1,263 research outputs found

    How Does Positive Work-Related Stress Affect the Degree of Innovation Development?

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    Many studies sustain that work-related stress exerts pervasive consequences on the employees’ levels of performance, productivity, and wellbeing. However, it remains unclear whether certain levels of stress might lead to positive outcomes regarding employees’ innovativeness. Hence, this paper examines how the five dimensions of work-related stress impact on the employees’ levels of innovation performance. To this aim, this study focused on a sample of 1487 employees from six Italian companies. To test the research hypotheses under assessment, we relied on the use of the partial least squares (PLS) technique. Our results reveal that, in summary, the stressors job autonomy, job demands, and role ambiguity exert a positive and significant impact on the employees’ levels of innovativeness. However, this study failed to find evidence that the supervisors’ support–innovation and colleagues’ support–innovation links are not statistically significant. View Full-Tex

    IL CONTRATTO A TEMPO INDETERMINATO A TUTELE CRESCENTI

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    La disciplina del licenziamento nel rapporto di lavoro ha comportato una dinamica evolutiva, che l'ha portata a distaccarsi dal regime disposto dal codice civile, incentrato, per entrambe le parti del rapporto di lavoro, sul principio, di matrice liberale, della libertà di recesso dal contratto di lavoro subordinato a tempo indeterminato. Con la Legge n. 108/1990 è stato portato a compimento una generalizzazione della giustificazione del licenziamento individuale. Una maggiore intensificazione della tutela contro i licenziamenti trova un freno, invece, con la riforma della tutela reale ad opera della Legge n. 92/2012. Questo ridimensionamento, da ultimo, viene confermato ed intensificato dalla disciplina in materia di contratto di lavoro a tempo indeterminato a tutele crescenti, di cui al D.lgs. n. 23/2015

    Interaction and coherence of a plasmon-exciton polariton condensate

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    Polaritons are quasiparticles arising from the strong coupling of electromagnetic waves in cavities and dipolar oscillations in a material medium. In this framework, localized surface plasmon in metallic nanoparticles defining optical nanocavities have attracted increasing interests in the last decade. This interest results from their sub-diffraction mode volume, which offers access to extremely high photonic densities by exploiting strong scattering cross-sections. However, high absorption losses in metals have hindered the observation of collective coherent phenomena, such as condensation. In this work we demonstrate the formation of a non-equilibrium room temperature plasmon-exciton-polariton condensate with a long range spatial coherence, extending a hundred of microns, well over the excitation area, by coupling Frenkel excitons in organic molecules to a multipolar mode in a lattice of plasmonic nanoparticles. Time-resolved experiments evidence the picosecond dynamics of the condensate and a sizeable blueshift, thus measuring for the first time the effect of polariton interactions in plasmonic cavities. Our results pave the way to the observation of room temperature superfluidity and novel nonlinear phenomena in plasmonic systems, challenging the common belief that absorption losses in metals prevent the realization of macroscopic quantum states.Comment: 23 pages, 5 figures, SI 7 pages, 5 figure

    Approaches for Future Internet architecture design and Quality of Experience (QoE) Control

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    Researching a Future Internet capable of overcoming the current Internet limitations is a strategic investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow applications to transparently, efficiently and flexibly exploit the available network resources with the aim to match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision problem

    Editorial: Exploring social networks, competitive actions, and dynamic capabilities in organizations

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    The COVID-19 pandemic has significantly changed workplace relations (Stewart, 2021). For instance, employee relationships have weakened, while working at home has become the norm. Consequently, employee networks are continually changing firms’ dynamic capabilities and competitive actions. Organizational network competitive actions and dynamic capabilities are crucial for understanding how to effectively manage internal and external organizational networks, especially when many employees are working in a hybrid or offline environment. Integrating social networks, competitive actions, and dynamic capabilities is important to address the growing crises in our natural, social, economic, and political environments since many decisions made are based on self and collective interests through networks and dynamic capabilities in organizations. Furthermore, there is scant literature that examines social networks, competitive actions and dynamic capabilities together. In order to address this important and under explored area in the literature, the editors submitted this Research Topic. We accepted 9 manuscripts that cover social networks and dynamic capabilities with a strong focus on trust and collaboration, followed by competitive advantage. The contributions to this Research Topic and to the literature point to a number of key insights within mechanisms and structures of dynamic capabilities, social networks, and competitive advantage/actions

    a diagnostics tool for aero engines health monitoring using machine learning technique

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    Abstract In this work an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. The aim is to link engine measurable data to its health status. A numerical tool has been implemented in order to calculate engine performance in design condition and to create a database of expected vales. Then different degradation levels have been introduced in the two main components, compressor and turbine of a single spool turbojet and the diagnostics instruments have been trained to detect the component fault. In order to evaluate the performance prediction two different machine learning based techniques, namely, artificial neural network (ANN) and support vector machine (SVM) have been compared. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The two main degradation causes considered are the compressor fouling and turbine erosion. The machine learning techniques were applied with two aims: aero-engine performance prediction and health diagnostics. The study was carried out based on three samples flights, whose data were used for the training and testing process of the prediction and diagnostics tools. The knowledge and the continuous monitoring of the engine health status can be crucial for maintenance and fleet management operations

    Implementation and validation of an extended Schnerr-Sauer cavitation model for non-isothermal flows in OpenFOAM

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    Abstract In the present work cavitation in liquid hydrogen and nitrogen was investigated by using the open source toolbox OpenFOAM. Simulations were performed by means of a mass transfer model, based on the homogeneous mixture approach in combination with the Volume of Fluid (VOF) method for the reconstruction the liquid-vapor interface. Two additional transport equations were considered, i.e. the liquid volume fraction advection and the temperature equation. The implementation of an extended Schnerr- Sauer model allowed for the introduction of the thermal effects in terms of latent heat release/absorption and convective heat transfer inside the liquid-vapor interface. A set of Antoine-like equations relate the saturation conditions to the local conditions

    Does the Danube exist? Versions of reality given by various regional climate models and climatological datasets

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    We present an intercomparison and verification analysis of several regional climate models (RCMs) nested into the same run of the same Atmospheric Global Circulation Model (AGCM) regarding their representation of the statistical properties of the hydrological balance of the Danube river basin for 1961-1990. We also consider the datasets produced by the driving AGCM, from the ECMWF and NCEP-NCAR reanalyses. The hydrological balance is computed by integrating the precipitation and evaporation fields over the area of interest. Large discrepancies exist among RCMs for the monthly climatology as well as for the mean and variability of the annual balances, and only few datasets are consistent with the observed discharge values of the Danube at its Delta, even if the driving AGCM provides itself an excellent estimate. Since the considered approach relies on the mass conservation principle and bypasses the details of the air-land interface modeling, we propose that the atmospheric components of RCMs still face difficulties in representing the water balance even on a relatively large scale. Their reliability on smaller river basins may be even more problematic. Moreover, since for some models the hydrological balance estimates obtained with the runoff fields do not agree with those obtained via precipitation and evaporation, some deficiencies of the land models are also apparent. NCEP-NCAR and ERA-40 reanalyses result to be largely inadequate for representing the hydrology of the Danube river basin, both for the reconstruction of the long-term averages and of the seasonal cycle, and cannot in any sense be used as verification. We suggest that these results should be carefully considered in the perspective of auditing climate models and assessing their ability to simulate future climate changes.Comment: 25 pages 8 figures, 5 table

    Sourcing Hydrogen for the Production of Sustainable Aviation Fuels

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    Sustainable aviation fuels (SAFs) are the near-term technological solution to decarbonize the aviation industry sector. There are several pathways to obtain biojet fuels, which can be classified into four main categories, namely oil-to-jet, alcohol-to-jet, gas-to-jet, and sugar-to-jet. All of them share the need for hydrogen to obtain a drop-in fuel that can be blended with petroleum-based jet fuel. The hydrogen input requirements affect the life cycle greenhouse gas emissions, increase the biojet fuel cost and hinder the construction of distributed processing plants. This study addresses the problem of hydrogen sourcing in the production of SAFs through a systematic literature review. Techno-economic studies of biojet fuel production using different feedstocks and conversion pathways are analyzed focusing on the methods of hydrogen provision. The technological options used to generate the required hydrogen within the conversion process itself as well as externally, along with the main strategies to reduce the hydrogen demand are identified. The production yields and the hydrogen consumption of several SAF production pathways are compared. The jet fuel yields reach values as high as 0.66 for hydroprocessing of vegetable oils with external hydrogen provision, while they drop to 0.10 for production from lignocellulosic biomass with internal hydrogen sourcing. The results of the analysis highlight the real potential of four among the most promising routes for the production of biojet fuels when the burden related to hydrogen demand is properly taken into account
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