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[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.

[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] 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.

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] 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] Sustainability practice within supply chains remains in an early development phase. Enterprises still need tools that support the integration of sustainability strategy into their activity, and to align their sustainability strategy with the supplier selection process. This paper proposes a methodology using a multi-criteria technique to support supplier selection decisions by taking two groups of inputs that integrate sustainability performance: supply chain performance and supplier assessment criteria. With the proposed methodology, organisations will have a tool to select suppliers based on their development towards sustainability and on their alignment with the supply chain strategy towards sustainability. The methodology is applied to an agri-food supply chain to assess sustainability in the supplier selection process., The authors of this publication acknowledge the contribution of Project GV/2017/065 'Development of a decision support tool for the management and improvement of sustainability in supply chains', funded by the Regional Valencian Government. Also, the authors acknowledge Project 691249, RUC-APS: Enhancing and implementing knowledge-based ICT solutions within high risk and uncertain conditions for agriculture production systems (www.ruc-aps.eu), funded by the European Union according to funding scheme H2020-MSCA-RISE-2015.

[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)

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.

[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

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.

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.

[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] 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.

Forecasting is an important task for intelligent agents involved in dynamical processes. A specific application domain concerns district heating networks, in which the future heating load generated by centralized power plants and distributed to buildings must be optimized for better plant maintenance, energy consumption and environmental impact. In this paper we present XM_HeatForecast a Python tool designed to support district heating network operators. The tool provides an integrated architecture for i) generating and updating in real-time predictive models of heating load, ii) supporting the analysis of prediction performance and errors, iii) inspecting model parameters and analyzing the historical dataset from which models are trained. A case study is presented in which the software is used on a synthetic dataset of heat loads and weather forecast from which a regression model is generated and updated every 24 h, while predictions of load in the next 48 h are performed every hour.

[EN] Wireless body area network (WBAN) has witnessed significant attentions in the healthcare domain using biomedical sensor-based monitoring of heterogeneous nature of vital signs of a patient's body. The design of frequency band, MAC superframe structure, and slots allocation to the heterogeneous nature of the patient's packets have become the challenging problems in WBAN due to the diverse QoS requirements. In this context, this paper proposes an Energy Efficient Traffic Prioritization for Medium Access Control (EETP-MAC) protocol, which provides sufficient slots with higher bandwidth and guard bands to avoid channels interference causing longer delay. Specifically, the design of EETP-MAC is broadly divided in to four folds. Firstly, patient data traffic prioritization is presented with broad categorization including Non-Constrained Data (NCD), Delay-Constrained Data (DCD), Reliability-Constrained Data (RCD) and Critical Data (CD). Secondly, a modified superframe structure design is proposed for effectively handling the traffic prioritization. Thirdly, threshold based slot allocation technique is developed to reduce contention by effectively quantifying criticality on patient data. Forth, an energy efficient frame design is presented focusing on beacon interval, superframe duration, and packet size and inactive period. Simulations are performed to comparatively evaluate the performance of the proposed EETP-MAC with the state-of-the-art MAC protocols. The comparative evaluation attests the benefit of EETP-MAC in terms of efficient slot allocation resulting in lower delay and energy consumption., The research is supported by Ministry of Higher Education Malaysia (MOHE) and conducted in collaboration with Research Management Center (RMC) at University Teknologi Malaysia (UTM) under VOT NUMBER: R.J130000.7828.4F859

[EN] Choosing a journal to publish a work is a task that involves many variables. Usually, the authors' experience allows them to classify journals into categories, according to their suitability and the characteristics of the article. However, there are certain aspects in the choice that are probabilistic in nature, whose modelling may provide some help. Suppose an author has to choose a journal from a preference list to publish an article. The researcher is interested in publishing the paper in a journal with a rank number less than or equal to k. For this purpose, a simple classification model is presented in order to choose the best journal from the list, from which some fundamental consequences can be deduced and simple rules derived. For example, if the list contains 100 journals and is ordered using 2-year impact factor, the rule "send to the journal at the k - 10 position" is adequate.

[EN] In this paper we describe a low-cost and low-power consumption messaging system based on LoRa technology. More that one billion people worldwide cannot access even the most basic connectivity services. For them even simple messaging services would be of great help, for example to farmers wishing to know the price of goods they want to sell or buy before deciding whether a possibly long, expensive and exhausting trip is undertaken. LoRa networks allow for very long wireless links that can connect villages and towns. This system falls in the category of community networks, where users build their own network where no commercial infrastructure is available. In addition to the simple messaging application, LoRa can be used to distribute sensor information to communities or to provide disaster alerts or meteorological data.

[EN] One of the main issues experienced in wireless body sensor networks (WBSNs) is the destructive impacts of "mutual interference" caused by neighboring WBSNs on each other's performance. Research communities have proposed several approaches to mitigate the impacts of mutual interference on the reliability of data transmission and sensor's energy consumption. However, the proposed approaches came with a number of limitations, such as significant modification of the standard protocol or imposing a high level of complexity. In this paper, a range of schemes are proposed, and their performances are evaluated in the presence of mutual interference experienced in a dynamic environment.More specifically, we consider a situation where a large number of people (each individual covered with a number of sensors to fetch the human vital sign) are gathered at a sport centre to enjoy an event. In such a dynamic environment, people would highly likely experience mutual interference which would destructively impact on WBSN's performances and eventually would result in an unreliable medical outcome. A simulation study is conducted in which a set of schemes proposed that indicates a gradual improvement of WBSN's performances in terms of reliability of data transmission and sensor's energy consumption. Our obtained results show that the frequency-adaptation strategy combined with phase-adaptation approach significantly improves the performance of WBSNs in the presence of mutual interference in a dynamic environment. Moreover, an experimental study is carried out to examine the feasibility of implementing the predominant scheme on real-world sensor devices and to further support the outcome of the simulation study.

The procurement decision-making process for complex military product systems (CoPS) has significant implications for military end-users, suppliers, and exchequers. This study examines the usefulness of adopting a fast and frugal decision-making approach for the acquisition of military CoPS. Defence procurement environment is complex. On the one hand, there are uncertainties and severe resource constraints due to regularly changing threat perceptions, limited flow of information about new technologies, and the growing demand to reduce defence related expenses. On the other hand, several stakeholders remain pre-occupied with the demand for ‘zero-error’ technologies. In such a setting, recurrent cost overruns and delays in supply are common in defence procurement programmes, across countries. Taking the illustrative examples of the missile system, fighter jet, and radar system acquisitions in India, we elucidate on ‘optimising’ versus ‘satisficing’ dynamics in the procurement decisions. The paper argues that a fast and frugal decision-making process by relying on judgement, experiential knowledge, and intuitive learning might make procurement processes, adaptively, more efficient. Such an approach would enable a ‘good enough’ technology to be inducted, and improved upon, through regular feedback from the actual environment. The study has implications for policy scholarships on innovation policy instruments under uncertainty.

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.

[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.

This work proposes the Bing-CSF-IDF+ recommender – a content-based recommender that makes use of semantic relationships, and combines the best features of our earlier introduced Bing-SF-IDF+ and CF-IDF+ systems. First, we make use of concepts and concept relationships from a domain ontology. Next, Bing-CSF-IDF+ employs the synsets and synset relationships from a semantic lexicon that have not been previously captured by the domain ontology. Last, named entities and their frequencies as provided by Bing – not present in the semantic lexicon and domain ontology – are utilized. Our experiments show that Bing-CSF-IDF+ significantly outperforms Bing-SF-IDF+ and CF-IDF+ on \(F_1\)-scores and Kappa statistics based on a news data set.

[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.

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.

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.

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] 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] 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.

In this paper, a semi-automatic approach for building a sentiment domain ontology is proposed. Differently than other methods, this research makes use of synsets in term extraction, concept formation, and concept subsumption. Using several state-of-the-art hybrid aspect-based sentiment analysis methods like Ont + CABASC and Ont + LCR-Rot-hop on a standard dataset, the accuracies obtained using the semi-automatically built ontology as compared to the manually built one, are slightly lower (from approximately 87% to 84%). However, the user time needed for building the ontology is reduced by more than half (from 7 h to 3 h), thus showing the usefulness of this work. This is particularly useful for domains for which sentiment ontologies are not yet available.

[EN] The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different type of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method., The work of Paolo Rosso was partially funded by the Spanish MICINN under the research Project MISMIS-FAKEn-HATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31).

[EN] Today's firms, particularly startups, require agile communication to sense and seize opportunities and to adapt to the market as quickly as possible (Picken 2017; Takeuchi and Nonaka 1986). However, scholars have predominantly focused on external communication, taking agile internal communication for granted. This paper analyzes the combinations of conditions (i.e., firm features and communication devices) that are present in startups with agile internal communication. The study considers the startup's life cycle, the size of the startup, and the size of the founding team. The use of direct communication channels, structured communication channels, and agile methods is also considered. Analysis of 88 Spanish startups shows that the size of the founding team influences the combination of present and absent communication methods when agile internal communication is present. In startups where the founding team is small, direct communication methods are absent, whereas the use of structured communication methods, online project management, and other communication tools is present. On the contrary, when the founding team size is large, direct communication is a core condition, as is the absence of structured communication methods. Agile methods represent a peripheral condition for the presence of agile internal communication when the founding team is small and structured communication is present as a core condition. Similarly, when the founding team is small and the presence of direct communication is a core condition, the presence of agile methods is a peripheral condition for the presence of agile internal communication., The authors thank the Catedra de Empresa y Humanismo de la Universidad de Valencia (Firm & Humanism Chair, Valencia University) for its financial support for this research.

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.

[EN] Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%., Research presented in this article has been partially funded by Horizon 2020 European Project grant INTER-IoT no. 687283, ACTIVAGE project under grant agreement no. 732679, the Escuela Politecnica Nacional, Ecuador, and Secretaria de Educacion Superior Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador.

[EN] This paper firstly presents a comprehensive minichannel evaporator model (MCHX-1D-MB) based on fin theory coupled with the moving boundary technique along fin height. To validate the presented model, experimental data for R-134a and R-744 (CO2) minichannel evaporators were used. The proposed model successfully predicted the cooling capacity of R-134a and CO2 evaporators with mean absolute error values of +/- 1.8 and +/- 4.3%, respectively. Regarding the outlet air temperature, the mean absolute errors in the estimated results were +/- 0.43 and +/- 0.9 degrees C for R-134a and CO2 evaporators, respectively. Finally, to evaluate the impact of widely used assumption of cut fin on the air-side performance of minichannel evaporators, another model was developed (MCHX-1D-CF). The comparative study revealed that the most remarkable deviations between the two models appear when the evaporator operates under partially wet conditions, which were up to approximate to 12% in the latent heat transfer rate., The authors would like to gratefully acknowledge the financial support from the Spanish Ministry of Economy and Finance to project number ENE2014-53311-C2-1-P.

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.

Attributed programmed graph grammars are introduced in this paper and their application to a particular problem in image understanding, the understanding of schematic drawings like circuit diagrams and flowcharts, is described.

[EN] Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identi cation consists in automatically recognizing document fragments that contain re-used text, which is intentionally hidden by means of some rewording practices such as semantic equivalences, discursive changes, and morphological or lexical substitutions. Our main hypothesis establishes that the original author's writing style ngerprint prevails in the plagiarized text even when paraphrases occur. Thus, in this paper we propose a novel text representation scheme that gathers both content and style characteristics of texts, represented by means of character-level features. As an additional contribution, we describe the methodology followed for the construction of an appropriate corpus for the task of paraphrase plagiarism identi cation, which represents a new valuable resource to the NLP community for future research work in this field., This work is the result of the collaboration in the framework of the CONACYT Thematic Networks program (RedTTL Language Technologies Network) and the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action. The first author was supported by CONACYT (Scholarship 258345/224483). The second, third, and sixth authors were partially supported by CONACyT (Project Grants 258588 and 2410). The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the Grant ALMAMATER (PrometeoII/2014/030).

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.

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)

[EN] The temperature and composition of food, during the printing process, maybe a key factor impacting on rheological properties. Currently, there is no evidence of authors analysing the effect of printing temperature on the characteristics of final products. The aim of this paper was to study the printability of potato puree when affected by printing variables, such as printing temperature and the composition of the potato puree. The printing temperature was studied at 10 degrees C, 20 degrees C and 30 degrees C, and the effect of the product composition on the printability was studied by analysing the rheological and textural properties. Viscosity-temperature profiles, flow curves and dynamic oscillation frequency analysis of potato puree were some of the techniques used in rheology analysis. Forward extrusion assays of formulated potato puree were used to study the compression force in the 3D printer. Results showed the formulation with higher content of dehydrated potato puree (38g of dehydrated potato puree in 250mL of whole milk) at a temperature of 30 degrees C were the most stable. The printability increase with the amount of the consistency index and the reduction of behaviour index. The mean force from extrusion test was correlated with printability but the effect of temperature did not help define this parameter.

[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".

[EN] In order to use the energy more efficiently, network algorithms and protocols must incorporate in their decision mechanisms some functions focused on saving energy. In this paper we have studied different features and parameters of wireless networks to establish their relationship with the power consumption. First, we have analyzed the variation of power consumption of access points (APs) in function of the antenna transmission power. Second, we have collected user information from real public wireless network to determine the real requirements of network resources in real time basis. Based on this information, a new extension for Openflow protocol over Software-Defined Networking (SDN) networks will be proposed to manage the wireless network, with the aim to keep the optimal network performance with minimal power consumption. This extension introduces new Openflow messages and a new function to be incorporated in SDN controllers and Openflow enabled devices. The proposal will be validated by appropriated simulations based on real scenarios: a shopping center and a municipal wireless network., This work has been supported by the Ministerio de Economía y Competitividad , through the Convocatoria 2014. Proyectos I + D - Programa Estatal de Investigación Científica y Técnica de Excelencia in the Subprograma Estatal de Generación de Conocimiento , Project TIN2014-57991-C3-1-P and the Programa para la Formación de Personal Investigador (FPI-2015-S2-884) by the Universitat Politècnica de València .

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.

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.

[EN] Record linkage is a technique widely used to gather data stored in disparate data sources that presumably pertain to the same real world entity. This integration can be done deterministically or probabilistically, depending on the existence of common key attributes among all data sources involved. The probabilistic approach is very time-consuming due to the amount of records that must be compared, specifically in big data scenarios. In this paper, we propose and evaluate a methodology that simultaneously exploits multicore and multi-GPU architectures in order to perform the probabilistic linkage of large-scale Brazilian governmental databases. We present some algorithmic optimizations that provide high accuracy and improve performance by defining the best algorithm-architecture combination for a problem given its input size. We also discuss performance results obtained with different data samples, showing that a hybrid approach outperforms other configurations, providing an average speedup of 7.9 when linking up to 20.000 million records., This work has been partially supported by CNPq, FAPESB, Bill & Melinda Gates Foundation, The Royal Society (UK), Medical Research Council (UK), NVIDIA Hardware Grant Program, Generalitat Valenciana (Grant PROMETEOII/2014/003), Spanish Government and European Commission through TEC2015-67387-C4-1-R (MINECO/FEDER), and network CAPAP-H. We have also worked in cooperation with the EU-COST Programme Action IC1305, "Network for Sustainable Ultrascale Computing (NESUS)

[EN] The cache hierarchy of current multicore processors typically consists of one or two levels of private caches per core and a large shared last-level cache. This approach incurs area and energy wasting due to oversizing the private cache space, data replication through the inclusive cache levels, as well as the use of highly set-associative caches. In this paper, we claim that although this is the commonly adopted approach, it presents important design issues that can be addressed by a more energy efficient organization. This work proposes Flat On-chip Storage (FOS), a novel cache organization that, aimed at addressing energy and area on low-power processors, resolves the mentioned issues. For this purpose, FOS combines L2 and L3 cache levels into a single one, organized as a flat space, and composed of a pool of private small cache slices. These slices are initially powered off to save energy, and they are powered on and assigned to cores provided that the system performance is expected to improve. To provide fast and uniform access from the private L1 caches to the FOS's cache slices, multiple architectural challenges are overcome, which entails the design of a custom optical network-on-chip. Experimental results show that FOS achieves significant energy savings on both static and dynamic energy over conventional cache organizations with the same storage capacity. FOS static energy savings are as much as 60% over an electrically connected shared cache; these savings grow up to 75% compared to optically connected baselines. Moreover, despite deactivating part of the cache space, FOS achieves similar performance values as those achieved by conventional approaches.