Penetrating soft palate injury by lollypop candy stick

Incidents of soft palate injury or laceration caused by unintended movement when holding a sharp object in the mouth in the pediatric population are usually rarely reported.Here we report a case of soft palate laceration in a child due to a lollypop stick tip.

Indian stock market prediction using artificial neural networks on tick data

Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

Consumption Value of Digital Devices: An Investigation through Facebook Advertisement

Data collection represents the most effort-intensive stage of any marketing research exercise, especially in cases sampling frame is unavailable. Sub-optimal bypasses in form of student surveys or surveys employing convenience sampling have become common. In modern era, where laptops and smartphones enable easy accessibility of respondents online, this study utilizes Facebook advertisement as a source of data collection to measure the construct of user experience for interactive products. Modern digital devices, like smartphones, are a source of a variety of experiences for the user. Design teams at various smartphone manufacturers are struggling every day to create products which provide complete consumer experiences. This work not only proposes a framework for describing the same with usability, social value and pleasure in use, but also tests the scales for each by empirical validation. Data collection process through Facebook, as a sample frame, is something yet to be seen in marketing literature. This work goes the distance in not only demonstrating the efficacy of using Facebook advertisement as a viable data collection tool but also developing a framework to measure consumption value. Outcomes of the study, should promote further research using this sampling frame for future research, especially in the area of marketing.

An artificial neural network based deep collocation method for the solution of transient linear and nonlinear partial differential equations

A combined deep machine learning(DML)and collocation based approach to solve the partial differential equations using artificial neural networks is proposed.The developed method is applied to solve problems governed by the Sine–Gordon equation(SGE),the scalar wave equation and elasto-dynamics.Two methods are studied:one is a space-time formulation and the other is a semi-discrete method based on an implicit Runge–Kutta(RK)time integration.The methodology is implemented using the Tensorflow framework and it is tested on several numerical examples.Based on the results,the relative normalized error was observed to be less than 5%in all cases.

Virtual preoperative planning and 3D printing are valuable for the management of complex orthopaedic trauma

Purpose:The technology of 3D printing(3DP)exists for quite some time,but it is still not utilized to its full potential in the field of orthopaedics and traumatology,such as underestimating its worth in virtual preoperative planning(VPP)and designing various models,templates,and jigs.It can be a significant tool in the reduction of surgical morbidity and better surgical outcome avoiding various associated complications.Methods:An observational study was done including 91 cases of complex trauma presented in our institution requiring operative fixation.Virtual preoperative planning and 3DP were used in the management of these fractures.Surgeons managing these cases were given a set of questionnaire and responses were recorded and assessed as a quantitative data.Results:In all the 91 cases,where VPP and 3DP were used,the surgeons were satisfied with the outcome which they got intraoperatively and postoperatively.Surgical time was reduced,with a better outcome.Three dimensional models of complex fracture were helpful in understanding the anatomy and sketching out the plans for optimum reduction and fixation.The average score of the questionnaire was 4.5,out of a maximum of 6,suggesting a positive role of 3DP in orthopaedics.Conclusion:3DP is useful in complex trauma management by accurate reduction and placement of implants,reduction of surgical time and with a better outcome.Although there is an initial learning curve to understand and execute the VPP and 3DP,these become easier with practice and experience.

Nanomaterial Based Biosensors for Detection of Viruses Including SARS-CoV-2:A Review

The COVID-19 outbreak led to an uncontrollable situation and was later declared a global pandemic.RT-PCR is one of the reliable methods for the detection of COVID-19,but it requires transporting samples to sophisticated laboratories and takes a significant amount of time to amplify the viral genome.Therefore,there is an urgent need for a large-scale,rapid,specific,and portable detection kit.Nowadays nanomaterials-based detection technology has been developed and it showed advancement over the conventional methods in selectivity and sensitivity.This review aims at summarising some of the most promising nanomaterial-based sensing technologies for detecting SARS-CoV-2.Nanomaterials possess unique physical,chemical,electrical and optical properties,which can be exploited for the application in biosensors.Furthermore,nanomaterials work on the same scale as biological processes and can be easily functionalized with substrates of interest.These devices do not require extraordinary sophistication and are suitable for use by common individuals without high-tech laboratories.Electrochemical and colorimetric methods similar to glucometer and pregnancy test kits are discussed and reviewed as potential diagnostic devices for COVID-19.Other devices working on the principle of immune response and microarrays are also discussed as possible candidates.Nanomaterials such as metal nanoparticles,graphene,quantum dots,and CNTs enhance the limit of detection and accuracy of the biosensors to give spontaneous results.The challenges of industrial-scale production of these devices are also discussed.If mass production is successfully developed,these sensors can ramp up the testing to provide the accurate number of people aff ected by the virus,which is extremely critical in today’s scenario.