Meaningful Groups Research Articles

Analysis of Medical Data Using Community Detection on Inferred Networks.

Performing network-based analysis on medical and biological data makes a wide variety of machine learning tools available. Clustering, which can be used for classification, presents opportunities for identifying hard-to-reach groups for the development of customized health interventions. Due to a desire to convert abundant DNA gene co-expression data into networks, many graph inference methods have been developed. Likewise there are many clustering and classification tools. This paper presents a comparison of techniques for graph inference and clustering, using different numbers of features, in order to select the best tuple of graph inference method, clustering method, and number of features according to a particular phenotype. An extensive machine learning based analysis of the REGARDS dataset is conducted, evaluating the CoNet and K-Nearest Neighbors (KNN) network inference methods, along with the Louvain, Leiden and NBR-Clust clustering techniques. Results from analysis involving five internal cluster evaluation indices show the traditional KNN inference method and NBR-Clust and Louvain clustering produce the most promising clusters with medical phenotype data. It is also shown that visualization can aid in interpreting the clusters, and that the clusters produced can identify meaningful groups indicating customized interventions.

Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings

Abstract HVAC control strategies have been considered as a means to reduce energy consumption in buildings. Personalizing the operation of HVAC systems depending on thermal zone could reduce energy consumption while minimizing the thermal discomfort of occupants in multi-zone buildings. Integrating smart metering technologies with building energy management systems in multi-zone buildings makes it possible to categorize individual rooms into several meaningful end-user groups (i.e., thermal zones) based on energy use behaviors. Unfortunately, it still remains unclear how energy efficient end-user groups are in multi-zone buildings. Therefore, this study evaluates the energy efficiency of end-user groups in multi-zone buildings. An energy efficiency assessment framework for end-user groups is developed using energy benchmarking data from seven dormitory buildings in Seoul, Korea. The results show that during the heating season, consistent energy users have the lowest energy efficiency among all end-user groups, especially during unoccupied hours. Also, the energy efficiency of nighttime-peak energy users is low when the outdoor dry-bulb temperature is higher than 0 °C. Last, the occupied buildings have a higher potential for energy savings at over 0 °C. The proposed framework could allow facility managers to determine appropriate control strategies for HVAC systems and personalize the control parameters depending on end-user group.

Normative Substance Use Antecedents among Sexual Minorities: A Scoping Review and Synthesis.

Although sexual minority stress remains the dominant perspective for understanding disproportionate substance use among lesbian, gay and bisexual (LGB) populations, social norms are among the most predictive and commonly targeted substance use antecedents in other high-risk groups. This scoping review seeks to bring clarity to the body of norms-focused alcohol, tobacco, and other drug (ATOD) research conducted with LGBs over the past 20 years, identify intervention implications, and present priority directions for future research. Fifty-two peer-reviewed studies published between June 1999 and June 2019 were identified from searches of PubMed, PsycInfo, and Medline databases using combinations of terms related to: social norms; sexual orientation or sexual minority status; and, the use of alcohol, tobacco, and other drugs. A far greater number of studies focused on actual ATOD norms than perceived ATOD norms or discrepancies between actual or perceived norms, illuminating the need for additional research focused on these levels of analysis. Taken together, this literature suggests that: (1) perceived ATOD norms are reliable predictors of LGBs' ATOD use; (2) actual ATOD use norms are low among LGBs participating in representative, population-based survey studies; and, (3) LGBs over-estimate the ATOD use of peers. Thus, intervention strategies including personalized normative feedback, psychoeducation, and social branding may hold promise in reducing LGBs substance use. However, additional research is needed to increase our understanding of injunctive ATOD norms, identify meaningful LGB reference groups, elucidate environmental influences on ATOD norms, and examine relationships between stigma experiences, perceived norms, and ATOD use.

Reduction and transformation of energy use data for end-user group categorization in dormitory buildings

The control strategy of heating, ventilating, and air-conditioning (HVAC) systems is cost-effective to achieve energy saving in buildings. It is believed that identifying representative behavioral patterns of end-users in multi-zone buildings helps personalize the control parameters of HVAC systems. Thus, this will result in energy saving while minimizing thermal discomfort of end-users. With advanced metering technologies, it is possible to capture how end-users consume energy in their rooms and then categorize the rooms into several meaningful groups based on similar energy use patterns. Unfortunately, it is still unknown how changes in numerical values and dimension of energy use data affect the performance of end-user group categorization. Therefore, this research examines the performance of end-user group categorization across different types of numerical values and dimensions of energy use data. A clustering analysis is conducted using energy use data from 959 rooms of seven dormitory buildings in Seoul, South Korea. The clustering results show that reducing the dimension of energy use data (i.e., data reduction) improves the similarity of end-users within a group. Also, transforming the numerical values of energy use data (i.e., data transformation) makes the group similarity higher. Lastly, when combining both data reduction and transformation during the categorization process, the best clustering method is dependent on the distribution of energy use data. These results indicate that facility managers can provide end-users with thermally comfortable conditions and achieve energy saving across all zones.

S57. CHARACTERIZING COGNITIVE HETEROGENEITY IN INDIVIDUALS WITH A SCHIZOPHRENIA SPECTRUM DISORDER: A CLUSTER ANALYTIC APPROACH

BackgroundCognitive impairment is a core symptom of patients with schizophrenia, yet substantial heterogeneity in the degree of impairment exists within and between diagnoses of the schizophrenia spectrum, leading to sub-optimal understanding of the general and individual pathology of schizophrenia. By means of a cluster-analytic approach, individuals are grouped based on profiles of traits regardless of specific diagnoses. As this approach has already proven itself in decreasing cognitive heterogeneity within specific psychotic disorders, we investigated the structure of cognitive heterogeneity within a broader spectrum of psychotic disorders by including a large sample of schizophrenia patients and healthy individuals.MethodsAn agglomerative hierarchical clustering analysis was performed using cognitive data (patients n = 324, healthy controls n = 40). All patients were diagnosed with a DSM-IV-TR diagnosis of schizophrenia (SZ), schizoaffective (SZA) or schizophreniform (SZP) disorder or another psychotic disorder/psychosis not otherwise specified (PNOS). To measure cognitive function, the composite Z-score was calculated, based on six cognitive domains as defined by the Brief Assessment of Cognition in Schizophrenia (BACS).ResultsFour clusters emerged from the clustering analysis, gradually and significantly distinguishable in cognitive impairment. The first cluster (n=50) contained relatively intact individuals, the second cluster (n=122) consisted of individuals with minimal to mild cognitive impairment, the third cluster (n=114) contained individuals with moderate cognitive impairment and individuals with most severe cognitive impairment were placed in the fourth cluster (n=78). All six cognitive domains show similar differences between clusters. While symptom severity and educational attainment show the same classification patterns as the neurocognitive profiles do, cluster assignment was not affected by illness duration and antipsychotic intake.DiscussionResults confirm that data-driven formed groups based on the clustering of neurocognitive profiles contain less cognitive heterogeneity compared to predefined diagnostic groups. Our results demonstrate that clustering patients with a schizophrenia spectrum disorder based on their neurocognitive profile decreases cognitive heterogeneity within the formed groups, creating more meaningful groupings and findings accentuate a cognitive continuum irrespective of a specific diagnosis, providing insight into the underlying pathology of clinical manifestation of schizophrenia.

Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images

High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth's surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento.

Analytical review of clustering techniques and proximity measures

One of the most fundamental approaches to learn and understand from any type of data is by organizing it into meaningful groups (or clusters) and then analyzing them, which is a process known as cluster analysis. During this process of grouping, proximity measures play a significant role in deciding the similarity level of two objects. Moreover, before applying any learning algorithm on a dataset, different aspects related to preprocessing such as dealing with the sparsity of data, leveraging the correlation among features and normalizing the scales of different features are required to be considered. In this study, various proximity measures have been discussed and analyzed from the aforementioned aspects. In addition, a theoretical procedure for selecting a proximity measure for clustering purpose is proposed. This procedure can also be used in the process of designing a new proximity measure. Second, clustering algorithms of different categories have been overviewed and experimentally compared for various datasets of different domains. The datasets have been chosen in such a way that they range from a very low number of dimensions to a very high number of dimensions. Finally, the effect of using different proximity measures is analyzed in partitional and hierarchical clustering techniques based on experiments.

Investigation of the personality, anger and anxiety situation of the professional and amateur football players and the veteran footballers

The aim of this study is to investigate the anger, personality and anxiety levels of the football players who actively play football and the football players who have left football. A total 496 football players, 162 women and 334 men, participated in the research in Istanbul in 2018. Spielberger's Anger Style scale, the Eysenck Personality Inventory short form, and Spielberger's continuous anxiety inventory were used as data collection tools. The percentage distribution and frequency values of the athletes participating in the research were determined with descriptive statistics and Shapiro Wilk test was applied as the normality test. Depending on the athletes’ situation, Mann Whitney U test was performed to compare the anger, personality and anxiety levels of the athletes. Kruskal Wallis test was used to compare the anger, personality and anxiety levels of the athletes depending on the level of the athlete, and Tamhane was used as the second level test to identify meaningful groups. Sperman correlation test was used to determine the relationship between the anger, personality and anxiety levels of the athletes. All the tests performed were done at α = 0.05 significance level. Microsoft Excell 2017 for Windows was used to enter the data into the computer environment and SPSS 25 package program was used to create graphics and tables. As a result of the findings obtained from the study, it was observed that there were significant differences between anger and inward anger scores according to the athletes' status, and anger, personality and trait anxiety dimensions according to the level of athletics. On the other hand, it was seen that there was a significant relationship between anger, personality and trait anxiety levels of the athletes participating in the research. As a result, it was determined that normal athletes were angrier than veteran athletes, and professional footballers had better level of anger controls and anger levels than amateur athletes. On the other hand, it has been determined that the personality and anxiety levels of professional athletes are higher than amateur athletes.
 

Highlights From the Circulation Family of Journals.

Highlights From the Circulation Family of Journals.

An Exploration Into the Importance of a Sense of Belonging for Online Learners

Fostering a sense of belonging and a personal connection is seen as fundamental by many educational researchers, regardless of the learning environment. Online learning certainly provides flexible learning opportunities but comes with notable issues. For online learners, nurturing a sense of belonging may present a way of improving their experiences and attainment, as well as reducing attrition rates. Research specifically exploring sense of belonging and online learning is limited. This article addresses that gap and reports on a small-scale exploratory study using qualitative data-collection and analysis methods to investigate the importance, or not, of sense of belonging for postgraduates’ online education by exploring the origins and nature of their lived experience of online learning and their sense of belonging therein. Our initial findings emphasise its importance for them as online learners and have identified three significant themes: interaction/engagement, the culture of the learning, and support. These early findings highlight the importance of these three themes in promoting a sense of belonging and in ensuring that there are opportunities for meaningful group and peer interactions; they will be of interest to all engaged in online education.

Deep Embedded Non-Redundant Clustering

Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records.

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes. TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical re-formulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14× faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 60 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.

A TECHNOLOGY OF COMMUNICATIVE COMPETENCE DEVELOPMENT OF FUTURE DOCTORS IN THE PROCESS OF PROFESSIONAL TRAINING

The article analyzed the problem of development of communicative competence of future doctors in the process of their professional training. The characteristic features of the physician communicative competent are distinguished.
 As a result of research, a peculiarity of communicators is highlighted. A slight percentage of the surveyed have quite significant personal problems that require long-term psychotherapeutic work, that should precede measures directly aimed to the development of their communicative competence.
 Models of communicative competence development are considered. It is presented a fairly a common feature, namely: competence in communication is understood as a result of quantitative accumulation of communication skills. It is assumed that at some point this number translates the person to a qualitatively new level of communicative behavior. Communicative skills are regarded as means of overcoming the drama of communication.
 The meaning of the training in the context of professional training of specialists are emphasized and substantiated.
 The basic training of communicative competence should be focused on developing the skills of active listening and regulation of emotional tension, since these skills allow to greatly solve the drama of communication.
 The logic scheme of the training is proposed based on the fact that the professional communication of a doctor is considered as a sequence of communication tasks of varying degrees of complexity. The task of the training was to correct the settings of participants regarding communication with people in general and patients in particular; development of abilities to action in unusual situations, forecasting the consequences of people's interaction. In accordance with these tasks, a meaningful group operation is highlighted.
 The implementation of the program included the use of various psychotechnical methods of work: exercises, rehearsal of behavior, situational and role-playing games, group discussions and discussions, bodily and motor techniques.

The Application of Cognitive-Behavior Therapy Program for Improving the Performance Strategy of TAEKWONDO Demonstration

The purpose of this study is to verify the effects by developing and applying a cognitive behavior therapy program for improvement of Taekwondo demonstration performance strategies. To achieve the purpose of this study, a program for improvement of Taekwondo demonstration performance strategies is developed through literature review. 9 people voluntarily expressing an intention of participating in this program among demon-stration members of the national team acting in Korean Taekwondo Association were formed as an experimental group, and 10 people were sampled and formed as a control group. Total 10 times of the developed program were applied to the experimental group during 90 minutes∼100 minutes. However, this program was not applied to the control group. As a result of analyzing a difference between the experimental group and the control group to verify the ef-fects of applying a cognitive behavior therapy program, first, there is a statistically meaningful difference be-tween groups in pre and post tests for sports performance strategy test between the experimental group and the control group. After implementation of the program, it showed that condition regulation, setup of image and purpose, easing the tension, and the ability of affect regulation were related to cognitive variable increase in sports performance strategies. Second, there is a statistically meaningful difference between groups in pre and post tests for an automatic thinking(negative) test between the experimental group and the control group. Es-pecially, negative automatic thinking decreased in the experimental group a cognitive behavior therapy program was conducted, and this experimental group also showed a statistically meaningful difference. Third, there is a statistically meaningful difference between groups in pre and post tests of a dysfunctional beliefs test between the experimental group and the control group. After implementation of a cognitive behavior therapy program, all variables of dysfunctional beliefs decreased in the experimental group, and the experimental group also showed a statistically meaningful difference.

Recognition of group patterns in geological maps by building similarity networks

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps.

Group meaningfulness and the causal direction of influence between the ingroup and the self or another individual: Evidence from the Induction-Deduction Paradigm.

The goal of the present study was to investigate the causal direction of influence between the ingroup as a whole and the self or another ingroup member considering a key feature of groups, i.e., their perceived meaningfulness. To this goal, in Study 1, 2, and 3 we predicted a preference for self-stereotyping and ingroup-stereotyping in the meaningful social categories of sorority women, left-handed people and psychology students. In Study 4 we further expect that the meaningfulness attributed to a group moderates the direction of causality between individual and ingroup perception. Thus, we used one’s Zodiac sign as the ingroup whose degree of meaningfulness varies across participants and we hypothesized higher levels of meaningfulness attributed to the ingroup to be associated with higher self- and ingroup-stereotyping. Using the methodologically stringent Induction Deduction Paradigm, participants were given information on unfamiliar dimensions, about either the ingroup or an individual (self or other ingroup member) and asked to make inferences on those same attributes about the ingroup (induction condition) or the individual (deduction condition). As predicted, a preference for deduction to the self (i.e., self-stereotyping) and deduction to another ingroup member (i.e., ingroup-stereotyping) were found for the meaningful groups of sorority women, left-handed people, and Psychology students (Studies 1, 2, and 3). In Study 4, consistent with predictions, the higher the level of attributed meaningfulness to the Zodiac system the higher the degree of deduction both to the self (self-stereotyping) and to another Zodiac ingroup member (ingroup-stereotyping). Several implications of these results are discussed, for example in relation to the possibility of educational interventions aimed at invalidating intergroup differences.

A NOVEL PROBABILISTIC GENERATION MODEL FOR GRID CONNECTED PV BASED DISTRIBUTED GENERATION

The feasibility of renewable energy resources such as solar energy is marked by uncertainties that make it an unpredictable mean of power generation. To guarantee an uninterrupted power supply, solar irradiance modelling can be taken as a useful step towards meeting the operational challenges of electric power grid. This paper proposes a dynamic Probabilistic generation model to estimate and generate the time-coupled solar irradiance patterns. Initially, clustering of yearly solar irradiance measurements is performed to obtain a meaningful grouping of similar days. One-hour time step is considered to construct a time-coupled probabilistic model of solar irradiance data based on a Beta distribution. The parameters of beta distribution are found by considering the variations of irradiance patterns at two successive time steps. The probabilistic model is then used to generate number of aggregate solar irradiance generation scenarios. The effectiveness of proposed scenario generation approach is evaluated through Average Mean Absolute Percentage Error (AMAPE) and comparison with the probabilistic model already available in the literature.

Factors Influencing Quality of Team Discussion: Discourse Analysis in an Undergraduate Team-Based Learning Biology Course.

Group activities as part of active-learning pedagogies are thought to be effective in promoting student learning in part because of the quality of discussion they engender in student teams. Not much is known, however, about which instructional factors are most important in achieving productive conversation or how these factors may differ among different collaborative pedagogies. We explored what provokes meaningful group discussions in a university physiology course taught using team-based learning (TBL). We were most interested in discussions that evoke explanations that go beyond statements of basic facts and into disciplinary reasoning. Using transcribed conversations of four randomly selected teams three times throughout the semester, we analyzed three distinct discursive phenomena—conceptual explanations, re-evaluations, and co-construction—that occurred in productive conversations. In this paper, we provide examples from student discussions showing the role of each of these elements in moving students toward conceptual understanding. These phenomena were more likely to occur in response to higher-order questions in Bloom’s taxonomy. Preclass preparation and student accountability as part of TBL may be important factors in this finding. We share implications for practice based on our results.

The Adolescent Knee Pain (AK-Pain) prognostic tool: protocol for a prospective cohort study.

Background: One in three children and adolescents experience knee pain. Approximately one in two adolescents with knee pain will continue to experience pain even five years later and have low quality of life. The general practitioner (GP) is the first point of contact for children and adolescents with knee pain in Denmark. There is a variety of treatments being delivered in general practice, despite similar symptoms and patients' characteristics. This suggests a need to support the GPs in identifying those at high risk of a poor outcome early on, in order to better allocate resources. The aim of this study is to develop a user-friendly prognostic tool to support GPs' management of children and adolescents' knee pain. Methods: A preliminary set of items in the prognostic tool were identified using systematic reviews and meta-analysis of individual participant data. Following feedback from GPs and children and adolescents on the content and understanding, the tool was piloted and implemented in general practice. A cohort of approximately 300 children and adolescents (age 8-19 years old) is being recruited from general practices (recruitment period, July 2019 - June 2020). Clinically meaningful risk groups (e.g. low/medium/high) for the recurrence/persistence of knee pain (at 3 and 6 months) will be identified. Discussion: If successful, this prognostic tool will allow GPs to gain insights into the likely prognosis of adolescents with knee pain and subsequently provide the first building blocks towards stratified care, where treatments will be matched to the patients' prognostic profile. This has the potential to improve the recovery of children and adolescents from knee pain, to improve the allocation of resources in primary care, and to avoid the decline in physical activity and potential associated health and social consequences due to adolescent knee pain. Registration: Registered with ClinicalTrials.gov on 24 June 2019 (ID NCT03995771).

The Adolescent Knee Pain (AK-Pain) prognostic tool: protocol for a prospective cohort study

Background: One in three children and adolescents experience knee pain. Approximately one in two adolescents with knee pain will continue to experience pain even five years later and have low quality of life. The general practitioner (GP) is the first point of contact for children and adolescents with knee pain in Denmark. There is a variety of treatments being delivered in general practice, despite similar symptoms and patients’ characteristics. This suggests a need to support the GPs in identifying those at high risk of a poor outcome early on, in order to better allocate resources. The aim of this study is to develop a user-friendly prognostic tool to support GPs’ management of children and adolescents’ knee pain. Methods: A preliminary set of items in the prognostic tool were identified using systematic reviews and meta-analysis of individual participant data. Following feedback from GPs and children and adolescents on the content and understanding, the tool was piloted and implemented in general practice. A cohort of approximately 300 children and adolescents (age 8-19 years old) is being recruited from general practices (recruitment period, July 2019 – June 2020). Clinically meaningful risk groups (e.g. low/medium/high) for the recurrence/persistence of knee pain (at 3 and 6 months) will be identified. Discussion: If successful, this prognostic tool will allow GPs to gain insights into the likely prognosis of adolescents with knee pain and subsequently provide the first building blocks towards stratified care, where treatments will be matched to the patients’ prognostic profile. This has the potential to improve the recovery of children and adolescents from knee pain, to improve the allocation of resources in primary care, and to avoid the decline in physical activity and potential associated health and social consequences due to adolescent knee pain. Registration: Registered with ClinicalTrials.gov on 24 June 2019 (ID NCT03995771).