Occupational Psychology faces challenges concerning the promotion of employees' well-being and health. The use of emergent technologies (e.g. smartphone) has revealed new opportunities to deliver effective, cheap and early interventions. By following the international PRISMA statement guidelines, this systematic review aims to bring together workplace smartphone-based interventions, targeting employees' well-being and psycho-physical health, to address the lack of studies focused on workplace settings. Results were drawn from 31 quantitative and qualitative studies, testing smartphone applications. The authors extracted multiple information for each article: focus, target, theoretical background, users' engagement and study design. Findings show the lack of theoretical background, reliable study design and the prevalence of physical health interventions. Moreover, our review identifies the importance of users' engagement for an intervention's effectiveness. It is relevant to design specific mHealth interventions, to provide employees with the skills to cope with and manage work-stress and enhance their general health and well-being.

Measuring economic growth is not only an arithmetic problem, it also involvesan economic vision and a philosophy for the choice of indicators (i.e.Gross Domestic Product). Today it seems more relevant to measure not onlythe economic well-being as it is, using Gross Domestic Product, but also thepeople's well-being, considering other dimensions such as health and happiness.Over the years, national accounts and Gross Domestic Product havenever changed their nature, instead combining social and economic indicatorscan oer important indications for well-being research. As pointed outby the World Health Organization, health is well-being, but it is not alwayscoincident with a high level of Gross Domestic Product. In particular, asregards the mental health condition it has become an important aspect inmeasuring people's well-being. Mental health has been considered an importantsignal of the society's discomfort due to economic growth that, generally,is argued to be caused by the disamenities of the "industrial lifestyle". Thisstudy involves an empirical investigation using a panel econometric model inItalian regions, considering antidepressant expenditures per capita (an expressionof society's unhappiness) as dependent variable and, as covariates,the old age index, the poverty rate, the employment rate, the Neet rate, andthe percentage of public expenditure in services.

The present paper announces and demonstrates some useful properties of the impliedcorrelation matrix built by the Finite Iterative Method (Elhadri and Hanafi,2015, 2016; Elhadri et al., 2019) The most important property is that the impliedcorrelation matrix is affine for each of its parameters. In other words, the firstderivative with respect to each parameter does not depend on this parameter. Moreover,two properties affirm that the first and the second derivatives can be builtiteratively using the previous property. The final property shows that the secondderivatives with respect to every pair ofparameters in the same structural equationare null. These properties are very important in the sense that they can be used toconstruct a new computational approach to estimate recursive model parameters.These findings can be exploited in the estimation stage implementation, especiallyin the computation of the Newton Raphson algorithm to make the first and the secondderivatives of the discrepancy function more explicit and simplistic.

The non-parametric kernel density estimation is used in practice to estimate population abundance using the line transect sampling. In general, the classical kernel estimator of f(0) tends to be underestimated. In this article, a shifted logarithmic transformation of perpendicular distance is proposed for the kernel estimator when the shoulder condition is violated. Mathematically, the proposed estimator is more efficient than the classical kernel estimator. A simulation study is also carried out to compare the performance of the proposed estimators and the classical kernel estimators.

: Use of suitable auxiliary information is always suggested in literature at the planning and estimation stage to make the estimators more powerful in terms of efficiency. Estimation using auxiliary information is common in sampling literature but using distribution of study and auxiliary information at the estimation stage is uncommon and useful specially when dealing with rare variable. This study utilizes the auxiliary information and Poisson distributed variates for proposing the log-type estimator and another generalized estimator for finite population mean under simple random sampling without replacement. The Mean Square Error expressions of the proposed estimators are obtained and mathematical conditions are established to prove the efficiency of proposed estimators. It is revealed from empirical (point estimation and interval estimation) & theoretical study that use of log type estimators along with suitable auxiliary information for Poisson distributed variates excels the performance of estimators in terms of efficiency.

The STATIS method is one of many strategies of analysis devoted to the unsupervised analysis of multiblock data. A new optimization criterion to define this method of analysis is introduced and an extension to the cluster analysis of several blocks of variables is discussed. This consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. Moreover, in order to improve the cluster analysis outcomes, an additional cluster called noise cluster which contains atypical blocks of variables is introduced. The general strategy of analysis is illustrated by means of two cases studies.

The vast availability of large scale, massive and big data has increased the computational cost of data analysis. One such case is the computational cost of the univariate filtering which typically involves fitting many univariate regression models and is essential for numerous variable selection algorithms to reduce the number of predictor variables. The paper manifests how to dramatically reduce that computational cost by employing the score test or the simple Pearson correlation (or the t-test for binary responses). Extensive Monte Carlo simulation studies will demonstrate their advantages and disadvantages compared to the likelihood ratio test and examples with real data will illustrate the performance of the score test and the log-likelihood ratio test under realistic scenarios. Depending on the regression model used, the score test is 30 - 6,000 times faster than the log-likelihood ratio test and produces nearly the same results. Hence this paper strongly recommends to substitute the log-likelihood ratio test with the score test when coping with large scale data, massive data, big data, or even with data whose sample size is in the order of a few tens of thousands or higher.

R 2 measure, which named coefficient of determination, is usually used as tools for evaluation the predictive power of the regression models. However, this measure, which is based on deviance for generalized linear models, is sensitive to the small samples. Therefore, it is necessary to adjust R 2 measure according to the number of covariates. Beta regression model has received much attention in several science fields in modeling proportions or rates data. In this paper, several adjusted R 2 measures are proposed in beta regression models. The performance of the proposed measures is evaluated through simulation and real data application. Results demonstrate the superiority of the proposed measures compared to others.

The role of Twitter as a platform to share opinions has been growing in the recent years especially since it has been widely used by public personae such as politicians, personalities of the show business, and other influencers to communicate with the public. For these reasons, the use of social bots to manipulate information and influence people's opinions is also growing. In this paper, we use a supervised classification model to distinguish bots from legitimate users on Twitter. More specifically, we show the importance of sentiment features in bot-human account detection. Moreover, we evaluate our detection model by testing on Russian bot accounts who are the most recent set of social bots that appeared on Twitter to show that these techniques may be easily adapted to work on new, unseen types of social bots.

The notion of the interpoint depth is applied to spherical spaces by us-ing an appropriate angular distance function for data lying on the surfaceof the unit hypersphere. The traditional multivariate methods, indeed, arenot suitable for the analysis of directional data and this holds true also fordistance measures and related depth based methods. The interpoint depthfor directional data possesses some nice properties and can be used for highdimensional data analysis. This notion of depth is particularly useful toinvestigate local features of distribution such as multimodality and can beexploited to deal with many statistical problems. The behavior of the pro-posed depth is investigated by means of simulated data. In addition threeinteresting applications are presented.