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Sense of community is a necessary ingredient for existing and future communities in developed and developing countries to thrive. However, there are socioeconomic factors that have the potential to influence the sense of community in developing countries. This is an aspect that is underreported in literature in developing countries like Nigeria. This study examines the influence of socioeconomic factors on the sense of community in selected public housing estates in Akure, Southwest, Nigeria. Structured questionnaire was used to collect data for the research. Single-factor descriptive Analysis, Mean Scoring and Categorical Regression Analysis were used to analyze the data. Findings showed that the sense of community in the study area was above average and that socioeconomic characteristics jointly predicted of sense of community. However, of the nine socioeconomic variables, four were significant predictors of sense of community in this context. The four significant variables were employment status, level of education, tenure status and age. The paper concludes by providing implications, considering the significant factors especially in developing strategies to enhance the sense of community in the study area. [ABSTRACT FROM AUTHOR]

The contents of his Red Book document experiences that C.G. Jung had during his confrontation with his unconscious. Jung's great imaginal work was possibly composed while he was in the midst of a pathological process. This paper identifies two works that foreshadowed The Red Book: Nietzsche's Thus spoke Zarathustra, and Hildegard's Scivias. Of interest is the fact that these two precursors of the red book were also great works of imaginal literature that were composed when their respective authors were in the throes of illness. The second part of this paper discusses the relationship between the imaginal and the pathological. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has revealed clear deficiencies in global public health policies and healthcare systems when confronted with the emergence of a novel and deadly infectious disease. Objectives: With 4 years elapsed since the onset of the pandemic, ample data now exist to analyse the associations between the implementation of diverse public health policies, sociodemographic factors and COVID-19 mortality rates. Methods: This study utilised the dataset compiled by 'Our World in Data' spanning the period of the COVID-19 pandemic from 2020 to 2022. Stochastic frontier analysis was employed to assess the influencing factors and their relationship with mortality rates resulting from COVID-19 infections across 156 countries or regions. Results: This study yielded several key findings: (1) There remains a 33% margin for improvement in the global mortality rate concerning the COVID-19 pandemic; (2) During the initial stage of the pandemic, when an effective vaccine was not yet available, implementing public health control policies could reduce both the infection and mortality rates; (3) Areas characterised by higher population densities, a greater proportion of individuals aged 65 and over, and elevated prevalence rates of diabetes demonstrated higher mortality rates; and (4) Increasing vaccination coverage emerged as an effective strategy for reducing mortality rates. Conclusions: As our understanding of the COVID-19 virus improves, global economies and social interactions have gradually returned to normality. It is anticipated that the findings of this study can serve as a valuable reference in combating potential future pandemics caused by unknown viruses. Paper context: Main findings: The initial social distancing and lockdown measures, and use of masks as a preventive measure against the spread of COVID-19 disease, among many public health policies, have proven effective. Added knowledge: This study uncovered several factors influencing COVID-19 mortality, including the infection rate, population density, proportion of elderly individuals, intensity of public health control measures, presence of cardiovascular disease and diabetes and the cumulative fully vaccinated rate. Global health impact for policy and action: The results of this study are anticipated to serve as valuable references and effective guides for policymakers and public health authorities worldwide, especially in the event of future unknown deadly epidemics or public health crises. [ABSTRACT FROM AUTHOR]

Applications of circular regression models are ubiquitous in many disciplines, particularly in meteorology, biology and geology. In circular regression models, variable selection problem continues to be a remarkable open question. In this paper, we address variable selection in linear-circular regression models where uni-variate linear dependent and a mixed set of circular and linear independent variables constitute the data set. We consider Bayesian lasso which is a popular choice for variable selection in classical linear regression models. We show that Bayesian lasso in linear-circular regression models is not able to produce robust inference as the coefficient estimates are sensitive to the choice of hyper-prior setting for the tuning parameter. To eradicate the problem, we propose a robustified Bayesian lasso that is based on an empirical Bayes (EB) type methodology to construct a hyper-prior for the tuning parameter while using Gibbs Sampling. This hyper-prior construction is computationally more feasible than the hyper-priors that are based on correlation measures. We show in a comprehensive simulation study that Bayesian lasso with EB-GS hyper-prior leads to a more robust inference. Overall, the method offers an efficient Bayesian lasso for variable selection in linear-circular regression while reducing model complexity. [ABSTRACT FROM AUTHOR]

Fertility rates in Nigeria are declining at such a modest rate, that if more proactive measures are not employed to reduce fertility, the nation may double its population before long. We empirically examined fertility behaviour as derivatives of specific subgroup social norms, and the variations in the factors responsible for different fertility behaviours, using the 2018 Nigeria Demographic and Health Survey. Descriptive bivariate, and multinomial logistic regression analyses were used to predict the contribution of demographic and sociocultural factors contributing to the fertility level, and findings were reported as odds ratios. Results revealed that age, religion and level of education are the most significant predictors of fertility level, with remarkable differences in birth rate across subgroups, whereas North-West Nigeria had the highest fertility level. Furthermore, marriage had a significantly negative effect on high fertility among North-Western women, whereas higher education significantly increased high fertility among North-Eastern women. In conclusion, subgroup social norms fertility behaviour is responsible for the persistent fertility differential outcome in Nigeria. Thus, the paper strongly advocates the need to intensify community-led, norm-based solution and not a universal approach in addressing fertility control in Nigeria. [ABSTRACT FROM AUTHOR]

This perspective paper illustrates the usefulness of explicitly integrating motor learning terminology with evolving therapeutic approaches. Physiotherapy specific scoliosis exercises (PSSEs) include a growing number of approaches to scoliosis management and serve as an example of this integration. Three quintessential patient cases (a young hypermobile adolescent, a post-pubescent teen, and an adult with childhood diagnosis of scoliosis) serve to contrast the clinical decision-making process for a PSSE plan of care when organized within a motor learning framework. As intervention approaches evolve, aligning the unique terminologies from different schools of thought with motor learning constructs would provide a common language for clinicians, academics and researchers to facilitate comparison of approaches and organize intervention care plans. Linking a motor learning framework and terminology to PSSE may facilitate comparison of PSSE treatment approaches by clinicians, academics, and researchers, as well as advance the global quality of care for patients with scoliosis. [ABSTRACT FROM AUTHOR]

In this article, a bi-level optimization problem covering upper (design) and lower (operation) levels is defined and a solution procedure for bi-level optimization problems is presented. This is devised as a dynamic multiobjective optimization problem, i.e. the values of the control and state variables change over a predefined time horizon and several competing criteria are optimized simultaneously. Moreover, the interaction between the upper and lower levels is analysed. The benefits of bi-level dynamic multiobjective optimization are illustrated in detail by examining an industrial case in which the design of a paper mill (upper level) and the mill operation (lower level) are optimized at the same time. However, the problem definition and the solution procedure are not limited to any specific application but can be exploited in many different industrial areas. [ABSTRACT FROM PUBLISHER]

MOOCs (massive open online courses) have attracted a great deal of research and attention; however, few studies have focused on the factors that influence MOOC teacher effectiveness, which is the key to the development of MOOCs. Based on the perspective of professional capital and social exchange theory, this paper aims to study the factors that influence MOOC teacher effectiveness. First, the research hypotheses are proposed from the teacher's perspective to build the research model based on factors affecting MOOC teacher effectiveness. Second, the iCourse platform is selected as the research object because it has the largest number of active users in China. As of March 1, 2019, the effective research data of 7380 teachers were obtained from iCourse to build a multivariate regression model. The results highlight the main factors that affect MOOC teacher effectiveness from the perspective of professional capital. Finally, relevant suggestions are proposed for the development of MOOC. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has exacerbated health inequalities worldwide, having a disproportionately harsh impact on unprivileged populations such as migrants and refugees. These populations are often more exposed to the virus, but less protected, while at the same time being at higher risk of suffering from poor living and working conditions, limited access to healthcare, and discrimination by the host society, all of which is challenging to their mental health. Empirical evidence on how the COVID-19 pandemic is affecting migrants and refugees is required to design effective actions aimed at ensuring health equity. Therefore, this paper aims to analyse how the pandemic has impacted the psychological well-being of migrants and refugees living in Spain. This study was carried out within the framework of the ApartTogether study sponsored by the World Health Organization. Data collection was carried out during March-November 2020, through an online survey completed by 241 participants (age: M = 37 years; 129 women). The results indicate that 78.7% of participants had suffered a decrease in their psychological well-being since the onset of the COVID-19 pandemic, with number of difficulties and worries experienced being the best individual predictors of this outcome. Enjoying social connections and perceiving positive treatment from the host society were positively associated with psychological well-being at a relational and community level, respectively. Based on these findings, we outline priority areas of psychosocial interventions aimed at guaranteeing the mental health of migrants and refugees in the face of the pandemic in Spain. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper was to discuss challenges to interdisciplinary evidence-based social work practice (EBP) within a context of xenotransplantation (XTx) using genetically modified animal organs, and the broader context of experimental medicine. Method This paper reviewed EBP terminology and discussed the meaning and function of evidence-based social work practice within an IDT in a XTx setting. Meta-analytic systematic reviews, and psychosocial surveys, were discussed to glean insights into ways in which IDTs can incorporate those research methodologies into EBP within an experimental medical (i.e., XTx) context. Several issues that arise while conducting psychosocial research in preparation for clinical trials were also discussed. Conclusions Social workers can assume leadership roles on IDT's within experimental medicine by using their group facilitation skills and training in EBP. An evidence-based pathway model for interdisciplinary care was proposed and briefly illustrated as a framework for collaboration among IDT members using EBP in XTx practice. [ABSTRACT FROM AUTHOR]

Obtaining values for latent variables in factor analysis models, also referred to as factor scores, has long been of interest to researchers. However, many treatments of factor analysis do not focus on inference about the latent variables, and even fewer do so from a Bayesian perspective. Researchers may therefore be ill-acquainted with Bayesian thinking on this issue, despite the fact that certain existing procedures may be seen as Bayesian to some extent. The focus of this paper is to provide a conceptual grounding for Bayesian inference for latent variables, articulating not only what Bayesian inference has to say about values for latent variables, but why Bayesian inference is suited for this problem. As to why, it is argued that the notion of exchangeability motivates the form of factor analysis, as well as Bayesian inference for latent variables. The argument is supported by documenting the widespread use of Bayesian inference in analogous settings, including latent variables in other measurement models, multilevel models, and missing data. As to what, this work describes a Bayesian analysis when other parameters are known, as well as partially and fully Bayesian analyses when other parameters are unknown. This facilitates a discussion of various choices researchers have when adopting Bayesian approaches to inference about latent variables. [ABSTRACT FROM AUTHOR]

The kinetic models were widely used to analyze the dynamic adsorption behaviors in a batch system and reveal the mass-transfer mechanisms. The previous review papers were mainly confined to the description of the kinetic models, assessment of the fitting quality, determination of the model parameters and practical application in the field of water and wastewater treatment. However, the curve characteristics of the kinetic models and their mathematical relations were rarely mentioned in the literature. How to select and determine the optimum model remained to be further discussed. Thus, in addition to improving previous work, the main objectives of this review were: (i) to identify the curve characteristics of the kinetic models by control variates; (ii) to reveal their mathematical relations by variable substitution; (iii) to determine the optimum model by error functions and residual plot; and (iv) to correct some common mistakes in the literature. The pseudo-first-order (PFO) and pseudo-second-order (PSO) equations were two special cases of mixed 1,2-order equation (MOE). The PFO and Furusawa–Smith equations were mathematically equivalent. This review is expected to help readers better understand and use the adsorption kinetic models and provide potential ideas for the development of new kinetic models. [ABSTRACT FROM AUTHOR]

For organisations utilising big data platforms, knowledge sharing helps spread contextualised information of data, increases efficiencies and reduces the cost of lost knowledge when employees leave. Query-Driven Knowledge Sharing Systems (QKSS) partially automate knowledge sharing in analytics teams by building context into data, enabling the reuse of complex queries. Although QKSS can improve knowledge sharing, encouraging reuse behaviour is a significant issue for adoption. This paper analyses the applicability of gamification for improving knowledge reuse in QKSS. In collaboration with a Sydney-based data analytics firm, we recruited professional data analysts to participate in an experiment. The recruited analysts were asked to complete Structured Query Language tasks using either the firm's QKSS platform or a gamified version which included a small number of gamified elements designed to increase the likelihood of query reuse. The results demonstrate the positive impact of gamification on query reuse and the efficiency of tasks. [ABSTRACT FROM AUTHOR]

Child abuse may potentially create the behavioral problems particularly in the children of parents with substance use disorder. Thus, the current study was conducted to investigate the effects of child abuse on the behavioral problems in the children of parents with substance use disorder using the emotional regulation and social skills as mediators. In this paper, the method of Structural Equation Modeling (SEM) was applied. The study population included 358 children of parents with substance use disorder whose parents had referred to the addiction treatment center in Kermanshah province, Iran (2017-2018). Conners Comprehensive Behavior Rating Scales (CBRS), Emotion Regulation Checklist (ERC), Social Skills Rating Scale (SSRS) Questionnaire, and Child Abuse Questionnaire were also used for data collection. IBM SPSS Amos 22 software was utilized for statistical analysis of the obtained data. The SEM was also analyzed to confirm fit of the model. The results showed a direct relationship between the child abuse and behavioral problems so that, the boys outperformed the girls in this regard. The findings also suggested a significant relationship between the family characteristics and behavioral problems. On the other hand, family characteristics and emotional regulation had direct and significant effects on improvement of the social skills (P < 0.001). The results revealed a direct effect of child abuse on the behavioral problems in the children of parents with substance use disorder. Thus, it is suggested to take a preventive approach toward child abuse in the children of parents with substance use disorder by employing a comprehensive program and intervention methods. [ABSTRACT FROM AUTHOR]

Hong Kong experienced the SARS pandemic in 2003. Seventeen years later, the Covid-19 pandemic now challenges Hong Kong and the world. This study aims to unveil the impact of the pandemic on health social workers. One hundred and sixty-six health care social workers in Hong Kong were recruited to join a cross-sectional online survey from November 2020 to March 2021. This paper includes the analysis of the demographic information, fear of contamination, resilience, perceived social support and physical health only. Irrespective of the demographic background, the level of fear of contamination, resilience, perceived social support and physical health were similar. The mean scores of the two dimensions of fear of contamination (Harm avoidance: m = 7.49, s.d. = 3.25; Disgust avoidance: m = 7.75, s.d. = 2.90) were higher than those of general public and clinical samples with Obsessive-Compulsive Disorder. No moderation effects were found in resilience and perceived social support in the relationship of fear of contamination and physical health. Instead, direct effects were shown.The impact of the pandemic on health social workers was universal. Psychoeducational programs to alleviate the fear of contamination and organizational-level interventions to improve workplace social support are highly needed. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to examine the probability problems which were posed based on continuous sample space and discrete sample space. Participants of the study consisted of 48 university students selected by using the convenience sampling method. Eight tasks related to continuous and discrete variables were designed by the researcher. Eight problems posed by each student in accordance with these tasks, their reflection papers and interviews conducted with them constituted the data of the study. Problems were analysed according to appropriateness, solvability, context, applicability, language and variable type dimensions. Findings revealed that most solvable problems were in real-life context, applicable and had clear language. However, most of the unsolvable problems had missing information. Inappropriate problems either were not generated depending on the task instructions or suitable variables were not used in them. Students had difficulties particularly in posing continuous sample space problems. It was concluded that problem posing was an effective practice for students since it enhanced their knowledge about probability and helped them to gain insight into problem posing. [ABSTRACT FROM AUTHOR]

Anecdotal evidence suggests that people with autism may have different processing strategies when accessing the web. However, limited empirical evidence is available to support this. This paper presents an eye tracking study with 18 participants with high-functioning autism and 18 neurotypical participants to investigate the similarities and differences between these two groups in terms of how they search for information within web pages. According to our analysis, people with autism are likely to be less successful in completing their searching tasks. They also have a tendency to look at more elements on web pages and make more transitions between the elements in comparison to neurotypical people. In addition, they tend to make shorter but more frequent fixations on elements which are not directly related to a given search task. Therefore, this paper presents the first empirical study to investigate how people with autism differ from neurotypical people when they search for information within web pages based on an in-depth statistical analysis of their gaze patterns. [ABSTRACT FROM AUTHOR]

In this paper, we present ArmMenu, a command input approach for distant displays. ArmMenu has a circular interface like pie menus and menu selection is performed by proprioception-based lateral arm movements. We implemented ArmMenu with an off-the-shelf body tracking device (Kinect) and conducted two experiments to validate its efficacy. In the first experiment, we explored the design space of ArmMenu by varying the number of menu items, with exposed or hidden menu modes. Users can operate up to 8-item menus with high selection accuracy (>98%). ArmMenu was fast and accurate even with the hidden menu mode. The second experiment compared the performance of ArmMenu and touchless marking menus. While having similar selection accuracy, ArmMenu was faster and more preferable by users. Our studies consequently demonstrate ArmMenu's effectiveness for command input on distant displays. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the finite population mean in stratified random sampling by using two auxiliary variables. This paper proposed a ratio-cum-product exponential type estimator of population mean under different situations: (i) when there is presence of non-response and measurement errors on the study as well as auxiliary variables; (ii) when there is non-response on the study and auxiliary variables but with no measurement error; (iii) when there is complete response on study variable but there is presence of non-response and measurement error on the auxiliary variables and (iv) when there are complete response and measurement error on study as well as auxiliary variables. The expressions of the bias and mean square error of the proposed estimator have been obtained up to the first degree of approximation. The proposed estimator has been compared with usual unbiased estimator, ratio estimator and other existing estimators and the conditions obtained to show the efficacy of the proposed estimator over other considered estimators. Simulation study is carried out to support the theoretical findings. [ABSTRACT FROM AUTHOR]

The goal of predictive policing is to forecast where and when crimes will take place in the future. The idea has captured the imagination of law enforcement agencies around the world. Many agencies are purchasing software tools with the goal of reducing crime by mapping the likely locations of future crime to guide the deployment of police resources. Yet the claims and promises of predictive policing have not been subject to critical examination. This paper provides a review of the theories, techniques, and assumptions embedded in various predictive tools and highlights three key issues about the use of algorithmic prediction. Assumptions: The algorithms used to gain predictive insights build on assumptions about accuracy, continuity, the irrelevance of omitted variables, and the primary importance of particular information (such as location) over others. In making decisions based on these algorithms, police are also directed towards particular kinds of decisions and responses to the exclusion of others. Evaluation: Media coverage of these technologies implies that they are successful in reducing crime. However, these claims are not necessarily based on independent, peer reviewed evaluations. While some evaluations have been conducted, additional rigorous and independent evaluations are needed to understand more fully the effect of predictive policing programmes. Accountability: The use of predictive software can undermine the ability for individual officers or law enforcement agencies to give an account of their decisions in important ways. The paper explores how this accountability gap might be reduced. [ABSTRACT FROM AUTHOR]

How can music therapy contribute to the future design and development of interactive and musical media for co-creation, and vice versa? This question is addressed through a reflective synthesis on selected results of the research project called RHYME (www.rhyme.no), in which the author took part. That project involved families of a child with disabilities and their co-creation with interactive musical media that were designed as home-environment objects (e.g. pillows, carpets, and toys). This paper examines aspects of the project results with regard to the subject-object interaction. One such aspect is that the users would experience the media as active collaborators and not “just” as another technological thing which they could manipulate into a cause-and-effect reaction. Another such aspect is that successful co-creation with the media seemed to demand an open and empathic (also called dialogical) mind-set among those who took on the role of helper. The paper argues that music therapy competency in human communication and interaction is significant for future developers of interactive media. Music therapy should also note the close connection humans have to the media, and learn from the ways in which this relationship too can be potentially health promoting. [ABSTRACT FROM AUTHOR]

In this paper, we address the analytical investigation into a model for adhesive contact introduced in a paper by Freddi and Fremond, which includes nonlocal sources of damage on the contact surface, such as the elongation. The resulting PDE system features various nonlinearities rendering the unilateral contact conditions, the physical constraints on the internal variables, as well as the contributions related to the nonlocal forces. For the associated initial-boundary value problem, we obtain a global-in-time existence result by proving the existence of a local solution via a suitable approximation procedure and then by extending the local solution to a global one by a nonstandard prolongation argument. [ABSTRACT FROM AUTHOR]

In this paper, we present recent developments in the global optimization software BARON to address problems with integer variables. A primary development was the addition of mixed-integer linear programming relaxations to BARON's portfolio of linear and nonlinear programming relaxations, aiming to improve dual bounds and offer good starting points for primal heuristics. Since such relaxations necessitate the solution of NP-hard problems, their introduction to a branch-and-bound algorithm raises many practical issues regarding their effective implementation. In addition to describing BARON's dynamic strategy for deciding under what conditions to activate integer programming relaxations in the course of branch-and-bound, the paper also describes cutting plane and probing techniques that originate from the literature of integer linear programming and have been adapted in BARON to solve nonlinear problems. Finally, we describe BARON's primal heuristics for finding good solutions of mixed-integer nonlinear programmes. For all these techniques, we report extensive computational results on a public data set, aiming to analyse the impact of each technique in the solution process and identify techniques that expedite solution the most. [ABSTRACT FROM AUTHOR]

If the population size is not a multiple of the sample size, then the usual linear systematic sampling design is unattractive, since the sample size obtained will either vary, or be constant and different to the required sample size. Only a few modified systematic sampling designs are known to deal with this problem and in the presence of linear trend, most of these designs do not provide favorable results. In this paper, a modified systematic sampling design, known as remainder modified systematic sampling (RMSS), is introduced. There are seven cases of RMSS and the results in this paper suggest that the proposed design is favorable, regardless of each case, while providing linear trend-free sampling results for three of the seven cases. To obtain linear trend-free sampling for the other cases and thus improve results, an end corrections estimator is constructed. [ABSTRACT FROM AUTHOR]

To promote trauma-informed professional supports and services to maltreated youth, it is imperative that researchers explore foster parents’ knowledge of child trauma. However, there is a dearth in the literature related to this topic. This exploratory study utilizes a convenience sample (N = 219) of foster parents in one southeastern state. Variables of interest include actual and perceived knowledge about child trauma. After a terse review of background literature, this paper will explicate key results, discuss these findings, and identify salient practice and policy implications derived from this study. The paper will conclude by delineating apposite areas for future research. [ABSTRACT FROM AUTHOR]

International statistics show that relapse rates associated with opiate and crack cocaine (OCC) misuse remain high. This has led to an increased scientific interest in the topic of relapse. However, there are limited studies reflecting on the state of this field. This review provides an overview of the topic and directions for future research. Method: Guided by the overall question of how relapse into OCC misuse is understood, a scoping review was conducted using Arksey and O'Malley's framework. A total of 126 studies between 1972 and 2019 were included. The literature was thematically grouped into relapse definitions, theories, associated factors and treatment approaches. Results: This review found that the majority of relapse understandings were derived from the USA and UK, and that these studies predominantly used quantitative research designs. Relapse definitions were controversial with the majority using biomedical concepts of disease. Theories on relapse were based on psychological theories and developed through clinical and neuroscientific research. Findings show that the majority of the literature focused on relapse risk factors with very few papers focusing on protective factors. Findings on treatment approaches indicate a steer toward harm-minimization strategies over relapse management strategies. Conclusions: This review demonstrates that drug relapse research needs to grow more diverse, inter-disciplinary and user-centred in perspective so as to respond to relevant challenges ahead. [ABSTRACT FROM AUTHOR]

Estimation of population mean of study variable Y suffers loss of precision in the presence of high variation in the data set. The use of auxiliary information incorporated in construction of an estimator under Rank set sampling scheme results in efficient estimation of population mean. In this paper, we propose an efficient generalized family of estimators to estimate finite population mean of study variable under ranked set sampling utilizing information on an auxiliary variable. Bias and Mean Square Error (MSE) of the proposed generalized family of estimators are derived. The conditions of efficiency of proposed generalized family of estimators from competitor estimators are also derived. The applications of estimator are discussed using simulation study and real-life data sets for comparisons of efficiency. It is concluded that when correlation between study and auxiliary variables increases, the proposed generalized family of estimators proves to be the efficient estimator of population mean of the study variable. [ABSTRACT FROM AUTHOR]

Hook-up apps are an increasingly popular way for women to meet other people for sex, dating, relationships, and more. As a mundane and habitual form of media, the multiple uses of hook-up apps allow for the production of intimacy in surprising and complex ways. This paper draws on narrative interviews with 15 self-identifying women to explore how dating and hook-up apps help produce 'intimate publics' for women. The field of intimate publics available to women users of hook-up apps is broader than those afforded by in-app interactions; there is an entire network of intimacy, sociality, and publicity that forms around hook-up apps. Our findings show that while both queer and straight women use hook-up apps to find sex, hook-ups, dates, and relationships, they are also central to building community, friendship, and sociality between women. [ABSTRACT FROM AUTHOR]

Trait Emotional Intelligence (trait EI) is a constellation of correlated emotion-related traits that capture an individual's typical way of processing emotion-related information and reacting in emotional situations. Numerous studies have shown that trait EI is a significant predictor of both subjective and objective health. This correlational meta-analysis (k = 106, N = 45,262) aims to explore the behavioural and biological variables that could account for these effects. It also aims to provide a roadmap for future research by identifying what should be studied (pinpointing dead-end roads and promising paths) and how (methodological improvements needed to draw stronger conclusions). The results revealed large associations of trait EI with social support, sleep quality, and hypothalamic–pituitary–adrenal axis activity in challenging situations as well as medium associations with dietary habits, physical activity, and substance use. Other candidate pathways have given rise to much less research. Based on both theoretical predictions and preliminary findings, the paper categorises these pathways as promising or not promising. Future research would benefit from using more diverse samples, measuring behavioural variables more objectively, controlling for personality, and systematically examining to what extent changes in EI (e.g., following training) lead to changes in behaviours and/or biological parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to design a Takagi–Sugeno (TS) descriptor observer for a class of TS descriptor systems. Unlike the popular approach that considers measurable premise variables, this paper considers the premise variables depending on unmeasurable vectors, e.g. the system states. This consideration covers a large class of nonlinear systems and represents a real challenge for the observer synthesis. Sufficient conditions to guarantee robustness against the unmeasurable premise variables and asymptotic convergence of the TS descriptor observer are obtained based on theH∞approach together with the Lyapunov method. As a result, the designing conditions are given in terms of linear matrix inequalities (LMIs). In addition, sensor fault detection and isolation are performed by means of a generalised observer bank. Two numerical experiments, an electrical circuit and a rolling disc system, are presented in order to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the era of Big Data, extracting the most important exploratory variables available in ultrahigh-dimensional data plays a key role in scientific researches. Existing researches have been mainly focusing on applying the extracted exploratory variables to describe the central tendency of their related response variables. For a response variable, its variability characteristic is as much important as the central tendency in statistical inference. This paper focuses on the variability and proposes a new model-free feature screening approach: sure explained variability and independence screening (SEVIS). The core of SEVIS is to take the advantage of recently proposed asymmetric and nonlinear generalised measures of correlation in the screening. Under some mild conditions, the paper shows that SEVIS not only possesses desired sure screening property and ranking consistency property, but also is a computational convenient variable selection method to deal with ultrahigh-dimensional data sets with more features than observations. The superior performance of SEVIS, compared with existing model-free methods, is illustrated in extensive simulations. A real example in ultrahigh-dimensional variable selection demonstrates that the variables selected by SEVIS better explain not only the response variables, but also the variables selected by other methods. [ABSTRACT FROM PUBLISHER]

Variable stiffness actuator (VSA) can significantly improve the dynamic performance of robots and ensure safety in human robot interaction. In this paper, a novel structure-controlled VSA which achieves a lower minimal stiffness while the size and load capacity remain unchanged is introduced. Stiffness variation is implemented by changing the effective length of parallel-assembled-folded serial leaf springs presented in this paper, which makes the adjustment of stiffness easier and driven by an independent motor. A modified analytical model of joint stiffness is built, which takes the gap between leaf springs and rollers into consideration. Experiments prove that the modified model is more accurate comparing with the ideal model which ignores the gap. Further analyses show that the gap can even make serious impacts on leaf spring-based structure-controlled VSA in other performances such as deformability and energy capacity. [ABSTRACT FROM AUTHOR]

This paper concerns a robust variable selection method in multiple linear regression: the robust S-nonnegative garrote variable selection method. In this paper the consistency of the method, both in terms of estimation and in terms of variable selection, is established. Moreover, the robustness properties of the method are further investigated by providing a lower bound for the breakdown point, and by deriving the influence function. The provided expressions nicely reveal the impact that the choice of an initial estimator has on the robustness properties of the variable selection method. Illustrative examples of influence functions for the S-nonnegative garrote as well as for the original (non-robust) nonnegative garrote variable selection method are provided. [ABSTRACT FROM AUTHOR]

This paper presents the first attempt to design a reaching law-based discrete-time sliding mode controller with a relative degree 2 switching variable. The current value of this variable is only affected by the control signal and disturbance generated two time instants ago. It is demonstrated that the new reaching law-based strategy offers a smaller quasi-sliding mode band width when compared to a similar control scheme with relative degree 1 switching variable. This in turn leads to reduced system output error in the control system proposed in the paper. [ABSTRACT FROM AUTHOR]

In this paper, we consider the method of moment estimation for dynamic panel models based on either forward demeaning (FOD) or first difference (FD) transformations to eliminate the individual-specific effects, using either all lags or one lag as instruments. We show that the Arellano–Bond-type generalized method of moment (GMM) based on FD is asymptotically biased of orderusing all lags or one lag as instruments whereasN,T→∞. For GMM based on FOD, it is asymptotically biased of orderwhen using all lags, but it is asymptotically unbiased when using only fixed number of lags as instruments. We also discuss these findings in light of the simple IV estimator. Monte Carlo simulations confirm our findings in this paper. [ABSTRACT FROM AUTHOR]

The Peaceman-Rachford splitting method (PRSM) is well studied for solving the two-block separable convex minimization problems with linear constraints recently. In this paper, we consider the separable convex minimization problem where its objective function is the sum of more than two functions without coupled variables, when applying the PRSM to this case directly, it is not necessarily convergent. To remedy this difficulty, we propose a proximal Peaceman-Rachford splitting method for solving this multi-block separable convex minimization problems, which updates the Lagrangian multiplier two times at each iteration and solves some subproblems parallelly. Under some mild conditions, we prove global convergence of the new method and analyse the worst-case convergence rate in both ergodic and nonergodic senses. In addition, we apply the new method to solve the robust principal component analysis problem and report some preliminary numerical results to indicate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Economic sanctions have become a popular tool of statecraft in international politics. This paper makes an attempt to investigate the effect of economic sanctions on ethnic violence by using a sample of 46 target states over the period 1984–2008. Our results indicate that the imposition of economic sanctions has a deleterious influence on ethnic violence. Moreover, an interesting by-product finding of this paper is that we find a U-shaped relationship between income and ethnic violence, which shed new light on the income-ethnic violence nexus. [ABSTRACT FROM AUTHOR]

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts’ estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts’ estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a new variable weight method, called the singular value decomposition (SVD) approach, for Kohonen competitive learning (KCL) algorithms based on the concept of Varshavskyet al.[18]. Integrating the weighted fuzzyc-means (FCM) algorithm with KCL, in this paper, we propose a weighted fuzzy KCL (WFKCL) algorithm. The goal of the proposed WFKCL algorithm is to reduce the clustering error rate when data contain some noise variables. Compared with thek-means, FCM and KCL with existing variable-weight methods, the proposed WFKCL algorithm with the proposed SVD's weight method provides a better clustering performance based on the error rate criterion. Furthermore, the complexity of the proposed SVD's approach is less than Palet al.[17], Wanget al.[19] and Hunget al.[9]. [ABSTRACT FROM PUBLISHER]

This paper aimed at providing an efficient new unbiased estimator for estimating the proportion of a potentially sensitive attribute in survey sampling. The suggested randomization device makes use of the means, variances of scrambling variables, and the two scalars lie between “zero” and “one.” Thus, the same amount of information has been used at the estimation stage. The variance formula of the suggested estimator has been obtained. We have compared the proposed unbiased estimator with that of Kuk (1990) and Franklin (1989), and Singh and Chen (2009) estimators. Relevant conditions are obtained in which the proposed estimator is more efficient than Kuk (1990) and Franklin (1989) and Singh and Chen (2009) estimators. The optimum estimator (OE) in the proposed class of estimators has been identified which finally depends on moments ratios of the scrambling variables. The variance of the optimum estimator has been obtained and compared with that of the Kuk (1990) and Franklin (1989) estimator and Singh and Chen (2009) estimator. It is interesting to mention that the “optimum estimator” of the class of estimators due to Singh and Chen (2009) depends on the parameter π under investigation which limits the use of Singh and Chen (2009) OE in practice while the proposed OE in this paper is free from such a constraint. The proposed OE depends only on the moments ratios of scrambling variables. This is an advantage over the Singh and Chen (2009) estimator. Numerical illustrations are given in the support of the present study when the scrambling variables follow normal distribution. Theoretical and empirical results are very sound and quite illuminating in the favor of the present study. [ABSTRACT FROM PUBLISHER]

In this paper we introduce %CEM, a macro package allowing researchers to automatically perform coarsened exact matching (CEM) in SAS environment. CEM is a non-parametric matching method widely used by researchers to avoid the confounding influence of pre-treatment control variables to improve causal inference in quasi-experimental studies. %CEM introduces a completely automated process which allows SAS users to efficiently perform CEM in fields in which large data sets are common and where SAS is the most popular statistical tool. In addition, such a macro may be used to test several coarsening combinations of numeric variables. This option also provides a visual representation of thematching frontier, thus enabling researchers to select the optimal setting which takes into account both theimbalance and the percentage of matched units. The paper concludes with an empirical application comparing computational performance and results obtained using alternative available software (SAS, R and STATA) using multiple administrative data sets from a large regional database. [ABSTRACT FROM PUBLISHER]

This paper considers posterior consistency in the context of high-dimensional variable selection using the Bayesian lasso algorithm. In a frequentist setting, consistency is perhaps the most basic property that we expect any reasonable estimator to achieve. However, in a Bayesian setting, consistency is often ignored or taken for granted, especially in more complex hierarchical Bayesian models. In this paper, we have derived sufficient conditions for posterior consistency in the Bayesian lasso model with the orthogonal design, where the number of parameters grows with the sample size. [ABSTRACT FROM AUTHOR]

Purpose:The purpose of this study is to assess and describe the meanings given to “gender” in scientific publications that evaluate multidisciplinary, interdisciplinary or multimodal rehabilitation for patients with chronic musculoskeletal pain. Method:A systematic literature search for papers evaluating multimodal rehabilitation was conducted. The PubMed and EBSCO databases were searched from 1995 to 2015. Two or three researchers independently read each paper, performed a quality assessment and coded meanings of gender using qualitative content analysis. Results:Twenty-seven papers were included in the review. Gender was used very differently in the MMR studies investigated but primarily it referred to factual differences between men and women. Only one paper provided a definition of the concept of gender and how it had been used in that study. In the content analysis, the meaning of gender formed three categories: “Gender as a factual difference”, “The man is the ideal” and “Gender as a result of social role expectations”. Conclusions:The meaning of the concept of gender in multimodal rehabilitation is undefined and needs to be developed further. The way the concept is used should be defined in the design and evaluation of multimodal rehabilitation in future studies.Implications for rehabilitationHealthcare professionals should reflect on gender relations in encounters with patients, selection of patients into rehabilitation programs and design of programs.In rehabilitation for chronic pain the patients’ social circumstances and cultural context should be given the same consideration as biological sex and pain symptoms. [ABSTRACT FROM PUBLISHER]

As known, the least-squares estimator of the slope of a univariate linear model sets to zero the covariance between the regression residuals and the values of the explanatory variable. To prevent the estimation process from being influenced by outliers, which can be theoretically modelled by a heavy-tailed distribution for the error term, one can substitute covariance with some robust measures of association, for example Kendall's tau in the popular Theil–Sen estimator. In a scarcely known Italian paper, Cifarelli [(1978), ‘La Stima del Coefficiente di Regressione Mediante l'Indice di Cograduazione di Gini’,Rivista di matematica per le scienze economiche e sociali, 1, 7–38. A translation into English is available athttp://arxiv.org/abs/1411.4809and will appear inDecisions in Economics and Finance] shows that a gain of efficiency can be obtained by using Gini's cograduation index instead of Kendall's tau. This paper introduces a new estimator, derived from another association measure recently proposed. Such a measure is strongly related to Gini's cograduation index, as they are both built to vanish in the general framework of indifference. The newly proposed estimator is shown to be unbiased and asymptotically normally distributed. Moreover, all considered estimators are compared via their asymptotic relative efficiency and a small simulation study. Finally, some indications about the performance of the considered estimators in the presence of contaminated normal data are provided. [ABSTRACT FROM AUTHOR]

Purpose: To examine whether differences in access to specialist disability services by people from culturally and linguistically diverse (CALD) backgrounds when compared with those born in Australia represent a service gap or the healthy migrant effect.Method: To use the latest disability statistics to measure the different rates of people with disability, and the rates of people with profound and severe disability, of people born in Australia and those born abroad; to compare the difference between those who mainly speak English with those who primarily speak a language other than English at home (LOTE); and examine the age-specific and standardised disability rates of these subgroups.Results: The rate of access of specialist disability services by people with disability who were from CALD backgrounds is highly disproportionate to their presence in the community. As a whole, people from CALD backgrounds have a similar level of disability as Australia-born people. They have a greater rate of profound and severe disability and a higher level of need for assistance in undertaking core activities. For younger age cohorts targeted by specialist disability services, there is little difference in the level of need for assistance between people from CALD backgrounds and the rest of community. Those people who mainly speak LOTE at home have a relatively higher level of need for assistance than those who speak mainly English at home.Conclusions: The paper reveals a substantial gap in specialist disability services between people from CALD and the broader community. This cannot be explained by the difference in the level of need for assistance between Australia-born and overseas-born populations, therefore raises some policy questions as to the barriers to the use of such services and how to effectively narrow the service access gap and improve utilisation rates.Implications for RehabilitationThe paper reveals a substantial accessibility gap in specialist disability services between people from culturally and linguistically diverse (CALD) backgrounds and the broader community in Australia.Rehabilitation is a large component of disability services. Therefore, understanding the gap, promoting the awareness of the services, developing appropriate and effective services to respond the need of people with disability from CALD backgrounds, are critically important to rehabilitation services and related research. [ABSTRACT FROM PUBLISHER]

Omission of some relevant explanatory variables and multicollinearity in regression models are very serious problems in applied works. There are some papers examining the multicollinearity and misspecification which is due to omission of some relevant explanatory variables, concurrently. To remedy the problem of multicollinearity, Kaçıranlar and Sakallıoğlu (2001) proposed the r-d class estimator that includes the ordinary least squares, principal components regression, and Liu estimators as special cases. The aim of this paper is to examine the performance of the r-d class estimator in misspecificied linear models. [ABSTRACT FROM PUBLISHER]

The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additionally, we provide an applied example using height and weight data from a gerontological study. Throughout the example, we emphasize caution in choosing which model (ARCL or LCS) to apply due to the risk of obtaining misleading results concerning the presence and direction of causal precedence between two variables. We suggest approaching model specification not only by comparing estimates and fit indices between the LCS and ARCL models (as well as other models) but also by giving appropriate weight to substantive and theoretical considerations, such as assessing the justifiability of the assumption of random effects in the slope factor scores. [ABSTRACT FROM AUTHOR]

Background:The primary purpose of this paper is to examine youth addiction and other justifications for adolescent smoking, and how they affect the level of consumption.Methods:Data from the Spanish ‘State Survey on Drug Use among High School Students’ aged between 14 and 18 years old were used in this paper. To account for the nature of the cigarette consumption data, several count data models were estimated in order to select the one that best fits adolescent smoking consumption.Results:Most adolescent smokers smoke because it relaxes them, and about a quarter of them recognize that they are addicted. Moreover, the latter group smoke 44% more cigarettes than the rest (IRR = 1.444), revealing the strong addictive nature of tobacco, even at early ages. Moreover, parents’ smoking increases the probability of smoking and has an impact on the level of consumption.Conclusions:The implications of these findings offer insight for parents, researchers, educators, and cessation interventionists, as awareness of self-reported and other predictors held by smoking youth creates a vantage point to facilitate changes in smoking behavior. [ABSTRACT FROM PUBLISHER]

This paper considers the use of orthogonal arrays of strength two as experimental designs for fitting a surrogate model. Contrary to standard space-filling designs or Latin hypercube designs, the points of an orthogonal array of strength two are well distributed when they are projected on the two-dimensional faces of the unit cube. The aim is to determine if this property allows one to fit an accurate surrogate model when the computer response is governed by second-order interactions of some input variables. The first part of the paper is devoted to the construction of orthogonal arrays with space-filling properties. In the second part, orthogonal arrays are compared with standard designs for fitting a Gaussian process model. [ABSTRACT FROM PUBLISHER]

The paper presents designs that allow detection of mixed effects when performing preliminary screening of the inputs of a scalar function ofdinput factors, in the spirit of Morris' Elementary Effects approach. We introduce the class of-cycle equitable designs as those that enable computation of exactlycsecond-order effects on all possible pairs of input factors. Using these designs, we propose a fast Mixed Effects screening method, that enables efficient identification of the interaction graph of the input variables. Design definition is formally supported on the establishment of an isometry between sub-graphs of the unit cubeequipped of the Manhattan metric, and a set of polynomials inon which a convenient inner product is defined. In the paper we present systems of equations that recursively define these-cycle equitable designs for generic values of, from which direct algorithmic implementations are derived. Application cases are presented, illustrating the application of the proposed designs to the estimation of the interaction graph of specific functions. [ABSTRACT FROM PUBLISHER]