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Public key cryptography is a concept used by many useful functionalities such as digital signature, encryption, key agreements, ... For those needs, elliptic curve cryptography is an attractive solution. Cryptosystems based on elliptic curve need a costly modular division. Depending on the choice of coordinates, this operation is requested at each step of algorithms, during a precomputation phase or at the end of the whole computation. As a result, efficient modular division implementations are useful for both area constrained designs working in affine coordinates and high-speed processors. For that purpose, this paper highlights the most efficient iterative modular division algorithm and explores different time and area tradeoffs on FPGA. First, thanks to a novel algorithm, the computational time is divided by two with an area increase of one half. Second, using the single-instruction multiple-data feature of the selected algorithm, the area is divided by two with a doubling of the computational time. To the best of our knowledge, it is the first report about an iterative digit-serial modular division algorithm, the first area and time tradeoff analysis of an iterative algorithm and the best result among the very few implementations on FPGA., Anglais

[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.

[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.

[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.

[EN] When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.

[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.

[EN] When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.

[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.

[EN] In this paper, the most usual matrix partial orders are considered on the ring of real matrices. These orders are studied on at most index 1 matrices having nonnegative group projector by using a specific block structure. The goal of this paper is to analyze how this structure is inherited by predecessors ordered under minus, sharp, star, and one-sided orders. In addition, the case of arbitrary index (greater than 1) is also investigated by means of the cn-partial order and using the Drazin inverse.

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.

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.

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.

In this paper, we describe a novel approach to the prediction of human blood glucose levels by analysing rich biometric human contextual data from a pioneering lifelog dataset. Numerous prediction models (RF, SVM, XGBoost and Elastic-Net) along with different combinations of input attributes are compared. An efficient ensemble method of stacking of multiple combination of prediction models was also implemented as our contribution. It was found that XGBoost outperformed three other models and that a stacking ensemble method further improved the performance.

In this paper, we describe a novel approach to the prediction of human blood glucose levels by analysing rich biometric human contextual data from a pioneering lifelog dataset. Numerous prediction models (RF, SVM, XGBoost and Elastic-Net) along with different combinations of input attributes are compared. An efficient ensemble method of stacking of multiple combination of prediction models was also implemented as our contribution. It was found that XGBoost outperformed three other models and that a stacking ensemble method further improved the performance.

[EN] In wireless ad hoc networks, neighbor discovery is necessary as an initial step. In this work we present LECDH (Low Energy Collision Detection Hello), an energy-aware randomized handshake-based neighbor discovery protocol for static environments. We carried out simulations through Castalia 3.2 simulator and compared LECDH with an existing protocol EAH (Energy Aware Hello) used as reference. We conclude that the proposal outperforms the reference protocol both in one-hop and multi-hop environments in terms of Energy consumption, Discovery time, Number of discovered neighbors, Throughput, and Discoveries per packet sent, for high duty cycles. Moreover, for low number of nodes in LECDH, as the duty cycle is reduced the performance is better according to all 5 metrics in both environments. Overall, we found that our proposal follows more realistic assumptions and still allows nodes to succeed at discovering all their neighbors almost with probability 1. Moreover, a qualitative comparison of the reference solution and our proposal is included in this paper.

[EN] In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.

In this paper, we describe a novel approach to the prediction of human blood glucose levels by analysing rich biometric human contextual data from a pioneering lifelog dataset. Numerous prediction models (RF, SVM, XGBoost and Elastic-Net) along with different combinations of input attributes are compared. An efficient ensemble method of stacking of multiple combination of prediction models was also implemented as our contribution. It was found that XGBoost outperformed three other models and that a stacking ensemble method further improved the performance.

India has committed to reducing the emissions intensity of GDP by 33-35% from the 2005 level by 2030 in alignment with objectives of the Paris Agreement. This will require a significant reduction in greenhouse gas (GHG) emissions from the food and land-use sector. In this paper, we construct three potential pathways for India to achieve its emissions target by 2050 involving moderate ambitions of mitigation action (BAU), moderate ambitions combined with achieving healthy diets (BAU + NIN), and high levels of mitigation action inclusive of healthy diets (SUSTAINABLE). Using an integrated accounting tool, the FABLE Calculator, that harmonizes various socioeconomic and biophysical data, we project these pathways under the conditions of cross-country balanced trade flows. Results from the projections show that the demand for cereals will increase by 2050, leading to increased GHG emissions under BAU. Under the SUSTAINABLE pathways, GHG emissions will decrease over the same period due to reduced demand for cereals, whereas significant crop productivity and harvest intensity gains would lead to increased crop production. The exercise reveals the indispensability of healthy diets, improved crop, and livestock productivity, and net-zero deforestation in achieving India's mid-century emission targets from the agriculture sector.

India has committed to reducing the emissions intensity of GDP by 33-35% from the 2005 level by 2030 in alignment with objectives of the Paris Agreement. This will require a significant reduction in greenhouse gas (GHG) emissions from the food and land-use sector. In this paper, we construct three potential pathways for India to achieve its emissions target by 2050 involving moderate ambitions of mitigation action (BAU), moderate ambitions combined with achieving healthy diets (BAU + NIN), and high levels of mitigation action inclusive of healthy diets (SUSTAINABLE). Using an integrated accounting tool, the FABLE Calculator, that harmonizes various socioeconomic and biophysical data, we project these pathways under the conditions of cross-country balanced trade flows. Results from the projections show that the demand for cereals will increase by 2050, leading to increased GHG emissions under BAU. Under the SUSTAINABLE pathways, GHG emissions will decrease over the same period due to reduced demand for cereals, whereas significant crop productivity and harvest intensity gains would lead to increased crop production. The exercise reveals the indispensability of healthy diets, improved crop, and livestock productivity, and net-zero deforestation in achieving India's mid-century emission targets from the agriculture sector.

The KM3NeT research infrastructure is under construction in the Mediterranean Sea. KM3NeT will study atmospheric and astrophysical neutrinos with two multi-purpose neutrino detectors, ARCA and ORCA, primarily aimed at GeV–PeV neutrinos. Thanks to the multi-photomultiplier tube design of the digital optical modules, KM3NeT is capable of detecting the neutrino burst from a Galactic or near-Galactic core-collapse supernova. This potential is already exploitable with the first detection units deployed in the sea. This paper describes the real-time implementation of the supernova neutrino search, operating on the two KM3NeT detectors since the first months of 2019. A quasi-online astronomy analysis is introduced to study the time profile of the detected neutrinos for especially significant events. The mechanism of generation and distribution of alerts, as well as the integration into the SNEWS and SNEWS 2.0 global alert systems are described. The approach for the follow-up of external alerts with a search for a neutrino excess in the archival data is defined. Finally, an overview of the current detector capabilities and a report after the first two years of operation are given, Postprint (published version)

As ongoing research efforts contribute to elucidating the consequences of climate change as well as adaptation and mitigation options, aligning the current research knowledge with stakeholder opinions and perceptions remains critical for adopting effective climate change policies. This paper utilizes an interactive survey to (1) address the aforementioned gap in studies involving three groups of stakeholders and opinion makers and (2) perform a comparative primary study of the climate change assumptions, risk perceptions, policy preferences, observations, and knowledge of Czech farmers, governmental policy-makers and researchers. This study shows that the stakeholder groups agree that the climate is clearly changing, attribute this change mostly to man-made causes and expect the negative effects to either prevail or be unevenly geographically distributed. The large majority of all three groups consider unmitigated climate change a major threat even by 2050 and agree that preparing in advance is the best sectoral strategy. Importantly, while investment in adaptation measures is considered the most efficient tool for accelerating the implementation of adaptation measures, the CAP and EU rules (as valid in 2016) are believed to hinder such measures. The results of this study have ramifications for the wider region of Central Europe.

As ongoing research efforts contribute to elucidating the consequences of climate change as well as adaptation and mitigation options, aligning the current research knowledge with stakeholder opinions and perceptions remains critical for adopting effective climate change policies. This paper utilizes an interactive survey to (1) address the aforementioned gap in studies involving three groups of stakeholders and opinion makers and (2) perform a comparative primary study of the climate change assumptions, risk perceptions, policy preferences, observations, and knowledge of Czech farmers, governmental policy-makers and researchers. This study shows that the stakeholder groups agree that the climate is clearly changing, attribute this change mostly to man-made causes and expect the negative effects to either prevail or be unevenly geographically distributed. The large majority of all three groups consider unmitigated climate change a major threat even by 2050 and agree that preparing in advance is the best sectoral strategy. Importantly, while investment in adaptation measures is considered the most efficient tool for accelerating the implementation of adaptation measures, the CAP and EU rules (as valid in 2016) are believed to hinder such measures. The results of this study have ramifications for the wider region of Central Europe.

The next generation of water Cherenkov neutrino telescopes in the Mediterranean Sea are under construction offshore France (KM3NeT/ORCA) and Sicily (KM3NeT/ARCA). The KM3NeT/ORCA detector features an energy detection threshold which allows to collect atmospheric neutrinos to study flavour oscillation. This paper reports the KM3NeT/ORCA sensitivity to this phenomenon. The event reconstruction, selection and classification are described. The sensitivity to determine the neutrino mass ordering was evaluated and found to be 4.4s if the true ordering is normal and 2.3s if inverted, after 3 years of data taking. The precision to measure ¿m2 32 and ¿23 were also estimated and found to be 85.10-6 eV2 and ( +1.9 -3.1)¿ for normal neutrino mass ordering and, 75.10-6 eV2 and ( +2.0 -7.0)¿ for inverted ordering. Finally, a unitarity test of the leptonic mixing matrix by measuring the rate of tau neutrinos is described. Three years of data taking were found to be sufficient to exclude ¿t event rate variations larger than 20% at 3s level., Peer Reviewed, Postprint (published version)

[EN] The agriculture value chain, from farm to fork, has received enormous attention because of its key role in achieving United Nations Global Challenges Goals. Food waste occurs in many different forms and at all stages of the food value chain, it has become a worldwide issue that requires urgent actions. However, the management of food waste has been traditionally segmented and in an isolated manner. This paper reviews existing work that has been done on food waste management in literature by taking a holistic approach, in order to identify the causes of food waste, food waste prevention strategies, and elicit recommendations for future work. A five step systematic literature review has been adopted for a thorough examination of the existing research on the topic and new insights have been obtained. The findings suggest that the main sources of food waste include food overproduction and surplus, food waste caused by processing, logistical inconsistencies, and households. Main food waste prevention strategies have been revealed in this paper include policy solutions, packaging solutions, date-labelling solutions, logistics solutions, changing consumers¿ behaviours, and reuse and redistribution solutions. Future research directions such as using value chain models to reduce food waste and forecasting food waste have been identified in this paper. This study makes a contribution to the extant literature in the field of food waste management by discovering main causes of food waste in the value chain and eliciting prevention strategies that can be used to reduce/eliminate relevant food waste.

[EN] This paper deals with bilinear operators acting in pairs of Banach function spaces that factor through the pointwise product. We find similar situations in different contexts of the functional analysis, including abstract vector lattices¿orthosymmetric maps, C¿-algebras¿zero product preserving operators, and classical and harmonic analysis¿integral bilinear operators. Bringing together the ideas of these areas, we show new factorization theorems and characterizations by means of norm inequalities. The objective of the paper is to apply these tools to provide new descriptions of some classes of bilinear integral operators, and to obtain integral representations for abstract classes of bilinear maps satisfying certain domination properties.

[EN] Since the end of the 1990s, the number of articles on coopetitiona relationship built on simultaneous competition and cooperationhas steadily increased in response to the growing prevalence of relationships of coopetition in many industries. The tension inherent in a relationship of coopetition with a direct competitor presents both a challenge for managers and, at the same time, an exciting and complex research area. Different researchers of coopetition have addressed the topic from vastly different perspectives, basing their research on different theoretical frameworks, types of analysis, methods, and aims. By classifying articles on coopetition published in the last 20years, this paper presents an application of cluster analysis to examine trends and tendencies in coopetition research. The paper also assesses whether this research field has followed a coherent progression during this period. The research reveals two independent research trends within the coopetition literature. The first research trend consists of studies that have mathematically modeled and simulated coopetition scenarios using game theory, whereas the second research stream consists of theoretical research describing the dynamics and tensions of coopetition based on evidence from case studies. Based on the cluster analysis, inter-firm alliances and their governance mechanisms emerge as the most promising theoretical and practical approach to improve cooperation between competitors.

[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.

[EN] CO2-free urban logistics is one of the 10 objectives to reach by 2030 as part of transport policy. What technologies can help to accomplish it? In this paper, we discuss the very complex situation that today¿s big and modern cities are facing with a tremendous environment of many urban logistics companies running in the same city. In the majority of cases, there is less or none coordination among them worsening traffic congestions. We believe that intelligent techniques are one of the key approaches that can aid to support smart and sustainable urban logistic applications. There are large open problems in the field of cooperative urban logistics that can greatly improve with the help of artificial intelligence. Some solutions are cited in this paper, but the overall conclusion is that there is still much work to be done.

[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.

[EN] Since the end of the 1990s, the number of articles on coopetitiona relationship built on simultaneous competition and cooperationhas steadily increased in response to the growing prevalence of relationships of coopetition in many industries. The tension inherent in a relationship of coopetition with a direct competitor presents both a challenge for managers and, at the same time, an exciting and complex research area. Different researchers of coopetition have addressed the topic from vastly different perspectives, basing their research on different theoretical frameworks, types of analysis, methods, and aims. By classifying articles on coopetition published in the last 20years, this paper presents an application of cluster analysis to examine trends and tendencies in coopetition research. The paper also assesses whether this research field has followed a coherent progression during this period. The research reveals two independent research trends within the coopetition literature. The first research trend consists of studies that have mathematically modeled and simulated coopetition scenarios using game theory, whereas the second research stream consists of theoretical research describing the dynamics and tensions of coopetition based on evidence from case studies. Based on the cluster analysis, inter-firm alliances and their governance mechanisms emerge as the most promising theoretical and practical approach to improve cooperation between competitors.

[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.

[EN] CO2-free urban logistics is one of the 10 objectives to reach by 2030 as part of transport policy. What technologies can help to accomplish it? In this paper, we discuss the very complex situation that today¿s big and modern cities are facing with a tremendous environment of many urban logistics companies running in the same city. In the majority of cases, there is less or none coordination among them worsening traffic congestions. We believe that intelligent techniques are one of the key approaches that can aid to support smart and sustainable urban logistic applications. There are large open problems in the field of cooperative urban logistics that can greatly improve with the help of artificial intelligence. Some solutions are cited in this paper, but the overall conclusion is that there is still much work to be done.

[EN] The agriculture value chain, from farm to fork, has received enormous attention because of its key role in achieving United Nations Global Challenges Goals. Food waste occurs in many different forms and at all stages of the food value chain, it has become a worldwide issue that requires urgent actions. However, the management of food waste has been traditionally segmented and in an isolated manner. This paper reviews existing work that has been done on food waste management in literature by taking a holistic approach, in order to identify the causes of food waste, food waste prevention strategies, and elicit recommendations for future work. A five step systematic literature review has been adopted for a thorough examination of the existing research on the topic and new insights have been obtained. The findings suggest that the main sources of food waste include food overproduction and surplus, food waste caused by processing, logistical inconsistencies, and households. Main food waste prevention strategies have been revealed in this paper include policy solutions, packaging solutions, date-labelling solutions, logistics solutions, changing consumers¿ behaviours, and reuse and redistribution solutions. Future research directions such as using value chain models to reduce food waste and forecasting food waste have been identified in this paper. This study makes a contribution to the extant literature in the field of food waste management by discovering main causes of food waste in the value chain and eliciting prevention strategies that can be used to reduce/eliminate relevant food waste.

[EN] In this paper, we deal with uncertainty quantification for the random Legendre differential equation, with input coefficient A and initial conditions X-0 and X-1. In a previous study (Calbo et al. in Comput Math Appl 61(9):2782-2792, 2011), a mean square convergent power series solution on (-1/e, 1/e) was constructed, under the assumptions of mean fourth integrability of X-0 and X-1, independence, and at most exponential growth of the absolute moments of A. In this paper, we relax these conditions to construct an L-p solution (1 <= p <= infinity) to the random Legendre differential equation on the whole domain (-1, 1), as in its deterministic counterpart. Our hypotheses assume no independence and less integrability of X-0 and X-1. Moreover, the growth condition on the moments of A is characterized by the boundedness of A, which simplifies the proofs significantly. We also provide approximations of the expectation and variance of the response process. The numerical experiments show the wide applicability of our findings. A comparison with Monte Carlo simulations and gPC expansions is performed.

[EN] The modelling of real world objects is not a straightforward subject. There are many different schemes; constructive solid geome-try (CSG), cell decomposition, boundary representation, etcetera. Obviously, somehow, any scheme will be related to any other since they have a common goal. The paper shows how to model general polyhedra as an unordered discrete and finite set of geometric numbers of a projective Clifford Algebra or Geometric Algebra (GA). Clearly, not any randomly generated finite set of geometric numbers will have the structure of an object, this set must have some well defined properties. The topological properties extracted from this set are mapped to a boundary representation scheme based on a type of combinatorial map called generalised map or n-gmap. The n-gmaps have different types of or-bits (in the mathematical sense) to which an attribute can be attached. When the attribute has a geometrical meaning, it is said that it is the geometrical embedding of the n-gmap. In this way the n-gmap holds explicitly the topology or structure already defined by the discrete geometry. In our proposal, each single element of a n-gmap is consistently embedded into a geometrical number also known as multi-vector. The scheme has been implemented by modifying an open source code [46] of n-gmaps. This representation has interesting properties. GA and n-gmaps complement and reinforce each other. For instance; it improves the robustness when computing the structure from the geometrical information. It is capable of computing lengths, areas and volumes of any polyhedral complex (with or without holes) using the orbits of the n-gmap (some examples are given). Finally the paper gives hints about other potentialities.

[EN] This paper deals with bilinear operators acting in pairs of Banach function spaces that factor through the pointwise product. We find similar situations in different contexts of the functional analysis, including abstract vector lattices¿orthosymmetric maps, C¿-algebras¿zero product preserving operators, and classical and harmonic analysis¿integral bilinear operators. Bringing together the ideas of these areas, we show new factorization theorems and characterizations by means of norm inequalities. The objective of the paper is to apply these tools to provide new descriptions of some classes of bilinear integral operators, and to obtain integral representations for abstract classes of bilinear maps satisfying certain domination properties.

[EN] In this paper, we deal with uncertainty quantification for the random Legendre differential equation, with input coefficient A and initial conditions X-0 and X-1. In a previous study (Calbo et al. in Comput Math Appl 61(9):2782-2792, 2011), a mean square convergent power series solution on (-1/e, 1/e) was constructed, under the assumptions of mean fourth integrability of X-0 and X-1, independence, and at most exponential growth of the absolute moments of A. In this paper, we relax these conditions to construct an L-p solution (1 <= p <= infinity) to the random Legendre differential equation on the whole domain (-1, 1), as in its deterministic counterpart. Our hypotheses assume no independence and less integrability of X-0 and X-1. Moreover, the growth condition on the moments of A is characterized by the boundedness of A, which simplifies the proofs significantly. We also provide approximations of the expectation and variance of the response process. The numerical experiments show the wide applicability of our findings. A comparison with Monte Carlo simulations and gPC expansions is performed.

[EN] The modelling of real world objects is not a straightforward subject. There are many different schemes; constructive solid geome-try (CSG), cell decomposition, boundary representation, etcetera. Obviously, somehow, any scheme will be related to any other since they have a common goal. The paper shows how to model general polyhedra as an unordered discrete and finite set of geometric numbers of a projective Clifford Algebra or Geometric Algebra (GA). Clearly, not any randomly generated finite set of geometric numbers will have the structure of an object, this set must have some well defined properties. The topological properties extracted from this set are mapped to a boundary representation scheme based on a type of combinatorial map called generalised map or n-gmap. The n-gmaps have different types of or-bits (in the mathematical sense) to which an attribute can be attached. When the attribute has a geometrical meaning, it is said that it is the geometrical embedding of the n-gmap. In this way the n-gmap holds explicitly the topology or structure already defined by the discrete geometry. In our proposal, each single element of a n-gmap is consistently embedded into a geometrical number also known as multi-vector. The scheme has been implemented by modifying an open source code [46] of n-gmaps. This representation has interesting properties. GA and n-gmaps complement and reinforce each other. For instance; it improves the robustness when computing the structure from the geometrical information. It is capable of computing lengths, areas and volumes of any polyhedral complex (with or without holes) using the orbits of the n-gmap (some examples are given). Finally the paper gives hints about other potentialities.

This paper presents a numerical procedure to couple shell to solid elements by using the Nitsche’s method. The continuity of displacements can be satisfied approximately with the penalty method, which is effective in setting the penalty parameter to a sufficiently large value. When the continuity of only displacements on the interface is applied between shell and solid elements, an unreasonable deformation may be observed near the interface. In this work, the continuity of the stress vector on the interface is considered by employing the Nitsche’s method, and hence a reasonable deformation can be obtained on the interface. The authors propose two types of shell elements coupled with solid elements in this paper. One of them is the conventional MITC4 shell element, which is one of the most popular elements in engineering applications. This approach shows the capability of discretizing the domain of the structure with the different types of elements. The other is the shell element with additional degrees of freedom to represent thickness–stretch developed by the authors. In this approach, the continuity of displacements including the deformation in the thickness direction on the interface can be considered. Several numerical examples are presented to examine the fundamental performance of the proposed procedure. The behavior of the proposed simulation model is compared with that of the whole domain discretized with only solid elements., This is a post-peer-review, pre-copyedit version of an article published in "Computational Mechanics". The final authenticated version is available online at: https://doi.org/10.1007/s00466-018-1585-6

In this paper, we show that the Chvátal–Gomory closure of any compact convex set is a rational polytope. This resolves an open question of Schrijver (Ann Discret Math 9:291–296, 1980) for irrational polytopes, and generalizes the same result for the case of rational polytopes (Schrijver in Ann Discret Math 9:291–296, 1980), rational ellipsoids (Dey and Vielma in IPCO XIV, Lecture Notes in Computer Science, vol 6080. Springer, Berlin, pp 327–340, 2010) and strictly convex bodies (Dadush et al. in Math Oper Res 36:227–239, 2011). An extended abstract of this paper appeared in [6]. After the completion of this work, it has been brought to our notice that the polyhedrality of the Chvátal–Gomory closure for irrational polytopes has recently been shown independently by Dunkel and Schulz [9]. The proof presented in this paper has been obtained independently., United States. National Science Foundation. (Grant CMMI-1030662), United States. National Science Foundation. (Grant CMMI-1030422)

[EN] The alarming increase in the average temperature of the planet due to the massive emission of greenhouse gases has stimulated the introduction of electric vehicles (EV), given transport sector is responsible for more than 25% of the total global CO2 emissions. EV penetration will substantially increase electricity demand and, therefore, an optimization of the EV recharging scenario is needed to make full use of the existing electricity generation system without upgrading requirements. In this paper, a methodology based on the use of the temporal valleys in the daily electricity demand is developed for EVrecharge, avoiding the peak demand hours to minimize the impact on the grid. The methodology assumes three different strategies for the recharge activities: home, public buildings, and electrical stations. It has been applied to the case of Spain in the year 2030, assuming three different scenarios for the growth of the total fleet: low, medium, and high. For each of them, three different levels for the EV penetration by the year 2030 are considered: 25%, 50%, and 75%, respectively. Only light electric vehicles (LEV), cars and motorcycles, are taken into account given the fact that batteries are not yet able to provide the full autonomy desired by heavy vehicles. Moreover, heavy vehicles have different travel uses that should be separately considered. Results for the fraction of the total recharge to be made in each of the different recharge modes are deduced with indication of the time intervals to be used in each of them. For the higher penetration scenario, 75% of the total park, an almost flat electricity demand curve is obtained. Studies are made for working days and for non-working days.

[EN] Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there is no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms.

[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] Data are the fundamental building blocks to conduct scientific studies that seek to understand natural phenomena in space and time. The notion of data processing is ubiquitous and nearly operates in any project that requires gaining insight from the data. The increasing availability of information sources, data formats and download services offered to the users, makes it difficult to reuse or exploit the potential of those new resources in multiple scientific fields. In this paper, we present a spatial extract-transform-load (spatial-ETL) approach for downloading atmospheric datasets in order to produce new biometeorological indices and expose them publicly for reuse in research studies. The technologies and processes involved in our work are clearly defined in a context where the GDAL library and the Python programming language are key elements for the development and implementation of the geoprocessing tools. Since the National Oceanic and Atmospheric Administration (NOAA) is the source of information, the ETL process is executed each time this service publishes an updated atmospheric prediction model, thus obtaining different forecasts for spatial and temporal analyses. As a result, we present a web application intended for downloading these newly created datasets after processing, and visualising interactive web maps with the outcomes resulting from a number of geoprocessing tasks. We also elaborate on all functions and technologies used for the design of those processes, with emphasis on the optimisation of the resources as implemented in cloud services

[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.

[EN] This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage of adaptive weights in the penalization. Adaptive estimators are usually focused on the study of the oracle property under asymptotic and double asymptotic frameworks. A key step on the demonstration of this property is to consider adaptive weights based on a initial root n-consistent estimator. In practice this implies the usage of a non penalized estimator that limits the adaptive solutions to low dimensional scenarios. In this work, several solutions, based on dimension reduction techniques PCA and PLS, are studied for the calculation of these weights in high dimensional frameworks. The benefits of this proposal are studied both in synthetic and real datasets.

[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.

[EN] One of the problems when conducting research in mathematical programming models for operations planning is having an adequate database of experiments that can be used to verify advances and developments with enough factors to understand different consequences. This paper presents a test bed generator and instances database for a rolling horizons analysis for multiechelon planning, multiproduct with alternatives processes, multistroke, multicapacity with different stochastic demand patterns to be used with a stroke-like bill of materials considering production costs, setup, storage and delays for operations management. From the analysis of the operations planning obtained from this test bed, it is concluded that a product structure with an alternative process obtains the lowest total cost and the highest service level. In addition, decreasing seasonal demand could present a lower total cost than constant demand, but would generate a worse service level. This test bed will allow researchers further investigation so as to verify improvements in forecast methods, rolling horizons parameters, employed software, etc.

[EN] Organisations are concerned about measuring the performance of the product/service they deliver to their customers. In all types of organisations, if a proper performance assessment is to be developed, it should be measured in different dimensions. At University, the new study programs include the development and assessment of transversal competences due to their importance in enhancing the abilities and improving the employability of students. The achievement of transversal competences can be assessed in different levels/stages; for example, the 1st and 2nd years of a Bachelor's degree; the 3rd and 4th years of a Bachelor's degree and at Master's level. The purpose of this paper is to integrate the research into performance management in organisations to develop an approach consisting of four components (a methodology, a solid and integrated performance management framework, graphical diagrams and quantitative techniques) to assess and manage the achievement of transversal competences through the different levels of study using a consolidated approach. The proposal uses the Analytic Network Process (ANP) to model dependences and feedback among the elements of the competences.

[EN] There is a growing interest in research on the role that space plays in defining and measuring well-being or quality of life. In this paper, we propose to evaluate the regional quality of life using the Multi-Reference Point based Weak Strong Composite Indicator approach, to further enhance the quality of the sub-national analysis. The major motivation is to facilitate assessing the regional quality of life performance at different geographical scales and compensability levels. As an example of application, we compute the composite indicators for 19 Spanish regions to paint a comprehensive picture of the regional quality of life using two different geographical scales: the Spanish and the European ones. Moreover, we provide warning signals to regional, national and European policy-makers on the quality of life dimensions in which each region needs further improvements.