Practical Tutorial Research Articles

Infection vs inflammation in prosthetic heart valves by [18F]FDG-PET/CT: A practical tutorial for accurate interpretation

Infection vs inflammation in prosthetic heart valves by [18F]FDG-PET/CT: A practical tutorial for accurate interpretation

GPT Alumni AI Pesquisa: A Practical Tutorial for the Adoption and Ethical Use of AI in Scientific Research

Objective: This tutorial introduces the use of Alumni AI Pesquisa, a chatbot based on GPT-4, publicly available on the OpenAI platform, designed to support the ethical and transparent use of Artificial Intelligence (AI) in academic research. The aim is to guide authors, editors, and reviewers in the responsible application of AI, ensuring scientific integrity throughout the editorial process. Method: The tutorial provides a step-by-step guide on how to access and utilize Alumni AI Pesquisa. The GPT-4 offers guidance on the ethical use of AI in academic writing, the oversight of editorial decisions assisted by AI, as well as compliance with editorial guidelines and manuscript formatting. Correct usage of AI is promoted through practical examples and references to global best practices. Results: Alumni AI Pesquisa provides immediate support tailored to different user profiles (authors, editors, and reviewers), encouraging transparent AI use in manuscripts. The tool ensures that AI-assisted decisions are validated by human supervisors, guaranteeing adherence to ethical and technical standards. Conclusions: Publicly available on the OpenAI platform, Alumni AI Pesquisa, powered by GPT-4, significantly contributes to the promotion of scientific integrity by facilitating responsible AI use in the academic environment. It is recommended that the use of the tool be cited in publications, explicitly mentioning the version (GPT-4).

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Nonlinear ray tracing in focused fields, part 1. Calculation of 3D focused wavefields: tutorial

In this three-part paper series, we develop a method to trace the lines of flux through a three-dimensional wavefield by following a direction that is governed by the derivative of the phase at each point, a process that is best described as flux tracing but which we interchangeably name “nonlinear ray tracing.” In this first part, we provide a tutorial on the high-speed calculation of three-dimensional complex wavefields, which is a necessary precursor to flux tracing. The basis of this calculation is the angular spectrum method, a well-known numerical algorithm that can be used to efficiently and accurately calculate diffracted fields for numerical apertures <0.7. It is known that this approach yields identical predictions to the first Rayleigh–Sommerfeld solution. We employ the angular spectrum method to develop two algorithms that generate the 3D complex wavefield in the region of focus of a lens. The first algorithm is based on the thin lens approximation, and the second is based on the concept of an ideal lens, which can be modeled using an optical Fourier transform. We review how these algorithms can be used to calculate focused laser beams with TEM00 and TEM01 laser profiles. The three-dimensional sampling requirements of the focused field are explained in detail, and expressions for the computational and memory efficiency of the two algorithms are developed. These two algorithms generate the 3D scaffold for the flux tracing method developed in the second paper, and in the third paper we highlight the application of the method to understanding monochromatic lens aberration. Disregarding the second and third papers, this first paper will serve as a practical tutorial for anyone seeking to compute focused fields in three dimensions.

Numerical thermalization in 2D PIC simulations: Practical estimates for low-temperature plasma simulations

The process of numerical thermalization in particle-in-cell (PIC) simulations has been studied extensively. It is analogous to Coulomb collisions in real plasmas, causing particle velocity distributions (VDFs) to evolve toward a Maxwellian as macroparticles experience polarization drag and resonantly interact with the fluctuation spectrum. This paper presents a practical tutorial on the effects of numerical thermalization in 2D PIC applications. Scenarios of interest include simulations, which must be run for many thousands of plasma periods and contain a population of cold electrons that leave the simulation space very slowly. This is particularly relevant to many low-temperature plasma discharges and materials processing applications. We present numerical drag and diffusion coefficients and their associated timescales for a variety of grid resolutions, discussing the circumstances under which the electron VDF is modified by numerical thermalization. Though the effects described here have been known for many decades, direct comparison of analytically derived, velocity-dependent numerical relaxation timescales to those of other relevant processes has not often been applied in practice due to complications that arise in calculating thermalization rates in 1D simulations. Using these comparisons, we estimate the impact of numerical thermalization in several examples of low-temperature plasma applications including capacitively coupled plasma discharges, inductively coupled plasma discharges, beam plasmas, and hollow cathode discharges. Finally, we discuss possible strategies for mitigating numerical relaxation effects in 2D PIC simulations.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code

BackgroundThe rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings.MethodsWe present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator’s descriptive accuracy.ResultsIn this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8–22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65–5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator.ConclusionsThe emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

How to apply zero-shot learning to text data in substance use research: An overview and tutorial with media data.

A vast amount of media-related text data is generated daily in the form of social media posts, news stories or academic articles. These text data provide opportunities for researchers to analyse and understand how substance-related issues are being discussed. The main methods to analyse large text data (content analyses or specifically trained deep-learning models) require substantial manual annotation and resources. A machine-learning approach called 'zero-shot learning' may be quicker, more flexible and require fewer resources. Zero-shot learning uses models trained on large, unlabelled (or weakly labelled) data sets to classify previously unseen data into categories on which the model has not been specifically trained. This means that a pre-existing zero-shot learning model can be used to analyse media-related text data without the need for task-specific annotation or model training. This approach may be particularly important for analysing data that is time critical. This article describes the relatively new concept of zero-shot learning and how it can be applied to text data in substance use research, including a brief practical tutorial.

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial.

One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound understanding of the component's physical behavior. Furthermore, this tutorial provides Python and MATLAB code examples to demonstrate these methods alongside explanatory videos. The goal of this article is to serve as a practical tutorial, enabling interested individuals with a background in signal processing to quickly learn the important principles of condition-based maintenance of rotating machinery using vibration analysis.

The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R.

Transparency and reproducibility are major prerequisites for conducting meaningful real-world evidence (RWE) studies that are fit for decision-making. Many advances have been made in the documentation and reporting of study protocols and results, but the principles for version control and sharing of analytic code in RWE are not yet as established as in other quantitative disciplines like computational biology and health informatics. In this practical tutorial, we aim to give an introduction to distributed version control systems (VCS) tailored toward the FAIR (Findable, Accessible, Interoperable, and Reproducible) implementation of RWE studies. To ease adoption, we provide detailed step-by-step instructions with practical examples on how the Git VCS and R programming language can be implemented into RWE study workflows to facilitate reproducible analyzes. We further discuss and showcase how these tools can be used to track changes, collaborate, disseminate, and archive RWE studies through dedicated project repositories that maintain a complete audit trail of all relevant study documents.

Penambahan Nilai Ekonomi Jajanan Tradisional Umkm “Bu Sie” Melalui Logo Produk Untuk Menunjang Sustainable Income

The logo is related to the product brand which can be a competitive advantage for MSMEs. A logo can become a business identity and visual attraction for traditional snack MSME products. The aim of this activity is to strengthen business identity through logo design training and obtain sustainable results. The method for implementing this service is socialization and design practice tutorials for Mrs. Sie's traditional snack business. The results of this service activity are giving product logos to Bu Sie's traditional snacks, increasing sales turnover, strengthening business identity and also as promotional media. MSMEs receive education about logo design and how to make them using tools commonly used in printing, thereby facilitating the production process while reducing the cost of printing logo stickers.

Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial

Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples, we present here a practical tutorial comparing multiple forms of cross-validation using a widely accessible, real-world electronic health care data set: Medical Information Mart for Intensive Care-III (MIMIC-III). This tutorial explored methods such as K-fold cross-validation and nested cross-validation, highlighting their advantages and disadvantages across 2 common predictive modeling use cases: classification (mortality) and regression (length of stay). We aimed to provide readers with reproducible notebooks and best practices for modeling with electronic health care data. We also described sets of useful recommendations as we demonstrated that nested cross-validation reduces optimistic bias but comes with additional computational challenges. This tutorial might improve the community’s understanding of these important methods while catalyzing the modeling community to apply these guides directly in their work using the published code.

Analyzing Educational Content on YouTube: Trends, User Behavior, and Recommendation Strategies

Our research journey took us deep into the realm of educational content on YouTube, a platform known for learning and exploration. We aimed to uncover patterns and trends in educational videos and use this knowledge to enhance the user experience through content recommendations. Using specialized tools, we analyzed data collected directly from YouTube. One intriguing finding was the varying lifespans of trending videos in different countries. Some videos enjoyed extended fame, while others had a shorter time in the spotlight. This cultural diversity in video lifecycles fascinated us. We also discovered rich user engagement across the globe by examining likes, dislikes, views, and comments. Different countries showed preferences in expressing themselves through engagement metrics. Science, technology, and practical tutorials consistently ranked highly in educational categories. We also found that the timing of content publication played a crucial role in a video's success. Understanding this correlation can guide content creators in maintaining relevance. Additionally, sentiment analysis revealed emotional tones associated with video tags, providing insights into what resonates with viewers. We also determined the best time for educational video consumption, based on user behavior and engagement. Finally, we implemented recommendation algorithms, combining collaborative and content-based filtering, to personalize the learning experience on YouTube. Overall, our research sheds light on the multifaceted world of educational content and provides guidance for creators and learners on this dynamic platform. As YouTube evolves, our findings will continue to shape the path towards a more engaging educational future. Key Words: YouTube, Educational Content, Trend Analysis, User Behavior, Recommendation Algorithms, Data Analysis, Video Trends, User Engagement.

Pharmacogenetic Testing for the Pediatric Gastroenterologist: Actionable Drug-Gene Pairs to Know.

Gastroenterologists represent some of the earlier adopters of precision medicine through pharmacogenetic testing by embracing upfront genotyping for thiopurine S-methyltransferase nucleotide diphosphatase (TPMT) before prescribing 6-mercaptopurine or azathioprine for the treatment of inflammatory bowel disease. Over the last two decades, pharmacogenetic testing has become more readily available for other genes relevant to drug dose individualization. Common medications prescribed by gastroenterologists for conditions other than inflammatory bowel disease now have actionable guidelines, which can improve medication efficacy and safety; however, a clear understanding of how to interpret the results remains a challenge for many clinicians, precluding wide implementation of genotype-guided dosing for drugs other than 6-mercaptopurine and azathioprine. Our goal is to provide a practical tutorial on the currently available pharmacogenetic testing options and a results interpretation for drug-gene pairs important to medications commonly used in pediatric gastroenterology. We focus on evidence-based clinical guidelines published by the Clinical Pharmacogenetics Implementation Consortium (CPIC®) to highlight relevant drug-gene pairs, including proton pump inhibitors and selective serotonin reuptake inhibitors and cytochrome P450 (CYP) 2C19, ondansetron and CYP2D6, 6-mercaptopurine and TMPT and Nudix hydrolase 15 (NUDT15), and budesonide and tacrolimus and CYP3A5.

Managing the Hyflex Scheduling Activity Using Excel Dynamic Arrays

The COVID-19 pandemic has forced the rapid adoption of remote teaching modalities including “hyflex” where students attend some class sessions in person and some online. Managing the hyflex course requires faculty to quickly generate several reports and to update these reports rapidly when the authorities adjust the rules, students add/drop, or the number of course sessions is changed. The creation of these reports is tedious and error-prone, so they need to be automated. However, the nature of the task precludes traditional Excel programming approaches. We use Excel’s new “dynamic array” capabilities (which are available only in Excel 365) to automate the creation and updating of the reports needed to manage the hyflex course. We show how to program the reports, and we discuss the importance of taking an iterative approach to creating effective, error-free cell formulas. To help the reader acquire genuine access to the dynamic array functions, we provide a practical tutorial on the principles and new concepts of dynamic arrays, explain how they relate to legacy array functions, and present selected dynamic array functions including SEQUENCE, FILTER, and XLOOKUP.

Correlative microscopy and monitoring of segregation processes in optoelectronic semiconductor materials and devices

The present work comprises a practical tutorial on the topic of correlative microscopy and its application to optoelectronic semiconductor materials and devices. For the assessment of microscopic structure–property relationships, correlative electron microscopy, combined also with scanning-probe and light microscopy, exhibits a collection of indispensable tools to analyze various material and device properties. This Tutorial describes not only the various microscopy methods but also the specimen preparation in detail. Moreover, it is shown that electron microscopy can serve to monitor phase segregation processes on various length scales in semiconductor nanoparticles and thin films. Algorithms used to extract phase information from high-resolution transmission electron micrographs are explained.

Exploring the association between self-efficacy and future utility beliefs in mathematics: A practical tutorial on correspondence analysis.

It has long been understood that there exists a strong association between a student's belief in the future utility of mathematics and their self-efficacy in mathematics. This study re-examines this association by studying these variables based on data collected from a sample of 21,444 ninth-grade students who participated in the 2009 High School Longitudinal Study (HSLS09). The nature of the association between future utility beliefs of students in mathematics and self-efficacy of students in mathematics is explored visually using the simple correspondence analysis technique. The main feature that will be utilised from this technique is a two-dimensional graphical display, referred to as a correspondence plot. By studying the HSLS09 data, the first two axes of such a plot summarised nearly 99% of the statistically significant association that exists between a student's beliefs in the future utility of mathematics and their mathematics self-efficacy. It is shown visually that students who strongly believe in the future importance of studying mathematics also perform strongly in the subject, while those who do not believe that there is any future utility from studying mathematics do not perform well at it. This study, therefore, suggests that mathematics ability is associated with a student's perception of its future importance.

Practical Tutorial on cylindrical structure vibro-acoustics Part 1 - Vibrations

The mathematics which describe the vibroacoustic behavior of cylindrical structures are imposing to say the least. Part 1 of this practical tutorial demystifies cylindrical shell vibration theory by using measured data from actual shells and pipes to explain key concepts. For any shell, you can estimate frequency ranges where shells behave like simple beams and flat plates, greatly simplifying calculations of modes of vibration and mobilities. The key is first calculating the ring frequency - the frequency where membrane waves can propagate fully around the shell circumference. Simple infinite structure theory may then be used to compute mean mobilities for beam, shell, and flat plate behavior. Modes of vibration for a cylinder depend on both longitudinal and circumferential harmonics, or a helical wavenumber. Cremer's simple approximate resonance frequency formula is used to show examples for a large diameter short shell and a small diameter long shell (a pipe). Finally, the measured modal densities of an elbowed pipe are compared to estimates from an empirical expression for modal density of a shell. In all cases in this tutorial, measurements and simple estimates agree well, showing that cylindrical shell vibrations may be estimated without difficult math or complex computer models.

WEO Newsletter.

WEO Newsletter.

A practical guide to random-effects Bayesian meta-analyses with application to the psychological trauma and suicide literature.

Bayesian meta-analyses offer several advantages over traditional approaches, including improved accuracy when using a small number of studies and enhanced estimation of heterogeneity. However, psychological trauma research has yet to see widespread adoption of these statistical methods, potentially due to researchers' unfamiliarity with the processes involved. The purpose of this article is to provide a practical tutorial for conducting random-effects Bayesian meta-analyses. Explanations and recommendations are provided for completing the primary steps of a Bayesian meta-analysis, ranging from model specification to interpretation of results. Furthermore, an illustrative example is used to demonstrate the application of each step. In the example, results are synthesized from six studies included in a previously published systematic review (Holliday et al., 2020), with a combined sample size of 21,244,109, examining the association between posttraumatic stress disorder and risk of suicide in veterans and military personnel. The posterior distributions for each model estimate, such as the pooled effect size and the heterogeneity parameter, are discussed and interpreted with regard to the probability of increased suicide risk. Our hope is that this tutorial, along with the provided data and code, facilitate the use of Bayesian meta-analyses in the study of psychological trauma. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Students' Acceptance of Online Learning during Covid-19

Objective - Universiti Tunku Abdul Rahman (UTAR) has implemented online learning methods during Covid-19. Students from the Faculty of Science, UTAR, face enormous challenges during this period as they need to attend all classes through a synchronous or asynchronous environment which is significantly different from their experiences within-person classes. Therefore, this aims to explore the level of acceptance of online learning among students from the Faculty of Science during the period that the movement control order was in place. Secondly, the study aims to determine the influence of perceived ease of use, perceived usefulness, and attitude toward online learning the students during the pandemic. Methodology/Technique - A quantitative study approach is employed in this paper. Hence, the targeted population comprises final-year to-year students from the Faculty of Science, UTAR, with data collected through a convenient sampling approach with a minimum size of 150. The survey was distributed through MS Teams. The conceptual research model is developed based on a technology acceptance model (TAM). The model is analysed by Partial Least Squares Structural Equation Modelling. Finding - The findings of this study have a significant impact on the level of acceptance of online learning among the Faculty of Science students. The study concludes that online learning systems and platforms are easy to use and indirectly benefit students' learning performance. In conclusion, learning attitude towards online learning, perceived ease of use, and perceived usefulness have a positive influence on the level of acceptance of online learning practices. Novelty - This study addresses the concern of stakeholders for how online learning affects student satisfaction during the period that the movement control order was in place, with a particular focus on science-based subjects that required in-person practical tutorials in the lab. Type of Paper - Empirical Keywords: Online Learning; Covid-19 Pandemic; Level of Student Acceptance; Partial Least Squares; Technology Acceptance Model JEL Classification: I21, I23 Reference to this paper should be referred to as follows: Lin, B.W; Hoe, K.Y. (2022). Students' Acceptance of Online Learning during Covid-19, GATR-Global J. Bus. Soc. Sci. Review, 10(4), 229–236. https://doi.org/10.35609/gjbssr.2022.10.4(2)