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Executive SummaryThe EC Directive 2013/59/Euratom states in article 56 that exposures of target volumes in nuclear medicine treatments shall be individually planned and their delivery appropriately verified. The Directive also mentions that medical physics experts should always be appropriately involved in those treatments. Although it is obvious that, in nuclear medicine practice, every nuclear medicine physician and physicist should follow national rules and legislation, the EANM considered it necessary to provide guidance on how to interpret the Directive statements for nuclear medicine treatments.For this purpose, the EANM proposes to distinguish three levels in compliance to the optimization principle in the directive, inspired by the indication of levels in prescribing, recording and reporting of absorbed doses after radiotherapy defined by the International Commission on Radiation Units and Measurements (ICRU): Most nuclear medicine treatments currently applied in Europe are standardized. The minimum requirement for those treatments is ICRU level 1 (“activity-based prescription and patient-averaged dosimetry”), which is defined by administering the activity within 10% of the intended activity, typically according to the package insert or to the respective EANM guidelines, followed by verification of the therapy delivery, if applicable. Non-standardized treatments are essentially those in developmental phase or approved radiopharmaceuticals being used off-label with significantly (> 25% more than in the label) higher activities. These treatments should comply with ICRU level 2 (“activity-based prescription and patient-specific dosimetry”), which implies recording and reporting of the absorbed dose to organs at risk and optionally the absorbed dose to treatment regions. The EANM strongly encourages to foster research that eventually leads to treatment planning according to ICRU level 3 (“dosimetry-guided patient-specific prescription and verification”), whenever possible and relevant. Evidence for superiority of therapy prescription on basis of patient-specific dosimetry has not been obtained. However, the authors believe that a better understanding of therapy dosimetry, i.e. how much and where the energy is delivered, and radiobiology, i.e. radiation-related processes in tissues, are keys to the long-term improvement of our treatments.

Infantile haemangioma (IH) is the most frequent childhood tumour. Although it is benign and self-limiting, severe complications can arise due to localisation and fast tumour growth. Management and therapy of IH has changed greatly after 2008 with propranolol. However, the pathogenesis remains elusive. This update provides an overview of all possible mechanisms currently considered. We discuss the possibility that several mechanisms act together, although local hypoxia seems to be important. Clinically, in about half of the cases, an IH is preceded by an anaemic macula (local ischaemia) or a so-called precursor lesion. Laboratory findings indicate stabilisation and an increased transcription activity of hypoxia-inducible factor 1 alpha (HIF1α), leading to up-regulation of its downstream target genes (such as vascular endothelial growth factor (VEGF)), which normally occurs in cases of hypoxia. Conclusion: Three main hypotheses have been proposed, namely (1) the theory of tissue hypoxia, (2) the theory of embolization of placental endothelial cells and (3) the theory of increased angiogenic and vasculogenic activity.

Background Multiparametric MRI (mpMRI) became recognised in investigating those with suspected prostate cancer between 2010 and 2012; in the USA, the preventative task force moratorium on PSA screening was a strong catalyst. In a few short years, it has been adopted into daily urological and oncological practice. The pace of clinical uptake, born along by countless papers proclaiming high accuracy in detecting clinically significant prostate cancer, has sparked much debate about the timing of mpMRI within the traditional biopsy-driven clinical pathways. There are strongly held opposing views on using mpMRI as a triage test regarding the need for biopsy and/or guiding the biopsy pattern. Objective To review the evidence base and present a position paper on the role of mpMRI in the diagnosis and management of prostate cancer. Methods A subgroup of experts from the ESUR Prostate MRI Working Group conducted literature review and face to face and electronic exchanges to draw up a position statement. Results This paper considers diagnostic strategies for clinically significant prostate cancer; current national and international guidance; the impact of pre-biopsy mpMRI in detection of clinically significant and clinically insignificant neoplasms; the impact of pre-biopsy mpMRI on biopsy strategies and targeting; the notion of mpMRI within a wider risk evaluation on a patient by patient basis; the problems that beset mpMRI including inter-observer variability. Conclusions The paper concludes with a set of suggestions for using mpMRI to influence who to biopsy and who not to biopsy at diagnosis. Key Points • Adopt mpMRI as the first, and primary, investigation in the workup of men with suspected prostate cancer. • PI-RADS assessment categories 1 and 2 have a high negative predictive value in excluding significant disease, and systematic biopsy may be postponed, especially in men with low-risk of disease following additional risk stratification. • PI-RADS assessment category lesions 4 and 5 should be targeted; PI-RADS assessment category lesion 3 may be biopsied as a target, as part of systematic biopsies or may be observed depending on risk stratification. Electronic supplementary material The online version of this article (10.1007/s00330-019-06166-z) contains supplementary material, which is available to authorized users.

Objectives To compare the cost-effectiveness of first-line gefitinib, erlotinib, afatinib, and osimertinib in patients with non-small cell lung cancer (NSCLC) harbouring epidermal growth factor receptor (EGFR) mutations. Methods A systematic review and network meta-analysis (NMA) were conducted to compare the relative efficacy of gefitinib, erlotinib, afatinib, and osimertinib in EGFR-mutated NSCLC. To assess the cost-effectiveness of these treatments, a Markov model was developed from Dutch societal perspective. The model was based on the clinical studies included in the NMA. Incremental costs per life-year (LY) and per quality-adjusted life-year (QALY) gained were estimated. Deterministic and probabilistic sensitivity analyses (PSA) were conducted. Results Total discounted per patient costs for gefitinib, erlotinib, afatinib, and osimertinib were €65,889, €64,035, €69,418, and €131,997, and mean QALYs were 1.36, 1.39, 1.52, and 2.01 per patient, respectively. Erlotinib dominated gefitinib. Afatinib versus erlotinib yielded incremental costs of €27,058/LY and €41,504/QALY gained. Osimertinib resulted in €91,726/LY and €128,343/QALY gained compared to afatinib. PSA showed that gefitinib, erlotinib, afatinib, and osimertinib had 13%, 19%, 43%, and 26% probability to be cost-effective at a threshold of €80,000/QALY. A price reduction of osimertinib of 30% is required for osimertinib to be cost-effective at a threshold of €80,000/QALY. Conclusions Osimertinib has a better effectiveness compared to all other TKIs. However, at a Dutch threshold of €80,000/QALY, osimertinib appears not to be cost-effective.

Objectives Aortic valve disease is the most frequent indication for heart valve replacement with the highest prevalence in elderly. Tissue-engineered heart valves (TEHV) are foreseen to have important advantages over currently used bioprosthetic heart valve substitutes, most importantly reducing valve degeneration with subsequent reduction of re-intervention. We performed early Health Technology Assessment of hypothetical TEHV in elderly patients (≥ 70 years) requiring surgical (SAVR) or transcatheter aortic valve implantation (TAVI) to assess the potential of TEHV and to inform future development decisions. Methods Using a patient-level simulation model, the potential cost-effectiveness of TEHV compared with bioprostheses was predicted from a societal perspective. Anticipated, but currently hypothetical improvements in performance of TEHV, divided in durability, thrombogenicity, and infection resistance, were explored in scenario analyses to estimate quality-adjusted life-year (QALY) gain, cost reduction, headroom, and budget impact. Results Durability of TEHV had the highest impact on QALY gain and costs, followed by infection resistance. Improved TEHV performance (− 50% prosthetic valve-related events) resulted in lifetime QALY gains of 0.131 and 0.043, lifetime cost reductions of €639 and €368, translating to headrooms of €3255 and €2498 per hypothetical TEHV compared to SAVR and TAVI, respectively. National savings in the first decade after implementation varied between €2.8 and €11.2 million (SAVR) and €3.2–€12.8 million (TAVI) for TEHV substitution rates of 25–100%. Conclusions Despite the relatively short life expectancy of elderly patients undergoing SAVR/TAVI, hypothetical TEHV are predicted to be cost-effective compared to bioprostheses, commercially viable and result in national cost savings when biomedical engineers succeed in realising improved durability and/or infection resistance of TEHV.

Background We aimed to investigate the burden of informal care in Hungary (HU), Poland (PL) and Slovenia (SI). Methods A cross-sectional online survey was performed involving representative samples of 1000 respondents per country. Caregiving situations were explored; health status of informal caregivers/care recipients and care-related quality of life were assessed using the EQ-5D-5L and CarerQol-7D. Results The proportion of caregivers was (HU/PL/SI) 14.9, 15.0 and 9.6%, respectively. Their mean age was 56.1, 45.6 and 48.0, and the average time spent on informal care was 27.6, 35.5 and 28.8 h/week. Chronic care was dominant (> 1 year: 78.5%, 72.0%, 74.0%) and care recipients were mainly (own/in-law) parents. Average EQ-5D-5L scores of care recipients were 0.53, 0.49 and 0.52. For Poland and Slovenia, EQ-5D-5L scores of informal care providers were signifcantly lower than of other respondents. Average CarerQol-7D scores were (HU/PL/SI) 76.0, 69.6 and 70.9, and CarerQol-VAS was 6.8, 6.4 and 6.6, respectively. Overall, 89, 87, and 84% of caregivers felt some or a lot fulflment related to caring. Problems with combining tasks with daily activities were most important in Hungary and Slovenia. Women had a higher probability of being a caregiver in Hungary. CarerQol-7D scores were signifcantly associated with caregivers’ EQ-5D-5L scores. In Hungary and Poland, living in a larger household was positively, while caring for patients with mental health problems was negatively associated with CarerQol-7D scores. Conclusions These frst results from the Central and Eastern European region using preference-based measures for the evaluation of informal care can serve as a valuable input for health economic analyses.

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Swiss National Science Foundation OnBehalf Swiss-AF Investigators Background Atrial fibrillation (AF) is associated with loss of cognition and dementia. Cardiac autonomic dysfunction has been linked to cognitive decline. We aimed to investigate if reduced cardiac autonomic function (CAF) is associated with cognitive impairment in AF patients. Methods Patients with paroxysmal, persistent and permanent AF were enrolled from a multicenter cohort study if they presented in AF ("AF group") or in sinus rhythm ("SR group") on a baseline 5-minute ECG recording. Parameters quantifying CAF (heart rate variability triangular index (HRVI), mean heart rate (MHR), the root mean square of successive differences (RMSSD) and the standard deviation of the normal-to-normal intervals (SDNN)) were calculated. We used the Montreal Cognitive Assessment (MoCA) to assess global cognitive function. Results 1,685 AF patients with a mean age of 73 ± 8 years, 29% females, were included. The MoCA score was 24.5 ± 3.2 in the AF group (n = 710 patients) and 25.4 ± 3.2 in the SR group (n = 975 patients). After adjusting for multiple confounders, lower HRVI was associated with lower MoCA scores, both in the SR group (β=0.049; 95% confidence interval (CI): 0.016 to 0.081; p = 0.003) and in the AF group (β=0.068; 95% CI: 0.020 to 0.116; p = 0.006). In the AF group, higher MHR was associated with a poorer performance in the MoCA (β=-0.008; 95% CI: -0.014 to -0.002; p = 0.014 ). Other parameters of CAF were not associated with cognition. Conclusion Our data suggest that impaired CAF is associated with worse cognitive performance in patients with AF. Elderly AF patients with impaired HRVI might undergo cognitive testing in order to screen for cognitive impairment.

We study the extremely high and low residual spenders in individual health insurance markets in three countries. A high (low) residual spender is someone for whom the residual—spending less payment (from premiums and risk adjustment)—is high (low), indicating that the person is highly underpaid (overpaid). We begin with descriptive analysis of the top and bottom 1% and 0.1% of residuals building to address the question of the degree of persistence in membership at the extremes. Common findings emerge among the countries. First, the diseases found among those with the highest residual spending are also disproportionately found among those with the lowest residual spending. Second, those at the top of the residual spending distribution (where spending exceeds payments the most) account for a massively high share of the unexplained variance in the predictions from the risk adjustment model. Third, in terms of persistence, we find that membership in the extremes of the residual spending distribution is highly persistent, raising concerns about selection-related incentives targeting these individuals. As our results show, the one-in-a-thousand people (on both sides of the residual distribution) play an outsized role in creating adverse incentives associated with health plan payment systems. In response to the observed importance of the extremes of the residual spending distribution, we propose an innovative combination of risk-pooling and reinsurance targeting the predictively undercompensated group. In all three countries, this form of risk sharing substantially improves the overall fit of payments to spending. Perhaps surprisingly, by reducing the burden on diagnostic indicators to predict high payments, our proposed risk sharing policy reduces the gap between payments and spending not only for the most undercompensated individuals but also for the most overcompensated people.

The limited efficacy of glucocorticoids (GCs) during therapy of acute relapses in multiple sclerosis (MS) leads to long-term disability. We investigated the potential of vitamin D (VD) to enhance GC efficacy and the mechanisms underlying this VD/GC interaction. In vitro, GC receptor (GR) expression levels were quantified by ELISA and induction of T cell apoptosis served as a functional readout to assess synergistic 1,25(OH)2D3 (1,25D)/GC effects. Experimental autoimmune encephalomyelitis (MOG35–55 EAE) was induced in mice with T cell-specific GR or mTORc1 deficiency. 25(OH)D (25D) levels were determined in two independent cohorts of MS patients with stable disease or relapses either responsive or resistant to GC treatment (initial cohort: n = 110; validation cohort: n = 85). Gene expression of human CD8+ T cells was analyzed by microarray (n = 112) and correlated with 25D serum levels. In vitro, 1,25D upregulated GR protein levels, leading to increased GC-induced T cell apoptosis. 1,25D/GC combination therapy ameliorated clinical EAE course more efficiently than respective monotherapies, which was dependent on GR expression in T cells. In MS patients from two independent cohorts, 25D deficiency was associated with GC-resistant relapses. Mechanistic studies revealed that synergistic 1,25D/GC effects on apoptosis induction were mediated by the mTOR but not JNK pathway. In line, 1,25D inhibited mTORc1 activity in murine T cells, and low 25D levels in humans were associated with a reduced expression of mTORc1 inhibiting tuberous sclerosis complex 1 in CD8+ T cells. GR upregulation by 1,25D and 1,25D/GC synergism in vitro and therapeutic efficacy in vivo were abolished in animals with a T cell-specific mTORc1 deficiency. Specific inhibition of mTORc1 by everolimus increased the efficacy of GC in EAE. 1,25D augments GC-mediated effects in vitro and in vivo in a T cell-specific, GR-dependent manner via mTORc1 inhibition. These data may have implications for improvement of anti-inflammatory GC therapy. Electronic supplementary material The online version of this article (10.1007/s00401-019-02018-8) contains supplementary material, which is available to authorized users.

Background Hypermobility of the first carpometacarpal joint is mostly surgically treated with a volar approached stabilization by Eaton, but recent studies indicate the importance of the dorsoradial and intermetacarpal ligaments (DRL and IML) for carpometacarpal joint stability. The aim of this study was to compare a dorsal and volar technique for primary carpometacarpal hypermobility regarding pain and functional outcome. Methods Patients with non-degenerative, painful carpometacarpal hypermobility were included and were randomly assigned to either the volar technique using the FCR, or a dorsal technique using the ECRL. After premature termination of the trial, we followed all patients treated with the volar approach. Pain, strength, and ADL function using DASH and Michigan Hand Questionnaires (MHQ) were measured at baseline and 3 and 12 months after surgery. Results After including 16 patients, the randomized trial comparing the volar and dorsal technique was terminated because of significant increased pain in the dorsal group. Although none of the other outcome measures were significant in the underpowered comparison, in line with the pain scores, all variables showed a trend towards a worse outcome in the dorsal group. Between 2009 and 2012, 57 thumbs were surgically stabilized. We found significant better pain and MHQ scores, and after 1 year improved grip and key pinch strength. Patients returned to work within 8 (±7) weeks, of which 85 % in their original job. Conclusions Surgical stabilization of the thumb is an effective method for patients suffering from hypermobility regarding pain, daily function, and strength. We recommend a volar approach. Level of Evidence: Level I, therapeutic study

[EN] The aim of this paper is to show how the number of conjugacy classes appearing in the product of classes affect the structure of a finite group. The aim of this paper was to show several results about solvability concerning the case in which the power of a conjugacy class is a union of one or two conjugacy classes. Moreover, we show that the above conditions can be determined through the character table of the group., Part of this paper was written during the stay of C.Melchor at the University of Cambridge in autumn 2017, which was financially supported by the grant E-2017-02, Universitat Jaume I of Castellón. C. Melchor would like to thank R. Camina and the Department of Mathematics for their warm hospitality. A. Beltrán, M.J. Felipe and C. Melchor are supported by Proyecto PGC2018-096872-B-100, Ministerio de Ciencia, Innovación y Universidades.

International audience; Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset.

Purpose: To estimate the prevalence of degenerative lumbar spinal stenosis (LSS) in adults, identified by clinical symptoms and/or radiological criteria. Methods: Systematic review of the literature. Pooled prevalence estimates by care setting and clinical or radiological diagnostic criteria were calculated and plotted [PROSPERO ID: CRD42018109640]. Results: In total, 41 papers reporting on 55 study samples were included. The overall risk of bias was considered high in two-thirds of the papers. The mean prevalence, based on a clinical diagnosis of LSS in the general population, was 11% (95% CI 4–18%), 25% (95% CI 19–32%) in patients from primary care, 29% (95% CI 22–36%) in patients from secondary care and 39% (95% CI 39–39%) in patients from mixed primary and secondary care. Evaluating the presence of LSS based on radiological diagnosis, the pooled prevalence was 11% (95% CI 5–18%) in the asymptomatic population, 38% (95% CI − 10 to 85%) in the general population, 15% (95% CI 13–18%) in patients from primary care, 32% (95% CI 22–41%) in patients from secondary care and 21% (95% CI 16–26%) in a mixed population from primary and secondary care. Conclusions: The mean prevalence estimates based on clinical diagnoses vary between 11 and 39%, and the estimates based on radiological diagnoses similarly vary between 11 and 38%. The results are based on studies with high risk of bias, and the pooled prevalence estimates should therefore be interpreted with caution. With an growing elderly population, there is a need for future low risk-of-bias research clarifying clinical and radiological diagnostic criteria of lumbar spinal stenosis. Graphic abstract: These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.]

[EN] Neighbor discovery represents a first step after the deployment of wireless ad hoc networks, since the nodes that form them are equipped with limited-range radio transceivers, and they typically do not know their neighbors. In this paper two randomized neighbor discovery approaches, called CDH and CDPRR, based on collision detection for static multi-hop wireless ad hoc networks, are presented. Castalia 3.2 simulator has been used to compare our proposed protocols against two protocols chosen from the literature and used as reference: the PRR, and the Hello protocol. For the experiments, we chose five metrics: the neighbor discovery time, the number of discovered neighbors, the energy consumption, the throughput and the number of discovered neighbors versus packets sent ratio. According to the results obtained through simulation, we can conclude that our randomized proposals outperform both Hello and PRR protocols in the presence of collisions regarding all five metrics, for both one-hop and multi-hop scenarios. As novelty compared to the reference protocols, both proposals allow nodes to discover all their neighbors with probability 1, they are based on collision detection and know when to terminate the neighbor discovery process. Furthermore, qualitative comparisons of the existing protocols and the proposals are available in this paper. Moreover, CDPRR presents better results in terms of time, energy consumption and number of discovered neighbors versus packets sent ratio. We found that both proposals achieve to operate under more realistic assumptions. Furthermore, CDH does not need to know the number of nodes in the network., This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.

[EN] Customer service measures are traditionally used to determine the performance or/and the control parameters of any inventory system. Among them, the fill rate is one of the most widely used in practice and is defined as the fraction of demand that is immediately met from shelf i.e. from the available on-hand stock. However, this definition itself set out several problems that lead to consider two different approaches to compute the fill rate: the traditional, which computes the fill rate in terms of units short; and the standard, which directly computes the expected satisfied demand. This paper suggest two expressions, the traditional and the standard, to compute the fill rate in the continuous reorder point, order quantity (s, Q) policy following these approaches. Experimental results shows that the traditional approach is biased since underestimate the real fill rate whereas the standard computes it accurately and therefore both approaches cannot be treated as equivalent. This paper focuses on the lost sales context and discrete distributed demands., This work was supported by the European Regional Development Fund and Spanish Government (MINECO/FEDER, UE) under the Project with reference DPI2015-64133-R.

[EN] Augmented Reality (AR) has become a mainstream technology in the development of solutions for repair and maintenance operations. Although most of the AR solutions are still limited to specific contexts in industry, some consumer electronics companies have started to offer pre-packaged AR solutions as alternative to video-based tutorials (VT) for minor maintenance operations. In this paper, we present a comparative study of the acquired knowledge and user perception achieved with AR and VT solutions in some maintenance tasks of IT equipment. The results indicate that both systems help users to acquire knowledge in various aspects of equipment maintenance. Although no statistically significant differences were found between AR and VT solutions, users scored higher on the AR version in all cases. Moreover, the users explicitly preferred the AR version when evaluating three different usability and satisfaction criteria. For the AR version, a strong and significant correlation was found between the satisfaction and the achieved knowledge. Since the AR solution achieved similar learning results with higher usability scores than the video-based tutorials, these results suggest that AR solutions are the most effective approach to substitute the typical paper-based instructions in consumer electronics., This work has been supported by Spanish MINECO and EU ERDF programs under grant RTI2018-098156-B-C55.

[EN] The mixed no-idle flowshop scheduling problem arises in modern industries including integrated circuits, ceramic frit and steel production, among others, and where some machines are not allowed to remain idle between jobs. This paper describes an exact algorithm that uses Benders decomposition with a simple yet effective enhancement mechanism that entails the generation of additional cuts by using a referenced local search to help speed up convergence. Using only a single additional optimality cut at each iteration, and combined with combinatorial cuts, the algorithm can optimally solve instances with up to 500 jobs and 15 machines that are otherwise not within the reach of off-the-shelf optimization software, and can easily surpass ad-hoc existing metaheuristics. To the best of the authors' knowledge, the algorithm described here is the only exact method for solving the mixed no-idle permutation flowshop scheduling problem., This research project was partially supported by the Scientific and Technological Research Council of Turkey (TuBITAK) under Grant 1059B191600107. While writing this paper, Dr Hamzaday was a visiting researcher at the Southampton Business School at the University of Southampton. Ruben Ruiz is supported by the Spanish Ministry of Science, Innovation and Universities, under the Project 'OPTEP-Port Terminal Operations Optimization' (No. RTI2018-094940-B-I00) financed with FEDER funds. Thanks are due to two anonymous reviewers for their careful reading of the paper and helpful suggestions.

It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs.

The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics. Validerad;2019;Nivå 2;2019-04-12 (johcin)

The aim of the sparse analytic hierarchy process (SAHP) problem is to rank a set of alternatives based on their utility/importance; this task is accomplished by asking human decision-makers to compare selected pairs of alternatives and to specify relative preference information, in the form of ratios of utilities. However, such an information is often affected by subjective biases or inconsistencies. Moreover, there is no general consent on the best approach to accomplish this task, and in the literature several techniques have been proposed. Finally, when more than one decision-maker is involved in the process, there is a need to provide adequate methodologies to aggregate the available information. In this view, the contribution of this paper to the SAHP body of knowledge is twofold. From one side, it develops a novel methodology to aggregate sparse data given by multiple sources of information. From another side, the paper undertakes an experimental validation of the most popular techniques to solve the SAHP problem, discussing the strength points and shortcomings of the different methodology with respect to a real case study.

[EN] The transport of sensitive products is very important because their deterioration may cause the value lost and even the product rejection by the buyer. In addition, it is important to choose the optimal way to achieve this end. In a data network, the task of calculating the best routes is performed by routers. We can consider the optimal path as the one that provides a shortest route. However, if a real transport network is considered the shortest path can sometimes be affected by incidents and traffic jams that would make it inadvisable. On the other hand, when we need to come back, due to features that symmetry provides, it would be interesting to follow the same path in reverse sense. For this reason, in this paper we present a symmetric routing mechanism for cooperative monitoring system for the delivery of fresh products. The systems is based on a combination of fixed nodes and a mobile node that stores the path followed to be able of coming back following the same route in reverse sense. If this path is no longer available, the system will try to maintain the symmetry principle searching the route that provide the shortest time to the used in the initial trip. The paper shows the algorithm used by the systems to calculate the symmetric routes. Finally, the system is tested in a real scenario which combines different kind of roads. As the results shows, the energy consumption of this kind of nodes is highly influenced by the activity of sensors., This work has been supported by the "Ministerio de Economia y Competitividad", through the "Convocatoria 2014. Proyectos I+D -Programa Estatal de Investigacion Cientifica y Tecnica de Excelencia" in the "Subprograma Estatal de Generacion de Conocimiento", (project TIN2014-57991-C3-1- P) and the "programa para la Formacion de Personal Investigador - (FPI-2015-S2-884)" by the "Universitat Politecnica de Valencia".

Access to research papers has changed in the last decades: from printed to digital sources, from close to open access. Despite these changes and broader access to research results, there are still accessibility barriers. A very few number of conferences and journals state an accessibility policy of their publications in their websites. The production of accessible documents is not a common practice in conferences devoted to accessibility. Purpose: The purpose of this paper is to present the case study of DSAI 2016 (Software Development and Technologies for Enhancing Accessibility and Fighting Infoexclusion) conference, wherein we were in charge of making accessible its proceedings. Methods: We discuss the methods and technical procedures we carried out to turn the original articles in MS Word and Latex formats into accessible PDFs and the steps necessary in authoring, conversion and validation. Results: the papers of DSAI 2016 were published in accessible format after much effort, best tools and procedure were using MS Word plus PDF Axes plus PDF Accessibility checker. Conclusion: We state the need to include a new role in the organizing committee of conferences for dealing with accessible publishing.

[EN] This paper presents and evaluates a system that provides TV and radio services in mobility using 4G communications. The system has mainly two blocks, one on the cloud and another on the mobile vehicle. On the cloud, a DVB (Digital Video Broadcasting) receiver obtains the TV/radio signal and prepares the contents to be sent through 4G. Specifically, contents are transcoded and packetized using the DASH (Dynamic Adaptive Streaming over HTTP) standard. Vehicles in mobility use their 4G connectivity to receive the flows transmitted by the cloud. The key element of the system is an on-board proxy that manages the received flows and offers them to the final users in the vehicle. The proxy contains a buffer that helps reduce the number of interruptions caused by hand over effects and lack of coverage. The paper presents a comparison between a live transmission using 4G connecting the clients directly with the cloud server and a near real-time transmission based on an on-board proxy. Results prove that the use of the proxy reduces the number of interruptions considerably and, thus, improves the Quality of Experience of users at the expense of slightly increasing the delay., This work is supported by the Centro para el Desarrollo Tecnologico Industrial (CDTI) from the Government of Spain under the project "Plataforma avanzada de conectividad en movilidad" (CDTI IDI-20150126) and the project "Desarrollo de nueva plataforma de entretenimiento multimedia para entornos nauticos" (CDTI TIC-20170102).

International audience; This paper reports on a four-year project that aims to raise the abstraction level through the use of model-driven engineering (MDE) techniques in the development of scientific applications relying on high-performance computing. The development and maintenance of high-performance scientific computing software is reputedly a complex task. This complexity results from the frequent evolutions of supercomputers and the tight coupling between software and hardware aspects. Moreover, current parallel programming approaches result in a mixing of concerns within the source code. Our approach relies on the use of MDE and consists in defining domain-specific modeling languages targeting various domain experts involved in the development of HPC applications, allowing each of them to handle their dedicated model in a both user-friendly and hardware-independent way. The different concerns are separated thanks to the use of several models as well as several modeling viewpoints on these models. Depending on the targeted execution platforms, these abstract models are translated into executable implementations by means of model transformations. To make all of these effective, we have developed a tool chain that is also presented in this paper. The approach is assessed through a multi-dimensional validation that focuses on its applicability, its expressiveness and its efficiency. To capitalize on the gained experience, we analyze some lessons learned during this project.

[EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170., The work reported in this paper is supported by a grant from the project “CLIA System Phase II” funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030).

[EN] In this paper, in order to reliably meet the homogeneity required by customers, a fuzzy model is proposed to support the promising process in LHP contexts after taking into account uncertainty in planned homoge- neous sublots. The fuzzy model is translated into an alpha- parametric equivalent crisp model. Here, it is important to highlight another important novelty of the paper related to the proposed methodology to analyse the suitability of the minimum degree of possibility (the a-cut), by an adapted TOPSIS-based fuzzy procedure. Finally, the experimental design, which is inspired in the ceramic sector, proves both the validity of the model and a better performance of the fuzzy model compared to the deterministic one, in several executions with forecasts of the real size of homogeneous sublots., This research is partly supported by: The Ministry of Science, Technology and Telecommunications of the of Costa Rica Government (MICITT), through the Programme of Innovation and Human Capital for Competitiveness (PINN)(Contract No. PED-019-2015-1); and the Spanish Ministry of Economy and Competitiveness Projects "Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity'' (PLANGES-FHP) (Ref. DPI2011-23597) and "Operations design and Management of Global Supply Chains'' (GLOBOP) (Ref. DPI2012-38061-C02-01).

[EN] Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian-Spanish and Italian-Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations., This research is partially funded by the EU FP7 PortDial Project No. 296170, FP7 SpeDial Project No. 611396, and Spanish contract TIN2014-54288-C4-3-R. The work presented in this paper was carried out while the author was affiliated with Universitat Politecnica de Valencia.

We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms' run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection., Comment: Extended version of a paper published in the proceedings of the BPM 2017 workshops

With a growing number of elderly people in the UK, more and more of them suffer from various kinds of cognitive impairment. Cognitive impairment can be divided into different stages such as mild cognitive impairment (MCI) and severe cognitive impairment like dementia. Its early detection can be of great importance. However, it is challenging to detect cognitive impairment in the early stage with high accuracy and low cost, when most of the symptoms may not be fully expressed. This survey paper mainly reviews the state of the art techniques for the early detection of cognitive impairment and compares their advantages and weaknesses. In order to build an effective and low-cost automatic system for detecting and monitoring the cognitive impairment for a wide range of elderly people, the applications of computer vision techniques for the early detection of cognitive impairment by monitoring facial expressions, body movements and eye movements are highlighted in this paper. In additional to technique review, the main research challenges for the early detection of cognitive impairment with high accuracy and low cost are analysed in depth. Through carefully comparing and contrasting the currently popular techniques for their advantages and weaknesses, some important research directions are particularly pointed out and highlighted from the viewpoints of the authors alone.

This Special Issue includes extended versions of selected papers from the XII Jornadas de Ingeniería Telemática (JITEL 2015), that took place in Palma, Spain, from October 14th to 16th, 2015. These papers underwent a rigorous review process, ensuring that they present enough new material so as to be considered original contributions while avoiding self-plagiarism.

The use of network technology to provide online courses is the latest trend in the training and development industry and has been defined as the “e-Learning revolution”. On the other hand, Online Social Networks (OSNs) represent today an effective possibility to have common and easy-to-use platforms for supporting e-Learning activities. However, as underlined by previous studies, many of the proposed e-Learning systems can result in confusion and decrease the learner’s interest. In this paper, we introduce the possibility to form e-Learning classes in the context of OSNs. At the best of our knowledge, any of the approaches proposed in the past considers the evolution of on-line classes as a problem of matching the individual users’ profiles with the profiles of the classes. In this paper, we propose an algorithm that exploits a multi-agent system to suitably distribute such a matching computation on all the user devices. The good effectiveness and the promising efficiency of our approach is shown by the experimental results obtained on simulated On-line Social Networks data.

International audience; When two people engage in a conversation, knowingly or unknowingly, they are playing a game. Players of such games have diverse objectives, or winning conditions: an applicant trying to convince her potential employer of her eligibility over that of a competitor, a prosecutor trying to convict a defendant, a politician trying to convince an electorate in a political debate, and so on. We argue that infinitary games offer a natural model for many structural characteristics of such conversations. We call such games message exchange games, and we compare them to existing game theoretic frameworks used in linguistics—for example, signaling games—and show that message exchange games are needed to handle non-cooperative conversation. In this paper, we concentrate on conversational games where players’ interests are opposed. We provide a taxonomy of conversations based on their winning conditions, and we investigate some essential features of winning conditions like consistency and what we call rhetorical cooperativity. We show that these features make our games decomposition sensitive, a property we define formally in the paper. We show that this property has far-reaching implications for the existence of winning strategies and their complexity. There is a class of winning conditions (decomposition invariant winning conditions) for which message exchange games are equivalent to Banach- Mazur games, which have been extensively studied and enjoy nice topological results. But decomposition sensitive goals are much more the norm and much more interesting linguistically and philosophically.

In the framework of a wider longitudinal empirical study on translation competence (TC) and its development, this paper investigates the attitudes of novice vs. professional translators towards the strategies of foreignization and domestication (Venuti 2008 ). Moving from the consideration that “expansion, [is] an unfolding of what, in the original, is ‘folded’” (Berman 2004:290), the study analyses the number and type of expansions and reductions in a parallel corpus of multiple translations of the same source texts produced by translators at different stages in the development of their TC. The working hypothesis is that the strategies of foreignization and domestication might be to some extent related to the translator’s supposed level of competence. This paper describes the trends observed in the corpus as concerns expansions and reductions from both a quantitative and a qualitative perspective and outlines some provisional results suggesting a tendency towards expansion and domestication in professional translators. (At the time of writing this article, the whole study had not been completed. The final results of this and other analyses on the same corpus are now available in C. Quinci (2015), Translators in the Making: An Empirical Longitudinal Study on Translation Competence and its Development, Ph.D. Thesis, Università degli Studi di Trieste. From April 2016 on, the thesis will be available for download at http://hdl.handle.net/10077/10986 .)

In the near future the number of equipment connected to the Internet will greatly increase, so that further development of applications meant to verify their operations will be required. Monitoring represents an important factor in improving the quality of the services provided in cloud computing, given the fact that it allows scaling resource utilization in an adaptive manner. This paper aims to provide a solution for the monitoring of network devices and services, allowing administrator to verify connectivity of the equipment, their performances and network security. The main contribution of the paper consists in proposing an integrated solution that is deployed in the cloud for monitoring all the network components. Finally, the paper discusses the main findings and advantages for a reference implementation of the monitoring system using a simulated network.

International audience; In the video analysis domain, automatic detection of actions performed in a recorded video represents an important scientific and industrial challenge. This paper presents a new method to approximate the boundaries of actions performed by a person while interacting with his environment (such as moving objects). This method relies on a Codebook quantization method to analyze the rough evolution of each pixel and then decide whether this evolution corresponds to an action or not; this decision is taken by an automated system. Statistics are then produced - at the scale of the whole frame - to estimate the start and the end of an action. According to our proposed evaluation protocol, this method produces interesting results on both real and simulated videos. This statistic-based protocol is discussed at the end of this paper. The interpretation of this evaluation protocol nominates this method to be a solid base to localize the exact boundaries of actions or - in the framework of this research activity - to associate prescriptive text with a visual content.

Functional data are occurring more and more often in practice, and various statistical techniques have been developed to analyze them. In this paper we consider multivariate functional data, where for each curve and each time point a p-dimensional vector of measurements is observed. For functional data the study of outlier detection has started only recently, and was mostly limited to univariate curves (p=1). In this paper we set up a taxonomy of functional outliers, and construct new numerical and graphical techniques for the detection of outliers in multivariate functional data, with univariate curves included as a special case. Our tools include statistical depth functions and distance measures derived from them. The methods we study are affine invariant in p-dimensional space, and do not assume elliptical or any other symmetry. ispartof: Statistical Methods and Applications vol:24 issue:2 pages:177-202 status: published

Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.

[EN] Agri-food production must increase while food waste needs to be reduced for improving the position of farmers. To do so it is necessary to sustainably manage agri-food supply chains beginning with the crop planning decisions. Although the centralized approach has usually been adopted for this purpose, it can lead to unfair solutions due to inequitable distribution of profits among farmers causing their unwillingness to collaborate in the implementation of decisions made. To solve this, in this paper a novel centralized multi-objective mathematical programming model is proposed to support the sustainable crop planning definition for a region that jointly optimize three objectives aligned to the sustainability aspects: supply chain profits maximization (economic objective), waste minimization (environmental objective) and unfairness among farmers minimization (social objective), being the last two objectives novel in the crop planning literature. It has also shown the conflicting nature of the three objectives finding trade-offs among them. Other novelties of this proposal are: (1) anticipation of operative decisions (such as harvest, transport, sale, clearance sale, waste and unmet demand) when defining the crop planning, (2) possibility of clearing the oversupply of crops as a means of increasing the farmers' profits and reducing waste, and (3) the modelling of a agri-food supply chain characterized by the lack of intermediaries between farmers and retailers, fostering the freshest product delivery and farmers' power position. The model is solved by applying the weighted sum method concluding that the crop waste generated along the chain and the unfairness among farmers can be considerably reduced by little decreasing the optimal SC profits., We acknowledge the support of the Project 691249, RUCAPS: "Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems", funded by the European Union's research and innovation programme under the H2020 Marie Sklodowska-Curie Actions.

There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

[EN] Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10., This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially founded by the Universitat Polite`cnica de Vale`ncia through the postdoctoral PAID-10-20 program.

Scholars have widely recognised the importance of academic relationships between students at the university. While much of the past research has focused on studying their influence on different aspects such as the students’ academic performance or their emotional stability, less is known about their dynamics and the factors that influence the formation and dissolution of linkages between university students in academic networks. In this paper, we try to shed light on this issue by exploring through stochastic actor-oriented models and student-level data the influence that a set of proximity factors may have on formation of these relationships over the entire period in which students are enrolled at the university. Our findings confirm that the establishment of academic relationships is derived, in part, from a wide range of proximity dimensions of a social, personal, geographical, cultural and academic nature. Furthermore, and unlike previous studies, this research also empirically confirms that the specific stage in which the student is at the university determines the influence of these proximity factors on the dynamics of academic relationships. In this regard, beyond cultural and geographic proximities that only influence the first years at the university, students shape their relationships as they progress in their studies from similarities in more strategic aspects such as academic and personal closeness. These results may have significant implications for both academic research and university policies.

[EN] Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user's emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction., Partially supported by the Spanish MINECO and FEDER founds under Project TIN2017-85854-C4-2-R. Work of J.A. Gonzalez is financed under Grant PAID-01-17

[EN] Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed., This work is supported by the Spanish Ministry of Science, Innovation, and Universities under Grant No.: TIN2017-84550-R, Adapt@Cloud Project and by the Generalitat Valenciana under Grant No.: AICO/2020/113, UX-Adapt Project. Arthur Sluyters is funded by the "Fonds de la Recherche Scientifique - FNRS" under Grant n40001931.

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.

[EN] Program slicing is an analysis technique that has a wide range of applications, ranging from compilers to clone detection software, and that has been applied to practically all programming languages. Most program slicing techniques are based on a widely extended program representation, the System Dependence Graph (SDG). However, in the presence of unconditional jumps, there exist some situations where most SDG-based slicing techniques are not as accurate as possible, including more code than strictly necessary. In this paper, we identify one of these scenarios, pointing out the cause of the inaccuracy, and describing the initial solution to the problem proposed in the literature, together with an extension, which solves the problem completely. These solutions modify both the SDG generation and the slicing algorithm. Additionally, we propose an alternative solution, that solves the problem by modifying only the SDG generation, leaving the slicing algorithm untouched., This work has been partially supported by the EU (FEDER) and the Spanish MCI/AEI under grants TIN2016-76843-C4-1-R and PID2019-104735RB-C41, by the Generalitat Valenciana under grant Prometeo/2019/098 (DeepTrust), and by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215

[EN] In this paper, the resource-based view is used to analyze the implementation of crowdsourcing as an IT-based practice. The study examines strategic positioning regarding market orientation and the role of two capabilities: transformational leadership and organizational learning capability, in the implementation of crowdsourcing. An empirical study of Spanish telecommunications and biotechnology companies confirms the moderating effect of these capabilities on the relationship between market orientation and crowdsourcing.

[EN] The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children¿s social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed., The authors have no relevant financial or non-financial interests to disclose. This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive Virtual Environment for the Evaluation and Training of Children with Autism Spectrum Disorder: T Room" (IDI-20170912). This work was also supported by the Spanish Ministry of Science and Innovation funded project "T-EYE: Monitoring system for children with ASD based on artificial intelligence and physiological measures" (IDI-20201146)

[EN] This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity., The authors would like to thank the European Union for the project "Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum" (AI-SPRINT), with code 101016577, funded under the H2020 Framework Programme and also the regional government of the Comunitat Valenciana (Spain) for the project IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020.

[EN] The problem of automatic text classification is an essential part of text analysis. The improvement of text classification can be done at different levels such as a preprocessing step, network implementation, etc. In this paper, we focus on how the combination of different methods of text encoding may affect classification accuracy. To do this, we implemented a multi-input neural network that is able to encode input text using several text encoding techniques such as BERT, neural embedding layer, GloVe, skip-thoughts and ParagraphVector. The text can be represented at different levels of tokenised input text such as the sentence level, word level, byte pair encoding level and character level. Experiments were conducted on seven datasets from different language families: English, German, Swedish and Czech. Some of those languages contain agglutinations and grammatical cases. Two out of seven datasets originated from real commercial scenarios: (1) classifying ingredients into their corresponding classes by means of a corpus provided by Northfork; and (2) classifying texts according to the English level of their corresponding writers by means of a corpus provided by ProvenWord. The developed architecture achieves an improvement with different combinations of text encoding techniques depending on the different characteristics of the datasets. Once the best combination of embeddings at different levels was determined, different architectures of multi-input neural networks were compared. The results obtained with the best embedding combination and best neural network architecture were compared with state-of-the-art approaches. The results obtained with the dataset used in the experiments were better than the state-of-the-art baselines., This work is partially supported by MINECO under Grant DI-15-08169 and by Sciling under its R+D program. The authors would like to thank NVIDIA for their donation of a Titan Xp GPU, which allowed us to conduct this research

In Panama City, the water supply sources for the population come mainly from surface waters, but in times of drought and extreme natural phenomena, the need to resort to underground sources is explored. This paper concerns an exploratory investigation with the aim of assessing the quality of groundwater in the southeastern part of the Province of Panama and to determine its possible use as drinking water during extreme natural phenomena. Monthly monitoring over a period of 15 months was carried out. The study included the sampling and analysis of groundwater by different physic-chemical, hydrochemical and biological parameters. The results showed that most of the parameters in the analysed groundwater conformed to the Panamanian drinking water norm and the water potability diagram, this is a convenient source of raw water for purification. As for other uses for this water source, it should be used with caution since it represents a medium risk of soil salinization for irrigation water; and it is a moderately hard water for industrial uses.