Needs of Head and Neck Cancer Patients and Stakeholders During Rehabilitation

The foreseen growth of Head and Neck cancer (HNC) incidents will require future rehabilitation services to meet the needs of a wider population. This study reports the empirical findings of a case study conducted at a cancer rehabilitation center in Copenhagen, aiming to elicit the needs of HNC patients, informal caregivers and healthcare professionals (HCPs). Our results point out that patients and stakeholders' needs are interrelated, as they faced common challenges pertinent to provision and distribution of information. This study, though preliminary, underlines the importance of inclusion of all actors in the design of future interventions

Mapping municipal solid waste to boost circular valorization practices in Łódzkie

ABSTRACT: Geographic Information System (GIS) is a powerful instrument that can be used for the spatial representation of waste and by-product flows at various levels, allowing to improve municipal solid waste (MSW) management. The mapping obtained can be advantageously targeted to build a regional network of technological, economic, social and environmental linkages and to boost circular economy practices. In this work, the data on MSW produced in the Łódzkie region, Poland, during 2021 were used to generate a geolocalized database and an interactive web map, using ArcGIS software. The geodatabase and the map visualization were organized in three layers of information with increasing detail to foster a map-driven symbiosis between waste suppliers and waste recipients, paving the way for a more circular regional economy.info:eu-repo/semantics/publishedVersio

A flux-differencing formulation with Gauss nodes

In this work, we propose an extension of telescopic derivative operators for the DGSEM with Gauss nodes, and we prove that this formulation is equivalent to its usual matrix counterpart. Among other possible applications, this allows extending the stabilization methods already developed for Gauss-Lobatto nodes to Gauss nodes, also ensuring properties such as entropy stability while retaining their improved accuracy.Comment: Short not

Pretreatments applied to microalgae residues to enhance anaerobic digestion

ABSTRACT: Biomass of microalga Chlorella protothecoides, grown under autotrophic and heterotrophic conditions and subjected to pretreatments, were energetically valorized through anaerobic digestion process according to the substrates: autotrophic algae (A), heterotrophic algae (H), heterotrophic algae extracted (HE), autoclave pretreated heterotrophic algae (HPA), enzyme pretreated heterotrophic algae (HPE), ultrasound pretreated heterotrophic algae (HPU), and inoculum (I). Despite the application of pretreatments, the highest methane production was obtained in the algae extracted digestion with 172 mL CH4, against 153, 126 and 142 mL obtained in HPA, HPE and HPU, respectively. The COD removal capacity was higher in the HPA sample while the TS and VS removal reached higher values in the autotrophic alga.N/

Truncation Error-Based Anisotropic pp-Adaptation for Unsteady Flows for High-Order Discontinuous Galerkin Methods

In this work, we extend the $\tau$-estimation method to unsteady problems and use it to adapt the polynomial degree for high-order discontinuous Galerkin simulations of unsteady flows. The adaptation is local and anisotropic and allows capturing relevant unsteady flow features while enhancing the accuracy of time evolving functionals (e.g., lift, drag). To achieve an efficient and unsteady truncation error-based $p$-adaptation scheme, we first revisit the definition of the truncation error, studying the effect of the treatment of the mass matrix arising from the temporal term. Secondly, we extend the $\tau$-estimation strategy to unsteady problems. Finally, we present and compare two adaptation strategies for unsteady problems: the dynamic and static $p$-adaptation methods. In the first one (dynamic) the error is measured periodically during a simulation and the polynomial degree is adapted immediately after every estimation procedure. In the second one (static) the error is also measured periodically, but only one $p$-adaptation process is performed after several estimation stages, using a combination of the periodic error measures. The static $p$-adaptation strategy is suitable for time-periodic flows, while the dynamic one can be generalized to any flow evolution. We consider two test cases to evaluate the efficiency of the proposed $p$-adaptation strategies. The first one considers the compressible Euler equations to simulate the advection of a density pulse. The second one solves the compressible Navier-Stokes equations to simulate the flow around a cylinder at Re=100. The local and anisotropic adaptation enables significant reductions in the number of degrees of freedom with respect to uniform refinement, leading to speed-ups of up to $\times4.5$ for the Euler test case and $\times2.2$ for the Navier-Stokes test case

Teaching and learning mathematics and statistics at an Agricultural Engineering School

This paper focuses on the teaching and learning of mathematical topics at the School of Agricultural Engineering of Barcelona in Spain. The teaching and learning process was hindered by under-achievement, absenteeism and lack of motivation on the student’s side. To overcome such obstacles we decided to set to work a new design for the subjects involved with the help of computer and other technologies. Therefore we devised a methodology based on the use of technical tools aiming at solving standard problems and fostering the communication teacher-student. This paper outlines the activities performed to the purpose, depending on the specific contents of each subject matter and the context where they are conducted. However, the use (and misuse) of technology entails some drawbacks, which can be sorted out by means of other kinds of activities, such as lectures, different types of examination questions or the achievement of a project work. Since the implementation of the sketched methodology absenteeism turns out to decrease, whereas students’ motivation seems to improve. In fact students employ statistical tools more frequently than in previous years to fulfil their final degree project. Likewise this methodology contributes to enhance students’ independent work, which matches perfectly the framework of the European Credit Transfer System.Peer Reviewe

Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information

Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID datasetOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Natur