teacher-lecturer model of learning commonly employed in university lecture halls often results in a lack of motivation and participation in learning. Personalized gamification is a pedagogical approach that combines gamification and personalized learning to motivate and engage students while addressing individual differences in learning. This approach integrates gamification and personalized learning strategies to inspire and involve students while addressing their unique learning needs and differences. A comprehensive literature search was conducted by including 81 studies that were analyzed based on their research design, intervention, outcome measures, and quality assessment. The findings suggest that personalized gamification can enhance student cognition in programming courses by improving motivation, engagement, and learning outcomes. However, the effectiveness of personalized gamification varies depending on various factors, such as the type of gamification elements used, the degree of personalization, and the characteristics of the learners. This paper provides insights into designing and implementing effective personalized gamification interventions in programming courses. The findings could inform educational practitioners and researchers in programming education about the potential benefits of personalized gamification and its implications for educational practice

Diagnostic Framework for Electricity Losses in Pakistan Using Data Visualization Techniques

To obtain the best results and leads to accuracy, data visualization techniques allows the big data and unprocessed data in a structured format. There are a lot of techniques that provides the best results for both high and low dimensional data preprocessing. Electricity crisis are becoming the part of day to day life and increasing rapidly. There are a lot of factors that affects the distribution and transmission losses of electricity. PEPCO, GENCO and other distribution companies are responsible for electricity distribution .There are 10-12% electricity losses during the distribution from main to other. At different sectors electricity production and installation from different resources of energy needs a proper monitoring system .Except of system losses a lot of electricity lost due to non-technical factors which leads to the shortage of electricity in a Country. .Grid stations should be planted according to the population of the province .Diagnostic framework includes the previous and present data comparison which predicts and forecasts the future consumption that helps to overcome losses by time series analysis algorithm (ARIMA

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction

Neuroprotective effects of melatonin and celecoxib against ethanol-induced neurodegeneration: A computational and pharmacological approach

© 2019 Al Kury et al. This work is published and licensed by Dove Medical Press Limited. Purpose: Melatonin and celecoxib are antioxidants and anti-inflammatory agents that exert protective effects in different experimental models. In this study, the neuroprotective effects of melatonin and celecoxib were demonstrated against ethanol-induced neuronal injury by in silico, morphological, and biochemical approaches. Methods: For the in silico study, 3-D structures were constructed and docking analysis performed. For in vivo studies, rats were treated with ethanol, melatonin, and celecoxib. Brain samples were collected for biochemical and morphological analysis. Results: Homology modeling was performed to build 3-D structures for IL1β), TNFα, TLR4, and inducible nitric oxide synthase. Structural refinement was achieved via molecular dynamic simulation and processed for docking and postdocking analysis. Further in vivo experiments showed that ethanol induced marked neuronal injury characterized by down-regulated glutathione, glutathione S-transferase, and upregulated inducible nitric oxide synthase. Additionally, ethanol increased the expression of TNFα and IL1β. Finally, neuronal apoptosis was demonstrated in ethanol-intoxicated animals using caspase 3 and activated JNK staining. On the other hand, melatonin and celecoxib treatment ameliorated the biochemical and immunohistochemical alterations induced by ethanol. Conclusion: These results demonstrated that ethanol induced neurodegeneration by activating inflammatory and apoptotic proteins in rat brain, while melatonin and celecoxib may protect rat brain by downregulating inflammatory and apoptotic markers

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Intra-vehicular verification and control: a two-pronged approach

Modern vehicles are equipped with hundreds of embedded networked components with computational, sensory and actuation powers. Reliable functioning and interaction of these components are vital for the safety of the vehicle and its passengers. We present an architecture that deals with the intra-vehicular network at both component and system levels. At the component level, our technique formally verifies compatibility of each component with the rest of the system. At the system level, we provide means to define overall behaviour by using first-order logic rules in an ontological space. Overall, we eliminate the hazards associated with integrating heterogeneous components in a car network domain and enable a knowledgeable user to define network behaviour easily

Priority Based Technique and Vehicle Location in VANET Using Google Maps

Google Maps is becoming popular in digital maps because of its user friendly human computerinteraction and easy to use Application Programming Interface (API) as a plugin to onlineapplications. Vehicular Ad-hoc Network (VANET) is conceptualizing moving cars as nodes ina dynamic road network. VANETs help manage the traffic through communication messagesamong the vehicles. In huge traffic loads too many messages create network congestion andstarvation. The basic objective of this research is to augment conventional VANET by addingmessage prioritization methodology, i.e. messages for top priority vehicles will be transmittedprior to the ones with lower priority. To this end, an algorithm has been developed andimplemented in a web application that incorporates Google maps for getting and displayingvehicle information. The proposed algorithm has been evaluated using experiments forthroughput and congestion avoidance in the network

COVID-19 Vaccine Supply chain management by Blockchain

The pandemic of COVID-19 is a big challenge to human health. Covid-19 pandemics is the worldwide disease starting from Wuhan in December 2019. It effect a huge amount of people. Now the whole world enter in the process of Immunization and vaccinationabst: The COVID-19 pandemic has as of now featured the absence of flexibility in supply chains, as worldwide organizations fall flat from disturbances at single hubs and associations. With regards to COVID-19 pandemic, the fast carry out of its vaccination and the execution of an overall overall immunization process is very difficult, yet its prosperity will rely upon the accessibility of a functional and straightforward conveyance chain that can be inspected by all important beneficiaries. In this research, we examine how blockchain innovation can help in a few parts of vaccine distribution of COVID-19 . We describe a framework in which blockchain innovation is utilized to surety information respectability and changelessness of recipient enlistment for inoculation, keeping away from character burglaries and pantomimes. Brilliant agreements are characterized to screen and track the legitimate immunization conveyance conditions against the protected dealing with rules characterized by antibody makers empowering the attention to all arrange peers. For antibody organization, a straightforward and sealed answer for incidental effects self-revealing is given thinking about recipient and administrated immunization affiliation. A model was carried out utilizing the Ethereum network test, which determine the conditions of COVID-19 Vaccine.. The outcomes got for each chain activity can be checked and approved on the Etherscan. As far as throughput and versatility, the described blockchain framework shows promising outcomes while the assessed cost as far as gas for vaccination situation in light of genuine information stays inside sensible cutoff points