Survey data to assess consumers’ attitudes towards circular economy and bioeconomy practices: A focus on the fashion industry

This data article presents data collected through a survey with the aim of understanding consumers' behavior in the fashion industry. The analyses of these data are elaborated in the article "The circular economy and bioeconomy in the fashion sector: Emergence of a "sustainability bias"" (Colasante and Adamo 2021). As highlighted in the literature, the fashion industry contributes significantly to environmental pollution in all steps, from the production to the delivery. Often, consumers are not aware of the impact of their fashion habits on the environment and this led to the emergence of the well-known fast fashion phenomenon. Even though there is a lack of evidence on this topic, shifting consumers to embrace bioeconomy as well as circular economy principles constitutes a possible solution to reduce the impact of the fashion sector on the environment. We collected these data on consumers' habits and preferences regarding both bioeconomy and circular economy by means of a questionnaire in which a total of 402 Italian people took part by using Amazon Mechanical Turk (MTurk) platform. This paper aims at presenting the data split in the three main blocks: (i) consumer perception; (ii) purchasing habits; and (iii) consumers' willingness to pay (WTP). The results obtained are of interest to citizens, business, academics and policy makers to understand consumers' perception of sustainability in the fashion industry. The proposed dataset can be replicated on a global scale, on specific market segments of the fashion industry and can be used to compare the perception of the circular bioeconomy in other sectors

Predicting the thermodynamics by using state-dependent interactions

We reconsider the structure-based route to coarse graining in which the coarse-grained model is defined in such a way to reproduce some distributions functions of the original system as accurately as possible. We consider standard expressions for pressure and chemical potential applied to this family of coarse-grained models with density-dependent interactions and show that they only provide approximations to the pressure and chemical potential of the underlying original system. These approximations are then carefully compared in two cases: we consider a generic microscopic system in the low-density regime and polymer solutions under good-solvent conditions. Moreover, we show that the state-dependent potentials depend on the ensemble in which they have been derived. Therefore, care must be used in applying canonical state-dependent potentials to predict phase lines, which is typically performed in other ensembles.Comment: 29 pages, 1 figure; To appear in J. Chem. Phy

CFD-3D and 1D modeling of fuel cell powertrain for a hydrogen vehicle

As it is known the transport sector represents a major contributor to climate change. In particular, private transport contributes to the degradation of the air quality inside the cities or the residential areas. To address this issue, a progressive reduction of the use of fossil fuels as a primary energy source for these vehicles and the promotion of cleaner powertrain alternatives is in order. This study focuses on designing a fuel cell powertrain for a hydrogen-powered passenger car using numerical modeling. To this purpose, we initially modeled a base fuel cell and optimized its performance by using various materials for the bipolar plates and adjusting the platinum loading between the anode and cathode. Then, a preliminary design of the new powertrain has been proposed in order to achieve a nominal power of 100 kW and it has been tested on a WLTP 3b homologation cycle. Finally, we have been able to numerically estimate the behavior of the three main feeding line: hydrogen line, air line and cooling line. In conclusion, the obtained results demonstrate how numerical modelling can be successfully used in the design of complex systems such as those related to alternative energy. This work also provides a solid basis for the future development of increasingly efficient and environmentally friendly hydrogen vehicles

Three-Dimensional CFD Simulation of a Proton Exchange Membrane Electrolysis Cell

The energy shift towards carbon-free solutions is creating an ever-growing engineering interest in electrolytic cells, i.e., devices to produce hydrogen from water-splitting reactions. Among the available technologies, Proton Exchange Membrane (PEM) electrolysis is the most promising candidate for coping with the intermittency of renewable energy sources, thanks to the short transient period granted by the solid thin electrolyte. The well-known principle of PEM electrolysers is still unsupported by advanced engineering practices, such as the use of multidimensional simulations able to elucidate the interacting fluid dynamics, electrochemistry, and heat transport. A methodology for PEM electrolysis simulation is therefore needed. In this study, a model for the multidimensional simulation of PEM electrolysers is presented and validated against a recent literature case. The study analyses the impact of temperature and gas phase distribution on the cell performance, providing valuable insights into the understanding of the physical phenomena occurring inside the cell at the basis of the formation rate of hydrogen and oxygen. The simulations regard two temperature levels (333 K and 353 K) and the complete polarization curve is numerically predicted, allowing the analysis of the overpotentials break-up and the multi-phase flow in the PEM cell. An in-house developed model for macro-homogeneous catalyst layers is applied to PEM electrolysis, allowing independent analysis of overpotentials, investigation into their dependency on temperature and analysis of the cathodic gas–liquid stratification. The study validates a comprehensive multi-dimensional model for PEM electrolysis, relevantly proposing a methodology for the ever-growing urgency for engineering optimization of such devices

Preterm birth after loop electrosurgical excision procedure (LEEP). how cone features and microbiota could influence the pregnancy outcome

OBJECTIVE: In the last years, the mean age of women who underwent cervical treatment for high-grade cervical intraepithelial neoplasia (CIN 2-3) is similar to the age of women having their first pregnancy. The aim of this study was to evaluate the risk of preterm birth in subsequent pregnancies after loop electrosurgical excision procedure (LEEP). PATIENTS AND METHODS: From January 2013 to January 2016 the study identified a total of 1435 women, nulliparous, who underwent LEEP for CIN 2-3, and who wished to have their first pregnancy. Before surgery, the lengths of the cervix were calculated by transvaginal sonography. After the treatment, the dimension of the removed tissue was evaluated. During the pregnancy, all women carried out periodic transvaginal sonography and vaginal-cervical swabs. RESULTS: The average age of patients was 31.96±5.24 years; the interval between the surgical procedure and pregnancy was 12.04±4.67 months; the gestational age at births was 37.53±2.91 weeks. The first vaginal and cervical swab performed during pregnancy was negative in 81.8% of patients. The most prevalent infections were related to C. Albicans, G. Vaginalis, and Group B Streptococcus (GBS). The rate of preterm delivery was significantly higher in women with a minor cervical length. CONCLUSIONS: The length and the volume of cervical tissue excised have been shown to be directly related to the risk for preterm birth. Furthermore, vaginal infections and their persistence during pregnancy in women with a history of LEEP may be associated with an increased risk for preterm birth, compared with women with no history of LEEP

Digitalization and artificial knowledge for accountability in SCM: a systematic literature review

Purpose: In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda. Design/methodology/approach: Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda. Findings: The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative. Research limitations/implications: The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities. Practical implications: This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models. Originality/value: This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda

Consistent coarse-graining strategy for polymer solutions in the thermal crossover from Good to Theta solvent

We extend our previously developed coarse-graining strategy for linear polymers with a tunable number n of effective atoms (blobs) per chain [D'Adamo et al., J. Chem. Phys. 137, 4901 (2012)] to polymer systems in thermal crossover between the good-solvent and the Theta regimes. We consider the thermal crossover in the region in which tricritical effects can be neglected, i.e. not too close to the Theta point, for a wide range of chain volume fractions Phi=c/c* (c* is the overlap concentration), up to Phi=30. Scaling crossover functions for global properties of the solution are obtained by Monte-Carlo simulations of the Domb-Joyce model. They provide the input data to develop a minimal coarse-grained model with four blobs per chain. As in the good-solvent case, the coarse-grained model potentials are derived at zero density, thus avoiding the inconsistencies related to the use of state-dependent potentials. We find that the coarse-grained model reproduces the properties of the underlying system up to some reduced density which increases when lowering the temperature towards the Theta state. Close to the lower-temperature crossover boundary, the tetramer model is accurate at least up to Phi<10, while near the good-solvent regime reasonably accurate results are obtained up to Phi<2. The density region in which the coarse-grained model is predictive can be enlarged by developing coarse-grained models with more blobs per chain. We extend the strategy used in the good-solvent case to the crossover regime. This requires a proper treatment of the length rescalings as before, but also a proper temperature redefinition as the number of blobs is increased. The case n=10 is investigated. Comparison with full-monomer results shows that the density region in which accurate predictions can be obtained is significantly wider than that corresponding to the n=4 case.Comment: 21 pages, 14 figure

An integrated 0D/1D/3D numerical framework to predict performance, emissions, knock and heat transfer in ICEs fueled with NH3–H2 mixtures: The conversion of a marine Diesel engine as case study

In the maritime transportation, e-fuels represent a valid alternative to fossil energy sour- ces, in order to accomplish the European Union goals in terms of climate neutrality. Among the e-fuels, the ammonia-hydrogen mixtures can play a leading role, as the combination of the two allows to exploit the advantages of each one, simultaneously compensating their gaps. The main goal of the present publication is the proposal of a robust numerical frame- work based on 0D, 1D and 3D tools for CFD analyses of internal combustion engines fueled with ammonia-hydrogen mixtures. The 1D engine model provides boundary conditions for the multi-dimensional in- vestigations and estimates the overall engine performance. 3D in-cylinder detailed ana- lyses are proficiently used to predict combustion efficiency (via the well-established G-equation model supported by laminar flame speed correlations for both ammonia and hydrogen) and emissions (with a detailed chemistry based approach). Heat transfer and knock tendency are evaluated as well, by in-house developed models. As for the 0D/1D chemical kinetics calculations, firstly they support 3D analyses (for example via the gen- eration of ignition delay time tables). Moreover, they allow insights on aspects such as NOx formation, to individuate mixture qualities able to strongly reduce the emissions