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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]

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]

The article offers author's insights on the evaluation on the impacts of activities of the Egyptian Social Fund for Development (SFD) using the propensity score matching (PSM) by Abou-Ali H. and colleagues. The author says that PSM results are unreliable because of unobservable variables that correlated with programme placement and capacity to benefit from intervention. It states that the paper of Abou-Ali and colleagues is the only paper in the area of development that applies analysis to PSM.

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]

Independent Component Analysis (ICA) has been extensively used for detecting faults in industrial processes. While applying ICA to process monitoring, the inability of identifying the important components affect the fault diagnosis ability. For further improving the competence of ICA, this paper proposes an approach integrating ICA, Durbin Watson (DW) criterion and Support Vector Data Description (SVDD) to monitor non-Gaussian process for detecting faults. In the proposed approach, namely ICA–DW–SVDD, ICA is a non-Gaussian information extractor from original variables, DW identifies dominating ICs, and SVDD plays the role of fault detector. This paper also discusses the retracing method to detect original variables causing disturbance in the process. One simulation case and the Tennessee Eastman Process are used to demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

Given a positive integern, letand. The generator matrixof thekth-order Reed–Muller code RMis an important tool in the study of Boolean functions' algebraic immunity. In this paper, choosing the lastscolumn vectors inas a basis of the vector space, we study the values of the coefficients in the linear expressions of's column vectors over this basis. As an application, we present a new construction of balanced even-variable Boolean functions onwith optimal algebraic immunity by modifying the outputs of majority function. The nonlinearities of these constructed functions are also determined. [ABSTRACT FROM AUTHOR]

The article focuses on modelling reference crop evapotranspiration using vector machines. It relates the evapotranspiration modelling work of O. Kisi and M. Cimen wherein support vector machines and artificial neural networks were applied to a non-complex hydrological modelling problem. It mentions that four meteorological input measurements were utilized in the modeling work including solar radiation (Rs), air temperature (T), and relative humidity (RH). It suggests that measurement units for all input and output variables must be provided in each scientific reporting of data in data-driven modelling paper. It adds that each paper must consist of a numerical description of every variable used in the model.

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]

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]

In this paper, we consider the following simple linear errors-in-variables regression model:where θ and β are the unknown parameters, ηi, ξi’s are observable andxi’s are unobservable. We assume the error variables {(ε1, δi), 1≤i≤n} form aL2stationary α-mixing sequences. In this paper, we obtained the moderate deviation principles for the least-square estimators of the unknown parameters θ and β. [ABSTRACT FROM AUTHOR]

This paper builds two factor discrete time models in order to investigate the effect of sovereign risk on the nominal exchange rates in a Markov switching framework. The empirical section of the paper uses seven currencies from Chile, the Czech Republic, Hungary, Iceland, Japan, Korea, and Mexico. To measure the sovereign risk, we use the credit rating agencies' ratings classes as proxy variable. In the empirical part, four different versions of the model are calibrated and their in-sample and out-of-sample data will be analyzed leading to the conclusion that none of the four versions dominates the others. As an additional result, it is revealed that risk has significant effect on the nominal exchange rates. [ABSTRACT FROM AUTHOR]

This piece includes discussion of some of the work of Bhaskar Kumar Ghosh. [ABSTRACT FROM AUTHOR]

Response surface methodology (RSM) is a technique for determining and representing the cause-and-effect relationship between true mean responses and input control variables influencing the responses as a two- or three-dimensional hypersurface. First, this paper presents a mathematical model for correlating the interactions of some drilling control parameters such as speed, feed rate and drill diameter, and their effects on some responses such as axial force and torque acting on the cutting tool during drilling by means of response surface methodology. For this exercise, a three-level full factorial design was chosen for experimentation using a computer-based computer numerically controlled drilling machine built in-house. Second, since finding the optimum set of experimental factors that produces maximum or minimum value of response(s), is a major step in RSM, the paper describes a new approach to this step, which is the optimization of the mathematical model realized from the RSM using one of the recent optimization techniques, Tribes. Comparing the optimization results for another published response problem between Gauss–Jordan algorithm and Tribes approaches, it was found that Tribes clocked a better result. Consequently, this paper reports not only on the use of RSM for analysing the cause and effect of process parameters on responses, but also on optimization of the process parameters themselves in order to realize optimal responses. [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]

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]

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 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]

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]

Regarding the output-capital ratio in heterodox macroeconomic simulation studies, a surprisingly wide range of numerical values can be found. The paper discusses quarterly US data that are publicly available where, in order to capture depreciation, the construction of the capital stock by the perpetual inventory method relies on detailed estimates of its lifetime. Subsequently the paper builds up a capital stock series by alternatively having recourse to the statistics about capital consumption and furthermore determining an initial level by an assumption about the long-term growth of capital. This procedure leads to somewhat different results. In addition, the rates of depreciation and profit are studied that are implied by the two approaches. The paper closes with two numerical proposals for the steady state values of these variables and the output-capital ratio that could be readily employed for macrodynamic modelling, and that are quite different from many of the aforementioned examples. [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]

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]

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]

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]

This paper contributes to the debate on the effects of fiscal stimuli by showing that the impact of government expenditure depends on its composition. Government spending is not homogeneous and its effects on economic variables vary depending on its type. Comparing the effects of three types of government spending (investment, wage and non-wage components of consumption), it is concluded that government investment shocks are not the most effective spending shocks in boosting output, contrary to common opinion. Instead, the wage component government consumption has the biggest impact, whereas the non-wage component government consumption has the least. This variation stems mainly from the effects on (private) consumption. [ABSTRACT FROM PUBLISHER]

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]

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]

This paper presents a crashworthiness optimum design of the tapered thin-walled tubes with square cross sections. In this study, three design variables that fully describe the tapered thin-walled square beam's geometry are optimised to maximise the beam's energy absorption capability during crashes. The specific energy absorption (SEA) and the ratio between the average and maximum crushing forces (Favg/Fmax), which is also referred to as the 'crush force efficiency', are considered as objective functions separately. During the optimisation, the objective of SEA and Favg/Fmax are formulated using the response surface method as function of the design variables. Optimal designs are decided based on the developed RS models for each design objective, and the results show that the optimised design variable values are same for both design objectives. Therefore, in such crashworthiness design of tapered square tubes, either SEA or Favg/Fmax can be considered as the design objective. After obtaining the optimal design, the sensitivity of the design variables on the tapered square beam's crash performance are analysed. Analyses involved in this paper are undertaken using finite element models and, the finite element solver LS-DYNA and the computer program MATLAB are used to perform all the analyses and the optimisation. [ABSTRACT FROM AUTHOR]

This paper examines the relationships between the mean residual life functions of parallel and k-out-of-n systems with the regression of order statistics. Using these relationships, the results and properties about the mean residual life function of those systems can be used for the regression of order statistics and vice versa. Finally, the paper proposes a definition for the mean residual life function of a k-out-of-n system when the number of failed components of the system is known. [ABSTRACT FROM AUTHOR]

In this paper, the Kalman–Yakubovich lemma is formulated and proved in the behavioural theoretic setting. The behavioural framework is used to generalize the lemma, for behaviours with two manifest variables, with respect to supply rates that are quadratic differential forms (QDFs). [ABSTRACT FROM AUTHOR]

In this paper, we prove a Hoeffding-like inequality for the survival function of a sum of symmetric independent identically distributed random variables, taking values in a segment [- b , b ] of the reals. The symmetric case is relevant to the auditing practice and is an important case study for further investigations. The bounds as given by Hoeffding in 1963 cannot be improved upon unless we restrict the class of random variables, for instance, by assuming the law of the random variables to be symmetric with respect to their mean, which we may assume to be zero. The main result in this paper is an improvement of the Hoeffding bound for i.i.d. random variables which are bounded and have a (upper bound for the) variance by further assuming that they have a symmetric law. [ABSTRACT FROM AUTHOR]

As politicians in the 1990s shrank welfare appropriations and tinkered with eligibility rules, many researchers examined American welfare attitudes. In doing so, separate lines of research argued that welfare-spending inclinations can be swayed by a person's class, race and gender statuses. Moreover, different studies connected welfare appraisals to a person's explanation of poverty, racial inequalities and gender hierarchies. As these inquiries provide critical insights these insights were hampered by a lack of theoretical breadth since they coalesced around a small set of independent variables. In effect, while theoreticians have linked class, race and gender matters, published quantitative works have routinely focused on only one set of class, race, or gender variables (i.e., numerous researchers consider welfare preferences in light of class and race interpretations as they neglect gender matters, or vice versa). In realizing that welfare attitudes probably emerge from all of these factors, this paper incorporates class, race and gender variables into a single explanatory model. With such a synthesis, this paper explores the welfare attitudes of college students from twelve randomly selected colleges (n = 575). In running a multivariate regression which explores the direct and separate contributions of each variable, the students' demographic affiliations seem to play a minimal role. Instead, attitudinal measures presented some significant results (especially the acceptance of modern racism, feminism and the belief of an open employment system). Thus, it seems that welfare antagonism was not contingent upon membership in certain race or gender groupings. Instead, welfare judgments were shaped by the perceived legitimacy of conventional class, race, and gender arrangements. [ABSTRACT FROM AUTHOR]

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]

This paper presents a novel training algorithm for adaptive neuro-fuzzy inference systems. The algorithm combines the error back-propagation algorithm with the variable structure systems approach. Expressing the parameter update rule as a dynamic system in continuous time and applying sliding mode control (SMC) methodology to the dynamic model of the gradient based training procedure results in the parameter stabilizing part of training algorithm. The proposed combination therefore exhibits a degree of robustness to the unmodelled multivariable internal dynamics of the gradient-based training algorithm. With conventional training procedures, the excitation of this dynamics during a training cycle can lead to instability, which may be difficult to alleviate owing to the multidimensionality of the solution space and the ambiguities concerning the environmental conditions. This paper shows that a neuro-fuzzy model can be trained such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate cost function (cost optimization), which is based on state tracking performance. In the application example, trajectory control of a two degrees of freedom direct drive robotic manipulator is considered. As the controller, an adaptive neuro-fuzzy inference mechanism is used and, in the parameter tuning, the proposed algorithm is utilized. [ABSTRACT FROM AUTHOR]

Thechart has been known as a powerful tool to monitor the variability of the normal process. This paper considers the variable sampling interval (VSI)chart to improve the performance of the originalchart. A VSIchart based on the average run length (ARL) unbiased and average time to signal (ATS) unbiased criteria is developed when the in-control variance is known. The performance of the proposed chart is investigated in terms of the mean and standard deviation of the RL and total time to signal. The impact of parameter estimation on the proposed VSIchart is also evaluated. The results show that the performance of the control chart is seriously different from the known parameter case when the parameter is estimated. Then, a new VSIchart with the desired in-control ARL and ATS is considered when the in-control variance is estimated. In evaluating the usefulness of the VSI feature, the VSIcharts are compared with the fixed sampling interval (FSI) versions in terms of their statistical properties. The comparison results show that the VSIchart can detect the shifts in the process variance quicker than the corresponding FSI chart, regardless of whether the in-control variance is known or unknown. Finally, two examples are used to illustrate the proposed control chart. [ABSTRACT FROM AUTHOR]

In a process with a large number of process variables (high-dimensional process), identifying which variables cause an out-of-control signal is a challenging issue for quality engineers. In this paper, we propose an adaptive step-down procedure using conditionalT2statistic for fault variable identification. While existing procedures focus on selecting variables that have strong evidence of a change, the proposed step-down procedure selects a variable having the weakest evidence of a change at each step based on the variables that are selected in previous steps. The information of selected unchanged variables is effectively utilised in obtaining a powerful conditionalT2test statistic for identifying the changed elements of the mean vector. The proposed procedure is designed to utilise the correlation information between fault and non-fault variables for the efficient fault variables identification. Further, the simulation results show that the proposed procedure has the better diagnostic performance compared with existing methods in terms of fault variable identification and computational complexity, especially when the number of the variables is high and the number of fault variables is small. [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]

In this paper, the distributed filtering for a class of sensor networks with randomly occurring filter gain variations is investigated, where the additively norm-bounded filter gain variations is addressed. For the filter implementation uncertainties, a set of stochastic variables are firstly employed to indicate random occurrence of the filter gain variation and a set of norm bounds are introduced to measure the variation size. Based on some stochastic analysis method and using the robust control technique, a new sufficient condition is derived such that the filtering error system is asymptotically stable in the mean-square sense and achieves a prescribedperformance level. The optimal filter gains are also obtained and the effectiveness of the proposed design is finally demonstrated via a numerical example. [ABSTRACT FROM PUBLISHER]

This paper examines the impact of military expenditure on stock market development in 36 countries over the period 1989–2010. Within a panel framework, the system GMM estimates is utilised to test the relationship with an array of control variables. We augment the traditional measure of military expenditure–military burden, with a newly constructed comprehensive index – Global Militarisation Index. Overall, the results show that military spending has a negative and significant effect on stock market performance in the selected countries. [ABSTRACT FROM AUTHOR]

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]

Let Xk, 1 ⩽ k ⩽ n, be n real-valued random variables, and θk, 1 ⩽ k ⩽ n, be another n non negative not-degenerate at zero random variables. Assume that random pairs (X1, θ1), …, (Xn, θn) are mutually independent, while each pair (Xk, θk) follows a wide type of dependence structure. Consider the randomly weighted sum Sθn = ∑k = 1nθkXk. In this paper, the tail asymptotics for Sθn in the case where Xk, 1 ⩽ k ⩽ n, belong to some heavy-tailed subclasses are firstly investigated. Then, as an application, we consider the tail behavior of the conditional tail expectation as q↑1, where . Under some technical conditions, the asymptotic estimate for the right tail of conditional tail expectation is also derived. The obtained results extend some existing ones in the literature. [ABSTRACT FROM AUTHOR]

In this paper, Galerkin finite methods for two-dimensional regularized long wave and symmetric regularized long wave equation are studied. The discretization in space is by Galerkin finite element method and in time is based on linearized backward Euler formula and extrapolated Crank-Nicolson scheme. Existence and uniqueness of the numerical solutions have been shown by Brouwer fixed point theorem. The error estimates of linearlized Crank-Nicolson method for RLW and SRLW equations are also presented. Numerical experiments, including the error norms and conservation variables, verify the efficiency and accuracy of the proposed numerical schemes. [ABSTRACT FROM AUTHOR]