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The Green Chemistry movement has led the change toward a more sustainable and environmentally benign chemistry industry and has explored new ways to reduce or eliminate the use or generation of hazardous substances from chemical products and processes. In the present chapter several extraction techniques will be seen and their applications to herbs, spices, and by‐products. Among pressurized technologies, supercritical fluid extraction (SFE) and pressurized liquid extraction (PLE) are revised in addition to techniques such as ultrasound‐assisted extraction (UAE), microwave‐assisted extraction (MAE), and enzyme‐assisted extraction (EAE). Moreover, the combination of these techniques for integrated processes will be covered. The reader is invited to learn about the huge potential of these techniques for extraction of high added‐value compounds (with improved bioactivity) while complying with Green Chemistry goals., A.P.S.C. thanks to the Administrative Department of Science, Technology and Innovation COLCIENCIAS (Colombia) for her postdoctoral research fellowship (784-1077 2017). M. H. would like to thank MICINN for his “Ramón y Cajal” research contract. The authors would also like to thank Projects AGL2017-89417-R (MINECO, Spain) and I-LINK1096 (Consejo Superior de Investigaciones Científicas, CSIC), for their financial support.

The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns, this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.

The spatial arrangement of a metropolis is of utmost importance to carry out daily activities, which are constrained by space and time. Accessibility is not only shaped by the spatial and temporal dimension, but it is also defined by individual characteristics, such as gender, impairments, or socioeconomic characteristics of the citizens living or commuting in this area. This study analyzes mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak, with special emphasis on gender and equality. The study draws on multiple sources of information, however, two main datasets are analyzed: two traditional travel surveys from the transport metropolitan area of Barcelona and two coming from smartphone data. The results show that gender plays a relevant role when analyzing mobility patterns, as already highlighted in other studies, but, after the pandemic outbreak, some population groups were more likely to change their mobility patterns, for example, highly educated population groups and those with higher income. This study also highlights that e-activities may shape new mobility patterns and living conditions for some population segments, but some activities cannot be replaced by IT technologies. For all these reasons, city and transport planning should foster sustainable development policies, which will provide the maximum benefit for society.

The estimation of Origin to Destination (OD) matrices is still object of continuous research interest, but the complexity of the problem, the many alternatives that it offers and the role that mobility patterns represented by ODs play in transport modelling, makes it an appealing research topic, namely in the domain of dynamic approaches. The availability of new traffic measurement due to the pervasive penetration of ICT measurements offers new paths to explore. This paper provides an insight of what can be achieved when, in addition to link flow counts, travel times are considered in the formulation of the Dynamic Origin-Destination Matrix Estimation (DODME). The analysis is conducted with an extension of conventional SPSA and a new hybrid formulation combining analytical and non-analytical formulations. Peer Reviewed Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles

Algae have been consumed for many years in Asian countries; at present, its consumption around the world either as food ingredient or directly as food commodity has increased considerably mainly due to the Asian influence and its cuisine, but also because of the growing interest in the beneficial health effects of algae. Although several biological activities have been attributed to different algae compounds such as pigments, lipids, polysaccharides, fiber, proteins or vitamins, in the present review we will highlight the different phenolic compounds present on seaweeds and their important role in the functional and health effects, paying special attention to the antioxidant, anti-proliferative, anti-obesity and antidiabetic activities.

Over the last century, cities have developed as a function of increased usage of automobiles as the standard transport mode. The number of cars increased along with the population as highways and parking spots became essential in city planning. Now, there is more focus on how the existing infrastructure could be used as efficiently as possible rather than increasing capacity by merely building new roads. An important part of traffic planning is a sustainable transport system, which thereby reduces congestion and emissions by using the available capacity in a more efficient way. Traffic simulation models are tools for assessing new mobility solutions and analysing changes in the infrastructure, such as rearranging intersections and building new roads. Transportation is undergoing a profound and significant transformation as it seeks to fulfil the promise of connected mobility for people and goods while limiting its carbon footprint. Physical changes to the road network mean large investments that must be comprehensively considered before acting. Modelling different scenarios of infrastructural changes allows making forecasts without any physical changes. Autonomous vehicles are potentially changing the economics of ownership as well as the use of the transportation networks, which will likely accelerate trends towards greater use of app-based ride hailing and/or sharing by private transportation network companies. American and European cities are seeing a rise in several potential business models with varying degrees of ride sharing and public vs. private involvement in delivering mobility services (MaaS). Implications for transit agencies and mobility service providers must be evaluated, and this can be done by traffic simulation models that provide a model-based framework for evaluating the mobility impact of new services.

Modern food analysis is directed to the characterization of as many components as possible in food and food-related materials. The use of –omics technologies within a Foodomics approach requires the use of high throughput analytical techniques able to offer increased resolving power and capability to separate a high number of compounds. From this perspective, two-dimensional liquid chromatography has the potential to unravel very complex food matrices, providing with high resolving power and enhanced identification potential, particularly when coupled to mass spectrometry. This review presents an overview including the most notable two-dimensional liquid chromatography applications developed in the last ten years to study food safety, food quality and the relationship between health and food as well as for the characterization of particular groups of food components within a Foodomics perspective. Moreover, the latest developments and advances, limitations, future evolution and needs related to the use of this technique are critically discussed and commented., Authors thank Project AGL2017-89417-R (MINECO, Spain) for financial support.

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ There are presently several companies that provide processed or raw global positioning system (GPS) measurements generated by fleets of commercial vehicles, internet applications or automobile companies. The aim of this paper is to deepen our understanding of GPS data applicability in transportation modelling by providing systematic and quantified insights into the representativeness of collected data in describing individual’ route choices. Unfortunately, real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. This is why pre-processing is one of the most critical steps in data analysis. Yet, pre-processing has not been properly systematized, which is why this paper focuses on the necessary steps for pre-processing GPS tracking data together and puts forth a proposal for systematizing it. Furthermore, the aggregation level is at waypoint location for low latency GPS positions along the trip trajectory. Travel time reliability on OD paths is addressed, along with other OD path characteristics. Dense urban networks pose multiple possibilities for route choice, and data from new technologies offers an opportunity to understand route choice behaviour. This research was funded by TRA2016-76914-C3-1-P Spanish R+D Programs and by Secretaria d’Universitats-iRecerca-Generalitat de Catalunya- 2017-SGR- 1749 and Industrial PhD Program 2017 DI 041.

OD matrices that describe mobility patterns provide major input to most transport analysis models. Since OD matrices are not yet directly observable, they are usually estimated indirectly. Data from new sources such as mobile phone records and GPS traces from mobile apps are emerging alternatives that allow for cheaper and timely estimates. However, they are still hindered by weaknesses that must be studied for use as input to transportation models. This paper presents a case study using mobility data from mobile phone records, with the goal of establishing a methodology for validating the obtained OD matrices and generating the appropriate input for traffic assignment models. Spatial and temporal consistency of OD data elaborated from mobile records has been proved to be useful for transportation modelling needs.

Meet-Up is an innovate pilot program for enrolled students at UPC (Universitat Politecnica de Catalunya) with Autism Spectrum Disorder (ASD). UPC is a Technical University one that provides majors in the STEM (Science, Technology, Engineering and Mathematics) area. As part of the UPC Inclusion Program, for the first time an orientation program for students with an ASD was launched on September 2017. Nowadays, colleges and universities are receiving more admission applications from young adults diagnosed with ASD than in the past, because ASD was sometimes confused with other disorders, but since the late 1990s the revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) seems to be an important factor on early diagnosis and intervention of ASD, so many of these students are now in a position to consider enrollment in higher education, which was not a viable option a few years ago. Since lecturers and schools at UPC have little experience of students with these disorders, the program was designed with the support of ASAC (Associacio Asperger de Catalunya) professionals, an Autism Spectrum local organization, and deployed with follow-up sessions conducted by ASAC psychologist team. Follow-up sessions are held at lunchtime, always at the same time and place, in order to provide students and Meet-Up staff with a familiar environment and a relaxed atmosphere conducive to free and open discussion. Finally, we experimented with the use of technology as a tool for cognitive support to help students focus on independence and control in order to reduce stress. In this initial stage, we confined the experience to electronic agendas, a contact list using WhatsApp and access to the virtual campus by means of smartphones. The promotion of the Meet-Up program was highly unsatisfactory in the first edition and a real concern for Meet-Up staff since students were allowed to incorporate ongoing in the course edition. This work presents the design of the initial Meet-Up pilot, the second edition revised program and qualitative results that cover the first edition feedback. Student profiling and initial screening tests (STAI, BDI, EQ and AQ) have been incorporated in the second edition while substantial promotion effort and organization issues have been addressed. First indicators, show a 100% increment in the number of students continuously attending to monthly follow-up sessions.

The CitScale tool is a software platform for visualizing, analyzing and comparing the impacts of new mobility concepts in urban models with special emphasis on new automotive vehicles or those based on innovative mobility concepts aimed at reducing environmental impacts. CitScale is simulation software agnostic. Using traffic simulation techniques requires executing multiple independent replications for each scenario in an experimental design, and each replication generates a huge amount of data that is not easy to analyze or compare. The analysis and comparison of experiments and scenarios is based on global, link-based and path-based key performance indicators from the perspective of users and mobility authorities while covering the scope of driver-type and transport mode.

The always more-demanding fields of food safety, quality and traceability are continuously fostering the development of robust, efficient, sensitive and cost-effective analytical methodologies. Mass spectrometry-based metabolomics is a key tool nowadays with great potential in many analytical fields and has been demonstrated to be capable of facing some important challenges related to these areas within the food science domain. The main aim of this review is to present a critical overview of the most recent applications of MS-based metabolomics approaches for food quality, safety and traceability assessment, covering the most relevant works published from 2014 to 2017. Information about the different steps needed to develop a MS-metabolomics approach, i.e. sample treatment, analytical platform, and data processing, is also provided and discussed., The authors would like to thank Projects AGL2014-53609-P (MINECO, Spain) and S2013/ABI-3028 (AVANSECAL-CM, from Comunidad Autónoma of Madrid, Spain, and the European funding from FEDER program) for financial support. M.C.P. thanks the Ministry of Economy and Competitiveness (Spain) for her “Ramón y Cajal” research contract (RYC-2013-12688). R.P.M. thanks the University of Alcalá for her pre-doctoral contract.

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ There is a wide evidence that sustainable mobility is not only a technological question, automotive technology will be part of the solution as a necessary but not sufficient condition, sufficiency is emerging as a combination of a paradigm shift from car ownership to vehicle usage consequence of socio-economic changes, withthe application of Information and Communication Technologies (ICT) that make possible for a userto have access to a mobility service from anywhere to anywhere at any time. Among the many emergent mobility services Multiple Passenger Ridesharing and its variants look the more promising. However, implementations of these systems accounting specifically for time dependencies, and time windows reflecting users’ needs raise challenges in terms of real-time fleet dispatching and dynamic route calculation. On the other handthe feasibility and impacts analysis in terms of the many factors influencing the behavior of the system, as for example the service demand, the size of the service fleet, the capacity of the shared vehicles, the time windows requirements, soft or tight. This paper analyzes both aspects. The first is approached in terms of a Decision Support System whose solutions are computed in terms of ad hoc heuristics of variants of Pick Up and Delivery Problems with Time Windows and Feasibility and Profitability criteria rooted on Dynamic Insertion Heuristics. For the evaluation of the applications a Simulation Framework is proposed based on a microscopic simulation model thatemulates real-time traffic conditions and a real traffic information system, and interacts with the Decision Support System feeding it with the required data to make the decisions that are implemented in the simulation to emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The paper is completed with the discussion of the achieved results

Estimation/updating of origin-destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in on-line contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks – except from closed highway systems – thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both off-line/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sources

This paper presents a model for designing a public transit network system combining the traditional approach of transport demand coverage in bimodal scenarios of operation with the recovery of possible disruptions due to limited reliability of the rolling stock. The model balances construction and operational costs with the benefits to the users for the optimization of their travel times. Two transportation modes have been considered, public and private transport and the proportion of the users choosing one mode or the other is assumed to obey to a bimodal logit choice model. While construction costs are a first stage decision, user travel costs and recovery action costs are scenario dependent. Two types of scenarios are taken into account: a) the scenarios of normal operation and b) disruption scenarios which are associated to a link's breakdown of the network. The disruptions in the links are assumed to follow a probability disruption model accordingly to the number of services that operate on them. The model can be used to analyze the influence of the rate of failures of the units on the reliability of the designed RTN. The proposed model can be considered as a two recourse stochastic programming model with a bi-level structure where the probabilities of failure are an implicit function of the number of services and the routing of the transit lines of the transport system. A heuristic solution method is examined for small to medium networks demonstrating the computational viability of the approach.

The aim of this paper is to provide a detailed description of a framework for the estimation of time-sliced origin-destination (OD) trip matrices in a transit network using counts and travel time data of Bluetooth Smartphone devices carried by passengers at equipped transit-stops. A Kalman filtering formulation defined by the authors has been included in the application. The definition of the input for building the space-state model is linked to network scenarios modeled with the transportation planning platform EMME. The transit assignment framework is optimal strategy-based, which determines the subset of paths related to the optimal strategies between all OD pairs

Traffic assignment models based on the user-equilibrium approach are one of the most widely used tools in transportation planning analysis. Resulting flows offer a static average view of the expected use of the road infrastructure under the modeling hypothesis. This information has usually been enough for the planning decisions. The planned infrastructure is probably sufficient for average demand, but time-varying traffic flows, i.e., at peak periods, combined with the influence of road geometry, can produce undesired congestion that can not be forecasted or analysed with the static tools. There is a clear case for a change in the analysis methodology such as combination of a traffic assignment tool, with a microscopic traffic simulator. This paper illustrates, by means of a case study, the combination of a well-known traffic assignment tool, the EMME/2 model, with a microscopic traffic simulator, Advanced Interactive Microscopic Simulator For Urban And Non-Urban Networks (AIMSUN2) with emphasis on the description of the specific interfaces that make consistent the combination of both tools in the Generic Environment for Traffic Analysis and Modeling (GETRAM) environment. Models for complex transportation systems should be the combination of mathematical models and computer models, to overcome, for example, the difficulties of the integration of modeling tools. GETRAM environment has an open and flexible computer architecture suitable for such purposes.

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.

Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, either in static or dynamic models for traffic assignment. However, OD matrices are not yet directly observable; thus, the current practice consists of adjusting an initial or a priori matrix from link flow counts, speeds, travel times and other aggregate demand data. This information is provided by an existing layout of traffic counting stations, as the traditional loop detectors. The availability of new traffic measurements provided by Information and Communications Technologies (ICT) applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. However, the efficiency strongly depends, among other factors, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator. The paper also analyzes the sensitivity of the on-line estimator with respect to the available traffic measurements.

Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, either in static or dynamic models for traffic assignment. However, OD matrices are not yet directly observable; thus, the current practice consists of adjusting an initial or a priori matrix from link flow counts, speeds, travel times and other aggregate demand data. This information is provided by an existing layout of traffic counting stations, as the traditional loop detectors. The availability of new traffic measurements provided by ICT applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. However, the efficiency strongly depends, among other factor, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator. The paper also analyzes the sensitivity of the on-line estimator with respect to the available ICT traffic measurements.

The primary data input used in principal traffic models comes from Origin-Destination (OD) trip matrices, which describe the patterns of traffic behavior across the network. In this way, OD matrices become a critical requirement in Advanced Traffic Management and/or Information Systems that are supported by Dynamic Traffic Assignment models. However, because OD matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts which are provided by an existing layout of traffic counting stations. The adequacy of the detection layout strongly determines the quality of the adjusted OD. The usual approaches to the Detection Layout problem assume that detectors are located at network links. The first contribution of this paper proposes a modified set that formulates the link detection layout problem with side constraints. It also presents a new metaheuristic tabu search algorithm with high computational efficiency. The emerging Information and Communication Technologies, especially those based on the detection of the electronic signature of on-board devices (such as Bluetooth devices) allow the location of sensors at intersections. To explicitly take into account how these ICT sensors operate, this paper proposes a new formulation in terms of a node covering problem with side constraints that, for practical purposes, can be efficiently solved with standard professional solvers such as CPLEX.