IMT Nord Europe

Abstract Plasma catalysis is gaining increasing interest for various gas conversion applications, such as CO 2 conversion into value-added chemicals and fuels, CH 4 activation into hydrogen, higher hydrocarbons or oxygenates, and NH 3 synthesis. Other applications are already more established, such as for air pollution control, e.g. volatile organic compound remediation, particulate matter and NO x removal. In addition, plasma is also very promising for catalyst synthesis and treatment. Plasma catalysis clearly has benefits over ‘conventional’ catalysis, as outlined in the Introduction. However, a better insight into the underlying physical and chemical processes is crucial. This can be obtained by experiments applying diagnostics, studying both the chemical processes at the catalyst surface and the physicochemical mechanisms of plasma-catalyst interactions, as well as by computer modeling. The key challenge is to design cost-effective, highly active and stable catalysts tailored to the plasma environment. Therefore, insight from thermal catalysis as well as electro- and photocatalysis is crucial. All these aspects are covered in this Roadmap paper, written by specialists in their field, presenting the state-of-the-art, the current and future challenges, as well as the advances in science and technology needed to meet these challenges.

The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users' identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users' identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

Magnetic-resonance-imaging rheometrical experiments show that concentrated suspensions or emulsions cannot flow steadily at a uniform rate smaller than a critical value ( ${\stackrel{\ifmmode \dot{}\else \.{}\fi{}}{\ensuremath{\gamma}}}_{c}$). As a result, a ``liquid'' region (sheared rapidly, i.e., at a rate larger than ${\stackrel{\ifmmode \dot{}\else \.{}\fi{}}{\ensuremath{\gamma}}}_{c}$) and a ``solid'' region (static) coexist. The behavior of the fluid in the liquid region follows a simple power-law model, while the extent of the solid region increases with the degree of jamming of the material.

One of the least understood aspects in atmospheric chemistry is how urban emissions influence the formation of natural organic aerosols, which affect Earth's energy budget. The Amazon rainforest, during its wet season, is one of the few remaining places on Earth where atmospheric chemistry transitions between preindustrial and urban-influenced conditions. Here, we integrate insights from several laboratory measurements and simulate the formation of secondary organic aerosols (SOA) in the Amazon using a high-resolution chemical transport model. Simulations show that emissions of nitrogen-oxides from Manaus, a city of ~2 million people, greatly enhance production of biogenic SOA by 60-200% on average with peak enhancements of 400%, through the increased oxidation of gas-phase organic carbon emitted by the forests. Simulated enhancements agree with aircraft measurements, and are much larger than those reported over other locations. The implication is that increasing anthropogenic emissions in the future might substantially enhance biogenic SOA in pristine locations like the Amazon.

A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization of objects in wireless sensor networks [1] and the Internet of Things [2]; multiple source reconstruction from electroencephalograms [3]; estimation of power spectral density for speech enhancement [4]; or inference in genomic signal processing [5]. Within the Bayesian signal processing framework, these problems are addressed by constructing posterior probability distributions of the unknowns. The posteriors combine optimally all of the information about the unknowns in the observations with the information that is present in their prior probability distributions. Given the posterior, one often wants to make inference about the unknowns, e.g., if we are estimating parameters, finding the values that maximize their posterior or the values that minimize some cost function given the uncertainty of the parameters. Unfortunately, obtaining closed-form solutions to these types of problems is infeasible in most practical applications, and therefore, developing approximate inference techniques is of utmost interest.

The fast development of intelligent transportation has paved the way for innovative techniques for highways, and an entirely new driving pattern of highway vehicular platooning might offer a solution to the persistent problem of road congestion, travel comfort, and road safety. In this vehicular platooning system, a platoon head vehicle provides platoon service to its user vehicles. However, some badly behaved platoon head vehicles may put the platoon in danger, which makes it crucial for user vehicles to distinguish and avoid them. In this paper, we propose a reliable trust-based platoon service recommendation scheme, which is called REPLACE, to help the user vehicles avoid choosing badly behaved platoon head vehicles. Specifically, at the core of REPLACE, a reputation system is designed for the platoon head vehicles by collecting and modeling their user vehicle's feedback. Then, an iterative filtering algorithm is designed to deal with the untruthful feedback from user vehicles. A detailed security analysis is given to show that our proposed REPLACE scheme is secure and robust against badmouth, ballot-stuffing, newcomers, and on-off attacks that exist in vehicular ad hoc networks (VANETs). In addition, we conduct extensive experiments to demonstrate the correctness, accuracy, and robustness of our proposed scheme.

Actual parallel-plate architecture of lithium-ion batteries consists of lithium-ion diffusion in one dimension between the electrodes. To achieve higher performances in terms of specific capacity and power, configurations enabling lithium-ion diffusion in two or three dimensions is considered. With a view to build these complex three-dimensional (3D) battery architectures avoiding the electrodes interpenetration issues, this work is focused on fused deposition modeling (FDM). In this study, the formulation and characterization of a 3D-printable graphite/polylactic acid (PLA) filament, specially designed to be used as negative electrode in a lithium-ion battery and to feed a conventional commercially available FDM 3D printer, is reported. The graphite active material loading in the produced filament is increased as high as possible to enhance the electrochemical performance, while the addition of various amounts of plasticizers such as propylene carbonate, poly(ethylene glycol) dimethyl ether average Mn ∼ 2000, poly(ethylene glycol) dimethyl ether average Mn ∼ 500, and acetyl tributyl citrate is investigated to provide the necessary flexibility to the filament to be printed. Considering the optimized plasticizer composition, an in-depth study is carried out to identify the electrical and electrochemical impact of carbon black and carbon nanofibers as conductive additives.

Abstract Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) methodology is one feasible approach. MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators. The most important families of MC algorithms are the Markov chain MC (MCMC) and importance sampling (IS). On the one hand, MCMC methods draw samples from a proposal density, building then an ergodic Markov chain whose stationary distribution is the desired distribution by accepting or rejecting those candidate samples as the new state of the chain. On the other hand, IS techniques draw samples from a simple proposal density and then assign them suitable weights that measure their quality in some appropriate way. In this paper, we perform a thorough review of MC methods for the estimation of static parameters in signal processing applications. A historical note on the development of MC schemes is also provided, followed by the basic MC method and a brief description of the rejection sampling (RS) algorithm, as well as three sections describing many of the most relevant MCMC and IS algorithms, and their combined use. Finally, five numerical examples (including the estimation of the parameters of a chaotic system, a localization problem in wireless sensor networks and a spectral analysis application) are provided in order to demonstrate the performance of the described approaches.

In this work, three-dimensional (3D) printing system based on fused deposition modeling (FDM) is used for the fabrication of conductive polymer nanocomposites. This technology consists in the additive multilayer deposition of polymeric nanocomposite based on poly(lactic acid) (PLA) and graphene by means of a in house made low-cost commercial bench-top 3D printer. Further, 3D printed PLA/graphene nanocomposites containing 10 wt% graphene in PLA matrix were characterized for their mechanical, electrical and electromagnetic induction shielding properties of the nanocomposite. Furthermore X-ray computed micro-tomography analyses showed that printed samples have good dimensional accuracy and are significantly closer to the predefined design and the results of scanning electron microscopy (SEM) printed samples showed a uniform dispersion of graphene in PLA matrix The proposed material has uniquely advantageous when implemented in 3D printed structures, because incorporation of multifunctional graphene has been shown to substantially improve the properties of the resulting nanocomposite.

The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

Abstract Among the 3D-printing technologies, fused deposition modeling (FDM) represents a promising route to enable direct incorporation of the battery within the final 3D object. Here, the preparation and characterization of lithium iron phosphate/polylactic acid (LFP/PLA) and SiO 2 /PLA 3D-printable filaments, specifically conceived respectively as positive electrode and separator in a lithium-ion battery is reported. By means of plasticizer addition, the active material loading within the positive electrode is raised as high as possible (up to 52 wt.%) while still providing enough flexibility to the filament to be printed. A thorough analysis is performed to determine the thermal, electrical and electrochemical effect of carbon black as conductive additive in the positive electrode and the electrolyte uptake impact of ceramic additives in the separator. Considering both optimized filaments composition and using our previously reported graphite/PLA filament for the negative electrode, assembled and “printed in one-shot” complete LFP/Graphite battery cells are 3D-printed and characterized. Taking advantage of the new design capabilities conferred by 3D-printing, separator patterns and infill density are discussed with a view to enhance the liquid electrolyte impregnation and avoid short-circuits.

Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellization and scalable methods). This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)Algorithms and Computational Methods > AlgorithmsStatistical and Graphical Methods of Data Analysis > Monte Carlo Methods.

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models.

Atrazine is one of the most widely-used chlorine herbicides in agriculture. In recent years, studies have shown a potential hazard of atrazine use in environmental health and human health. Due to its toxicity, widespread use, relatively high stability in water and soil, determining safe and efficient methods of its removal is crucial. The main aim of this review was to showcase the recent progress of atrazine degradation methods, along with their main advantages, disadvantages, potential efficiency, and degradation pathways. The overall goal was to create an information gateway for researchers, and stakeholders interested in choosing the best method for atrazine degradation. Thus, the current technologies for atrazine degradation are systematically reviewed and can be used for future improvements or the selection of the most appropriate strategy for a specific place.

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

Importance sampling (IS) methods are broadly used to approximate posterior distributions or their moments. In the standard IS approach, samples are drawn from a single proposal distribution and weighted adequately. However, since the performance in IS depends on the mismatch between the targeted and the proposal distributions, several proposal densities are often employed for the generation of samples. Under this multiple importance sampling (MIS) scenario, extensive literature has addressed the selection and adaptation of the proposal distributions, interpreting the sampling and weighting steps in different ways. In this paper, we establish a novel general framework with sampling and weighting procedures when more than one proposal is available. The new framework encompasses most relevant MIS schemes in the literature, and novel valid schemes appear naturally. All the MIS schemes are compared and ranked in terms of the variance of the associated estimators. Finally, we provide illustrative examples revealing that, even with a good choice of the proposal densities, a careful interpretation of the sampling and weighting procedures can make a significant difference in the performance of the method.

Cement/lime-based solidification is an environmentally sound solution for the management of dredged marine sediments, instead of traditional solutions such as immersion. Based on the mineralogical composition and physical characteristics of Dunkirk sediments, the effects of cement and lime are assessed through Atterberg limits, modified Proctor compaction, unconfined compressive strength and indirect tensile strength tests. The variation of Atterberg limits and the improvement in strength are discussed at different binder contents. The potential of sediments solidified with cement or lime for road construction is evaluated through a proposed methodology from two aspects: I-CBR value and material classification. The test results show the feasibility of solidified dredged sediments for beneficial use as a material in road construction. Cement is superior to lime in terms of strength improvement, and adding 6% cement is an economic and reasonable method to stabilize fine sediments.

Natural biopolymers, a class of materials extracted from renewable sources, is garnering interest due to growing concerns over environmental safety; biopolymers have the advantage of biocompatibility and biodegradability, an imperative requirement. The synthesis of nanoparticles and nanofibers from biopolymers provides a green platform relative to the conventional methods that use hazardous chemicals. However, it is challenging to characterize these nanoparticles and fibers due to the variation in size, shape, and morphology. In order to evaluate these properties, microscopic techniques such as optical microscopy, atomic force microscopy (AFM), and transmission electron microscopy (TEM) are essential. With the advent of new biopolymer systems, it is necessary to obtain insights into the fundamental structures of these systems to determine their structural, physical, and morphological properties, which play a vital role in defining their performance and applications. Microscopic techniques perform a decisive role in revealing intricate details, which assists in the appraisal of microstructure, surface morphology, chemical composition, and interfacial properties. This review highlights the significance of various microscopic techniques incorporating the literature details that help characterize biopolymers and their derivatives.

Polyethylene terephthalate glycol (PETG) is a thermoplastic formed by polyethylene terephthalate (PET) and ethylene glycol and known for his high impact resistance and ductility. The printability of PETG for fused deposition modelling (FDM) is studied by monitoring the filament temperature using an infra-red camera. The microstructural arrangement of 3D printed PETG is analysed by means of X-ray micro-tomography and tensile performance is investigated in a wide range of printing temperatures from 210 °C to 255 °C. A finite element model is implemented based on 3D microstructure of the printed material to reveal the deformation mechanisms and the role of the microstructural defects on the mechanical performance. The results show that PETG can be printed within a limited range of printing temperatures. The results suggest a significant loss of the mechanical performance due to the FDM processing and particularly a substantial reduction of the elongation at break is observed. The loss of this property is explained by the inhomogeneous deformation of the PETG filament. X-ray micro-tomography results reveal a limited amount of process-induced porosity, which only extends through the sample thickness. The FE predictions point out the combination of local shearing and inhomogeneous stretching that are correlated to the filament arrangement within the plane of construction.