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Abstract We propose and experimentally investigate two distinct power-over-Fiber (PoF) approaches, aiming to remotely power Internet-of-Things (IoT) sensing systems for Industry 4.0 environments. The first proof-of-concept is focused on demonstrating a 1-W optical power transmission through a 50-m-fiber-optic link for powering an Arduino Uno, a temperature sensor (DS1820B), and a 433-MHz transceiver (FS1000A). The designed PoF link is able to provide up to 280 mW with power transmission efficiency (PTE) of 28.9%. The second implementation is based on a 100-m PoF link capable of transmitting over 0.6-W optical power and delivering 140-mW electrical power with PTE of 23%. In this scheme, an Arduino Pro Mini, another temperature sensor (LM35), and a 2.4-GHz transceiver (nRF24L01+) are employed. A voltage stability analysis enables to demonstrate that our PoF system is capable of delivering stable output voltage at 8.5 V and 5 V, with only 0.6% and 0.2% voltage fluctuations. In addition, an industrial oven is employed to evaluate the sensor performance considering temperature measurements from both sensing systems. The obtained results demonstrate that PoF might be considered as a potential technology to optically-power IoT wireless sensing systems for Industry 4.0 scenarios.

We report the experimental implementation of optically-powered wireless sensor nodes based on the power-over-fiber (PoF) technology, aiming at Industrial Internet of Things (IIoT) applications. This technique employs optical fibers to transmit power and is proposed as a solution to address the hazardous industrial environment challenges, e.g., electromagnetic interference and extreme temperatures. The proposed approach enables two different IIoT scenarios, in which wireless transmitter (TX) and receiver (RX) nodes are powered by a PoF system, enabling local and remote temperature data monitoring, with the purpose of achieving an intelligent and reliable process management in industrial production lines. In addition, the system performance is investigated as a function of the delivered electrical power and power transmission efficiency (PTE), which is the primary performance metric of a PoF system. We report 1.4 W electrical power deliver with PTE = 24%. Furthermore, we carry out a voltage stability analysis, demonstrating that the PoF system is capable of delivering stable voltage to a wide range of applications. Finally, we present a comparison of temperature measurements between the proposed approach and a conventional industrial programmable logic controller (PLC). The obtained results demonstrate that PoF might be considered as a potential technology to power and enhance the energy efficiency of IIoT sensing systems.

IntroductionSoft skills, also known as transversal skills, have gained significant attention in the organizational context due to their positive impact on various work-related outcomes. The present study aimed to develop and validate the Multiple Soft Skills Assessment Tool (MSSAT), a short self-report instrument that evaluates interpersonal skills (initiative-resourcefulness, assertiveness, conflict management), interpersonal communication skills, decision-making style (adaptive and maladaptive), and moral integrity.MethodsThe scale development process involved selecting and adapting relevant items from existing scales and employing a cross-validation approach with a large sample of workers from diverse organizational settings and job positions (N = 639). In the first step, 28 items were carefully chosen from an item pool of 64 items based on their content, factor loadings, item response theory analyses, differential item functioning, and fit statistics. Next, the structure of the resulting scale was evaluated through confirmatory factor analyses.ResultsThe MSSAT demonstrated gender invariance and good reliability and validity. The results of a network analysis confirmed the relationships between soft skills and positive work-related outcomes. Notably, interpersonal communication skills and moral integrity emerged as crucial skills.DiscussionThe MSSAT is a valuable tool for organizations to assess the soft skills of their employees, thereby contributing to design targeted development programs.

The annual congress of the Italian Association of Plastic Aesthetic Surgery (AICPE) is one of the most relevant conference meetings in Europe concerning aesthetic plastic surgery as there are a number of participants and a parterre of invited speakers chosen for their renowned scientific value [...]

This work reports the implementation of a simple and accurate indoor sensing and positioning system using a two-dimensional (2D) Light Detecting and Ranging (LiDAR) to fulfill the vigorous requirements from the Beyond Fifth Generation of mobile networks (B5G), including the Sixth Generation of Mobile Networks (6G). Particularly, we present the development of an Artificial Neural Network (ANN)-based 2D-LiDAR system, renowned for its electromagnetic interference resilience and superior accuracy compared to radiofrequency signal methodologies. The proposed framework integrates 2D-LiDARs and Artificial Intelligence (AI) functionalities to enhance the performance of pedestrian sensing and positioning systems in indoor environments. The proposed system architecture incorporates an array of up to four LiDAR sensors, taking advantage of combined data as the input for the exploited ANN aiming to obtain precise user positions within the indoor environment. Those positional data are pivotal for B5G systems, enabling optimized control and management of antenna arrays and Reconfigurable Intelligent Surfaces (RIS), significantly improving the user experience. The main contributions include the development and implementation of the proposed system; the demonstration of this innovative system applicability for 6G, tested in a 16 m2 research laboratory space divided into up to 64 quadrants; and the experimental performance analysis under real indoor conditions, evaluated in terms of accuracy, precision, recall, and F1 score. Experimental results underscore and demonstrate the proposed system efficiency and applicability for accurately mapping pedestrian locations, achieving remarkable accuracy, precision, recall and F1 Score rates of up to 99%.

In this paper, a quad-core oscillator is presented, which exploits a novel stacked two-port inductor topology to couple four oscillator cores. The topology guarantees excellent low-phase-noise performance, while overcoming the drawbacks of multi-core oscillators in terms of power consumption and silicon area occupation. Three different 19.125-GHz quad-core VCOs were designed in a fully depleted silicon on insulator CMOS technology to demonstrate the high design flexibility of the proposed solution that allows power consumption and area occupation to be traded off according to the specific requirements. Although the quad-core VCO design was aimed at 77-GHz automotive radar, the proposed topology can be profitably exploited in RF/mm-wave wireless communication systems.

In this paper the design of a galvanically isolated data link for gate-driver applications in GaN technology is presented. The isolation channel exploits the near-field RF planar coupling between micro-antennas placed on two side-by-side co-packaged chips. Adopted package-scale isolation has several benefits i.e., the applicability to any integration technology and the capability to achieve both very high isolation rating and common-mode transient immunity by means of a proper distance between chips. The isolation channel adopts an RF carrier of 2 GHz that is modulated by a PWM signal. For the first time, a voltage-combining approach based on multiple antennas has been explored in a galvanically isolated data link. Specifically, the TX front-end consists of two capacitively coupled RF oscillators connected to two differential antennas designed for voltage combining. The RX front-end combines the transmitted RF signals by means of one differential antenna and extracts the PWM signal by means of a rectifier and an amplifier with a dynamic offset compensation. Emphasis is given to the design of micro-antennas, which is essential to minimize channel loss. The data link was tested by using a chip-on-board assembly to validate the proposed approach. The overall current consumption was lower than 4 mA for a PWM signal of 500 kHz and a duty cycle of 50%.

This work reports the development of an efficient and precise indoor positioning system utilizing two-dimensional (2D) light detection and ranging (LiDAR) technology, aiming to address the challenging sensing and positioning requirements of the beyond fifth-generation (B5G) mobile networks. The core of this work is the implementation of a 2D-LiDAR system enhanced by an artificial neural network (ANN), chosen due to its robustness against electromagnetic interference and higher accuracy over traditional radiofrequency signal-based methods. The proposed system uses 2D-LiDAR sensors for data acquisition and digital filters for signal improvement. Moreover, a camera and an image-processing algorithm are used to automate the labeling of samples that will be used to train the ANN by means of indicating the regions where the pedestrians are positioned. This accurate positioning information is essential for the optimization of B5G network operation, including the control of antenna arrays and reconfigurable intelligent surfaces (RIS). The experimental validation demonstrates the efficiency of mapping pedestrian locations with a precision of up to 98.787%, accuracy of 95.25%, recall of 98.537%, and an F1 score of 98.571%. These results show that the proposed system has the potential to solve the problem of sensing and positioning in indoor environments with high reliability and accuracy.

The measure obtained from the Implicit Association Test (IAT; Greenwald et al., 1998. DOI: 10.1037/0022-3514.74.6.1464) is often used to predict people’s behaviors. However, it has shown poor predictive ability potentially because of its typical scoring method (the D score), which is affected by the across-trial variability in the IAT data and might provide biased estimates of the construct. Linear Mixed-Effects Models (LMMs) can address this issue while providing a Rasch-like parametrization of accuracy and time responses. In this study, the predictive abilities of D scores and LMM estimates were compared. The LMMs estimates showed better predictive ability than the D score, and allowed for in-depth analyses at the stimulus level that helped in reducing the across-trial variability. Implications of the results and limitations of the study are discussed.

Abstract Background This study investigated changes in the prevalence of insomnia in Italy during COVID-19, starting from the first lockdown period (8 March 2020). We hypothesized that lockdown precipitated increased prevalence of insomnia symptoms relative to the pre-pandemic period; b) the gradual relaxation of containment measures – post-lockdown period (Phase 2 and Phase 3) – reduced insomnia severity, leading to a relative recovery of pre-pandemic levels; and c) we tested age-related heterogeneity in sleep responses, with an expected higher increase in insomnia in younger and middle-age groups. Methods Analyses drew on a subsample (N = 883) of respondents to ITA.LI – Italian Lives, a recently established panel study on a probability sample of individuals aged 16 + living in Italy. To estimate patterns of change in insomnia, we first fitted a random-effects ordered logistic model on the whole sample. We then added an interaction term between policy phases and the respondent age to test whether the relationship between insomnia and policy phases differed across age groups. Analyses accounted for survey non-response weights. Results The fraction of respondents reporting moderate (“somewhat” + 0.159, S.E. 0.017) or severe (“very much” + 0.142, S.E. 0.030) sleep disturbances significantly increased during Phase 1. The prevalence of insomnia followed an inverted U-shaped curve across policy phases, with further increases from baseline levels (“somewhat” + 0.168, S.E. 0.015; “very much” + 0.187, S.E. 0.030) during Phase 2, followed by a relative reduction in Phase 3, although it remained significantly higher than in the pre-pandemic period (“somewhat”, + 0.084, S.E. 0.016; “very much”, + 0.045, S.E. 0.010). There were significant age-related differences in insomnia patterns, as the discrete change from pre-pandemic levels in the probability of not suffering from insomnia was negative and significant for the younger age group (− 0.269, S.E. 0.060) and for respondents aged 35–54 (− 0.163, S.E. 0.039). Conclusion There is reason to believe that the emergency policy response to the COVID-19 crisis may have had unintended and possibly scarring effects in terms of increased prevalence of insomnia. The hardest hit were young adults and, to a lesser extent, the middle-aged; however, older respondents (55 +) remained resilient, and their insomnia trajectory bounced back to pre-pandemic levels.

Assessing executive functions in individuals with disorders or clinical conditions can be challenging, as they may lack the abilities needed for conventional test formats. The use of more personalized test versions, such as adaptive assessments, might be helpful in evaluating individuals with specific needs. This paper introduces PsycAssist, a web-based artificial intelligence system designed for neuropsychological adaptive assessment and training. PsycAssist is a highly flexible and scalable system based on procedural knowledge space theory and may be used potentially with many types of tests. We present the architecture and adaptive assessment engine of PsycAssist and the two currently available tests: Adap-ToL, an adaptive version of the Tower of London-like test to assess planning skills, and MatriKS, a Raven-like test to evaluate fluid intelligence. Finally, we describe the results of an investigation of the usability of Adap-ToL and MatriKS: the evaluators perceived these tools as appropriate and well-suited for their intended purposes, and the test-takers perceived the assessment as a positive experience. To sum up, PsycAssist represents an innovative and promising tool to tailor evaluation and training to the specific characteristics of the individual, useful for clinical practice.

Background To limit the spread of the COVID-19 emergency, a massive vaccination program was implemented and restrictive measures were imposed on the population. However, the propensity to adhere to the vaccination program has struggled to take off. Moreover, complying with the restrictive rules and maintaining social distancing have been highly distressing for many individuals. Participants and procedure Italian participants (N = 140, females = 65%, mean age = 29.50, SD = 10.80) were presented with an online survey consisting of multiple-choice questions and two single-category implicit association tests (SC-IATs). One SC-IAT evaluated the ten-dency of participants to automatically associate personal protective equipment (PPE) and vaccines with safety or danger; the other evaluated their tendency to automatically associate social situations with good or bad. Multiple-choice questions ex-plored individual, social, and environmental factors that were expected to contribute to vaccine propensity, compliance with restrictive rules, and feelings of distress. Results Using scientific information sources was related to implicitly associating PPE and vaccines with safety, which in turn was associated with the propensity to get the vaccine. Moreover, being female, young, unsatisfied with social relationships, having suffered health and economic consequences due to the pandemic, and having negative implicit attitudes toward so-cial situations contributed to increasing feelings of distress. Conclusions Communication may contribute to individuals’ behavior and preferences and it can also be associated with implicit atti-tudes, becoming consequently one of the main leverages to reduce vaccine hesitancy. Recovery programs should prioritize the development of interventions aimed at fostering psychological well-being through the enhancement of social contacts.

Este artigo tem como base um diálogo entre duas dissertações de mestrado: Vallilo (2018) e Silva (2018), considerando os temas linguagem e conexões matemáticas. O objetivo deste artigo é evidenciar como a linguagem ajuda o aluno a expressar o que sabe e o que aprendeu, de forma que o professor proponha problemas que possibilitem conexões entre as áreas da Matemática. A compreensão da linguagem, matemática e materna, com as conexões entre as diferentes áreas da Matemática são itens de destaque neste estudo para análise do aprendizado do aluno. Essa união entre esses estudos, linguagem e conexões, é discutida neste artigo por meio de três atividades trabalhadas na educação básica, uma envolvendo fração, e duas envolvendo áreas e perímetro de figuras planas. Conclui-se que a Matemática existe por meio das uniões entre seus diferentes ramos Aritmética, Geometria e Álgebra, de forma que a linguagem possibilita a união desses tópicos.

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Departing from pioneering Heyman modern rational investigations on the purely-rotational collapse mode of least-thickness circular masonry arches, the hypothesis that joint friction shall be high enough to prevent inter-block sliding is released. The influence of a reducing Coulomb friction coefficient on the collapse modes of the arch is explicitly inspected, both analytically and numerically, by tracing the appearance of purely-rotational, mixed sliding-rotational and purely-sliding modes. A classical doubly built-in, symmetric, complete semi-circular arch, with radial joints, under self-weight is specifically considered, for a main illustration. The characteristic values of the friction coefficient that limit the ranges associated to each collapse mode are first analytically derived and then numerically identified, with self-consistent outcomes. Explicit analytical representations are provided to estimate the geometric parameters that define the limit equilibrium states of the arch, specifically the minimum thickness to radius ratio, at reducing friction. These formulas, starting from the analysis of classical Heymanian instance of purely-rotational collapse, make new explicit reference to the mixed sliding-rotational collapse mode, arising within a narrow range of limited friction coefficients (or friction angles). The obtained results are consistently compared to existing numerical ones from the competent literature.

The accumulation of volatile fatty acids (VFAs) through the anaerobic fermentation of organic waste, such as strawberry extrudate, is proposed for this work. A hydrothermal treatment was carried out, and it was proposed to break the complex matrix of the strawberry extrudate to favour the hydrolysis stage of the anaerobic digestion process. The production of volatile fatty acids from treated and untreated strawberry was evaluated by adjusting the pH to 5 and 9. After the hydrothermal treatment of the strawberry extrudate, an increase in the solubilisation of organic matter, such as sugars and phenols, was observed. In the production of VFAs by means of anaerobic digestion of the pretreated strawberry extrudate, a significant increase in the accumulation of volatile fatty acids was demonstrated at a pH of 9 with respect to the untreated strawberry extrudate. In addition, the operational pH also had a strong effect on the individual VFA profile. A stream enriched in acetic acid was obtained at a pH of 9 (around 65% of the VFAs), whereas the operation at a pH of 5 resulted in a more complex composition with a high percentage of propionic acid (29% of the VFAs).

This study used a subsample of a household panel study in Italy to track changes in mental health before the onset of COVID-19 and into the first lockdown period, from late April to early September 2020. The results of the random-effects regression analyses fitted on a sample of respondents aged 16 years and older (N = 897) proved that there was a substantial and statistically significant short-term deterioration in mental health (from 78,5 to 67,9; β = -10.5, p < .001; Cohen’s d -.445), as measured by a composite index derived from the mental component of the 12-item Short-Form Health Survey (SF-12). The findings also showed heterogeneity in the COVID-related effects. On the one hand, evidence has emerged that the pandemic acted as a great leveller of pre-existing differences in mental health across people of different ages: the decrease was most pronounced among those aged 16–34 (from 84,2 to 66,5; β = -17.7, p < .001; Cohen’s d -.744); however, the magnitude of change reduced as age increased and turned to be non-significant among individuals aged 70 and over. On the other hand, the COVID-19 emergency widened the mental health gender gap and created new inequalities, based on the age of the youngest child being taken care of within the household.

The paper is part of a case study concerning the structural assessment of a historical infrastructure in the local territory, a road three-span reinforced concrete arch bridge over a river, built by the end of World War I (1917). The purpose of the paper is twofold: first, in-situ acquired response data are systematically analysed by specific signal processing techniques, to form a devoted methodological procedure and to extract useful information toward possible interpretation of the current structural conditions; second, the deciphered information is elaborated, in view of obtaining peculiar conceptualisations of detailed features of the structural response, as meant to achieve quantitative descriptions and modelling, for final Structural Health Monitoring (SHM) and intervention purposes. The proposed methodology, integrating self-implemented and adapted classical signal processing methods, and refined techniques, such as Wavelet analysis and ARMA models, assembles a rather general, systematic methodological approach to signal processing, highlighting the capability to extract useful and fundamental information from acquired response data, also endowed of a non-stationary character, toward final structural interpretation, identification and modelling, thus enabling for developing a reliable and effective SHM platform, on strategic ageing infrastructures. For the present case study, non-stationary characteristics of the response signals are revealed and flattened out, to identify the underlying fundamental frequencies of the infrastructure and to advance particular interpretations of its current structural behaviour, in forming an enlarging structural consciousness of the bridge at hand.

A growing body of evidence has been produced on the adverse effects of job insecurity on health and well-being. Current research in the field conveys a few concerns, some of which are related to gender and cross-national differences in the experience of job insecurity. In order to fill these gaps this study draws on three waves (2005, 2010, 2015) of the European Working Conditions Survey and investigates, using mixed-effects logistic regression: (i) the relationship between anxiety and perceived job insecurity; and whether such relationship: (ii) is different for men and women; (iii) and varies across European countries. Results indicate that male and female workers perceiving the risk of involuntary job loss have similar odds of reporting anxiety. Furthermore, the variance in slopes across countries, relative to the general trend, is significant but modest, too, thus suggesting that the relationship under investigation is fairly similar across European countries. Implications of the findings for future research and practice are discussed.

Several options are available for computing the most common score for the Implicit Association Test, the so-called D-score. However, all these options come with some drawbacks, related to either the need for a license, for being tailored on a specific administration procedure, or for requiring a degree of familiarity with programming. By using the R shiny package, a user-friendly, interactive, and open source web application (DscoreApp) has been created for the D-score computation. This app provides different options for computing the D-score algorithms and for applying different cleaning criteria. Beyond making the D-score computation easier, DscoreApp offers the chance to have an immediate glimpse on the results and to see how they change according to different settings configurations. The resulting D-scores are immediately available and can be seen in easy-readable and interactive graphs, along with meaningful descriptive statistics. Graphical representations, data sets containing the D-scores, and other information on participants' performance are downloadable. In this work, the use of DscoreApp is illustrated on an empirical data set.

This paper reviews state-of-the-art design approaches for low-voltage radio frequency (RF) and millimeter-wave (mm-wave) CMOS circuits. Effective design techniques at RF/mm-wave frequencies are described, including body biasing in fully depleted (FD) silicon-on-insulator (SOI) CMOS technologies and circuit topologies based on integrated reactive components (i.e., capacitors, inductors and transformers). The application of low-voltage design techniques is discussed for the main RF/mm-wave circuit blocks, i.e., low-noise amplifiers (LNAs), mixers and power amplifiers (PAs), highlighting the main design tradeoffs.

Three measures of internal consistency – Kuder-Richardson Formula 20 (KR20), Cronbach’s alpha (α), and person separation reliability (R) – are considered. KR20 and α are common measures in classical test theory, whereas R is developed in modern test theory and, more precisely, in Rasch measurement. These three measures specify the observed variance as the sum of true variance and error variance. However, they differ for the way in which these quantities are obtained. KR20 uses the error variance of an “average” respondent from the sample, which overestimates the error variance of respondents with high or low scores. Conversely, R uses the actual average error variance of the sample. KR20 and α use respondents’ test scores in calculating the observed variance. This is potentially misleading because test scores are not linear representations of the underlying variable, whereas calculation of variance requires linearity. Contrariwise, if the data fit the Rasch model, the measures estimated for each respondent are on a linear scale, thus being numerically suitable for calculating the observed variance. Given these differences, R is expected to be a better index of internal consistency than KR20 and α. The present work compares the three measures on simulated data sets with dichotomous and polytomous items. It is shown that all the estimates of internal consistency decrease with the increasing of the skewness of the score distribution, with R decreasing to a larger extent. Thus, R is more conservative than KR20 and α, and prevents test users from believing a test has better measurement characteristics than it actually has. In addition, it is shown that Rasch-based infit and outfit person statistics can be used for handling data sets with random responses. Two options are described. The first one implies computing a more conservative estimate of internal consistency. The second one implies detecting individuals with random responses. When there are a few individuals with a consistent number of random responses, infit and outfit allow for correctly detecting almost all of them. Once these individuals are removed, a “cleaned” data set is obtained that can be used for computing a less biased estimate of internal consistency.

In psychophysics, the point of subject equality (PSE) is any of the points along a stimulus dimension at which a variable stimulus (visual, tactile, auditory, and so on) is judged by an observer to be equal to a standard stimulus. Rasch models have been found to offer a valid solution for computing the PSE when the method of constant stimuli is applied in the version of the method of transitions. The present work provides an overview of the procedures for computing the PSE using Rasch models and proposes some new developments. An adaptive procedure is described that allows for estimating the PSE of an observer without presenting him/her with all stimuli pairs. This procedure can be particularly useful in those situations in which psychophysical conditions of the individuals require that the number of trials is limited. Moreover, it allows for saving time that can be used to scrutinize the results of the experiment or to run other experiments. Also, the possibility of using Rasch-based fit statistics for identifying observers who gave unexpected judgments is explored. They could be individuals who, instead of carefully evaluating the presented stimuli pairs, gave random, inattentive, or careless responses, or gave the same response to many consecutive stimuli pairs. Otherwise, they could be atypical and clinically relevant individuals who deserve further investigation. The aforementioned developments are implemented using procedures and statistics that are well established in the framework of Rasch models. In particular, computerized adaptive testing procedures are used for efficiently estimating the PSE of the observers, whereas infit and outfit mean-squares statistics are used for detecting observers who gave unexpected judgments. Results of the analyses carried out on simulated data sets suggest that the proposed developments can be used in psychophysical experiments.

É crescente o número de estudantes que têm recorrido às videoaulas dispostas em redes sociais. Muito disso é devido ao desmantelamento dos modos de ensino enciclopédicos vigentes em nossas escolas e da falta de atenção dos estudantes ao ensino tradicional. Por meio de uma Análise de Discurso arqueogenealógica procuramos compreender e tensionar os mecanismos que se colocam como propulsores dessa nova forma de se ensinar e aprender matemática da atualidade. Trata-se de um recorte de nosso projeto de pesquisa atual, onde vinculamos e analisamos seus efeitos nesse meio inovador sob um olhar da Teoria da Atenção, a qual nos apresenta alguns fatores condizentes e fundamentais pelo qual tantos alunos quanto professores virtuais vêm a procurar e trabalhar nesse ambiente.

We report the experimental implementation of optically-powered wireless sensor nodes based on the power-over-fiber (PoF) technology, aiming at Industrial Internet of Things (IIoT) applications. This technique employs optical fibers to transmit power and is proposed as a solution to address the hazardous industrial environment challenges, e.g., electromagnetic interference and extreme temperatures. The proposed approach enables two different IIoT scenarios, in which wireless transmitter (TX) and receiver (RX) nodes are powered by a PoF system, enabling local and remote temperature data monitoring, with the purpose of achieving an intelligent and reliable process management in industrial production lines. In addition, the system performance is investigated as a function of the delivered electrical power and power transmission efficiency (PTE), which is the primary performance metric of a PoF system. We report 1.4 W electrical power deliver with PTE = 24%. Furthermore, we carry out a voltage stability analysis, demonstrating that the PoF system is capable of delivering stable voltage to a wide range of applications. Finally, we present a comparison of temperature measurements between the proposed approach and a conventional industrial programmable logic controller (PLC). The obtained results demonstrate that PoF might be considered as a potential technology to power and enhance the energy efficiency of IIoT sensing systems. [ABSTRACT FROM AUTHOR]

The present work aims at developing a new version of the short form of the Eysenck Personality Questionnaire-Revised, which includes Psychoticism, Extraversion, Neuroticism, and Lie scales (48 items, 12 per scale). The work consists of two studies. In the first one, an item response theory model was estimated on the responses of 590 individuals to the full-length version of the questionnaire (100 items). The analyses allowed the selection of 48 items well discriminating and distributed along the latent continuum of each trait, and without misfit and differential item functioning. In the second study, the functioning of the new form of the questionnaire was evaluated in a different sample of 300 individuals. Results of the two studies show that reliability of the four scales is better than, or equal to that of the original forms. The new version outperforms the original one in approximating scores of the full-length questionnaire. Moreover, convergent validity coefficients and relations with clinical constructs were consistent with literature.