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Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death‐valley toward a general use implementation by society (the market) rather than on development ; The authors would like to thank the Catalan Water Agency (Agència Catalana de l'Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291

Biogas production is a growing market and the existing conversion technologies require different biogas quality and characteristics. In pursuance of assisting decision-makers in biogas upgrading an environmental decision support system (EDSS) was developed. Since the field is rapidly progressing, this tool is easily updatable with new data from technical and scientific literature through the knowledge acquisition level. By a thorough technology review, the diagnosis level evaluates a wide spectrum of technologies for eliminating siloxanes, H2S, and CO2 from biogas, which are scored in a supervision level based upon environmental, economic, social and technical criteria. The sensitivity of the user towards those criteria is regarded by the EDSS giving a response based on its preferences. The EDSS was validated with data from a case-study for removing siloxanes from biogas in a sewage plant. The tool described the flow diagram of treatment alternatives and estimated the performance and effluent quality, which matched the treatment currently given in the facility. Adsorption onto activated carbon was the best-ranked technology due to its great efficiency and maturity as a commercial technology. On the other hand, biological technologies obtained high scores when economic and environmental criteria were preferred. The sensitivity analysis proved to be effective allowing the identification of the challenges and opportunities for the technologies considered ; This work was cofunded by the Ministerio de Economica y Competitividad (Spain - CTQ2014-53718-R), FEDER and University of Girona. ES-C thanks Universitat de Girona for his predoctoral grant (IFUdG-2015/51). AC-C acknowledges support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia (TECSPR16-1-0045). HM acknowledges support from the Spanish Ministry of Economy, Industry and ...

Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. METHODS: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. RESULTS: Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint ( P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) ( P = .03). There was also a reduction in number of carbohydrate recommendations. CONCLUSION: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 689810.

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients' outcomes and then tailor their therapies ; This project has been partially supported by the Spanish Government, Ministerio de Economía y Competitividad (MINECO) (Grants DPI2016-78831-C2-1-R, DPI2016-78831-C2-2-R, RTI2018-095518-B-C21), Agencia Estatal de Investigación (PID2019-107722RB-C21/ AEI/10.13039/501100011033), the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7, 207688/2014-1 and EU through FEDER funds

Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods: A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results: We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients' quality of life ; This work was funded by the Spanish Government through grant DPI2016-78831-C2-2-R and the European Union through Fondo Europeo de Desarrollo Regional (FEDER)

Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development. ; The authors would like to thank the Catalan Water Agency (Agència Catalana de l'Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291. ; Peer Reviewed ; Postprint (published version)

Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development. ; The authors would like to thank the Catalan Water Agency (Agència Catalana de l'Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291. ; Peer Reviewed ; Postprint (published version)

The objective of the DSS is to provide a guide to the user about the most convenient process for the used and / or damaged equipment: recycling, repair, recovery, .i.e, to assist managers in determining management options for different types of such equipment, giving priority to sustainable use of resources, reuse and recycling. For the decision, the DSS will be based on real-time data of reverse logistics costs, costs of materials and processes necessary for the treatment, environmental costs, etc., all of them averaged according to flexible and programmable criteria. The DSS will consist of several modules, including a data entry repository in a local database, a cost calculation module, a repository of applicable legislation and regulations, a decision module, a user interface and an API to highlight toDDBB remotes, etc. The DSS system willbe applied to a real project.

Pòster de congrés presentat a: ATTD 2020 - 13th International Conference on Advanced Technologies & Treatments for Diabetes", celebrat a Madrid del 19 al 22 de febrer de 2020 ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded H2020 project that provides a personalized decision support system for type 1 diabetes selfmanagement. This work describes the application program interface (API) developed for this purpose ; This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 689810

Pòster de congrés presentat a: ATTD 2020 - 13th International Conference on Advanced Technologies & Treatments for Diabetes", celebrat a Madrid del 19 al 22 de febrer de 2020 ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded H2020 project that provides personalised insulin bolus advice for people with Type 1 diabetes (T1D). Here, we evaluate the safety, feasibility and usability of the PEPPER system compared to a standard bolus calculator ; This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 689810

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives ; This work was partially supported by the Spanish Ministry of Science and Innovation through grant PID2019-107722RB-C22/AEI/10.13039/ 501100011033, PID2020-117171RA-I00 funded by MCIN/AEI/ 10.13039/501100011033, the Government of Catalonia under 2017SGR1551 and the EU through the European Regional Development Fund (ERDF) (2018 PROD 00041)

Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon ; This work has been partially funded by the Spanish Government (PID2019-107722RB-C22) and the Government of Catalonia under 2017SGR1551 and 2020 FI_B 0096

El problema de decidir las ubicaciones de un máximo de K plantas, (almacenes, centros de distribución, etc.) dado un conjunto de J, [J]>K, emplazamientos posibles, para satisfacer las demandas de un conjunto I de centros de consumo, de manera que los costes totales (costes fijos de las decisiones de abrir plantas + coste de transporte para satisfacer la demandas de los centros desde las plantas abiertas) sean mínimos, es un problema frecuente en diferentes contextos (administración pública: regionalización escolar, hospitalaria, etc.; empresas privadas: distribución de mercancías, etc.), incluído el de las aplicaciones militares. El problema puede ser tratado por diferentes procedimientos, en este trabajo se comentan algunas de las alternativas de formulación como problema de programación entera, se discuten las ventajas e inconvenientes de los algoritmos aplicables a tales formulaciones y se destaca la necesidad de procedimientos aproximados de tipo heurístico para obtener soluciones aproximadas cuando el contexto de la decisión requiere rapidez. Se analiza detalladamente una heurística ad hoc para el caso de los problemas enteros puros, y se discute, a través de un ejemplo real, la conveniencia de la inserción de los Sistemas de Ayuda a la Toma de Decisiones. ; Postprint (published version)

El problema de decidir las ubicaciones de un máximo de K plantas, (almacenes, centros de distribución, etc.) dado un conjunto de J, [J]>K, emplazamientos posibles, para satisfacer las demandas de un conjunto I de centros de consumo, de manera que los costes totales (costes fijos de las decisiones de abrir plantas + coste de transporte para satisfacer la demandas de los centros desde las plantas abiertas) sean mínimos, es un problema frecuente en diferentes contextos (administración pública: regionalización escolar, hospitalaria, etc.; empresas privadas: distribución de mercancías, etc.), incluído el de las aplicaciones militares. El problema puede ser tratado por diferentes procedimientos, en este trabajo se comentan algunas de las alternativas de formulación como problema de programación entera, se discuten las ventajas e inconvenientes de los algoritmos aplicables a tales formulaciones y se destaca la necesidad de procedimientos aproximados de tipo heurístico para obtener soluciones aproximadas cuando el contexto de la decisión requiere rapidez. Se analiza detalladamente una heurística ad hoc para el caso de los problemas enteros puros, y se discute, a través de un ejemplo real, la conveniencia de la inserción de los Sistemas de Ayuda a la Toma de Decisiones. ; Postprint (published version)

Pòster de congrés presentat a: ICT 2018: Imagine Digital - Connect Europe (4-6 Desembre: Viena, Austria) ; PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is a research and innovation project to develop a personalised clinical decision support system for Type 1 diabetes self-management. The tool provides insulin bolus dose advice and carbohydrate recommendations, tailored to the needs of individuals. The former is determined by Case-Based Reasoning (CBR, Fig. 1), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a model-based approach (Fig. 2) that also promotes safety by providing glucose alarms, low-glucose insulin suspension and fault detection ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 689810