Psychometric properties of the French version of the Zuckerman-Kuhlman Personality Questionnaire

Este estudio instrumental fue diseñado para investigar las propiedades psicométricas de la versión francesa y replicabilidad transcultural del Zuckerman-Kuhlman Personality Questionnaire (ZKPQ) en sus factores y facetas. El ZKPQ es un instrumento destinado a evaluar los cinco factores básicos del Alternative Five-Factor Model (AFFM). Los participantes fueron 843 suizos francófonos, principalmente estudiantes universitarios. Obtenidos los factores estos mostraron una fiabilidad entre 0,73 y 0,87, y sus facetas entre 0,57 y 0,77. Las diferencias entre géneros son similares a las informadas en la muestra americana. Las mujeres alcanzaron puntuaciones superiores en N-Anx, y puntuaciones más bajas en ImpSS y Act. El resultado de los análisis factoriales exploratorios respaldó la estructura de cinco factores y sus correspondientes facetas. Las correlaciones entre las escalas sostienen que los cinco factores básicos del AFFM son ortogonales. Los coeficientes de congruencia muestran la elevada eplicabilidad transcultural de los factores y sus facetas. Se puso a prueba el ajuste del modelo en sus factores y facetas mediante análisis factorial confirmatorio. Los resultados indican que la versión en lengua francesa del ZKPQ es un instrumento fiable y válido y posee buena replicabilidad transcultural.This instrumental study was designed to ivestigate the psychometric properties of the French version and the cross-language replicability of the Zuckerman-kuhlman Personality Questionnaire (ZKPQ) at the factor- and at the facet-level. The ZKPQ is an instrument aimed at assessing the five basic factors of Zuckerman’s Alternative Five-Factor Model (AFFM). Subjects were 843 French-speaking Swiss, mainly students. At the factor-level, the reliability ranged from .73 to .87 and at the facet level, the reliability ranged from .57 to .77. Differences between genders are congruent with those found in the American sample. Women scored higher on N-Anx, and lower on ImpSS, and Act. A series of exploratory factor analyses supported the overall five-factor structure and the structure at the facet-level. The correlations among the scales support that the five basic factors of the AFFM are orthogonal. Targeted factor analyses and congruence coefficients show high cross-language replicability at the factor- and at the facet-level. The adequacy of the model at the factor- and facet-level was tested using confirmatory factor analyses. The results show that the French version of the ZKPQ is a reliable and valid instrument and has a high cross-language replicability

D1.3 Data Management Plan

This report is the second version of the Data Management Plan (DMP) for the OPTIMAI project, provided as D1.3 - Data Management Plan - 2nd version in month 12 of the project. The overall purpose of this document is to support the data management lifecycle for all data that will be collected, stored, processed or generated by the project in order to maximise its access, according to the H2020 Pilot on Open Research Data (ORDP) in which the project participates. The DMP aims to identify the scope for data management within the project and then to consider in turn the datasets present within the project. The OPTIMAI approach will be in full compliance with the EU legislative and regulatory framework for ethics and data protection. So, in this document main regulations and basic concepts of the EU legal framework are summarised. A Data Management Plan Methodology is defined to provide the general rules and mechanisms for the access management of project data. Each dataset in the project is identified and described and information is provided about the extent to which it is standard compliant, and how the data will be available, accessible, interoperable and reusable. The approach to data management within OPTIMAI is presented by establishing the types of data likely to be encountered, the FAIR approach to data management and how it is specifically applied within the project, processes for the management of personal data and further ethical and security considerations. The complete legal and ethical framework of OPTIMAI will be developed and reported in WP9. The final part of the report then reviews each work package and task for any related data management requirements identified until M12, establishing an initial list of datasets within the project. So far, a total of 22 datasets have been identified in this version of the deliverable, being most of them data that will be collected and processed by the four pilot use cases. Other datasets are mostly stakeholders' information and public deliverables produced within the project scope. Although at this stage, some of this information remains incomplete or is yet to be determined this overview provides a solid basis for the data management lifecycle. Overall, the OPTIMAI data management plan is intended to be a living document that is regularly updated throughout the remainder of the project as information about the data present within the project becomes more complete and new datasets emerge. As living document, it will be updated during the course of the project in new versions at M24 and M3

D8.6 OPTIMAI commercialization and exploitation strategy

Deliverable D8.6 OPTIMAI commercialization and exploitation strategy 1 st version is the first version of the OPTIMAI Exploitation Plan. Exploitation aims at ensuring that OPTIMAI becomes sustainable well after the conclusion of the research project period so as to create impact. OPTIMAI intends to develop an industry environment that will optimize production, reducing production line scrap and production time, as well as improving the quality of the products through the use of a variety of technological solutions, such as Smart Instrumentation of sensors network at the shop floor, Metrology, Artificial Intelligence (AI), Digital Twins, Blockchain, and Decision Support via Augmented Reality (AR) interfaces. The innovative aspects: Decision Support Framework for Timely Notifications, Secure and adaptive multi-sensorial network and fog computing framework, Blockchain-enabled ecosystem for securing data exchange, Intelligent Marketplace for AI sharing and scrap re-use, Digital Twin for Simulation and Forecasting, Embedded Cybersecurity for IoT services, On-the-fly reconfiguration of production equipment allows businesses to reconsider quality management to eliminate faults, increase productivity, and reduce scrap. The OPTIMAI exploitation strategy has been drafted and it consists of three phases: Initial Phase, Mid Phase and Final Phase where different activities are carried out. The aim of the Initial phase (M1 to M12), reported in this deliverable, is to have an initial results' definition for OPTIMAI and the setup of the structures to be used during the project lifecycle. In this phase, also each partner's Individual Exploitation commitments and intentions are drafted, and a first analysis of the joint exploitation strategies is being presented. The next steps, leveraging on the outcomes of the preliminary market analysis, will be to update the Key Exploitable Results with a focus on their market value and business potential and to consolidate the IPR Assessment and set up a concrete Exploitation Plan. The result of the next period of activities will be reported in D8.7 OPTIMAI commercialization and exploitation strategy - 2nd version due at month 18 (June 2022

Reviewing Product Lifecycle Management Models For Complex Sectors: A Proposal

The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the partitions calculated. In this paper we propose a multi-step procedure bridging the fastest, but less accurate algorithms (coarse clustering), with the slowest, most effective ones (refinement). By adopting heuristic ranking of the nodes, and classifying a fraction of them as `critical', a refinement step can be restricted to this subset of the network, thus saving computational time. Preliminary numerical results are discussed, showing improvement of the final partition

D1.2 - OPTIMAI - Data Management Plan - 1st version

This report is the first version of the OPTIMAI Data Management Plan (DMP), delivered as D1.2 - Data Management Plan - 1st version. The purpose of the data management plan is to identify the scope for data management within the project and then to consider in turn the datasets present within the project. Overall, the OPTIMAI data management plan is intended to be a living document that is regularly updated throughout the remainder of the project as information about the data present within the project becomes more expressive and new datasets emerge. In this document, the initial approach to data management within OPTIMAI has been presented by establishing the types of data likely to be encountered, the FAIR approach to data management and how it is specifically applied within the project, processes for the management of personal data and further ethical and security considerations. The complete legal and ethical framework of OPTIMAI will be developed and reported in WP9. The final part of the report then reviews each work package and task for any related data management requirements identified until M6, establishing a first list of datasets within the project. Although at this stage, some of this information remains incomplete or is yet to be determined this overview provides a solid basis for the data management lifecycle

Defining Product Lifecycle Management: A Journey across Features, Definitions, and Concepts

Product lifecycle management (PLM) has become more important in companies providing technologies and methodologies to manage data, information, and knowledge along the whole product lifecycle. In recent years, several authors have argued about PLM using a managerial or a technological view. The paper analyses these studies and integrates different author's points of view using focus groups, blogs, and face-to-face meetings in a university community of practice. Three sets of features (i.e., managerial, technological, and collaborative ones) have been used to review the existing definitions shared between academic and industrial ones and to propose an extended PLM definition describing its key concepts. The paper is a useful reference for managers and academics who want to have a clear and critical understanding of PLM using a unique source to collect lines of evidence on several PLM definitions, features, and concepts

The 16PF5 and the NEO-PI-R in Spanish and Swiss samples: A cross-cultural comparison

This study compared the Spanish (Castilian) and French versions of the 16PF5 and of the NEO-PI-R in Spanish and Swiss samples. The five-factor solution for the 16PF5 only seems clear for the Castilian version, but not for the French version. Indeed, the congruence coefficients for the Tough-Mindedness and the Self-Control dimensions are low. On the other hand, the five-factor solutions are highly similar for both countries concerning the NEO-PI-R, and the congruence coefficients are above .95 for all five dimensions. The low cross-cultural replicability for the 16PF5 makes it difficult to analyze the differences at the mean level for this inventory. For the NEO-PI-R, the differences are generally very small and globally account for 2.6% of the total variance. Spaniards seem to have slightly lower scores on Actions and slightly higher scores on Dutifulness. These differences could either be due to translation problems, sample selection, or cultural differences

D2.4 - OPTIMAI - The OPTIMAI architecture specifications

This document contains the preliminary description of the OPTIMAI smart manufacturing solution architecture based on the elicited stakeholders' requirements and use case scenario definitions that preceded it. This first version architecture (M12) is the outcome of a three-step design methodology which started with relevant technology exploration in the context of D2.3. This process was followedup through a closer examination of the most prominent reference architectural models provisioned for smart manufacturing and industrial Internet of Things applications. A top-down design approach was then carried out using the original OPTIMAI architecture proposition as a starting point so as to identify the various components and subsystems that deliver on the specified needs and requirements of the end-users. Through this exercise, the architecture was broken down into 36 basal components. Each one of those base elements was then elaborated by project partners responsible for their implementation through a bottom-up functional specification. Through this process, three architectural viewpoints are defined for the OPTIMAI envisioned solution in this document, namely the functional, information and deployment view. The functional view delivers a high-level overview of the envisioned system functionality broken down into the identified subsystems and individual components, all of whom are described in terms of their foreseen roles and responsibilities within the runtime operation of the system. Aspects related to integration, such as the interrelationships among platform components are presented, in order to guide the development of the necessary intercommunication mechanisms between components. This process is complemented by means of aligning the resulting architectural components to prominent Industry 4.0 reference architecture models and principles. The Information view then elaborates on the flow of information through the system, highlighting how components create, communicate and consume information during envisioned system operation to deliver on the use cases' goals. Finally, the deployment view presents topological considerations in terms of defining the execution environment for the various system components at a later stage during the project lifetime. The contents of this deliverable are provided as a first version documentation of the envisioned system's shape and structure, and are expected to be updated upon completion of the architecture and system specification activities in M18 of the project lifetime

Psychometric Properties of the Marlowe-Crowne Social Desirability Scale in Eight African Countries and Switzerland

The purpose of this study was to assess the cross-cultural validity of the Marlowe-Crowne Social Desirability scale short form C, in a large sample of French-speaking participants from eight African countries and Switzerland. Exploratory and confirmatory analyses suggested retaining a two-factor structure. Item bias detection according to country was conducted for all 13 items and effect was calculated with R2. For the two-factor solution, 9 items were associated with a negligible effect size, 3 items with a moderate one, and 1 item with a large one. A series of analyses of covariance considering the acquiescence variable as a covariate showed that the acquiescence tendency does not contribute to the bias at item level. This research indicates that the psychometric properties of this instrument do not reach a scalar equivalence but that a culturally reliable measurement of social desirability could be developed