Latent Structure Research Articles

Measuring the teachers’ talent management

PurposeA crucial issue in educational management refers to helping teachers reach their full potential and manage their talents. Although managing talent is advised as an essential resource for organizational transformation to maximize performance and to promote a school’s knowledge capital increase, Teachers’ talent management (TTM) is not an agreed-upon concept in the literature. The aim of this study is threefold: (1) to propose a theoretical and operational TTM definition, (2) to develop a test to measure TTM and (3) to discuss the value of TTM implementation in educational settings.Design/methodology/approachA psychometric validation design according to international standards for test development is implemented. A cross-sectional intentional sampling with 200 schools is used for obtaining reliability and validity evidence. We operationalized TTM using 9 factors (10 items per factor, 6-point Likert scale): attraction, selection, development, retention, succession, climate, culture, evaluation and knowledge management.FindingsResults show adequate evidence of reliability and validity, focusing on items' content and latent structure. The combined use of structural equation models (SEM) and psychometric networks (PN) detects a six-factor model with a test reduced version including 61 items, getting 64.28% of the total explained variance and adequate reliability indices and factor loadings.Originality/valueThe Teachers' Talent Management Assessment Test (TTMAT) can contribute educational institutions to create a culture of excellence and deliver the best possible education to their students. Educational transformation will only happen if teachers are professionalized, trained, motivated and supported to inspire and to guide their learners to reach their objectives and well-being.

Characteristic flavor compounds in Guizhou green tea and the environmental factors influencing their formation: Investigation using stable isotopes, electronic nose, and headspace-gas chromatography ion migration spectrometry

The Guizhou province is a significant contributor to the production of green tea within China. The present study aims to examine the characteristic flavor compounds found in Guizhou green tea and explore the environmental factors that contribute to the development of these unique flavor compounds. Accordingly, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and electronic nose were employed in conjunction with odor activity value (OAV) analysis to develop an orthogonal projections to latent structures discriminant analysis (OPLS-DA) model that was capable of discerning between the three distinct habitats (QE, QM, QW) of Guizhou green tea based on their flavor constituents. The findings show that the primary constituents contributing to the flavor profile of Guizhou green tea consisted of 3-methyl-2-butenal, butanal, pentanal, linalool, 6-methyl-5-hepten-2-one, dimethyl disulfide, and dimethyl sulfide. Subsequently, the stable isotope technique was employed to examine the growth environmental factors of Guizhou green tea, based on the identification of the flavoring compounds that differentiate green tea from various habitats within the Guizhou province. Accordingly, temperature and sunshine were found to have the greatest impact on the flavor compounds present in Guizhou green tea.

Image‐based characterization of flocculation processes through PLS inspired representation learning in convolutional neural networks

AbstractMonitoring of flocculation processes such as those used in downstream processing of a fermentation broth is essential for process control. One approach is to apply microscopic imaging combined with image analysis for characterizing the state of the process. In this work, we investigate and compare the use of supervised feedforward convolutional neural network (CNN) architectures to predict the process states from the image information and compare the results with the traditional alternative of characterizing flocs based on manually engineered image features guided by human expertise. From a well‐defined image data set representing six process states, the objective is to establish end‐to‐end classification models which are accurate but at the same time learn meaningful latent variable space representations. Specifically, we evaluate three different CNN architectures with varying degrees of regularization and compare results with logistic regression models based on inputs from two different traditional feature engineering methods. By applying global average pooling as a structural regularizer to the CNN architecture, we significantly improve the generalization performance in comparison with the classification accuracies of the traditional feature engineered models. Furthermore, we show that by imposing a projection to latent structures (PLS) like regularization framework onto the CNN, it can also learn a latent variable representation that mimics the features selected by human expertise.

Developmental origins of psycho-cardiometabolic multimorbidity in adolescence and their underlying pathways through methylation markers: a two-cohort study.

Understanding the biological mechanisms behind multimorbidity patterns in adolescence is important as they may act as intermediary risk factor for long-term health. We aimed to explore relationship between prenatal exposures and adolescent's psycho-cardiometabolic intermediary traits mediated through epigenetic biomarkers, using structural equation modeling (SEM). We used data from mother-child dyads from pregnancy and adolescents at 16-17years from two prospective cohorts: Northern Finland Birth Cohort 1986 (NFBC1986) and Raine Study from Australia. Factor analysis was applied to generate two different latent factor structures: (a) prenatal exposures and (b) adolescence psycho-cardiometabolic intermediary traits. Furthermore, three types of epigenetic biomarkers were included: (1) DNA methylation score for maternal smoking during pregnancy (DNAmMSS), (2) DNAm age estimate PhenoAge and (3) DNAm estimate for telomere length (DNAmTL). Similar factor structure was observed between both cohorts yielding three prenatal factors, namely BMI (Body Mass Index), SOP (Socio-Obstetric-Profile), and Lifestyle, and four adolescent factors: Anthropometric, Insulin-Triglycerides, Blood Pressure, and Mental health. In the SEM pathways, stronger direct effects of F1prenatal-BMI (NFBC1986=β: 0.27; Raine=β: 0.39) and F2prenatal-SOP (β: -0.11) factors were observed on adolescent psycho-cardiometabolic multimorbidity. We observed an indirect effect of prenatal latent factors through epigenetic markers on a psycho-cardiometabolic multimorbidity factor in Raine study (P<0.05). The present study exemplifies an evidence-based approach in two different birth cohorts to demonstrate similar composite structure of prenatal exposures and psycho-cardiometabolic traits (despite cultural, social, and genetic differences) and a common plausible pathway between them through underlying epigenetic markers.

Prevalence and concordance of International Classification of Diseases-11th Revision and Diagnostic and Statistical Manual-Fifth Edition posttraumatic stress diagnostics among Northern Irish military veterans.

There are two primary competing diagnostic criteria for posttraumatic stress disorder (PTSD); the International Classification of Diseases (ICD) and the Diagnostic and Statistical Manual (DSM). These systems differ in terms of the number and nature of PTSD symptoms, the implied latent structure of the disorder, and associated posttraumatic diagnostic classifications. To investigate the prevalence and concordance of ICD-11th Revision (ICD-11) and DSM-Fifth Edition (DSM-5) PTSD, complex-PTSD (C-PTSD), and dissociative subtype of PTSD (D-PTSD) criteria in a sample of Northern Irish military veterans. Data were collected from a community sample of military veterans living in Northern Ireland (NI). Prevalence rates of ICD-11 PTSD and C-PTSD and DSM-5 PTSD and D-PTSD were estimated using validated self-report measures for each system. Concordance of caseness determined by diagnostic criteria was compared using Cohen's kappa. The DSM-5 algorithm criteria produced significantly higher prevalence estimates of PTSD (39.26%) relative to the ICD-11 algorithm criteria (32.09%). Both C-PTSD and D-PTSD were identified for subgroups within the sample (24.66% and 27.96%, respectively). There was moderate agreement between the diagnostic systems regarding PTSD caseness, and between C-PTSD and D-PTSD caseness. These findings have implications regarding the measurement of PTSD and associated diagnostic groupings for the wider literature, suggesting that the choice of diagnostic screening framework may influence classification. Both C-PTSD and D-PTSD may be relevant diagnostic considerations for the NI military veteran group. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measuring Hypochondria with Short Health Anxiety Inventory: Psychometric Properties in Colombian University Students

Introduction. The Short Health Anxiety Inventory is a commonly used tool for assessing health anxiety, but its psychometric properties and internal structure have not been examined in a Latin American Spanish-speaking population. This study aimed to establish the psychometric properties among Colombian university students. Method. The goodness of fit of four latent structure models of the Short Health Anxiety Inventory was tested using confirmatory factor analysis in a sample of 1004 Colombian university students. Results. The results show that the original model's structure does not fit well (CFI = .808; RMSEA = .074), and the reliability was .796 and .703 for the original two variables. Conclusions. The findings do not support the utilization of the Colombian version of the Short Health Anxiety Inventory by researchers and clinicians among Colombian university students.

Validation of the difficulties in emotion regulation scale (ders-28) in spanish young adult substance users

The 28-item Difficulties in Emotion Regulation Scale (DERS) is widely used to measure Emotional Dysregulation (ED). However, its psychometric performance has not been examined in Spanish-speaking substance users. This study aimed to validate the DERS-28 in 1,676 [M = 19.56 (SD = 1.70), % women = 64.6] young adults with past-month substance use. Exploratory and confirmatory factor analyses, as well as reliability assessments using Classical Test and Item Response Theories, were conducted. The analyses supported a bifactor solution (invariant to sex) comprising five specific dimensions and a general factor (ED), each demonstrating adequate score reliability. Bifactor indices indicated an essentially unidimensional latent structure (ωHG = .808; ECVG = .554; H = .951; FD = .966). Convergent validity (values of |r| ranged between .036-.645) was supported by associations with clinical variables, including measures of substance use related problems, anxiety, stress, and depression. Among the DERS-28 dimensions, lack of control exhibited the largest effects. Despite its reliability and validity, limitations such as self-reported substance use and recruitment in college settings should be considered. In conclusion, the DERS-28 proves to be a reliable and valid tool, demonstrating utility for prevention purposes among young adults.

Compilation and Validation of Two Short Forms to Measure HEXACO Dimensions in Elementary School Children

Personality trait measures for children are rarely based on the HEXACO Model of Personality, although research using this framework could provide important insights into the structure and development of children’s personalities. There is no HEXACO measure for elementary school children to date, and existing measures for older children seem inappropriate for this age group (e.g., due to some item content). We thus compiled two HEXACO-based short forms for measuring personality in elementary school children (8-10 years old) via parent reports. We applied a meta-heuristic item sampling algorithm (Ant Colony Optimization) in a training sample with 1,641 parent reports of 122 administered items. We selected a 54-Item Short Form comprising a latent facet structure and an 18-Item Ultra-Short Form comprising a correlated factors model for all six HEXACO dimensions but no facet structure. Both short forms showed good model fit in a holdout sample (n = 411) and sufficiently high re-test correlations after six months. Convergent and divergent validities for maximal performance measures and socio-emotional constructs (also measured six months after the initial personality assessment) were largely in line with theoretical assumptions. Overall, our study provides support for construct, re-test, and (predictive) criterion validity for the selected short forms.

Development and Validation of the SPSQ-26

Abstract: The aim of this article was to develop an abbreviated version of the Sensory Processing Sensitivity Questionnaire (SPSQ), that maintains a similar factorial structure to the original and demonstrates good psychometric properties. The Short Form of the SPSQ (SPSQ-SF) was developed using a split-sample validation design. Items were retained based on impact on internal consistency reliability, fit to the hierarchical structure of the original SPSQ, and information curves based on a Graded Response Model. Subsequently, the dimensionality and fit to the latent structure of the SPSQ were evaluated. Correlations with the original SPSQ, other measures of Sensory Processing Sensitivity, personality, and clinical outcomes were examined. The SPSQ-SF demonstrated a good fit and high correlation with the original SPSQ. Positive correlations were observed with subscales of the Adult Temperament Questionnaire and the Highly Sensitive Person Scale. Neuroticism and Openness showed strong associations with the negative and positive higher-order dimensions of the SPSQ-SF, respectively. The negative higher-order dimension and Emotional and Physiological Reactivity subscale exhibited significant relationships with physical and psychological distress. Thus, the SPSQ-SF demonstrated good convergent, discriminant, and concurrent validity. The SPSQ-SF offers a valid and reliable alternative to the original SPSQ, allowing for the collection of a large amount of information with fewer items.

The Felt Realism of “Unreal” Environments: Testing a Dual Awareness Model of Subjective Realism

In this paper, we empirically investigate VR users’ subjective experiences of realism. We conceptualise two operationally distinct processes: a top-down critical evaluation of realism and a bottom-up felt experience of realism. Study 1 investigated the latent factor structure of a measure of subjective realism responses to different virtual environments. Results indicated a three-factor structure: (i) felt realism – user’s feelings of realism, (ii) perceptual realism – evaluation of the realism of stimulus form or perceptual elements, and (iii) conceptual realism – evaluation of stimulus content or conceptual elements. Study 2 investigated the effect of two “objective realism” manipulations on the three “subjective realism” factors. We manipulated the visual quality and normality of the virtual setting. Results indicated that participants rated abnormal environments lower in perceptual realism and conceptual realism. However, the abnormal environments were reported higher in felt realism. The findings demonstrate the power of abnormal and “unreal” environments in inducing subjective realism for entertainment users. Due to COVID-19 restrictions, this research was conducted using 360 video stimuli, future research should test these findings with full HMD VR.

Screening for Light Personalities in Portugal: A Cross-Cultural Validation of the Light Triad Scale With an At-risk-of-delinquency Sample

The Light Triad of personality refers to three prosocial personality traits—Faith in Humanity, Humanism, and Kantianism—that promote the worth and dignity of other people, focus on ethical behavior and empathy, and confidence that other people are naturally good. The aim of the present study is to examine the psychometric properties of the Light Triad Scale (LTS)—Portuguese version. Our convenience sample consisted of 242 male and female participants ( M = 30.19 years, SD = 12.78, range = 16–77) from Portugal. The proposed latent structure models of the LTS obtained adequate fits. Internal consistency/reliability, as measured by the alpha and omega coefficients, was adequate to good. Construct validity with other psychometric measures (i.e., empathy, dark traits of personality, propensity to morally disengage, and antisociality/criminality measures) and criterion-related validity (with justice involvement variables such as problems with the law, arrested by the police, sentenced to prison and alcohol/drug abuse variables) were demonstrated. Cross-gender measurement invariance was established, with females scoring higher than males. The findings support the use of the LTS as a valid and reliable measure.

ICD-11 post-traumatic stress disorder and complex post-traumatic stress disorder in mental health support-seeking former-serving Australian defence force veterans.

ICD-11 complex post-traumatic stress disorder is a more severe condition than post-traumatic stress disorder, and recent studies indicate it is more prevalent among military samples. In this study, we tested the psychometric properties of the International Trauma Questionnaire, assessed the relative prevalence rates of post-traumatic stress disorder and complex post-traumatic stress disorder in the sample population and explored relationships between complex post-traumatic stress disorder and post-traumatic stress disorder and a range of risk factors. Survey participants (N = 189) were mental health support-seeking former-serving veterans of the Australian Defence Force (ADF) recruited from primary care. Confirmatory factor analysis was used to test the factorial validity of the International Trauma Questionnaire. The latent structure of the International Trauma Questionnaire was best represented by a two-factor second-order model consistent with the ICD-11 model of complex post-traumatic stress disorder. The International Trauma Questionnaire scale scores demonstrated excellent internal reliability. Overall, 9.1% (95% confidence interval = [4.8%, 13.5%]) met diagnostic requirements for post-traumatic stress disorder and an additional 51.4% (95% confidence interval = [44.0%, 58.9%]) met requirements for complex post-traumatic stress disorder. Those meeting diagnostic requirements for complex post-traumatic stress disorder were more likely to have served in the military for 15 years or longer, had a history of more traumatic life events and had the highest levels of depression, anxiety and stress symptoms. The International Trauma Questionnaire can effectively distinguish between post-traumatic stress disorder and complex post-traumatic stress disorder within primary care samples of Australian Defence Force veterans. A significantly greater proportion of Australian Defence Force veterans met criteria for complex post-traumatic stress disorder than post-traumatic stress disorder. Australian military mental health services should adopt the International Trauma Questionnaire to routinely screen for complex post-traumatic stress disorder and develop complex post-traumatic stress disorder specific interventions to promote recovery in Australian Defence Force veterans with complex post-traumatic stress disorder.

Nature Relatedness Scale: psychometric properties of the Portuguese version

Environmental education would strongly benefit from a socioecological approach that encompasses the human-nature relationship in all its dimensions. Nature relatedness appears to be an essential trait to ensure effective approaches, promoting the development of pro-environmental attitudes and behaviors. but also, human well-being through nature-derived benefits. To our knowledge, there are no available instruments validated for the Portuguese population to assess nature connectedness. This study aims to fill this gap, by validating the short and full versions of the Nature Relatedness Scale for the Portuguese population. The translation and adaptation of the questionnaire comply with international guidelines in this field and validation was conducted with 456 Portuguese participants with a mean age of 32.95 (SD = 11.45, age range = 18–77) years, mostly female (n = 328; 71.9%). Confirmatory factor analysis indicated that the original three-factor latent structure of the full Nature Relatedness Scale, and the short version of the instrument with six items, obtained an adequate fit. These two versions of the instrument showed adequate validity to be used with the Portuguese population.

Distinguishing heart failure with reduced ejection fraction from heart failure with preserved ejection fraction: A phenomics approach.

Pathophysiological differences between patients with heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction (EF) remain unclear. Therefore we used a phenomics approach, integrating selected proteomics data with patient characteristics and cardiac structural and functional parameters, to get insight into differential pathophysiological mechanisms and identify potential treatment targets. We report data from a representative subcohort of the prospective Singapore Heart Failure Outcomes and Phenotypes (SHOP), including patients with HFrEF (EF <40%, n = 217), HFpEF (EF ≥50%, n = 213), and age- and sex-matched controls without HF (n = 216). We measured 92 biomarkers using a proximity extension assay and assessed cardiac structure and function in all participants using echocardiography. We used multi-block projection to latent structure analysis to integrate clinical, echocardiographic, and biomarker variables. Candidate biomarker targets were cross-referenced with small-molecule and drug databases. The total cohort had a median age of 65 years (interquartile range 60-71), and 50% were women. Protein profiles strongly discriminated patients with HFrEF (area under the curve [AUC] = 0.89) and HFpEF (AUC = 0.94) from controls. Phenomics analyses identified unique druggable inflammatory markers in HFpEF from the tumour necrosis factor receptor superfamily (TNFRSF), which were positively associated with hypertension, diabetes, and increased posterior and relative wall thickness. In HFrEF, interleukin (IL)-8 and IL-6 were possible targets related to lower EF and worsening renal function. We identified pathophysiological mechanisms related to increased cardiac wall thickness parameters and potentially druggable inflammatory markers from the TNFRSF in HFpEF.

A multiplex metabolomic approach for quality control of Spirulina supplement and its allied microalgae (Amphora & Chlorella) assisted by chemometrics and molecular networking

Microalgae species are of economic importance regarded as “green gold” being rich in bioactive compounds. Spirulina and Chlorella are the most popular microalgal species and are marketed as healthy food supplements. At the same time, Amphora holds potential as a source of healthy lipids and essential fatty acids. Yet, there are considerable variations in their reported chemical composition, and less is known about their compositional differences. A multiplexed metabolomic approach was adopted for the quality control (QC) of Spirulina supplements and to compare its constitutive metabolome to Chlorella and Amphora. The adopted protocol comprised gas chromatography-mass spectrometry (GC–MS), ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS), and ultraviolet–visible spectrophotometry (UV/Vis) for mapping their primary and secondary metabolome. Interestingly, UPLC-HRMS/MS analysis delineated the abundance of fatty acids in Amphora versus glycolipids enrichment in Spirulina, and porphyrins were the main pigments identified in Spirulina, with scarce occurrence in Chlorella. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) analysis of GC–MS data set revealed palmitic acid, 3-mannobiose, and glyceryl-glycoside as being most enriched in Spirulina, versus sucrose and leucine in Chlorella and Amphora, respectively. Despite being of low discriminatory potential, UV/Vis OPLS-DA modeling showed that Spirulina was distinguished with the UV absorbances of carotenoids and chlorophyll pigments, as indicated by its OPLS-DA derived S-plot. Our study provides a QC approach for the analysis of the microalgal species and poses alternative spectral and compositional markers for their discrimination.

Topic Modeling: A Consistent Framework for Comparative Studies

In recent years, the field of Topic Modeling (TM) has grown in importance due to the increasing availability of digital text data. TM is an unsupervised learning technique that helps uncover latent semantic structures in large sets of documents, making it a valuable tool for finding relevant patterns. However, evaluating the performance of TM algorithms can be challenging as different metrics and datasets are often used, leading to inconsistent results. In addition, many current surveys of TM algorithms focus on a limited number of models and exclude state-of-the-art approaches. This paper has the objective of addressing these issues by presenting a comprehensive comparative study of five TM algorithms across three different benchmark datasets using five different metrics. We offer an updated survey of the latest TM approaches and evaluation metrics, providing a consistent framework for comparing different algorithms while introducing state-of-the art approaches that have been disregarded in the literature. The experiments, which primarily use Context Vectors (CV) Topic Coherence as an evaluation metric, show that Top2Vec is the best-performing model across all datasets, disrupting the tendency for Latent Dirichlet Allocation to be the best performer. Doi: 10.28991/ESJ-2024-08-01-09 Full Text: PDF

Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph.

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.

The association between hydration state and the metabolism of phospholipids and amino acids among young adults: a metabolomic analysis

BackgroundWater is vital for humans' survival and general health, which is involved in various metabolic activities. ObjectivesThe aim of this study was to investigate the variation in urine metabolome and associated metabolic pathways among people with different hydration states. MethodsA metabolomic analysis was conducted using 24-h urine samples collected during a cross-sectional study on fluid intake behavior from December 9 to 11, 2021, in Hebei, China. Subjects were divided into the optimal hydration (OH, ≤500 mOsm/kg, n = 21), middle hydration (500–800 mOsm/kg, n = 33), and hypohydration groups (HH, >800 mOsm/kg, n = 13) based on the 3-d average 24-h urine osmolality. Collected 24-h urine samples from 67 subjects (43 males and 34 females) were analyzed for urine metabolome using liquid chromatography-MS. ResultsThe untargeted metabolomic analysis yielded 1055 metabolites by peak intensities. Integrating the results of the orthogonal projections to latent structures discriminant analysis and fold change test, 115 differential metabolites between the OH and HH groups, including phospholipids (PLs) and lysophospholipids, were identified. Among the 115 metabolites identified as differential metabolites, 85 were recorded by the Human Metabolome Database and uploaded to the Kyoto Encyclopedia of Genes and Genomes databases for pathway analysis. Twenty-one metabolic pathways were recognized. Phenylalanine metabolism (0.50, P = 0.007), phenylalanine, tyrosine, and tryptophan biosynthesis (0.50, P = 0.051), glycerophospholipid metabolism (0.31, P < 0.001), sphingolipid metabolism (0.27, P = 0.029), and cysteine and methionine metabolism (0.10, P = 0.066) had the leading pathway impacts. ConclusionsWe found variations in the urinary PLs and amino acids among subjects with different hydration states. Pathways associated with these differential metabolites could further impact various physiologic and pathologic functions. A more comprehensive and in-depth investigation of the physiologic and pathologic impact of the hydration state and the underlying mechanisms to elucidate and advocate optimal fluid intake habits is needed.This trial was registered at Chinese Clinical Trial Registry as ChiCTR2100045268.

Differences in Supervision on Peer Learning Wards: A Pilot Survey of the Supervisor's Perspective.

Multiple students are placed on clinical wards simultaneously due to increasing student numbers. This has the potential to create stress for the supervisor and reduce quality of student learning. Peer learning as a pedagogical framework to supervise multiple students has been widely shown to have advantages for the students by developing teaching skills, team collaboration, and independence. However, whether peer learning impacts the characteristics of supervision and the experience of the supervisor is less understood. It is unknown whether wards that use peer learning as a pedagogical framework (peer learning wards) are any different compared to wards that do not (non-peer learning wards), from the supervisor's perspective. We aimed to develop and pilot test a questionnaire to compare peer-learning wards and non-peer learning wards from the supervisor's perspective. We used the AMEE 7-step guide to develop questions investigating supervision, the learning environment and satisfaction. We piloted the questionnaire with 46 nurse supervisors working on inpatient hospital wards in Stockholm, Sweden. We compared answers from peer learning with non-peer learning wards. We used Orthogonal Projections to Latent Structures (OPLS) discriminant analysis to show what differed between the wards. Peer learning wards compared to non-peer learning wards had more student-centred activities, the physical space had more adaptations for students, more support available to the supervisor, and supervisors perceived greater overall satisfaction with the quality of education and with the ward as a whole. The variables that had most influence on the discrimination between the two ward types related to peer learning activities and perceptions (p=0.0034). This pilot study shows that peer learning wards differ compared to non-peer learning wards regarding peer learning activities and perceptions among supervisors. Our questionnaire needs to be distributed on a larger scale to validate our findings and explore further the way in which the pedagogical framework and peer learning can affect supervision and satisfaction.

Multigraph approximate-representation learning for hyperspectral band selection

ABSTRACT Unsupervised hyperspectral image (HSI) band selection methods have been attracting ever-increasing attention. However, the local structural features captured by most of the existing methods suffer from a certain degree of risk of feature ambiguity. Moreover, these methods typically have difficulty preserving the latent manifold structure of the HSI in selected subsets of the bands. To address these problems, firstly, the graph approximate representation learning (GARL) model is proposed in this article by incorporating graph-regularized sparse coding into self-representation learning to preserve the local manifold structure information of HSI in the selected subset of bands. Meanwhile, to alleviate the ambiguity of the local graph structures, we design a local-global feature relationship expression model to construct the latent long-distance contextual connectivity (LDCC) graphs between the obtained local graph structures. Then, to preserve HSI’s local and non-local manifold structure information as much as possible in the selected subset of bands, a multigraph approximate representation learning (MGARL) model is proposed in this article by incorporating the obtained LDCC graph into the proposed GARL model. Next, we design a solution method to solve the proposed formulation. Finally, extensive experimental results on four datasets reveal the promising performance of MGARL over some state-of-the-art methods.