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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]

The peer‐review component of the editorial process is designed to facilitate quality control, legitimize scientific research, and self‐regulate scientific communities. Even though serving as a reviewer undoubtedly has direct and indirect benefits, the peer‐review system and the methods of teaching scholars to conduct reviews are nascent and relatively underdeveloped. This article describes the peer‐review process and provides recommendations for writing reviews for scientific journals. The recommendations were developed based on the expertise and preferences of editors in chief and associate editors for behavior‐analytic journals (Cengher & LeBlanc, 2024), and they include honoring your responsibility, knowing your audience, being constructive and kind, and carefully evaluating the merits of the study or review. These guidelines may serve as a primer for scholars who want to conduct reviews for scientific journals in behavior analysis. [ABSTRACT FROM AUTHOR]

Multilevel modeling techniques have gained traction among experimental psychologists for their ability to account for dependencies in nested data structures. Increasingly, these techniques are extended to the analysis of binary data (e.g., correct or incorrect responses). Despite their popularity, the information in logistic multilevel models is often underutilized when researchers focus solely on fixed effects and ignore important heterogeneity that exists between participants. In this tutorial, we review four techniques for estimating and quantifying the relative degree of between-person variability in logistic multilevel models in an accessible manner using real data. First, we introduce logistic multilevel modeling, including the interpretation of fixed and random effects. Second, we review the challenges associated with the estimation and interpretation of within- and between-participant variation in logistic multilevel models, particularly computing the intraclass correlation coefficient (ICC), which is usually a first, simple step in a linear MLM. Third, we demonstrate four existing methods of quantifying the ICC in logistic multilevel models and discuss their relative advantages and disadvantages. Fourth, we present bootstrapping methods to make statistical inference about these ICC estimates. To facilitate reuse, we developed R code to implement the discussed techniques, which is provided throughout the text and as supplemental materials. [ABSTRACT FROM AUTHOR]

Feedforward control can be considered as the most well-known control approach to deal with measurable disturbances. It started to be used almost 100 years ago, and since then it is being used in most industrial processes. It is a very simple technique that has been used typically as a complement to PID control, although it can be combined with any feedback controller. The feedforward control design has traditionally been performed assuming perfect cancellation of the disturbance signal, and when this solution was not possible because of non-realisable problems, static design solutions were usually implemented. In the last decade, this issue has been researched and analysed, and new designs for feedforward control have appeared to face these problems. As a result, a set of new simple tuning rules have been obtained providing considerable improvements in the control system performance. This paper presents a summary of these contributions and a short history of feedforward control. [ABSTRACT FROM AUTHOR]

This document provides a practical guide to using Bayesian statistical methods in clinical and translational research. The authors explain the concepts and methods of Bayesian analysis, including Gibbs sampling and Metropolis-Hastings algorithms. They demonstrate how to apply Bayesian analyses using different software programs and discuss the importance of specifying prior beliefs and using domain knowledge. The document also covers topics such as model selection, informative priors, and model diagnostics. It aims to help researchers understand and evaluate Bayesian analyses in medical research. [Extracted from the article]

Background: Epistemic Network Analysis (ENA) is a unified, quantitative – qualitative method aiming to draw from both methodological worlds by leveraging a data set containing raw and quantified qualitative data, as well as metadata about data providers or the data itself. ENA generates network models depicting the relative frequencies of co-occurrences for each unique pair of codes in designated segments of qualitative data. Methods: This step-by-step tutorial demonstrates how to model qualitative data with ENA through its quantification via coding and segmentation. Data was curated with the Reproducible Open Coding Kit (ROCK), a human- and machine-readable standard for representing coded qualitative data, enabling researchers to document their workflow, as well as organize their data in a format that is agnostic to software of any kind. Results: ENA allows researchers to obtain insights otherwise unavailable by depicting relative code frequencies and co-occurrence patterns, facilitating a comparison of those patterns between groups and individual data providers. Conclusions: ENA aids reflexivity, moves beyond code frequencies to depict their interactions, allows researchers to easily create post-hoc groupings of data providers for various comparisons, and enables conveying complex results in a visualization that caters to both qualitative and quantitative sensibilities. [ABSTRACT FROM AUTHOR]

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices to achieve precise light–matter interactions using structural parameters and materials is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods. In recent years, deep learning techniques have emerged as promising tools for tackling the inverse design of nanophotonic devices. While several review articles have provided an overview of the progress in this rapidly evolving field, there is a need for a comprehensive tutorial that specifically targets newcomers without prior experience in deep learning. Our goal is to address this gap and provide practical guidance for applying deep learning to individual scientific problems. We introduce the fundamental concepts of deep learning and critically discuss the potential benefits it offers for various inverse design problems in nanophotonics. We present a suggested workflow and detailed, practical design guidelines to help newcomers navigate the challenges they may encounter. By following our guide, newcomers can avoid frustrating roadblocks commonly experienced when venturing into deep learning for the first time. In a second part, we explore different iterative and direct deep learning-based techniques for inverse design, and evaluate their respective advantages and limitations. To enhance understanding and facilitate implementation, we supplement the manuscript with detailed Python notebook examples, illustrating each step of the discussed processes. While our tutorial primarily focuses on researchers in (nano-)photonics, it is also relevant for those working with deep learning in other research domains. We aim at providing a solid starting point to empower researchers to leverage the potential of deep learning in their scientific pursuits. [ABSTRACT FROM AUTHOR]

Building on the growing body of research highlighting the capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT), this paper presents a structured pipeline for the annotation of cultural (big) data through such LLMs, offering a detailed methodology for leveraging GPT’s computational abilities. Our approach provides researchers across various fields with a method for efficient and scalable analysis of cultural phenomena, showcasing the potential of LLMs in the empirical study of human cultures. LLMs proficiency in processing and interpreting complex data finds relevance in tasks such as annotating descriptions of non-industrial societies, measuring the importance of specific themes in stories, or evaluating psychological constructs in texts across societies or historical periods. These applications demonstrate the model’s versatility in serving disciplines like cultural anthropology, cultural psychology, cultural history, and cultural sciences at large.

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.

This article is a tutorial describing the process and issues related to developing a state-space model for the stochastic errors affecting an Inertial Measurement Unit (IMU). The starting point is the instrument error characterization data sheet provided by the manufacturer, which is typically either an Allan Variance graph or the parameters extracted from that graph. Along with this tutorial, supplementary software is available for open source distribution that calculates and plots the Allan Variance for a given set of data; extracts optimal parameters (e.g., $Q_N$, $Q_B$, $Q_K$, and $T_B$) from Allan Variance data for a given model structure; and, computes and simulates of the discrete-time equivalent model.

Abstract Background Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention “works” and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation. Methods In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software. Results Drawing on Rubin’s potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset. Discussion Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable “weak spots” of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Competencies in statistical data processing are becoming increasingly important for modern scientists. The apparent advantages of open-source software for statistical analysis are its accessibility and adaptability. The programming language and the corresponding software R, available as a minimalistic console interface or a complete development environment RStudio/Posit, have the widest possibilities among free solutions. We present a practical guide for comparing two groups using the software R. This study compares the effective doses of standard computed tomography with low-dose computed tomography for COVID-19 patients. The practical guide summarizes theoretical approaches to medical data processing and recommendations for correctly formulating research tasks and selecting optimal statistical analysis methods. The main goal of the practical guide is to introduce the reader to the Posit interface and the basic functionality of the R language by using a practical example of treating a real medical problem. The presented material can be useful as an introduction to statistical analysis using the programming language R.

The UTEC-UNED-TECSUP University Consortium took part in the eighth edition of the Support programme for the design and implementation of strategies for continuity of the Higher Education service in public universities of the Peruvian Ministry of Education (PMESUT), devised to promote the continuity of non-classroom-based educational service in public universities. This article presents the assessment carried out in four universities, with the aim of ensuring the ongoing digitization of education in the aftermath of the pandemic. Once the diagnosis had been made, the techno-pedagogical virtualization of 138 model courses was carried out in three phases: planning; curriculum design and teaching support; and implementation of the courses in the virtual classroom. Limitations were related to existing digital divides: time constraints, personal self-regulation and limited training in pedagogical and digital competences of teachers. Individualised support and tutoring measures were sought, increasing the number of synchronous sessions to facilitate assessment. The feedback focused on information, interaction and accountability on what has been done and the improvement of the didactic act in virtual mode. In conclusion, the complexity of the process was notable, as well as the high level of demand to achieve results which, although positive, encountered several obstacles arising from the different agents and elements involved.

Causal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.

Abstract Background: Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher’s ability to gain confidence in interpreting and implementing Bayesian analyses. Methods: We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research. Results: The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems. Conclusion: Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

ABSTRACTBackground: Epistemic Network Analysis (ENA) is a unified, quantitative – qualitative method aiming to draw from both methodological worlds by leveraging a data set containing raw and quantified qualitative data, as well as metadata about data providers or the data itself. ENA generates network models depicting the relative frequencies of co-occurrences for each unique pair of codes in designated segments of qualitative data. Methods: This step-by-step tutorial demonstrates how to model qualitative data with ENA through its quantification via coding and segmentation. Data was curated with the Reproducible Open Coding Kit (ROCK), a human- and machine-readable standard for representing coded qualitative data, enabling researchers to document their workflow, as well as organize their data in a format that is agnostic to software of any kind. Results: ENA allows researchers to obtain insights otherwise unavailable by depicting relative code frequencies and co-occurrence patterns, facilitating a comparison of those patterns between groups and individual data providers. Conclusions: ENA aids reflexivity, moves beyond code frequencies to depict their interactions, allows researchers to easily create post-hoc groupings of data providers for various comparisons, and enables conveying complex results in a visualization that caters to both qualitative and quantitative sensibilities.

Background: Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation. Methods: In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software. Results: Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset. Discussion: Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care. [ABSTRACT FROM AUTHOR]

Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques facilitate estimation of response time models. Implementations of these models in standard statistical software, however, are still sparse. In this accessible tutorial, we discuss one of the most common response time models—the lognormal response time model—embedded in the hierarchical framework by van der Linden (2007). We provide detailed guidance on how to specify and estimate this model in a Bayesian hierarchical context. One of the strengths of the presented model is its flexibility, which makes it possible to adapt and extend the model according to researchers' needs and hypotheses on response behaviour. We illustrate this based on three recent model extensions: (a) application to non‐cognitive data incorporating the distance‐difficulty hypothesis, (b) modelling conditional dependencies between response times and responses, and (c) identifying differences in response behaviour via mixture modelling. This tutorial aims to provide a better understanding of the use and utility of response time models, showcases how these models can easily be adapted and extended, and contributes to a growing need for these models to answer novel substantive research questions in both non‐cognitive and cognitive contexts. [ABSTRACT FROM AUTHOR]

It is widely recognised that an important aspect of doctoral study is the development of a researcher identity. However, little is known about how to support this. Although previous research has highlighted the importance of discursive engagement for researcher identity development, no studies examine talk or discuss how identities are constructed through interaction. This article examines how doctoral students’ researcher identities develop during tutorials on a professional doctorate in education. Analysis reveals how researcher identities are constructed and accomplished, turn by turn, during study-based talk. Researcher identities are co-constructed with and confirmed by peers, and verified by a tutor who validates students’ actions and describes experiencing similar, often difficult, processes herself. Knowledge and understanding facilitated by discussion prompts identity development and tutorial talk builds a sense of belonging and confidence, acculturating students into the research community. The article makes an original contribution to research by analysing situated talk to show identity accomplishment in action. The article also makes recommendations for both practice and further research which include setting up opportunities for doctoral students to talk and share experiences, and close analysis of doctoral interactional events such as supervision meetings and peer support groups. [ABSTRACT FROM AUTHOR]

Floods are an increasingly frequent phenomenon with catastrophic consequences for citizens and cities. Virtual reality (VR) has become a valuable training tool, simulating inaccessible or hazardous situations like emergencies and risk environments. This study evaluates a designed virtual training simulating a riverbank flood scenario. Beyond imparting factual knowledge, this environment aims to facilitate experiential learning by enabling users to repeatedly practice correct behaviors safely. We investigate the impact that different tutorials have on the ability of users to comprehend and retain the knowledge conveyed during the VR experience. To achieve this, three training experiences with different levels of contextualization and interaction tutorials were compared in a between-participant design. Data collection involved both self-reported measures (e.g., questionnaire) and objective measures (e.g., behavioral data). The results demonstrate the effectiveness of the virtual environment in acquiring knowledge of flood contents, particularly when the tutorial offers high levels of interaction, thereby reducing cognitive load. Furthermore, we observed that a lower level of contextualization in the tutorial phase enhances user efficiency in acquainting themselves with the virtual environment. This study emphasizes the potential of virtual training in augmenting preparedness, providing a valuable framework for the development of experiential learning tools in flood emergencies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the College Of Basic Education is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate classifications supply climate visualization with inference about general vegetation types. The Köppen classification system of thermal classes and an arid class is widely used, but options are available to strengthen climate change detection. For this study, I incorporated temperature and aridity information into all climate classes to isolate climate change, added a hypertropical class to better detect warming and drying in tropical zones, and developed a consistent ruleset of thermal classes with one temperature variable for streamlined application, yet maintained primary Köppen thermal classes. I compared climate currently to 6000 years ago (ka; Mid-Holocene) and 22 ka (Last Glacial Maximum) worldwide. Growing degree days > 0 °C was the most efficient variable for modeling thermal classes. Climate classes based on growing degree days matched 86% of Köppen thermal classes. Current climate shared 80% and 23% of class assignments with the Mid-Holocene and Last Glacial Maximum, respectively, with dry conditions shifting to the tropical and hypertropical classes under current climate. Contributing to our understanding of global environmental change, this classification demonstrated that the hypertropical class experienced the greatest change in area since 6 ka and the second greatest change in area since 22 ka, and the greatest increase in percentage arid classes during both intervals. The added hypertropical class with aridity information delivered sensitive detection of warming and drying for relevant climate classes under climate change. [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).

The assessment of physiological complexity via the estimation of monofractal exponents or multifractal spectra of biological signals is a recent field of research that allows detection of relevant and original information for health, learning, or autonomy preservation. This tutorial aims at introducing Whittle’s maximum likelihood estimator (MLE) that estimates the monofractal exponent of time series. After introducing Whittle’s maximum likelihood estimator and presenting each of the steps leading to the construction of the algorithm, this tutorial discusses the performance of this estimator by comparing it to the widely used detrended fluctuation analysis (DFA). The objective of this tutorial is to propose to the reader an alternative monofractal estimation method, which has the advantage of being simple to implement, and whose high accuracy allows the analysis of shorter time series than those classically used with other monofractal analysis methods.

This tutorial offers a full introduction to Building and Deploying a Classification schema using Open Standards and Technology. Beyond accessing the command line, no prior knowledge is assumed as all the steps are described in detail. However, the tutorial will take you through some more complex technical steps. In order to profit best from the example workflows of the last chapter, it is good if you have some idea of how to work with OpenRefine and/or to have a TEI Publisher server set up to play around with. With this tutorial, you ‑ the reader ‑ should gain an understanding of: ‑ what different classification schemes can contribute to your project, ‑ what to pay attention to when building a classification scheme, ‑ how to code the classification scheme (in SKOS), ‑ how to publish the classification scheme (on GitHub pages), ‑ what some possible scenarios of using classifications schemes in actual project workflows are.

Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data. [ABSTRACT FROM AUTHOR]

Social network analysis (SNA) is a highly flexible research method that allows for novel exploration of a wide variety of research phenomena. Evidence from fields as disparate as public health, education, informatics, sociology, and medicine has demonstrated the importance of recognizing the complexity inherent in individuals' connections with others. In this article, we provide a brief conceptual overview of social network theory and methodology, and then demonstrate how to apply SNA to an applied psychological research context studying students embedded in classrooms. We also provide numerous supporting materials on our OSF page, including R code for all analyses, a dataset containing social network data, and a glossary of key terms in social network analysis. We conclude with a set of recommendations for researchers interested in applying SNA to their own contexts and content areas. [ABSTRACT FROM AUTHOR]

Electromembrane extraction (EME) is a microextraction technique where charged analytes are extracted from an aqueous sample solution, through a liquid membrane, and into an aqueous acceptor, under the influence of an external electric field. The liquid membrane is a few microliters of organic solvent immobilized in a polymeric support membrane. EME is a green technique and provides high selectivity. The selectivity is controlled by the direction and magnitude of the electric field, the chemical composition of the liquid membrane and the pH. Recently, commercial prototype equipment for EME was launched based on the use of conductive vials, and interest in EME is expected to increase. The current article is a tutorial and discusses the principle and practical work with EME. The practical information is related to the commercial prototype equipment but is valid also for other technical configurations of EME. The tutorial is intended to give readers a fundamental understanding of EME, which is required for method development and operation, and for avoiding common pitfalls. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The quantum nature of light enables potentially revolutionary communication technologies. A key to advancing this area of research is a clear understanding of the concepts of states, modes, fields, and photons. The concept of field modes carries over from classical optics, while the concept of state has to be considered carefully when treating light quantum mechanically. The term “photon” is an overloaded identifier in the sense that it is often used to refer to either a quantum particle or the state of a field. This overloading, often used without placing it in context, has the potential to obfuscate the physical processes that describe the reality we measure. We review the uses and relationships between these concepts using modern quantum optics theory, including the concept of a photon wave function, the modern history of which was moved forward in a groundbreaking paper in this journal by Iwo BiałynickiBirula, to whom this article is dedicated. [ABSTRACT FROM AUTHOR]

Big Data can bring enormous benefits to psychology. However, many psychological researchers show skepticism in undertaking Big Data research. Psychologists often do not take Big Data into consideration while developing their research projects because they have difficulties imagining how Big Data could help in their specific field of research, imagining themselves as "Big Data scientists," or for lack of specific knowledge. This article provides an introductory guide for conducting Big Data research for psychologists who are considering using this approach and want to have a general idea of its processes. By taking the Knowledge Discovery from Database steps as the fil rouge, we provide useful indications for finding data suitable for psychological investigations, describe how these data can be preprocessed, and list some techniques to analyze them and programming languages (R and Python) through which all these steps can be realized. In doing so, we explain the concepts with the terminology and take examples from psychology. For psychologists, familiarizing with the language of data science is important because it may appear difficult and esoteric at first approach. As Big Data research is often multidisciplinary, this overview helps build a general insight into the research steps and a common language, facilitating collaboration across different fields. Technological advances have led to an abundance of widely available data on every aspect of life, called Big Data. Big Data provide psychologists with new means to research psychological constructs in new ways. Psychologists often show excitement when talking about these new data opportunities. However, this enthusiasm has not yet led to extensive use of Big Data in the psychological community. One reason for this is that psychologists usually do not acquire the various skills and competencies that successful Big Data research requires during their training. This work provides an introductory guide for conducting Big Data research for psychologists who are considering using this approach and want to have a general idea of its processes and techniques. It gives useful indications for finding data suitable for psychological investigations, describes how these data can be pre-processed, enlists some techniques and tools to analyze them. [ABSTRACT FROM AUTHOR]

Background and Aim: To shift the paradigm from passive tutor driven tutorials to more responsible active learning by the students, a co operative teaching learning method could be a good alternative. The Jigsaw approach, a model of co operative learning, focuses on learning in groups with fellow learner co operation and reassures team work. Thus, this study was aimed at assessing the effectiveness of jigsaw strategy in comparison to tutorials in enhancing cognitive skills among the medical students in microbiology. Materials and Methods: A cross sectional study was carried out at microbiology department for 50 consenting MBBS students of professional year II and 50 consenting B.Sc nursing students of 2nd years. Students were randomly divided into two batches: Aand B of 25 students each. They were prepared for either a jigsaw (Experimental) or tutorial (Control) a week prior on predecided topics. The same groups were crossed over for another topic. Students' performance was assessed by pretest, posttest, academic achievement level, and retention test by prevalidated multiple choice questionnaires. The student perception on TL methods was evaluated by using learning experience questionnaires. Results: Posttest scores of both groups were found to be significant at the level of P < 0.05. Among medical students, mean of posttest scores in the jigsaw is more than tutorial (12.4 v/s 9.7, crossover 13.3 v/s 11.2) and in nursing students (11.7 v/s 8.2, crossover 11.6 v/s 9.9). Mean retention tests scores and absolute achievement level were high in jigsaw groups. The students preferred jigsaw over the tutorial. Conclusion: Jigsaw strategy is a robust instructional tool, well perceived by the students to enhance cognitive skills as inferred by the results and can be tailored to the needs of varied topics across different disciplines.

During the Covid-19 pandemic, learning process cannot be done by face-to-face so teachers are required to be creative in choosing learning media. Based on these problems, a video powerpoint tutorial product was developed in the thematic learning for the Second Grade of Muhammadiyah Elementary School 2 Pontianak in order to describe the results of validation and student responses.. The research design used in this research was Research and Development (RnD) by utilizing 4D theory from Thiagarajan that consisted of 4 steps such as define, design, develop, and dissemination. The product of this research and development was a PowerPoint Tutorial Video for Thematic teaching and Learning in which the validity had been tested by the experts. The result from the experts' validation proposed that the product scored 91.25% for its advisability in the aspect of teaching & learning (materials), 88.75% in the aspect of its media (display), and 72.5% in the aspect of language. Furthermore, the simplicity of this product also had been tested based on the 60 students' responses with the average score 3.37 in percentage of 84%. Therefore, according to the students’ responses score, it can be concluded the product was considered good and advisable to use.

The purpose of this study was to determine the effect of using Photoshop tutorial on student learning outcomes in the subject of class XI Design at SMK Negeri 1 Pallangga, Gowa Regency. The approach used in this study is a quantitative approach, with the type of experimental, and the research design used is the pretest posttest group design, which compares the results of the pretest and posttest in the control class and the experimental class. The population in this study amounted to 58 people, consisting of 28 women and 20 men. The sampling technique used is a random sampling technique, which takes all population groups randomly as a sample. Data collection techniques used are observation, tests, and documentation. The data analysis used is descriptive statistical analysis and inferential statistical analysis using SPSS. The results of this study indicate that there is a significant difference between after using the photoshop tutorial in the face subject, photoshop editing material in the experimental class compared to the control class. This at the same time answers the hypothesis "H1an media photoshop tutorial on student learning outcomes in the subject matter of Class XI Design at SMK Negeri 1 Pallangga, Gowa Regency", is declared accepted Keywords: Media video tutorial, photoshop, SMK

Interest in moderation and mediation models have gained momentum since the 1980s and have become widespread in numerous fields of research including clinical, social, and health psychology in addition to behavioral, educational, and organizational research. There are resources available to help the user understand an analysis of a moderated mediation using the PROCESS macro and its resultant output, however, many are in video format (e.g., YouTube) or lack detailed instructions based on real world examples. To our knowledge, there are no resources that provide a thorough yet accessible step-by-step explanation of the procedure involved in using PROCESS v4.1 to analyze and interpret a moderated mediation model using real data in SPSS v28. The aim of this guide is to address this knowledge gap. An overview of mediation, moderation, and moderated mediation models is presented followed by instructions for verifying that assumptions are respected. Finally, a procedure to analyze data using PROCESS v4.1 is presented along with an interpretation of the resultant output.

Purpose: Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data. Methods: The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step‐by‐step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research. Results: The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted. Conclusions: Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user‐friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization. [ABSTRACT FROM AUTHOR]

Peer tutoring is a learning method that entails student partnerships that pair good students with lower achievers or pupils with comparable achievement for systematic reading and math study times. The main purpose of this study is to determine the problems of selected grade 6 students in mathematics that would help the students enhance their academic performance in the subject. The data gathering will be done by selecting grade 6 students, both female and male, with separate results from the conducted pretest and posttest questionnaires. Other students who do not fall into the category are not included in the collection of information. The only purpose of this study is to achieve its goal, and it won't investigate anything else. After using dependent T test at the scores of the students after pretest and posttest, we got a computed value (T Value) of -5.6803. With a critical value of -2.0195, we can reject the null hypothesis and conclude that peer tutoring can bring a significant difference to the combined result of critical thinking and problemsolving test. The finding suggests that school heads and classroom teachers may use "peer tutoring" as a learning method for helping the learners improve their performance in mathematics. The school and the classroom teachers may use peer tutoring as a learning method that entails student partnerships that pair good students with lower achievers or pupils with comparable achievement for systematic reading and math study times. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has accelerated the digitalization of assessment, creating new challenges for measurement professionals, including big data management, test security, and analyzing new validity evidence. In response to these challenges, Machine Learning (ML) emerges as an increasingly important skill in the toolbox of measurement professionals in this new era. However, most ML tutorials are technical and conceptual‐focused. Therefore, this tutorial aims to provide a practical introduction to ML in the context of educational measurement. We also supplement our tutorial with several examples of supervised and unsupervised ML techniques applied to marking a short‐answer question. Python codes are available on GitHub. In the end, common misconceptions about ML are discussed. [ABSTRACT FROM AUTHOR]

The model of Spiking Neural P systems (SNP systems) is a widespread computational model in the area of membrane computing. It has numerous applications, especially related to machine learning. Most of these applications require a custom variant of SNP systems, differing by the rule form and by semantics. The model of network of cells and the formal framework for SNP systems were developed to help the analysis of such custom models, to compare and relate them to each other and to other models of computing. The model specifies the data structure, the rules and the update procedure, while the formal framework concentrates on how the input, output and the choice of the update strategy are handled. Together, these concepts specify a concrete instance of a network of cells that strongly bisimulates the desired model, thus making easier the process of the creation of new models and the extension of existing ones. Since the formal framework is rather generic, it might be slightly complex to use it for concrete cases. This paper provides a tutorial that explains the model of networks of cells and the basic concepts used in the formal framework for SNP systems. It gives a series of examples for the analysis of existing models, their bisimulation and their extension by different features. [ABSTRACT FROM AUTHOR]