Measuring Care and Justice Moral Orientation: Italian Adaptation and Revision of the MMO-2 Scale

This study presents the Italian adaptation of the Measure of Moral Orientation, second revision (MMO-2). Based on Carol Gilligan’s theory of the Ethics of Care, the MMO-2 was designed to measure two complementary moral stances, namely, Care and Justice. For this study, questionnaire responses from 683 university students were assessed against an Italian-adapted MMO-2 scale. Data were analyzed through exploratory structural equation modeling first as separate scenarios and then as a single model. The final model comprises 4 intercorrelated pairs of latent variables and shows highly satisfactory goodness of fit indices with moderate construct validity and reliability. Strengths, limitations, and directions for the future developments of the MMO-2 are discussed.     

Consciousness in a multilevel architecture: What causes the lateralization of effective connectivity under resting state?

Here we present our answers to a critical commentary by Elkhonon Goldberg on our recent publication (Velichkovsky et al., 2018). To avoid discussions about novelty effects in the human brain activity and memory processes, we narrowed down this response to a reanalysis of our data along the lines proposed in the commentary, namely to comparing the effective links between symmetrical brain structures during the first and the last parts of a prolonged resting-state fMRI experiment. We also tested for sex differences in our results and checked for a stability of top-down interactions during the course of experiment because learning is often expressed in the weakening of upper level control over low-level mechanisms. Our attempts to test the predictions based on the novelty hypothesis has led to mixed results suggesting that the discovered right-to-left dominance of causal connections at rest may have a deeper origin than supposed in the Goldberg’s commentary.     

Complementary variable- and person-centred approaches to the dimensionality of work engagement: a longitudinal investigation

This study illustrates complementary variable- and person-centred approaches to the investigation of the underlying dimensionality of the work engagement construct. A sample of 730 participants completed a questionnaire twice across a four-month period. The results showed that employees’ ratings of their work engagement simultaneously reflected a global overarching work engagement construct, which co-existed with three specific dimensions (vigour, dedication, and absorption). Relying on factor scores from this initial measurement model, the present study examined latent profiles of employees defined based on their global (work engagement) and specific (vigour, dedication, and absorption) levels of work engagement. The results revealed five distinct work engagement profiles, which proved to be fully identical, and highly stable, across the two time points. These profiles characterized disengaged-vigorous, normative, totally disengaged, vigorously absorbed, and engaged yet distanced employees. These profiles were also showed to be meaningfully related to employees’ levels of stress, intentions to leave the organization, health, and job satisfaction.     

A general theoretical framework for the design of artificial emotion systems in Autonomous Agents

Autonomous Agents (AAs) capable of exhibiting emotional behaviors have contributed to the development of natural human-machine interactions in several application domains. In order to provide AAs with emotional mechanisms, their underlying architecture must implement an Artificial Emotion System (AES), a computational model that imitates specific facets of human emotions. Although several AES have been reported in related literature, their design is generally supported on several emotion theories, leading researchers to model and integrate isolated emotion components and mechanisms into the architectures of AES. This theoretical foundation of AES contributes to ambiguities in the analysis and comparison of their underlying architectures, which demands the definition of standards, design guidelines, and integrative frameworks. In this paper, we present a psychologically inspired theoretical framework designed to serve as a platform for the unification of AES' components, the comparison of AES, and the design and implementation of AES in AAs. We analyze common emotion-related requirements of AES, emotion components involved in the design of this type of computational model, and emotion theories that drive the design of most AES. The validation of this framework demonstrates its compatibility with current AES and its feasibility as a model for unifying multiple emotional theories.     

Personal growth initiative: Relation to Hedonia and Eudaimonia happiness among South African university students

This study examined the relationship between personal growth initiative (PGI) and hedonic and eudaimonic happiness among a sample of South African university students (N = 235; mean age = 20.38, SD = 1.55; female = 61.70%). Students completed measures on personal growth initiative, hedonic happiness, and eudaimonic happiness. Regression analysis indicated personal growth initiative to positively predict students’ reported scores on hedonic and eudaimonic measures. The findings underscore the importance of PGI among student populations in advancing well-being among university students. It is proposed that higher education student development and support services should tap into PGI as a resource for enhancing student self-directed change and happiness.     

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

ObjectivesLiver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method.MethodsIn this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET).ResultsWe have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process.ConclusionsOur developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.     

Analyzing Job Analysis Data Using Mixture Rasch Models

An important piece of validity evidence to support the use of credentialing exams comes from performing a job analysis of the profession. One common job analysis method is the task inventory method, where people working in the field are surveyed using rating scales about the tasks thought necessary to safely and competently perform the job. This article describes how mixture Rasch models can be used to analyze these data, and how results from these analyses can help to identify whether different groups of people may be responding to job tasks differently. Three examples from different credentialing programs illustrate scenarios that can be found when applying mixture Rasch models to job analysis data. Discussion of what these results may imply for the development of credentialing exams and other analyses of job analysis data is provided.     

When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration

The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment. But is it always necessary to use latent covariates? How well do analysis of covariance (ANCOVA) or propensity score (PS) methods estimate the ATE when latent covariates are used? We first analytically delineate the conditions under which latent instead of fallible covariates are necessary to obtain the ATE. Then we empirically examine the difference between ATE estimates when adjusting for fallible or latent covariates in an applied example. We discuss the issue of fallible covariates within a stochastic theory of causal effects and analyse data of a within-study comparison with recently developed ANCOVA and PS procedures that allow for latent covariates. We show that fallible covariates do not necessarily bias ATE estimates, but point out different scenarios in which adjusting for latent covariates is required. In our empirical application, we demonstrate how latent covariates can be incorporated for ATE estimation in ANCOVA and in PS analysis.     

Attachment Styles and Employee Performance: The Mediating Role of Burnout

Researchers are becoming increasingly interested in how early experiences within the family are relevant to an individual’s behavior at work. Drawing on Bowlby’s attachment theory, the present study addresses this topic by examining the relationship between attachment in adulthood and job performance, and the mediating role of burnout in that relationship. We used data from two samples (201 Dutch employees and 178 Romanian working students) and structural equation modeling to test this mediation model and its possible invariance across both samples. The results showed that in both samples, attachment-related anxiety was positively related to burnout, which was in turn negatively related to job performance. Attachment-related avoidance was not significantly associated with burnout or performance. These results were similar in both samples, thus increasing their validity. The results suggest that childhood and early socialization experiences play a role in shaping the employee’s behavior and well-being at work. The study is one of the few examining attachment styles in relation to burnout and performance.