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Given the benefits both individuals and collectivity have achieved over the past few years thanks to Mobility-as-Service (MaaS) systems, various studies were conducted to predict the level of acceptance of MaaS bundles from different territorial scales and in different countries. Results obtained are in some cases contradictory. Literature is lacking in the study of small-to-medium-sized urban contexts and in the effects of the ongoing COVID-19 pandemic. This paper aims to understand (1) what factors influence respondents’ preferences between their usual transportation means and a possible MaaS alternative and (2) what leads a user to prefer one MaaS bundle to another. A logistic regression and a mixed logit model were developed to reach the two aims, respectively. These models were calibrated using questionnaires administered to employees of the Municipality of Padua, a medium-sized city in Italy. Aspects concerning the perception of health safety in relation to the COVID-19 pandemic were included in the analyses. In 37% of the cases, users stated they would be willing to adopt at least one of the proposed MaaS bundles. The results suggest that MaaS solutions can be a useful tool for managing mobility even in medium-sized cities, provided users’ biosecurity concerns are addressed by appropriate countermeasures.

Mobility as a Service (MaaS) is often seen as a promising solution to address societal and environmental challenges. Despite the importance of quantifying its potential benefits, few previous works have focused on the impacts on the environment, and all of them considered large cities. This study aims to forecast the diffusion of MaaS in a medium-sized city and quantify the consequent reduction in pollutant emissions for commuting trips. Answers from a mobility survey administered to employees of the Municipality of Padua (Italy) were used to calibrate a model predicting MaaS adoption, which was applied to real working trips to estimate daily vehicle emissions savings in future scenarios with different MaaS bundles. The results indicated that the opportunity to have multimodal mobility options providing door-to-door travel is a fundamental element to ensure wide MaaS diffusion. Furthermore, public transport was confirmed to be the backbone of such a system. Compared to the current scenario, we observed up to a 41% reduction in pollutant emissions. The analysis pointed out that MaaS adoption is highly dependent on the characteristics of the proposed bundles, thus highlighting the importance of a proper design of the service and ex ante evaluation of emission savings.

In many scientific disciplines datasets contain many more variables than observational units (so-called thick data). A common hypothesis of interest in this setting is the global null hypothesis of no difference in multivariate distribution between different experimental or observational groups. Several permutation-based nonparametric tests have been proposed for this hypothesis. In this paper we investigate the potential differences in performance between different methods used to test thick data. In particular we focus on an extension of the Nonparametric combination procedure (NPC) proposed by Pesarin and Salmaso, a rank-based approach by Ellis, Burchett, Harrar and Bathke, and a distance-based approach by Mielke. The effect of different combining procedures on the NPC is also explored. Finally, we illustrate the use of these methods on a real-life dataset.

Sepiolite clay is a natural filler particularly suitable to be used with polysaccharide matrices (e.g., in starch-based bio-nanocomposites), increasing their attractiveness for a wide range of applications, such as packaging. Herein, the effect of the processing (i.e., starch gelatinization, addition of glycerol as plasticizer, casting to obtain films) and of the sepiolite filler amount on the microstructure of starch-based nanocomposites was investigated by SS-NMR (solid-state nuclear magnetic resonance), XRD (X-ray diffraction) and FTIR (Fourier-transform infrared) spectroscopy. Morphology, transparency and thermal stability were then assessed by SEM (scanning electron microscope), TGA (thermogravimetric analysis) and UV–visible spectroscopy. It was demonstrated that the processing method allowed to disrupt the rigid lattice structure of semicrystalline starch and thus obtain amorphous flexible films, with high transparency and good thermal resistance. Moreover, the microstructure of the bio-nanocomposites was found to intrinsically depend on complex interactions among sepiolite, glycerol and starch chains, which are also supposed to affect the final properties of the starch–sepiolite composite materials.

Ladder-like polysilsesquioxanes (LPSQs) are characterized by a double-stranded siloxane backbone, whose chemical and structural properties depend on both the synthesis parameters and the nature of the organic side-chains. In the case of ladder-like (methacryloxypropyl) polysilsesquioxanes (LPMASQ), polymer matrices can be produced by exploiting the presence of photocurable methacrylate groups. Consequently, they can be used to prepare functional nanocomposites (NCs), either by blending with organic polymers such as polybutadiene or exploiting the inorganic fillers’ dispersion. Since the properties of LPMASQ-based NCs are strongly related to their structure, the structural changes of polymerized LPMASQ were investigated upon addition of low loadings of TiO2 nanoparticles (up to 3 wt%) by solid state nuclear magnetic resonance and X-ray diffraction. The filler addition leads to the reduction of the polymerization capacity of the LPMASQ organic side-chains. Moreover, a different organization of ladder chains has been highlighted, ascribable to the increase in fully condensed linear ladder units at the expenses of folded chains and defective structures. The methodological approach here adopted can be extended to other composite systems and may help to describe the properties at the filler-matrix interface, offering valuable hints for a better design of these materials. Graphical Abstract

Ceramic aerogels possess intriguing thermophysical properties which make them excellent candidates for high temperature thermal insulators. However, their properties can degrade at high temperature because of crystallization phenomena or because of densification (causing a sensible reduction of their specific surface area and porosity). The polymer derived ceramic (PDC) route is a relatively new way of developing ceramic aerogels. Several aspects influence the properties of the final product when dealing with preceramic polymers, among them their chemical composition and molecular architecture. In this work, we investigated the possibility of producing aerogels belonging to the SiCN system from polysilazanes mixtures, namely perhydropolysilazane (PHPS) and a methyl/vinyl-containing polysilazane, namely Durazane 1800®, thus changing the C/Si ratio of the amorphous pyrolyzed products. It is shown that the chemical composition of the ceramic aerogel affects the main properties of the porous materials, such as thermal stability and specific surface area (SSA). Results show that the presence of carbon in the aerogels inhibits crystallization of Si3N4 up to 1600 °C in N2 and allows to maintain a SSA of ~90 m2/g up to this temperature.

Permutation tests are highly versatile non-parametric procedures that can be used to address a wide set of statistical problems, without strict assumptions on data distribution. The Non-Parametric ...

The influence of bromide ions on systems containing highly reactive radical species is of great interest for environmental remediation, atmospheric chemistry, and the synthesis of high-added-value ...

To contain the sudden spread of SARS-CoV-2, many governments encouraged people to work from home, generating an unprecedented diffusion of this activity. Furthermore, Covid-19 has induced drastic changes in everyday life and travel habits, which might persist in the future. This paper aims to understand and estimate the potential long-term impacts of telework on the environment due to the pandemic, by analyzing factors affecting the frequency of telecommuting, the mode choice for traveling to work, and pollutant emissions generated by these trips. Data from a mobility survey administered in Padova (Italy) was used. Results indicate that Covid-19 could cause a rebound effect reversing the positive impacts of working from home, since, even if the number of trips could be reduced, many shifts towards non-sustainable travel modes could occur. The promotion of telework should be combined with measures fostering sustainable travel habits to pave the way towards a future green mobility.

An increasing number of publications present the joint application of Design of Experiments (DOE) and machine learning (ML) as a methodology to collect and analyze data on a specific industrial phenomenon. However, the literature shows that the choice of the design for data collection and model for data analysis is often not driven by statistical or algorithmic advantages, thus there is a lack of studies which provide guidelines on what designs and ML models to jointly use for data collection and analysis. This article discusses the choice of design in relation to the ML model performances. A study is conducted that considers 12 experimental designs, 7 families of predictive models, 7 test functions that emulate physical processes, and 8 noise settings, both homoscedastic and heteroscedastic. The results of the research can have an immediate impact on the work of practitioners, providing guidelines for practical applications of DOE and ML., Supplemental material at the end of the manuscript. 7 tables, 8 figures. Quality and Reliability Engineering International (2022)

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.

The present study reports on the synthesis of a new alkoxysilane-bearing light-responsive cinnamyl group and its application as a surface functionalization agent for the development of SiO2 nanoparticles (NPs) with photoreversible tails. In detail, cinnamic acid (CINN) was activated with N-hydroxysuccinimide (NHS) to obtain the corresponding NHS-ester (CINN-NHS). Subsequently, the amine group of 3-aminopropyltriethoxysilane (APTES) was acylated with CINN-NHS leading to the generation of a novel organosilane, CINN-APTES, which was then exploited for decorating SiO2 NPs. The covalent bond to the silica surface was confirmed by solid state NMR, whereas thermogravimetric analysis unveiled a functionalization degree much higher compared to that achieved by a conventional double-step post-grafting procedure. In light of these intriguing results, the strategy was successfully extended to naturally occurring sepiolite fibers, widely employed as fillers in technological applications. Finally, a preliminary proof of concept of the photoreversibility of the obtained SiO2@CINN-APTES system has been carried out through UV diffuse reflectance. The overall outcomes prove the consistency and the versatility of the methodological protocol adopted, which appears promising for the design of hybrid NPs to be employed as building blocks for photoresponsive materials with the ability to change their molecular structure and subsequent properties when exposed to different light stimuli.

E-commerce is a continuously growing sector worldwide, with important repercussions on the delivery system in urban areas and especially in the Business to Consumer (B2C) sector. The delivery of a package to a consumer's address involves not only high costs for couriers (greater number of kilometres travelled), but also increased congestion and greater environmental pollution (greater volume of pollutants released into the air). To rationalize deliveries in urban areas the use of collection points, equipped with lockers, to store the goods that users have ordered has been considered in literature. This work compares two alternative delivery options: deliveries to the consumer's home versus to Lockers. To make this comparison we used a cluster first route second math-heuristic approach. In the clustering phase, we experimented a new clustering function, while the routing phase consists in solving an instance of the Traveling Salesman Problem for each generated cluster. Finally, we applied the math-heuristic to a real case (the Italian municipality of Dolo near Venice) and compared the two delivery alternatives. We evaluate the performance considering two different fleets of vehicles, with small and medium capacity. In addition, since additional trips might be performed by consumers to pick up parcels at Lockers, a sensitivity analysis was carried out to analyse the sustainability of the proposed city logistics scheme.

Photocatalytic chemical transformations in the presence of irradiated TiO2 are generally considered in terms of interfacial electron transfer. However, more elusive energy-transfer-driven reactions have been also hypothesized to occur, mainly on the basis of the indirect evidence of detected reaction products whose existence could not be justified simply by electron transfer. Unlike in homogeneous and colloidal systems, where energy transfer mechanisms have been investigated deeply for several organic syntheses, understanding of similar processes in heterogeneous systems is at only a nascent level. However, this gap of knowledge can be filled by considering the important achievements of synthetic heterogeneous photocatalysis, which bring the field closer to industrial exploitation. The present manuscript summarizes the main findings of previous literature reports and, also on the basis of some novel experimental evidences, tentatively proposes that the energy transfer in TiO2 photocatalysis could possess a Förster-like nature.

Background: Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in the urine headspace that can be detected by an electronic nose. The diagnostic performance of an electronic nose in detecting BCa was investigated in a pilot study. Methods: A prospective, single-center, controlled, non-randomized, phase 2 study was carried out on 198 consecutive subjects (102 with proven BCa, 96 controls). Urine samples were evaluated with an electronic nose provided with 32 volatile gas analyzer sensors. The tests were repeated at least two times per sample. Accuracy, sensitivity, specificity, and variability were evaluated using mainly the non-parametric combination method, permutation tests, and discriminant analysis classification. Results: Statistically significant differences between BCa patients and controls were reported by 28 (87.5%) of the 32 sensors. The overall discriminatory power, sensitivity, and specificity were 78.8%, 74.1%, and 76%, respectively, 13/96 (13.5%) controls and 29/102 (28.4%) BCa patients were misclassified as false positive and false negative, respectively. Where the most efficient sensors were selected, the sensitivity and specificity increased up to 91.1% (72.5–100) and 89.1% (81–95.8), respectively. None of the tumor characteristics represented independent predictors of device responsiveness. Conclusions: The electronic nose might represent a potentially reliable, quick, accurate, and cost-effective tool for non-invasive BCa diagnosis.

Photocatalytic chemical transformations in the presence of irradiated TiO

Barium Titanate (BaTiO3) is one of the most promising lead-free ferroelectric materials for the development of piezoelectric nanocomposites for nanogenerators and sensors. The miniaturization of electronic devices is pushing researchers to produce nanometric-sized particles to be embedded into flexible polymeric matrices. Here, we present the sol-gel preparation of crystalline BaTiO3 nanoparticles (NPs) obtained by reacting barium acetate (Ba(CH3COO)2) and titanium (IV) isopropoxide (Ti(OiPr)4). The reaction was performed both at ambient conditions and by a hydrothermal process carried on at 200 °C for times ranging from 2 to 8 h. Doped BaTiO3 nanoparticles were also produced by addition of Na, Ca, and Bi cations. The powders were annealed at 900 °C in order to improve NPs crystallinity and promote the cubic-to-tetragonal (c⟶t) phase transformation. The microstructural features of nanoparticles were investigated in dependence of both the hydrothermal reaction time and the presence of dopants. It is found that short hydrothermal treatment (2 h) can produce BaTiO3 spherical and more homogeneous nanoparticles with respect to longer hydrothermal treatments (4 h, 6 h, 8 h). These particles (2 h) are characterized by decreased dimension (approx. 120 nm), narrower size distribution and higher tetragonality (1.007) in comparison with particles prepared at ambient pressure (1.003). In addition, the short hydrothermal treatment (2 h) produces particles with tetragonality comparable to the one obtained after the longest process (8 h). Finally, dopants were found to affect to different extents both the c⟶t phase transformation and the crystallite sizes.

This study explores the co-deposition of thin polymeric films loaded with nanoparticles for its possible future application as radiation detectors. Thin films containing zinc oxide (ZnO) nanoparticles in plasma polymerized n-hexane (PPH) were deposited on silicon substrates using an atmospheric pressure plasma jet (APPJ). Crystalline ZnO nanoparticles were produced by wet chemistry, characterized, and injected through the plasma with an aerosol buffer. The precursor hydrocarbon was polymerized in atmosphere at room temperature by the plasma, resulting in a highly crosslinked structure chemically stable against common solvents. The polymer structure was characterized by FT-IR, NMR, and thermal analyses. Photoluminescence analysis revealed that ZnO UV excitonic emission is recovered owing to the passivation through polymeric encapsulation, with a remarkable increase in luminescence yield.

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees, furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

Dielectric barrier discharge plasma and photocatalysis have been proposed as tools for decontamination of process water, especially in food industry. The present investigation aims to redefine and identify the features of coupling the two technologies in terms of degradation efficiency of a model compound. Results show that, when the process is carried out in plasma activated water in the presence of irradiated TiO2, the efficiency of the integrated process is lower than the sum of the two processes acting separately. It is proposed that afterglow species, e.g. hydrogen peroxide and/or peroxynitrites could be activated by UVA light irradiation producing hydroxyl radicals in the liquid phase. Even if TiO2 limits this additional effect by acting as UVA screen barrier material, its decontamination efficiency under certain conditions results higher than that obtained with plasma systems. These results open the route to chlorine-free decontamination processes and redefine the application framework of this integrated approach.

Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are no...

Several parametric and nonparametric methods have been proposed to deal with stratified C-sample problems where the main interest lies in evaluating the presence of a certain treatment effect, but the strata effects cannot be overlooked. Stratified scenarios can be found in several different fields. In this paper we focus on a particular case study from the field of education, addressing a typical stochastic ordering problem in the presence of stratification. We are interested in assessing how the performance of students from different degree programs at the University of Padova change, in terms of university credits and grades, when compared with their entry test results. To address this problem, we propose an extension of the Non-Parametric Combination (NPC) methodology, a permutation-based technique (see Pesarin and Salmaso, 2010), as a valuable tool to improve the data analytics for monitoring University students’ careers at the School of Engineering of the University of Padova. This new procedure indeed allows us to assess the efficacy of the University of Padova’s entry tests in evaluating and selecting future students.

The increasing concentration of human activities in cities has been leading to a worsening in air quality, thus negatively affecting the lives and health of humans living in urban contexts. Transport is one of the main sources of pollution in such environments. Several local authorities have therefore implemented strict traffic-restriction measures. The aim of this paper is to evaluate the effectiveness and limitations of these interventions, by analyzing the relationship between traffic flows and air quality. The used dataset contains concentrations of NO, NO2, NOx and PM10, vehicle counts and meteorology, all collected during the COVID-19 lockdown in the city of Padova (Italy), in which severe limitations to contain the spread of the virus simulated long and large-scale traffic restrictions in normal conditions. In particular, statistical tests, correlation analyses and multivariate linear regression models were applied to non-rainy days in 2020, 2018 and 2017, in order to isolate the effect of traffic. Analysis indicated that vehicle flows significantly affect NO, NO2, and NOx concentrations, although no evidence of a relationship between traffic and PM10 was highlighted. According to this perspective, measures to limit traffic flows seem to be effective in improving air quality only in terms of reducing nitrogen oxide.

This study aims to demonstrate the remarkable features of graphene-based fillers, which are able to improve the protective performance of acrylic coatings. Furthermore, the joint application of a cataphoretic primer and a spray top coat, containing graphene and functionalized graphene oxide flakes, respectively, enables the deposition of a double-layer coating with high conductivity and abrasion resistance properties, capable of offering excellent corrosion resistance to the metal substrate. The surface morphology of the single- and double-layer coatings was investigated by optical and electron microscopies, analysing the defectiveness introduced in the polymer matrix due to the filler agglomeration. The behavior in aggressive environments was assessed by exposure of the samples in the salt spray chamber, evaluating the blister formation and the adhesion level of the coatings. Electrochemical impedance spectroscopy measurements were employed to study the corrosion protection properties of the coatings, whose conductivity and abrasion resistance features were analysed by conductivity assessment and scrub tests, respectively. The incorporation of graphene-based fillers in the cataphoretic primer improves the corrosion protection properties of the system, while the graphene flakes provide the top coat spray layer with high conductivity and excellent abrasion resistance features. Thus, this work demonstrates the possibility of employing different types of graphene-based fillers and deposition methods for the creation of multifunctional coatings.

Composites comprised of Ag3PO4 and bare TiO2 (TiO2@Ag3PO4) or silver doped TiO2 (Ag@TiO2&ndash, Ag3PO4) have been synthesized by coupling sol&ndash, gel and precipitation methods. For the sake of comparison, also the bare components have been similarly prepared. All the samples have been characterized by X-ray diffraction (XRD), UV-vis diffuse reflectance spectroscopy (DRS), scanning electron microscopy (SEM), Fourier transformed infrared spectroscopy (FTIR), photoelectrochemical measurements, and specific surface area (SSA) analysis. The optoelectronic and structural features of the samples have been related to their photocatalytic activity for the degradation of 4&ndash, nitrophenol under solar and UV light irradiation. Coupling Ag3PO4 with silver doped TiO2 mitigates photocorrosion of the Ag3PO4 counterpart, and remarkably improves the photocatalytic activity under solar light irradiation with respect to the components, to the TiO2&ndash, Ag3PO4 sample, and to the benchmark TiO2 Evonik P25. These features open the route to future applications of this material in the field of environmental remediation.

The concern about plastic marine debris has steadily been rising year after year, because millions of tonnes of plastics are released in the ocean every year, causing tremendous ecological and economic repercussions. The degradation of polymers in sea water is studied under different aspects, as it could lead to (i) the fragmentation of plastic objects and the production of microplastics, (ii) leaching of environmentally harmful additives, and (iii) preclusion of the possibility of plastics recycling [1, 2, 3].

This study aims to demonstrate the excellent protective performance of functionalized graphene oxide (fGO) flakes in acrylic cataphoretic coatings. The filler content provides an important contribution in improving the chemical and mechanical resistance of the acrylic matrix. The morphology of the fillers was first investigated by optical and electron microscopy, analysing the distribution of the fGO flakes within the polymer matrix. After that, the flakes were added to the cataphoretic bath in different concentrations, resulting in four series of samples. The cathodic delamination of the coatings was assessed with cathodic polarization cycles and with measurements carried out with a scanning Kelvin probe. Finally, the abrasion resistance at the macroscopic and microscopic level was studied by scrub testing and scratching atomic force microscopy analysis, respectively. The incorporation of fGO at the optimized concentration of 0.2 wt.% greatly increases the cathodic delamination resistance of the acrylic matrix, resulting in an effective barrier against the effects of absorbed aggressive substances. Graphene-based fillers also enhance abrasion resistance, thanks to their high mechanical strength. Thus, this work demonstrates the great protective benefits that can be obtained when using fGO flakes as reinforcing fillers in cataphoretic coatings.

Accessibility is a fundamental aspect for cities and a good degree of connection among different urban areas can be reached by smoothing the travel experience, through the efficient use of public and private transport modes. On the whole, a variety of methods has been developed for measuring accessibility, based on different kinds of dataset and using various types of metrics and indices. In this paper, accessibility is analysed focusing on travel times, as perceived by travellers, the measured travel distances and the average speed, derived from these two attributes, to connect various areas of the city. The methodology developed in the paper is tested on a real dataset for the city of Turin (Italy). This comes to be a proper test site through the availability of citizens’ mobility data collected through a travel diary survey proposed in three waves in 2016 and 2017. Such dataset provides useful information to measure the accessibility of different areas of the city. In this way, it is possible to measure how the zones of a city are differently connected, with a strong dependence on the available transport modes and their quality. Results are compared using both active and passive accessibility indicators, to assess the difference between travellers’ perception and the scheduled service for the public transport network. The evaluation proposed can provide useful information to estimate the accessibility at city level and map the zones for their ranking assessment.