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For forcibly displaced people, high educational attainment is economically and socially empowering. Using experiences of African refugee youth in Australia as an empirical case and drawing on the capability approach to social justice, this paper aims to assess the substantiveness of education opportunities of refugees. Qualitative data were generated through policy review and semi-structured interviews. The analysis shows that not only are refugees invisible in equity policies, but educational inequality is also framed homogeneously as a lack of access. The restrictive framing disregards differences in people's ability to convert resources into valuable outcomes. Specifically, the paper identifies four overlooked factors of educational inequality among African refugee youth: early disadvantage, limited navigational capacity, adaptive preferences, and racial stereotypes. Without an expansive view of disadvantage, it is hardly possible to break the link between marginal social position and low educational attainment of refugees. [ABSTRACT FROM AUTHOR]

Background: Pain and anxiety management in patients undergoing medical and surgical procedures is an important competence area for nurses. Aim: This study aimed to determine and compare the effects of virtual reality and acupressure interventions on pain, anxiety, vital signs and comfort levels in the process of femoral catheter extraction for patients undergoing coronary angiography. Methods: The study was a single‐blind, three‐group, randomized controlled trial conducted in the cardiology clinics of a university hospital in 2021. A total of 153 patients (51 virtual reality, 51 acupressure, 51 control) participated in the study. Data were collected using a Visual Analogue Scale, the State–Trait Anxiety Inventory, a vital signs follow‐up form and the Perianesthesia Comfort Scale. Results: Both intervention groups had significantly lower pain and anxiety scores, as well as higher comfort scores, compared to the control group (p < 0.001). The virtual reality group had lower systolic blood pressure, respiratory rate and pulse rate than the control group (p < 0.05). The acupressure group had lower systolic and diastolic blood pressure and respiratory rate than the control group (p < 0.05). Conclusions: While neither intervention group was found to be superior to the other, both interventions improved vital signs and comfort levels by reducing pain and anxiety. Summary statement: What is already known about this topic? Uncontrolled pain and anxiety during medical and surgical procedures may increase the risk of complications and have negative effects on the pulmonary and cardiovascular systems, leading to changes in vital signs and compromising patients' hemodynamics.Virtual reality has demonstrated its potential to restore cognitive attention capacities, enabling patients to remain engaged and mentally focused during procedures, and thus reducing pain and anxiety.Acupressure, as a non‐pharmacological intervention, involves applying pressure to specific points on the body to alleviate pain and anxiety. It constitutes a safe, side‐effect‐free and cost‐effective option for improving patient outcomes.What this paper adds? This paper contributes to the knowledge of virtual reality and acupressure interventions as non‐pharmacological approaches that nurses can safely incorporate within their independent professional roles.The study evaluates the effects of traditional acupressure and technological virtual reality interventions on patients, comparing the two interventions for the first time to the best of our knowledge. Furthermore, to the best of our knowledge, these procedures were applied for the first time in femoral catheter extraction after coronary angiography.The implications of this paper: This study emphasizes the positive impacts of virtual reality and acupressure interventions administered by nurses for patients undergoing medical procedures.Overall, the findings suggest that non‐pharmacological techniques such as virtual reality and acupressure can contribute to a similar degree in improving patient outcomes during medical procedures. [ABSTRACT FROM AUTHOR]

In this paper, we develop statistical methodology for the analysis of data under nonnormal distributions, in the context of mixed effects models. Although the multivariate normal distribution is useful in many cases, it is not appropriate, for instance, when the data come from skewed and/or heavy‐tailed distributions. To analyse data with these characteristics, in this paper, we extend the standard linear mixed effects model, considering the family of generalized hyperbolic distributions. We propose methods for statistical inference based on the likelihood function, and due to its complexity, the EM algorithm is used to find the maximum likelihood estimates with the standard errors and the exact likelihood value as a by‐product. We use simulations to investigate the asymptotic properties of the expectation‐maximization algorithm (EM) estimates and prediction accuracy. A real example is analysed, illustrating the usefulness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper explores school principals' and teachers' views of the Italian teachers' appraisal system and related indicators. In particular, the research aimed at examining the opinions of school principals and teachers regarding the domains to be considered for teacher appraisal purposes. An investigation involving 37 school principals and 868 in service teachers, from three Italian regions, is presented. The data was collected through a questionnaire and statistically analysed. Findings suggest operationalising new data sources - specifically students and parent, - is beneficial to evaluate teacher quality as a construct in data-based professional learning communities. They show different positioning: teachers seem to be really inclined to be evaluated, especially by students and parents, while school principals still seem very cautious in entrusting the teachers' appraisal to an external point of view. This paper adds to the existing research showing the perspective of a new teachers' appraisal, inspired by multi-faceted models based on multiple points of view (students, parents, not only principals) and not reduced to simplistic solutions of an accounting type. Furthermore, the study answers the call for research into teachers' appraisal as part of the professional learning actions and in view of more integrated professional leadership. [ABSTRACT FROM AUTHOR]

Background: Over the last 20 years, educational large‐scale assessments have undergone dramatic changes moving away from simple paper‐pencil assessments to innovative, technology‐based assessments. This comprehensive switch has led to some rather technical improvements such as identifying early guessing or improving standardization. Objectives: At the same time, process data on student interaction with items has been shown to carry value for obtaining, reporting, and interpreting additional results on student skills in international comparisons. In fact, on the basis of innovative simulated assessment environments, news about student rankings, under‐ and overperforming countries, and novel ideas on how to improve educational systems are prominently featured in the media. At the same time, few of these efforts have been used in a sustainable way to create new knowledge (i.e., on a scientific level), to improve learning and instruction (i.e., on a practical level), and to provide actionable advice to political stakeholders (i.e., on a policy level). Methods: This paper will adopt a meta‐perspective and discuss recent and current developments with a focus on these three perspectives. There will be a particular emphasis on new assessment environments that have been recently employed in large‐scale assessments. Results and Conclusions: Most findings remain very task specific. We propose a necessary steps that need to be taken in order to yield sustainable change from analysing process data on all three levels. Implications: New technologies might be capable of contributing to the research‐policy‐practitioner gap when it comes to utilizing the results from large‐scale assessments to increase the quality of education around the globe but this will require a more systematic approach towards researching them. Lay Description: What is already known about this topic: Technology added new possibilities to educational large‐scale assessmentNext to conventional tasks, new interactive tasks were introduced to measure problem‐solving competenciesThis transition to computerized assessment and interactive tasks allowed analysing behavioural processes in addition to correct or incorrect responses What this paper adds: In this paper we argue that the enormous potential of analysing behavioural processes from interactive tasks is not fully used to yield sustainable change in educationWhile analysing behavioural data has led to substantial advances in measurement quality, much of this research has been atheoretical and difficult to replicate Implications for practice and/or policy: We propose necessary steps for research on behavioural processes from interactive tasks necessary to yield sustainable change on a scientific, practical and political level [ABSTRACT FROM AUTHOR]

Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work‐integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro‐level relations. Objectives: The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. Method: This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem‐solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. Results and Conclusions: We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Implications: Applying temporal motifs as an analytics‐based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion. Lay Description: What is already known about this topic?: The development of team cohesion has been widely studied for decades in face‐to‐face educational contexts but remains underexplored in online environmentsObtrusive tools, such as questionnaire instruments are still the primary method used to examine team cohesionTeam cohesion is an emergent state that changes over time, representing the synchrony of different behavioural interactionsResearch that explores and examines the temporality of cohesion as it unfolds in team learning is limited What this paper adds?: Underlines how emerging team behaviours are characterised by their engagement, performance and cohesion development using temporal network motifsPresents an analytics‐based measure to examine cohesion using temporal network motifs as a proxyProvides a learning analytics approach for researchers and instructors to evaluate cohesion in online learning environments through online learner trace data The implications of study findings for practitioners: The paper provides insights into distinct teams with emerging learning behaviours, performance associations and cohesion in an online learning platform.The paper provides instructors with a way to monitor and support a team's cohesion over time by examining the way temporal motifs occur.The paper offers a new avenue to understanding learner engagement beyond summarisations [ABSTRACT FROM AUTHOR]

Estimation of variance components is essential in data analysis based on a linear mixed model. Among different estimation methods, the most commonly used in practice are likelihood-based approaches: maximum likelihood and restricted maximum likelihood. These methods do not guarantee positive-unbiased estimators, however. The method of moments ensures unbiasedness but may yield negative estimates, which can cause problems when estimating fixed effects by generalized least squares. The paper develops an algorithm to derive strictly positive unbiased estimators with minimum variance in a well-defined class. The key idea is to take the method-of-moments estimator, given by a quadratic form of a symmetric matrix A , as a starting point and modify it using the class of square non-singular regularization matrices Q while preserving unbiasedness in addition to ensuring positivity. Different subclasses of structured Q are possible for convenience instead of all possible Q matrices. A search algorithm then finds a local or global optimal matrix A depending on Q and the corresponding optimal variance component estimator by minimizing the variance, a function of the unknown variance components and kurtosis parameters in the model. Availability of an optimal matrix A means that the theoretical and numerical properties of the estimator can be conveniently studied, also in small samples. The proposed method further allows finding matrices A leading to quadratic forms closely approximating the corresponding numerical values of the likelihood-based estimates. The paper investigates the dependence of variance functions on the unknown model parameters. Using two illustrative examples, Examples I and II, the paper also illustrates the use of the matrix Q and the determination of the kurtosis parameters in the search for the optimal variance component estimators by keeping the bias zero and the variance trim for the bias–variance trade-offs. [ABSTRACT FROM AUTHOR]

The inverter is considered the core of the PV power plant. The inverter's failure leads to generation loss and decreases plant availability. So, it is required to investigate a clear Root Cause Analysis (RCA) to deduce the failure causes and implement the required corrective action in addition to the preventive action to avoid more inverter failure, hereby maintaining the plant available to a certain value. This paper discusses real-time mode operation data analysis of the PV grid-connected inverter due to real central inverter incidents in Benban solar park located in Egypt.The central inverter plays an important role in the Mega-Scale PV power plant. The main function of this inverter is to convert the DC power produced by the PV modules to AC power to be injected into the utility grid by considering specific characteristics based on the grid code. The availability of any PV power plant directly depends on the healthy inverter's operation. The more increases for the installed inverters, the less availability loss in the case of inverter partial or catastrophic failures. So, it is required to focus on the failure causes of the central inverter by implementing a technical analysis using the available operational data. The monitored data of the central inverter in the PV power plant is classified into two types. The first type is the continuous time data stored in the memory. It represents the waveforms of inverter outputs like voltage, current, frequency, .... etc. Unfortunately, in case of a catastrophic failure, the central inverter is completely charred, and the continuous time data is lost due to storage memory damage. The second type is the operation data that is recorded by the SCADA system (per one-minute interval). Hereby, the operation data is the sole data in the case of the completely charred inverter. The representation of the operational data in curves indicates symptoms that can be used for the RCA processes. The investigation outcomes include three results. The first result is detecting the signature of the IGBT thermal stress on the voltage balance of the DC link capacitor. The second result is verifying a scenario for the cause of the IGBT failure by implementing a technical mathematical model based on the detected symptoms that denote the fault signature which is considered the thread-tip for detecting the failure cause. The third result is the simulating scenario for the interpretation of a DC link capacitors explosion due to the short circuit fault that occurred due to IGBT failure. The investigation in this paper is performed based on operation data analysis of the PV grid-connected inverter (central type) due to a real incident. The analysis methodology is based on mathematical calculation for the IGBT junction temperature using the measured heatsink temperature. The study concludes that after the IGBT failure occurred, it was a short circuit for a while and closed the terminals of the DC link capacitors. So, the DC link capacitors exploded and produced heavy sparks that led to enough fire to burn the inverter container completely. [ABSTRACT FROM AUTHOR]

This paper serves as an introduction to the special issue of Prevention Science entitled, "Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science." This special issue includes a collection of original papers from multiple disciplines that apply individual-level data synthesis methodologies, including IDA, individual participant meta-analysis, and other related methods to harmonize and integrate multiple datasets from intervention trials of the same or similar interventions. This work builds on a series of papers appearing in a prior Prevention Science special issue, entitled "Who Benefits from Programs to Prevent Adolescent Depression?" (Howe, Pantin, & Perrino, 2018). Since the publication of this prior work, the use of individual-level data synthesis has increased considerably in and outside of prevention. As such, there is a need for an update on current and future directions in IDA, with careful consideration of innovations and applications of these methods to fill important research gaps in prevention science. The papers in this issue are organized into two broad categories of (1) evidence synthesis papers that apply best practices in data harmonization and individual-level data synthesis and (2) new and emerging design, psychometric, and methodological issues and solutions. This collection of original papers is followed by two invited commentaries which provide insight and important reflections on the field and future directions for prevention science. [ABSTRACT FROM AUTHOR]

This commentary on the special issue, "Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science" summarizes the utility of data synthesis techniques to elucidate prevention effects in important ways, including effects on low base–rate conditions and effects across multiple small-scale studies of preventive interventions, long-term and crossover effects of preventive interventions, and addressing for whom preventive interventions work, and for how long. In addition, articles tackle methodological challenges by integrating and harmonizing data. Much progress has occurred in the past 5 years. We consider in this commentary the full collection of papers in the special issue, and their ongoing contributions of data synthesis methods for advancing research on the prevention of mental, emotional, and behavioral health problems. We organize our observations by several themes noted across the papers. We also highlight the National Institute of Mental Health's investments that align with many of the efforts summarized here to advance our understanding of prevention research. [ABSTRACT FROM AUTHOR]

Aim: The aim of this paper is to critically reflect on our team's experience of using netnography to explore vaccine‐hesitant parents and pregnant women, a group who have traditionally been difficult to recruit to research studies and a methodology that is underutilized in nursing research. Design: This paper takes the form of a discussion paper that will utilize data obtained from a qualitative netnographic study. Relevant literature was searched including 2015–2023. Method: This paper utilized data obtained from a qualitative study that used netnography as methodology and an online minable data source. Netnography is a relatively new methodology that uses the online environment to explore digital cultures and study networked society. It pays particular attention to cultural insights and conditions that impact the human experience. This methodology is particularly relevant to nursing research which is often humanistic and always conducted to ensure optimal patient outcomes. Results: Using netnography for the first time has resulted in four main insights. These include the adaptability of the process; the creativity involved in designing the site; the ready acceptance of the site by participants and the co‐creation knowledge that resulted. Conclusions: Netnography is a creative methodology that was successful in accessing and engaging the vaccine‐hesitant community, a group who are often marginalized. Netnography has the advantage of using a platform that is familiar and safe for many people and provides access to an extensive minable data source. [ABSTRACT FROM AUTHOR]

Recently, there has been a paradigm shift from considering physical activity and sedentary behaviour as "independent" risk factors of health to acknowledging their co-dependency and compositional nature. The focus is now on how these behaviours relate to each other rather than viewing them in isolation. Compositional data analysis (CoDA) is a methodology that has been developed specifically for compositional data and the number of publications using CoDA in physical activity and sedentary behaviour research has increased rapidly in the past years. Yet, only a small proportion of the published studies in physical activity and sedentary behaviour research have investigated the time-use composition as dependent variables. This could be related to challenges regarding the interpretation of the results and the lack of guidelines for deciding which statistical approach to use. Therefore, in this paper, four different approaches for analysing the time-use composition as dependent variables are presented and discussed. This paper advocates that the aim of research should guide how the dependent variable is defined and which data analysis approach is selected, and it encourages researchers to consider analysing time-use components as dependent variables in physical activity and sedentary behaviour research. [ABSTRACT FROM AUTHOR]

Metainferences, or the insights derived from integrating quantitative and qualitative inferences at the end of a study, are crucial for achieving added value and synergy in mixed methods research. There is an ongoing need to understand how researchers generate metainferences, especially considering their pivotal role in helping researchers achieve full quantitative and qualitative integration. While some examples of metainferences generation are available in the mixed methods literature, more explicit guidance is required. Approaches to developing metainferences must also be contextual, as inferences of this type are contingent on the nature and purpose of the mixed methods study, the type of mixed methods design, and the quality of the research data. This paper describes a seven-step process for generating metainferences using a convergent mixed methods study as an exemplar. These steps consist of identifying knowledge, experience, and data-driven inferences from the quantitative and qualitative data; developing inference association maps to draw metainferences; and assessing the validity of metainferences using backward working heuristics. This paper contributes to mixed methods research by shedding light on the development of metainferences in convergent designs and by providing practical and tangible tools for making sense of the complexity of the analysis and interpretation tasks involved in the process of generating metainferences. [ABSTRACT FROM AUTHOR]

Unbalanced and insufficient development is a prominent problem in China's period of economic transformation, and an accurate grasp of the current situation and evolutionary trend of innovation and economic development is of great significance in finding a solution and promoting regional coordinated development. This paper introduces the framework of functional data analysis to the study of both innovation and economic development in China, examining its spatial dynamic distribution and convergence characteristic in greater depth. Empirical results revealed the following. (1) Significant regional differences exist in the absolute level of China's innovation development, with a balanced improvement in the development speed and growth potential. Significant differences exist in the absolute level and speed of economic development, but not in the potential energy of development as reflected by acceleration. (2) The σ(t) function of nationwide and regional innovation development shows a downward trend. There is σ(t) convergence in economic development nationwide and in the northeastern, eastern, and western regions, but the regional disparities in economic development within the six central provinces have not yet been effectively mitigated. (3) Nationwide and regional innovation development are in a state of β(t) convergence. There is significant β(t) convergence in the nationwide economic development, with a transition point from divergence to convergence in the northeastern, eastern, and western regions. However, the β(t) function of economic development in the central region shows a region-wide divergence. The findings of this paper have important policy implications for recognizing the balance and disparities in innovation and economic development among regions in China and promoting coordinated regional development. [ABSTRACT FROM AUTHOR]

The paper explores the application of Steiner's most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method's versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 μ Gy for 1342 h of exposure, with an uncertainty range of + 3.41 to − 3.59 μ Gy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 μ Gy, with an uncertainty range of + 3.58 to − 3.48 μ Gy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research. [ABSTRACT FROM AUTHOR]

Current literature dealing with the theory of sampling process flows is limited in scope, a situation addressed by papers presented at conferences by the author. This paper provides a summary of methods to quantify process sampling variance in a sound statistical manner. Two methods of calculation of the process sampling variance are presented here with models for robust estimation of the process covariance/variogram. The strategies for process sampling to acquire the data for process characterisation are described. The method for process characterisation can be based on special sampling campaigns, on the analysis of on-line measurement data, or on the analysis of historical data, which may be based on composite samples, such as shift samples. These scenarios are all dealt with as well as methods based on analysis of process dynamics. The latter analysis demonstrates that use of covariance functions drawn from geostatistical practice can be inappropriate for process analysis. The work is supported with plant data examples and quantitative analyses of process conditions. [ABSTRACT FROM AUTHOR]

This paper outlines an example of Special Collections materials being used to support master's-level students' data and digital skills development. It evidences positive student learning outcomes through an unconventional interdepartmental collaboration between a digital media teaching team and Special Collections staff, which combined digital practices and tangible Special Collections material. This paper outlines the readings used for the course under review, the assignments used to evaluate students, student experiences of those assignments, and staff observations of student success and the logistics of course delivery. Findings show that in completing their assignments students (a) negotiated uncertainty; (b) increased understandings of data and digital methods; (c) embraced the lifeworlds of data; and (d) blurred the boundaries of analog and digital. More generally, this work finds that Special Collections material can be effectively used to support data and digital skills development, as well as help students gain broader understandings of digital contexts and media. Value is, however, dependent upon embracing uncertainty: something that universities tend to avoid. But, as is shown, staff and students alike can thrive in states of bewilderment. [ABSTRACT FROM AUTHOR]

In this paper, we present a continuous model as an analog to the discrete atomic model often used in the interpretation/analysis of magnetometry studies of magnetic structures with bilinear and/or biquadratic coupling. For a typical set of parameters, the continuous model is shown to be nearly indistinguishable from its discrete counterpart; validating it for data analysis purposes. Furthermore, we also find a very simple analytic expression which can approximately solve the continuous model and also provides insight into the behaviors of the magnetic moment within the magnetic structure. The main advantage of the continuous model is that numerically it can be solved as a boundary value problem, which can be significantly faster than the energy minimization required for the discrete model improving computational speed and allowing for much more rapid data analysis. [ABSTRACT FROM AUTHOR]

Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a probe to investigate the physical properties inside supernovae. However, how to search for and extract the GW signals from CCSNe remains an open question owing to their complicated time–frequency structure. In this paper, we apply the ensemble empirical mode decomposition (EEMD) method to decompose and reconstruct simulated GW data generated by the magnetorotational mechanism and the neutrino-driven mechanism within the Advanced LIGO, using the match score as the criterion for assessing the quality of the reconstruction. The results indicate that by decomposing the data, the sum of the first six intrinsic mode functions (IMFs) can be used as the reconstructed waveform. To determine the probability that our reconstructed waveform corresponds to a real GW waveform, we calculate the false alarm probability of reconstruction (FAPR). By setting the threshold of the match score to be 0.75, we obtain the FAPRs of GW sources at distances of 5 and 10 kpc to be 6 × 10−3 and 1 × 10−2, respectively. If we normalize the maximum amplitude of the GW signal to 5 × 10−21, the FAPR at this threshold is 4 × 10−3. Furthermore, in our study, the reconstruction distance is not equivalent to the detection distance. When the strain of GWs reaches 7 × 10−21, and the match score threshold is set at 0.75, we can reconstruct GW waveforms up to approximately 36 kpc. [ABSTRACT FROM AUTHOR]

Bootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the analysis of data where predictors are collinear. This paper develops a nonparametric bootstrap-quantile approach for the estimation of ridge parameter in the linear regression model. The proposed method is illustrated using some popular and widely used ridge estimators, but this idea can be extended to any ridge estimator. Monte Carlo simulations are carried out to compare the performance of the proposed estimators with their baseline counterparts. It is demonstrated empirically that MSE obtained from our suggested bootstrap-quantile approach are substantially smaller than their baseline estimators especially when collinearity is high. Application to real data sets reveals the suitability of the idea. [ABSTRACT FROM AUTHOR]

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data. [ABSTRACT FROM AUTHOR]

Variable selection approaches are often employed in high-dimensionality and multicollinearity problems. Since lasso selects variables by shrinking the coefficients, it has extensive use in many fields. On the other, we may sometime have extra information on the model. In this case, the extra information should be considered in the estimation procedure. In this paper, we propose a stochastic restricted lasso estimator in linear regression model which uses the extra information as stochastic linear restrictions. The estimator is a generalization of mixed estimator with L1 type penalization. We give the coordinate descent algorithm to estimate the coefficient vector of the proposed method and strong rules for the coordinate descent algorithm to discard variables from the model. Also, we propose a method to estimate the tuning parameter. We conduct two real data analyses and simulation studies to compare the new estimator with several estimators including the ridge, lasso and stochastic restricted ridge. The real data analyses and simulation studies show that the new estimator enjoys the automatic variable selection property of the lasso while outperforms standard methods, achieving lower test mean squared error. [ABSTRACT FROM AUTHOR]

This study introduces parallel coordinates plot as an innovative visualization method, offering an alternative to traditional score plots in Principal Component Analysis (PCA) for exploratory data analysis (EDA). EDA is pivotal for unraveling intricate data structures, and PCA serves as a powerful tool for simplifying complex datasets. The paper explores the application of parallel coordinates plot in visualizing and interpreting PCA results, emphasizing their effectiveness in revealing multivariate patterns and detecting outliers. Through the examination of simulated datasets, where two distinct classes are generated in three-dimensional and five-dimensional spaces and real Raman spectroscopic data, the research demonstrates the utility of parallel coordinates plot in enhancing data exploration and providing valuable insights in pattern recognition methodologies. The results highlight the complementary nature of traditional score plots and parallel coordinates plots, offering a comprehensive approach to understanding high-dimensional datasets. This innovative visualization technique proves valuable in discerning subtle patterns and relationships within complex data structures, contributing to a more profound exploration of datasets in various domains. [ABSTRACT FROM AUTHOR]

This methodological paper offers a five-step model for preparing bilingual data for analysis. The article is guided by research in translating bilingual data into qualitative research. A systematic analysis of the research studies was conducted to identify workable options for preparing bilingual data for analysis. The proposed five-step model is comprised of (1) translator's worldview and professionalism, (2) epistemological, ontological, and methodological considerations, (3) line of translation, (4) responsiveness to cultural terms and (5) eagerness to trustworthiness and reliability of the data. Researcher's worldviews encompass the visibility of translator's positionality for reflexivity, avoidance of biasness, and demonstration of professionalism. The bilingual data needs treatment based on epistemological, ontological, and methodological orientations which the translators need to be mindful of. The researcher must be clear on the line of translation in opting for direct translation, back-translation, single translation, or multiple translations. The translated data must be responsive to the cultural contexts to carry on sense and meaning. Depending on the methodological approaches and contextual requirements, the translation work must meet the needs of trustworthiness and validity. The literature is silent on the translations of non-verbal cues. Therefore, this problem should be dealt with separately in future research endeavors. Similarly, future research on linguistic contexts and translation work in diverse research approaches such as quantitative, qualitative, and mixed methods is needed. [ABSTRACT FROM AUTHOR]

Global 21-cm experiments are built to study the evolution of the Universe between the cosmic dawn and the epoch of reionization. FlexKnot is a function parametrized by freely moving knots stringed together by splines. Adopting the FlexKnot function as the signal model has the potential to separate the global 21-cm signal from the foregrounds and systematics while being capable of recovering the crucial features given by theoretical predictions. In this paper, we implement the FlexKnot method by integrating twice over a function of freely moving knots interpolated linearly. The function is also constrained at the lower frequencies corresponding to the dark ages by theoretical values. The FlexKnot model is tested in the framework of the realistic data analysis pipeline of the REACH global signal experiment using simulated antenna temperature data. We demonstrate that the FlexKnot model performs better than existing signal models, e.g. the Gaussian signal model, at reconstructing the shape of the true signals present in the simulated REACH data, especially for injected signals with complex structures. The capabilities of the FlexKnot signal model is also tested by introducing various systematics and simulated global signals of different types. These tests show that four to five knots are sufficient to recover the general shape of most realistic injected signals, with or without sinusoidal systematics. We show that true signals whose absorption trough is of amplitude between 120 and 450 mK can be well recovered with systematics up to about 50 mK. [ABSTRACT FROM AUTHOR]

Long-period comets C/2019 L3 (ATLAS) and C/2020 P3 (ATLAS) remain active at heliocentric distance greater than 4 au. Broad-band Charge-Coupled Device (CCD) photometry of long-period comets C/2019 L3 and C/2020 P3 is analysed in this paper. The observation was obtained with three telescopes: ZEISS-1000 at Simeiz Observatory, Maksutov at Abastumani Astrophysical Observatory, and ZTSh at Crimean Astrophysical Observatory. The R -band A (0) f ρ values of C/2019 L3 range from (5043 ± 244) to (13611 ± 1874) cm, and tend to decrease first and then increase. The average colour indices of C/2019 L3 are B – V = 0.75 ± 0.06, V – R = 0.28 ± 0.05, and R – I = 0.21 ± 0.05. The reddening of C/2019 L3 exhibits variations during the observational runs, ranging from (13.75 ± 1.07) to (−15.69 ± 0.37) per cent/kÅ with an average value of (0.94 ± 0.23) per cent/kÅ. It possesses a non-steady coma since the average gradient of its surface brightness profile is −1.60. For comet C/2020 P3, its R -band A (0) f ρ values range from (606 ± 31) to (869 ± 20) cm, the colour indices are B–V = 1.04 ± 0.09, V–R = 0.26 ± 0.05, and R – I = 0.76 ± 0.04, and the reddening is (−6.65 ± 0.01) per cent/kÅ. The colour indices of the two comets differ from those of other long-period comets. Compared with other long-period comets, comet C/2019 L3 is very active at heliocentric distance of ∼4 au, while C/2020 P3 is moderately active at heliocentric distance of ∼7 au. [ABSTRACT FROM AUTHOR]

In behavioral research, it is very common to have manage multiple datasets containing information about the same set of individuals, in such a way that one dataset attempts to explain the others. To address this need, in this paper the Tucker3-PCovR model is proposed. This model is a particular case of PCovR models which focuses on the analysis of a three-way data array and a two-way data matrix where the latter plays the explanatory role. The Tucker3-PCovR model reduces the predictors to a few components and predicts the criterion by using these components and, at the same time, the three-way data is fitted by the Tucker3 model. Both the reduction of the predictors and the prediction of the criterion are done simultaneously. An alternating least squares algorithm is proposed to estimate the Tucker3-PCovR model. A biplot representation is presented to facilitate the interpretation of the results. Some applications are made to empirical datasets from the field of psychology. [ABSTRACT FROM AUTHOR]

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source Python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of distinguishing the leading agents in the group. The problem is considered in the framework of classification problems, where the agents in the group select the items with respect to certain properties. The suggested method of distinguishing the leading agents utilizes the connectivity between the agents and the Rokhlin distance between the subgroups of the agents. The method is illustrated by numerical examples. The method can be useful in considering the division of labor in swarm dynamics and in the analysis of the data fusion in the tasks based on the wisdom of the crowd techniques. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score. [ABSTRACT FROM AUTHOR]

Teacher shortages represent a global challenge for educator preparation programs (EPPs) and the districts they serve. Using mixed methods approach to data collection and analysis, this paper presents the findings of the three cases involving the use of student data and partner voices to highlight the benefits of intentionally integrating the concepts of Improvement Sciences, Equitable Experiences, educator preparation learning communities, and the changemaker mindset in the design of initiatives for addressing local teacher shortages. Results provide a ground-up perspective on promising strategies for local EPPs and school districts seeking to create a sustainable pipeline of quality educators by focusing on expanding access, success, and mobility for non-traditional student teachers (teacher candidates). [ABSTRACT FROM AUTHOR]

Non-negative integer-valued time series are usually encountered in practice, and a variety of integer-valued autoregressive processes based on various thinning operators are commonly used to model these count data with temporal dependence. In this paper, we consider a first-order integer-valued autoregressive process constructed by the negative binomial thinning operator with random coefficients, to address the problem of constant thinning parameters which might not always accurately represent real-world settings because of numerous external and internal causes. We estimate the model parameters of interest by the two-step conditional least squares method, obtain the asymptotic behaviors of the estimators, and furthermore devise a technique to test the constancy of the thinning parameters, which is essential for determining whether or not the proposed model should consider the parameters' randomness. The effectiveness and dependability of the suggested approach are illustrated by a series of thorough simulation studies. Finally, two real-world data analysis examples reveal that the suggested approach is very useful and flexible for applications. [ABSTRACT FROM AUTHOR]

Formalistic tasks are widely utilized in modern companies due to their ability to increase productivity and contribute to the achievement of corporate goals at a lower cost. However, these tasks are often meet with resistance from individuals because they do not provide direct short-term rewards for their efforts. Drawing on social cognitive theory, this study examined the influence of individual quality and organizational attachment on the completion of formalistic tasks. To address this, the study conducted a questionnaire survey to collect data from 602 Chinese respondents and built a structural equation model for data analysis. Through empirical research, the study confirmed the positive role of individual quality, including knowledge and personality, in the completion of formalistic tasks. Furthermore, the study proved that avoidant attachment could significantly weaken the effect of some components of individual quality on formalistic task completion. This paper is the first to reveal the influence of individual and environmental factors on individuals' completion of formalistic tasks, progressing from bottom to top. The implications of these results are discussed. [ABSTRACT FROM AUTHOR]

This paper explores how social workers integrate personal spirituality within nursing homes, where a highly regulated, stressful environment marginalizes professional identity and challenges retention. Researchers offer new evidence of how spirituality informs professional identity, ethical practice, and continuation in the role. Twenty BSW/MSW licensed nursing home social workers (NHSWs) reported how they daily navigate the personal spirituality-at-work opportunity and challenge. Qualitative analysis of in-depth interviews resulted in three primary codes and related subcodes: Spirituality-Integrated Professional Identity (Vocational Affirmation and Value Alignment), Spirituality-Informed Practice (Intervention Asset, Relational Affinity, and Ambiguous Boundaries), and Spiritually-Sustained Career Resilience. Respondents reported how spirituality animated professional identity, informed daily role enactment, and sustained role involvement. Recommendations are provided for normalizing ethical spirituality-at-work through social work education and practical guidance in navigating the spirituality and practice space. [ABSTRACT FROM AUTHOR]

The gas blast arc-extinguishing lightning protection gap is a new type of lightning protection device based on airflow active arc-extinguishing. This paper establishes an airflow and arc mathematical model for calculating and analyzing air flow and arc motion processes. Besides, the finite element software ANSYS 14.0 is employed for the arc-extinguishing simulation in the pressure field, interaction arc, and airflow processes. Accordingly, the test analysis effects of the arc-extinguishing gap lightning protection test are verified. High-speed cameras, recording oscillograph arc-extinguishing images, and arc waveforms are employed in the process to obtain the airflow response time and arc-extinguishing time effectively based on the data analysis. Comparing the actual results with the simulation results demonstrates the arc-extinguishing reliability and provides an effective scientific basis for its practical application. [ABSTRACT FROM AUTHOR]

Raman spectroscopy can be effectively used for detection and analysis of chemical agents that are serious threats in modern warfare, but the detection and analysis performance is prone to deterioration due to noise. The existing denoising technique has limitations that there is no criterion for selecting the window length and that the filtering distorts the peaks, key features for Raman spectral data analysis. To overcome such limitations, in this paper, we propose the peak‐aware adaptive denoising for Raman spectroscopy based on machine learning approach. The proposed technique utilizes the information of detected peaks to eliminate noise effectively using different window values optimal for each region in the Raman spectrum while preserving the shape of peaks. We conducted the various analyses and experiments, and the proposed technique showed a 28% lower Euclidean distance and a 48% lower Fréchet inception distance compared to the existing technique, meaning the proposed technique outperformed the existing one. [ABSTRACT FROM AUTHOR]

Terahertz frequency domain spectroscopy provides a superior resolution in the broad range from 50 GHz to 5 THz. However, temporal frequency drift and the standing wave pattern in the spectrometer, which often act simultaneously, produce intensive coherent noise in the transmittance spectra of the measured samples. The paper presents a spectrum processing method allowing to reduce the coherent noise and thus significantly enhance the accuracy of spectral data analysis. The idea of the method is to remove the small-period (0.25 GHz) oscillations in the standing wave pattern of the measured signal by applying the windowed Fourier filtering. The large-period (4 GHz) oscillations in the measured spectra are broadened and then used to compensate the frequency drift of the two consecutive measurements of the signal with the sample and without it. The proposed approach is tested on the measured spectra of metamaterial and silicon wafer. Its advantage over the classical method based on the averaging of adjacent data points is confirmed. Our work benefits to optimization of the frequency domain terahertz systems and paves the way for fast and accurate analysis of spectral data. [ABSTRACT FROM AUTHOR]

One of the essential tools for predicting the business environment and analysis and timely and low-cost decisions for SMEs is cloud business intelligence. This research paper aims to present a model of cloud business intelligence for SMEs.In the current study, the Meta-synthesis method has been utilised to identify the fundamental categories of cloud business intelligence. The model is presented by exploiting the interpretive structural modelling. In this regard, the data analysis process has been performed by MATLAB and MicMac software. Consequently, presented Six main factors; 24 sub-factors were obtained from the extracted parameters of relevant researches scripts. It is evident that 'Drivers' is the most influential variable of the model. They then used SWARA to rank the sub-Factors. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the impact of the indoor classroom environment on students' learning efficiency. The research is based on a classroom smart monitoring and a questionnaire about the students' assessment of the comfort conditions and learning efficiency. Multisensor devices are used to measure the indoor temperature, relative humidity, and CO2 concentration at the students' desks. Data analysis concerned an investigation of the spatial and temporal variation of the comfort parameters and their correlation with students' assessment of comfort conditions and learning efficiency. The results show a significant spatial variation in the indoor comfort conditions, particularly for temperature and CO2 concentration. The indoor temperature could exceed by up to 5 °C, the temperature threshold limits value in France's public buildings. At the beginning of the class, the learning efficiency correlates well with the students' assessment of comfort conditions. At the end of the class, the results show a weak correlation with both recorded comfort parameters and the students' assessment of the indoor conditions. The results indicate a decrease in learning efficiency during the class. However, students do not mainly attribute this decrease to the degradation in indoor conditions. [ABSTRACT FROM AUTHOR]

Large pulsation and impact occur during the working process of hydraulic rock drill, which has a negative influence on the system. Aiming at this phenomenon, the related characteristics of hydraulic fluid inertia are studied in this paper. The dynamic model of impact and reversing mechanism of rock drill is established. Through the parameter analysis of the model, the concepts of hard parameter quantity and soft parameter quantity are proposed. The influence coefficient C of the initial section of crevice flow including the influence of fluid inertia is deduced, and the comprehensive factors such as hydraulic oil velocity, unstable flow and friction resistance are analyzed, The value of soft parameter quantity ζ is (ξ+1) ~ (ξ+ 1.5)。The pressure test of the working chamber is designed and 7 groups of data are obtained. Through data analysis, the influence of C on the simulation results is obtained, which has a great influence on the reversing valve. The deviation of simulation data of left and right valve chamber pressure is increased from ± 8% to ± 3%. It has a certain impact on the impact piston. The deviation of simulation data increases from ± 5% to ± 3%, and has little impact on the accumulator. The deviation of simulation data increases from ± 4% to ± 3%. [ABSTRACT FROM AUTHOR]

This paper reports on a case study conducted in Mid-western Nepal, aiming to explore the potential impacts of precarious working conditions on part-time teachers' lives and identify ways to eliminate those working conditions. The data for the study were collected using semi-structured interviews from purposefully selected part-time teachers teaching in public higher secondary schools in the study area. The data analysis reveals that part-time teachers work in highly precarious working contexts that have negatively impacted their personal and professional lives. They think that they do not have financial stabilities, job securities and professional development opportunities. Additionally, the findings suggest that they can unsnare themselves from the precarious working conditions by lobbying the government to reform its teacher hiring policies, rejecting this kind of teaching and running their own academic institutions. [ABSTRACT FROM AUTHOR]

Cyber ranges have gained significant importance in cybersecurity training in recent years, and they are still playing a role of paramount importance, thanks to their ability to give trainees hands-on experience with real-world exercises. This paper presents the motivation and objective of the AERAS project, including a thorough analysis of data from ad hoc interviews and surveys specifically designed and administered for the project's goals. AERAS aims to apply the cyber range concept to the critical healthcare sector. The AERAS platform will be a virtual cyberwarfare solution that will simulate the operation and effects of security controls and offer hands-on training on their development, assessment, use, and management. [ABSTRACT FROM AUTHOR]