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It is important for dental researchers to have a general idea of the different types of data that can be collected from a study and the available statistical tools that can be used with such data. By knowing about what is available, researchers will have an informed idea of the types of studies that should be conducted, the data that should be collected, and the proper statistical methods for analyzing collected data. In the first part of this general overview of statistical methods, we walked through the various types of data that traditional statistical techniques, such as t tests and linear regression, can handle. In this second part, we explore more complex types of data that traditional statistical techniques are unable to handle. Specifically, we discuss longitudinal and time-to-event data because both occur frequently in dental studies and require special modeling techniques in order to analyze correctly. Using two different simulated dental datasets, the proper application of techniques such as repeated measures ANOVA, linear mixed modeling, generalized estimating equations, log-rank test, and Cox proportional hazards models are discussed and illustrated in depth. [ABSTRACT FROM AUTHOR]

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years. [ABSTRACT FROM AUTHOR]

In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and the more recently introduced Transformers. The choice of architecture depends on the data characteristics and the specific task at hand. In the 1D domain, one-dimensional CNNs (1D CNNs) are widely used, particularly for tasks involving the classification and recognition of 1D signals. While there are many applications of 1D CNNs in the literature, the technical details of their training are often not thoroughly explained, posing challenges for those developing new libraries in languages other than those supported by available open-source solutions. This paper offers a comprehensive, step-by-step tutorial on deriving feedforward and backpropagation equations for 1D CNNs, applicable to both regression and classification tasks. By linking neural networks with linear algebra, statistics, and optimization, this tutorial aims to clarify concepts related to 1D CNNs, making it a valuable resource for those interested in developing new libraries beyond existing ones. [ABSTRACT FROM AUTHOR]

This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions that can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples, specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial. [ABSTRACT FROM AUTHOR]

Over the last decade, access to global data has become increasingly critical for research, allowing insights into diverse biological, environmental and societal questions at a macro scale. Digitisation has greatly enhanced the use of herbarium data in the analysis of species distributions and ecological niche modelling. Yet, sources on modelling and mapping methodology using open-source software is greatly lacking for beginners. We have created a replicable and thorough tutorial to visualise species occurrence data and exploratory analysis that was developed by undergraduates with broad backgrounds and levels of experience. This tutorial integrates the open-source programmes QGIS, MaxEnt and R to develop distribution maps, using bryophytes as a case study, to promote the accessibility of open-source software and remote access learning. This tutorial has already set the foundation for further research into distribution modelling of rare Illinois bryophytes to better understand the potential impact of climate change.

One of the most important parts of any research project is the statistical analysis through which results are described and deemed significant or otherwise. Given this role of statistical analyses, it is imperative to understand what statistical tests are available and, most importantly, when they are appropriate. This is because choosing an improper statistical test can lead to inaccurate results and meaningless conclusions. We have written this data analysis guide to assist dental researchers with carrying out a proper analysis plan in consultation with a statistician. In this first part, we detail common types of outcome and predictor variables and include some common tests that can be used for each scenario. Using different simulated datasets, we also provide examples of common data visualization techniques and how a proper statistical test can be applied. We hope this guide provides some general background knowledge on common statistical techniques and allows for easier and more effective conversations with your statistician. [ABSTRACT FROM AUTHOR]

With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs. Here, we provide guidance for model specification and interpretation in Bayesian hierarchical modeling and describe common pitfalls that can arise in the process of model fitting and evaluation. Our introduction gives particular emphasis to prior specification and prior sensitivity, as well as to the calculation of Bayes factors for model comparisons. We illustrate the use of state-of-the-art software programs Stan and brms. The result is an overview of best practices in Bayesian hierarchical modeling that we hope will aid psychologists in making the best use of Bayesian hierarchical modeling. [ABSTRACT FROM AUTHOR]

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells—and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data (https://github.com/GfellerLab/MetacellAnalysisTutorial) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods (https://github.com/GfellerLab/MetacellAnalysisToolkit). Several approaches have been designed to tackle the size and inherent noise of single-cell genomics data. This Review discusses the metacell concept, provides recommendations on when and how to use them and includes a tutorial and an integrated pipeline for building and analyzing metacells. [ABSTRACT FROM AUTHOR]

GSCA Pro is free, user-friendly software for generalized structured component analysis structural equation modeling (GSCA-SEM), which implements three statistical methods for estimating models with factors only, models with components only, and models with both factors and components. This tutorial aims to provide step-by-step illustrations of how to use the software to estimate such various models after briefly discussing model specification, estimation, and evaluation in GSCA-SEM. [ABSTRACT FROM AUTHOR]

The aim of this paper is twofold. First, it serves as a comprehensive tutorial on Data‐Driven Soft Independent Modelling of Class Analogy (SIMCA) (DD‐SIMCA) method for one‐class classification. It covers all practical aspects of developing, validation, and application of DD‐SIMCA models, using a set of simple examples. Second, it introduces web application that implements the main DD‐SIMCA functionality. This application is freely available for everyone and does not require registration or installation. All calculations run locally in a browser without sending any information on a server, hence removing any obstacles to the dissemination of the data and models. The paper serves as a comprehensive tutorial on Data‐Driven SIMCA method for one class classification. It covers all practical aspects of developing, validation, and application of DD‐SIMCA models, using a set of simple examples. It also introduces web application that implements the main DD‐SIMCA functionality. [ABSTRACT FROM AUTHOR]

We report that introducing online teaching to Access students studying a Level 3 pre-degree Higher Education module at The Open University has long-lasting beneficial effects. This study shows that a single online teaching session, which focuses on a heavily-weighted summative final piece of assessment, encourages students who participate to subsequently attend online teaching sessions on future modules to a significantly greater extent than those students who did not undertake the introductory Access module. This effect was seen in students progressing to three subsequent modules. Qualitative interviews of students who had undertaken the introductory online teaching session showed that the students could recall the session some three years after it had taken place, were able to report the benefits of having attended the session and also reported that it made them far more likely to attend future online teaching events in subsequent modules they undertook. [ABSTRACT FROM AUTHOR]

Navigation and interaction in the virtual world will occur via controllers. Previous literature suggests that not all buttons on these controllers are suitable for all functions and that unclear or uncomfortable operations can lead to frustration. This study aimed to determine if an operation with freely selectable buttons differs from one with mapped buttons, in which human-factor differences can be identified, and if there are differences between primary and advanced training. A field experiment with professionals employed by Swiss Federal Railways and apprentices and future apprentices (N = 60) was conducted in a VR tutorial that was previously developed using design cues from existing literature. Controller operation was varied in the groups. The results show significant differences and interaction effects, which indicate that a different operation is more appropriate for apprentices than for professionals in further education. [ABSTRACT FROM AUTHOR]

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy – a critical period of rapid brain development and plasticity – microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience. [ABSTRACT FROM AUTHOR]

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses. [ABSTRACT FROM AUTHOR]

Using R to conduct univariate meta-analyses is becoming common for publication. However, R can also conduct multivariate meta-analysis (MMA). However, newcomers to both R and MMA may find using R to conduct MMA daunting. Given that, R may not be easy for those unfamiliar with coding. Likewise, MMA is a topic of advanced statistics. Thus, it may be very challenging for most newcomers to conduct MMA using R. If this holds, this can be viewed as a practice gap. In other words, the practice gap is that researchers are not capable of using R to conduct MMA in practice. This is problematic. This paper alleviates this practice gap by illustrating how to use R (the metaSEM package) to conduct MMA on educational psychology data. Here, the metaSEM package is used to obtain the required MMA text outputs. However, the metaSEM package is not capable of producing the other required graphical outputs. As a result, the meta for package is also used as a complimentary to generate the required graphical outputs. Ultimately, we hope that our audience will be able to apply what they learn from this method paper to conduct MMA using R in their teaching, research, and publication. [ABSTRACT FROM AUTHOR]

Dermatology holds significant importance in the curriculum of medical studies, encompassing various aspects of medical practice. Therefore, it is essential for medical graduates to acquire a proficient understanding of dermatological principles. This study endeavors to assess the impact of integrating problem-based learning (PBL) with traditional didactic lectures among final-year medical undergraduates. Methodology: A comparative study was conducted involving 150 final-year M.B.B.S. students who voluntarily participated. Initially, all students received didactic lectures. Subsequently, they were randomly assigned to two groups: Group A and Group B, with 75 students in each. Both groups participated in tutorial sessions and PBL activities focused on four selected dermatology topics. Pre-tests and post-tests were administered to evaluate the knowledge gained by the students. Additionally, selfadministered feedback was collected to discern their preferred learning methodology. [ABSTRACT FROM AUTHOR]

Objectives: The peer review process is critical to maintaining quality, reliability, novelty, and innovation in the scientific literature. However, the teaching of scientific peer review is rarely a component of formal scientific or clinical training, and even the most experienced peer reviewers express interest in continuing education. The objective of this review article is to summarize the collective perspectives of experienced journal editors about how to be a good reviewer in a step-by-step guide that can serve as a resource for the performance of peer review of a scientific manuscript. Methods: This is a narrative review. Results: A review of the history and an overview of the modern-day peer review process are provided with attention to the role played by the reviewer, including important reasons for involvement in scientific peer review. The general components of a scientific peer review are described, and a model for how to structure a peer review report is provided. These concepts are also summarized in a reviewer checklist that can be used in real-time to develop and double-check one's reviewer report before submitting it. Conclusions: Peer review is a critically important service for maintaining quality in the scientific literature. Peer review of a scientific manuscript and the associated reviewer's report should assess specific details related to the accuracy, validity, novelty, and interpretation of a study's results. We hope that this article will serve as a resource and guide for reviewers of all levels of experience in the performance of peer review of a scientific manuscript. [ABSTRACT FROM AUTHOR]

Background and aimThe COVID-19 pandemic necessitated the transition to online medical education. This study evaluated the efficacy of online case-based tutorials using a serious game tutorial [PlayMed™ (PM)], as compared to a traditional slideshow tutorial (TT).MethodsWe performed a prospective, mixed-methods, randomised controlled trial on undergraduate medical students during the COVID-19 pandemic, from May 2020 to January 2021. Students were block randomised into the PM or TT groups. Tutors conducted online teaching on bronchiolitis and gastroenteritis cases using PM or TT to facilitate the presentation. Educational experience was assessed using a continuous interval scale (0–100; with pre-defined categories) and free text responses. Immediate and long-term knowledge acquisition was assessed using 6 multiple-choice questions (MCQ) for each case (total of 12 MCQ). A modified intention-to-treat mixed methods and a sensitivity per-protocol analysis were performed to compare outcomes between PM and TT groups.ResultsIn total, 80 PM and 73 TT participants attended at least one tutorial. Sixty-five (81%) PM and 52 (71%) TT participants completed at least one survey and were included for analysis. PlayMed™ students had an increased likelihood of completing the surveys, which included the MCQ [odds ratio (95% CI) of 2.4 (1.6–3.8), p

Objetivo: Ampliar o estudo da modelagem PBPK para profissionais e estudantes falantes de português ao desenvolver um tutorial básico de modelagem PBPK simples usando um programa de acesso livre. Métodos: Para o desenvolvimento do tutorial foi utilizado o software PK-Sim® (versão 11.2). Resultados: Foi desenvolvido um tutorial no PK-Sim, todo em português, que viabilizará aos leitores a possibilidade de realizar uma análise PBPK para fornecer uma abordagem mecanicista para estudar e prever a farmacocinética de medicamentos com base nas características fisiológicas e anatômicas de uma população de interesse, bem como nas propriedades físicas e químicas de um determinado fármaco. Conclusões: Considerando a importância das tecnologias em saúde e sua posterior avaliação, este tutorial se prova como ferramenta relevante, tanto para pesquisa e desenvolvimento quanto para a prática clínica, além de alavancar a democratização do ensino e pesquisa das Ciências Farmacêuticas entre pessoas falantes de língua portuguesa.

Abstract The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells—and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

This guide presents a detailed overview of the most used machine learning (ML) techniques for psychologists who may not be familiar with advanced statistical methods, algorithms, or programming. Recognizing the growing interest in using data-driven approaches within psychological research, this guide describes applying ML techniques to investigate complex psychological phenomena. The paper covers the spectrum of algorithms, including decision trees, random forests, gradient boosting, stochastic gradient boosting, and XGBoost, highlighting their concepts and practical applications in psychology. Aiming to bridge the gap between theoretical understanding and practical performance, this paper offers step-by-step instructions on data preprocessing, correlation exploration, feature selection, and model evaluation within the Python programming environment. Readers are offered the necessary tools to apply ML in their research through explanations, examples, and visualization.

QR decomposition is widely used for solving the least squares problem. However, existing materials about it may be too abstract for non-mathematicians, especially STEM students, and/or require serious background in linear algebra. The paper describes theoretical background and examples of GNU Octave compatible MATLAB scripts that give relatively simple but complete explanations about how to use QR decomposition for the least squares problem solution. Only basic knowledge of linear algebra and calculus are required. Both Givens rotations and Householder reflections usage for the linear least squares problem were considered. It was shown that the algorithm based on Givens rotations is even easier to program than explicit formation of the normal equations with subsequent usage of Gaussian elimination. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To accurately characterize human health hazards, human, animal, and mechanistic data must be integrated and the relevance to the research question of all three lines of evidence must be considered. Mechanistic data are often critical to the full integration of animal and human data and to characterizing relevance and uncertainty. This novel evidence integration framework (EIF) provides a method for synthesizing data from comprehensive, systematic, quality-based assessments of the epidemiological and toxicological literature, including in vivo and in vitro mechanistic studies. It organizes data according to both the observed human health effects and the mechanism of action of the chemical, providing a method to support evidence synthesis. The disease-based component uses the evidence of human health outcomes studied in the best quality epidemiological literature to organize the toxicological data according to authors' stated purpose, with the pathophysiology of the disease determining the potential relevance of the toxicological data. The mechanism-based component organizes the data based on the proposed mechanisms of effect and data supporting events leading to each endpoint, with the epidemiological data potentially providing corroborating information. The EIF includes a method to cross-classify and describe the concordance of the data, and to characterize its uncertainty. At times, the two methods of organizing the data may lead to different conclusions. This facilitates identification of knowledge gaps and shows the impact of uncertainties on the strength of causal inference. [ABSTRACT FROM AUTHOR]

Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy‐to‐use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short‐term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software (https://github.com/gchowell/paramEstimation%5fforecasting%5fODEmodels/) and detailed guidance on estimating parameters and forecasting time‐series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included. [ABSTRACT FROM AUTHOR]

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field -- the training of system-specific MLPs for reactive systems -- with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction. [ABSTRACT FROM AUTHOR]

This paper has been developed to provide a clear lens of the new normal education, particularly in improving students' academic performance through the utilization of TUTORIAL AND HOME VISITATION TOWARDS IMPROVED ACADEMIC PERFORMANCE (THIAP). With the birth of the new normal curriculum, the situation presents a unique challenge to every educational leader, particularly in the decision-making process. Hence, to sustain the delivery of quality instruction to every school, this study presents opportunities for responding to issues, problems and trends that are arising and will arise in the future due to the COVID-19 pandemic. This study aimed to improve the academic performance of Grade 10 students in science subjects on MELC #4-Describe the possible causes of plate movement through the utilization of THIAP. The study utilized experimental design. The group is exposed to the utilization of intervention. The salient findings of the study are summarized as follows: The pre-assessment mean score of 13.433 was even below the 50% mean percentage score. This shows that the experimental group needs improvement in their MELC #4. After the utilization of THIAP, the post-assessment mean score of 18.233 was above the 50% mean percentage score. This shows that the experimental group improved in their MELC #4. With a t. test computed value of -5.558 and a p-value of 0.000, this shows that there is a significant difference between the pretest and post-test mean scores of the students. This implies that the students had enhanced their performance in Science MELC #4 after implementing THIAP. Since the result of the existing study enhanced students' performance; the Principals may encourage teachers to be innovative by experimenting with new interventions, teachers may administer pre-assessment before applying any intervention and post-assessment after intervention as a basis of its effectiveness, and lastly students may accept the challenges of any intervention or innovation. This action research has its unique contribution of improving the teaching-learning process through the utilization of THIAP even the time of the pandemic. [ABSTRACT FROM AUTHOR]

The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries). [ABSTRACT FROM AUTHOR]

Electrochemical Raman spectroscopy can provide valuable insights into electrochemical reaction mechanisms. However, it also shows various pitfalls and challenges. This paper gives an overview of the necessary theoretical background, crucial practical considerations for successful measurement, and guidance for in situ/in operando electrochemical Raman spectroscopy. Several parameters must be optimized for suitable reaction and measurement conditions. From the experimental side, considerations for the setup, suitable signal enhancement methods, choice of material, laser, and objective lens are discussed. Different interface phenomena are reviewed in the context of data interpretation and evaluation. [ABSTRACT FROM AUTHOR]

Synthesis of metal oxides is typically the first step of any materials science research in a field or application involving oxides materials. However, the synthesis is rarely the prime focus in materials science, which usually describe properties and characterizations of said materials. Consequently, synthesis protocols are often given too little attention in the literature and hence poorly described. For scientists starting in the field, it becomes confusing to make the right choice of synthesis route and conditions to successfully prepare what will be the base of their research, which is a pure, single phase, complex oxide powder. With this tutorial article, we are giving basic knowledge on the underlying chemistry of oxide synthesis, and simple explanations on what motivates the need of various synthesis routes. Then, four main synthesis routes are described, namely the solid state reaction route, the Pechini route, the combustion route, and the precipitation route. For each routes, the approach is described, and the relevant parameters to be considered are developed. Finally, a step by step general protocol for each route is proposed, which can serve as a solid foundation for unexperienced researchers to become more confident when approaching metal oxide synthesis. [ABSTRACT FROM AUTHOR]

This paper provides an introductory tutorial on Value Function Approximation (VFA), a solution class from Approximate Dynamic Programming. VFA describes a heuristic way for solving sequential decision processes like a Markov Decision Process. Real-world problems in supply chain management (and beyond) containing dynamic and stochastic elements might be modeled as such processes, but large-scale instances are intractable to be solved to optimality by enumeration due to the curses of dimensionality. VFA can be a proper method for these cases and this tutorial is designed to ease its use in research, practice, and education. For this, the tutorial describes VFA in the context of stochastic and dynamic transportation and makes three main contributions. First, it gives a concise theoretical overview of VFA's fundamental concepts, outlines a generic VFA algorithm, and briefly discusses advanced topics of VFA. Second, the VFA algorithm is applied to the taxicab problem that describes an easy-to-understand transportation planning task. Detailed step-by-step results are presented for a small-scale instance, allowing readers to gain an intuition about VFA's main principles. Third, larger instances are solved by enhancing the basic VFA algorithm demonstrating its general capability to approach more complex problems. The experiments are done with artificial instances and the respective Python scripts are part of an electronic appendix. Overall, the tutorial provides the necessary knowledge to apply VFA to a wide range of stochastic and dynamic settings and addresses likewise researchers, lecturers, tutors, students, and practitioners. [ABSTRACT FROM AUTHOR]

In this study, the novelty effect or initial fascination with new technology is addressed in the context of an immersive Virtual Reality (iVR) experience. The novelty effect is a significant factor contributing to low learning outcomes during initial VR learning experiences. The aim of this research is to measure the effectiveness of a tutorial at mitigating the novelty effect of iVR learning environments among first-year undergraduate students. The iVR tutorial forms part of the iVR learning experience that involves the assembly of a personal computer, while learning the functions of the main components. 86 students participated in the study, divided into a Control group (without access to the tutorial) and a Treatment group (completing the tutorial). Both groups showed a clear bimodal distribution in previous knowledge, due to previous experience with learning topics, giving us an opportunity to compare tutorial effects with students of different backgrounds. Pre- and post-test questionnaires were used to evaluate the experience. The analysis included such factors as previous knowledge, usability, satisfaction, and learning outcomes categorized into remembering, understanding, and evaluation. The results demonstrated that the tutorial significantly increased overall satisfaction, reduced the learning time required for iVR mechanics, and improved levels of student understanding, and evaluation knowledge. Furthermore, the tutorial helped to homogenize group behavior, particularly benefiting students with less previous experience in the learning topic. However, it was noted that a small number of students still received low marks after the iVR experience, suggesting potential avenues for future research. [ABSTRACT FROM AUTHOR]

Ultrasound is an emerging tool for muscle-based measurements. However, the analysis of hyperechoic images (high muscle fat infiltration, as in type 2 diabetes [T2D]) can be challenging. Objective: To evaluate the reliability of ultrasound image analysis of muscle thickness and echointensity in older adults with T2D and non-diabetic controls by experienced and novice analysts. We hypothesized that reliability would be high for the experienced analysts, and lower, but acceptable, for a novice analyst. Methods: We recruited three groups: 1) adults >60 years with T2D; 2) age- and sex-matched normoglycemic controls; and 3) healthy adults 18–35 years. All participants underwent ultrasound imaging of the abdomen and anterior thigh. Ultrasound images were de-identified, randomized, and separately analyzed by two expert analysts (>3 years experience each). A subset of images was also analyzed by a novice analyst with no prior experience. Inter- and intra-rater reliability was assessed using coefficients of variation (CVs), intraclass correlation coefficients (ICCs), and Bland-Altman analysis. Results: For the expert analysts, ICCs were >0.90 and CVs were 0.90, except for rectus abdominus thickness measurements in the T2D group (ICC: 0.659 [95%CI: -0.134, 0.911]). CVs were

Abstract Experimental psychology is witnessing an increase in research on individual differences, which requires the development of new tasks that can reliably assess variations among participants. To do this, cognitive researchers need statistical methods that many researchers have not learned during their training. The lack of expertise can pose challenges not only in designing good, new tasks but also in evaluating tasks developed by others. To bridge the gap, this article provides an overview of test psychology applied to performance tasks, covering fundamental concepts such as standardization, reliability, norming and validity. It provides practical guidelines for developing and evaluating experimental tasks, as well as for combining tasks to better understand individual differences. To further address common misconceptions, the article lists 11 prevailing myths. The purpose of this guide is to provide experimental psychologists with the knowledge and tools needed to conduct rigorous and insightful studies of individual differences.

We introduce here an approach that presents the basics of quantum mechanics to electrical engineers using common matrix algebra and Fourier series knowledge. Designed to quickly impart a fundamental understanding, this strategy bypasses the complex mathematics typically required. Amidst the unfolding second quantum revolution, the accelerated development of quantum engineering in academia and industry underscores the urgent need for a skilled workforce. Scientific and technological fields like quantum computing, quantum sensing, and quantum communications are gaining significant momentum, necessitating the urgent involvement of electrical engineers skilled in management, design, manufacturing, and research. These professionals require foundational quantum mechanics knowledge to effectively integrate these principles with their existing technical skills in areas like automatic control theory and signal processing. Traditionally, educational methods have targeted physicists rather than engineers, often treating quantum mechanics as too abstract for practical engineering applications. However, the rise of quantum technology now demands the inclusion of engineers from diverse backgrounds. Our proposal offers this primer as an innovative, electrical engineering-focused educational tool. We strongly believe that our approach prepares engineers to make significant contributions to the rapidly expanding field of quantum technology.

Augmented Reality (AR) has experienced a significant resurgence in popularity and interest in recent years. Despite numerous surveys and reviews in the field, information remains scattered and challenging to consolidate for newcomers. This paper addresses this gap with a comprehensive study of AR’s state of the art. We begin with an introduction to AR and related terminologies. We then describe three enabling of AR and four enhancing technologies. A survey then covers different types of commercial AR hardware with detailed specifications and elaborations. We then address AR software and discuss its basic and advanced features, mapping them to software with feature matrices. Based on a thematic literature analysis, we extract a comprehensive set of guidelines for developing AR solutions into seven themes, from ideation to best practices for implementation and deployment. Finally, we explore some essential challenges in the field of AR. This paper serves as an essential resource for those looking to understand what AR is, what is needed to get started, how to approach development, and what the future holds for AR. By consolidating essential information and providing a solid foundation, this paper aims to help researchers and developers capitalize on the current cycle of interest in AR.

Abstract Background The 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. Methods We 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. Results In 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 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.

In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and the more recently introduced Transformers. The choice of architecture depends on the data characteristics and the specific task at hand. In the 1D domain, one-dimensional CNNs (1D CNNs) are widely used, particularly for tasks involving the classification and recognition of 1D signals. While there are many applications of 1D CNNs in the literature, the technical details of their training are often not thoroughly explained, posing challenges for those developing new libraries in languages other than those supported by available open-source solutions. This paper offers a comprehensive, step-by-step tutorial on deriving feedforward and backpropagation equations for 1D CNNs, applicable to both regression and classification tasks. By linking neural networks with linear algebra, statistics, and optimization, this tutorial aims to clarify concepts related to 1D CNNs, making it a valuable resource for those interested in developing new libraries beyond existing ones.

Experimental psychology is witnessing an increase in research on individual differences, which requires the development of new tasks that can reliably assess variations among participants. To do this, cognitive researchers need statistical methods that many researchers have not learned during their training. The lack of expertise can pose challenges not only in designing good, new tasks but also in evaluating tasks developed by others. To bridge the gap, this article provides an overview of test psychology applied to performance tasks, covering fundamental concepts such as standardization, reliability, norming and validity. It provides practical guidelines for developing and evaluating experimental tasks, as well as for combining tasks to better understand individual differences. To further address common misconceptions, the article lists 11 prevailing myths. The purpose of this guide is to provide experimental psychologists with the knowledge and tools needed to conduct rigorous and insightful studies of individual differences. [ABSTRACT FROM AUTHOR]

Background: The 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. Methods: We 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. Results: In 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. Conclusions: The 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this research is to evaluate e-PBL tutorials and compare these sessions with face-to-face PBL sessions. Materials and Methods: This research is a program evaluation study in which quantitative methods were used. In the research, four 90-minute e-PBL sessions held between April and June 2020 were evaluated. Sessions conducted online were realised using the seven-step approach in groups of 13-14 students and a tutor. Video recordings were analysed with the thin slicing method. In addition, various quantitative data on evaluation were analysed using multiple tools and materials, including the end-of-program evaluation form. Friedman test and Mann-Whitney U test were used in the analyses of quantitative data. Results: Upon evaluating the analyses of the feedback received from the students about the structure, content and process of the program regarding the e-PBL tutorials, the students gave a positive opinion of 80% or more. In regards with the cases, the students had positive opinions of over 80% in terms of "motivation for learning and researching", "daily life and its relation to their individual development", "suitability to their levels of knowledge and skills", "reinforcement of topics". Support, guidance and feedback received from the tutor as a group and individually during online tutorials were statistically significantly higher than the face-to-face PBL tutorials (P<0.05). Conclusion: Research on the effectiveness of e-PBL tutorials, including ours, point out that e-PBL practices may constitute a viable alternative besides face-to face ones. However, for a sounder framing and better results, the subject should be studied in different aspects and more evidences be gathered in this area. These studies will provide evidence to educational institutions and practitioners on how to adapt and modify educational practices, including PBL. [ABSTRACT FROM AUTHOR]