Waste to Energy from Municipal Wastewater Treatment Plants: A Science Mapping

Energy recovery, according to circular economy and sustainable principles, has established itself as an inevitable field of action in wastewater treatment plants (WWTPs). Energy costs are forcing the optimization of processes and increases in the development of applicable waste-to-energy (WtE) technologies. This study aims to analyze the existing knowledge on WtE research in municipal WWTPs using a systematic literature review and a bibliometric analysis from 1979 to 2021. For this purpose, Science Mapping Analysis Tool (SciMAT) and VosViewer, two softwares for analyzing performance indicators and visualizing scientific maps, were used to identify the most relevant figures in the research. The results show an exponential increase in the number of publications over time, which has yet to reach a stage of maturity. The analysis of the evolution of the topics exposes variability in the keywords over the years. The main field of WtE research has focused on sludge treatment, with technologies ranging from anaerobic digestion to more recently-emerging ones such as microalgae or membrane technologies. The analysis also identified the need for more publications on other wastes in WWTPs, which are necessary to achieve zero waste.EMASAGRA S.A 432

Analyzing the production, quality, and potential uses of solid recovered fuel from screening waste of municipal wastewater treatment plants

Over time, wastewater management evolves into a circular model, producing energy and moving towards zero waste. The usual screening waste treatment is the elimination, with no energy recovery processes. As an alternative, the production of solid recovered fuel (SRF) from screening has been studied, both non-densified and densified, in pellet form. The densification was developed, taking as variables the input moisture and size of the die, obtaining 20 different samples. The optimum pelletizing conditions are an input moisture content of 10% and dies with a compression ratio of 6/20, 6/24 and 8/32. SRF properties have been evaluated based on a quality proposal presented in this paper, which has been developed given the lack of uniformity in the existing SRF standards. The SRF produced complies with fuel quality requirements, such as lower calorific value, with values between 13.37 and 25.65 MJ/kg; Cl and Hg content, with maximums of 0.066% and 1.0 × 10����� 5 mg/MJ, respectively; and ash content, between 7.22% and 9.85%. Energy from waste plants could be the destination for all the SRF produced. Its use in cement plants and gasification processes, more restrictive than the previous one, would require manufacturing processes with adequate moisture levels and die size.EMASAGRA 4325University of Granada / CBU

The validity and reliability of a new instrumented device for measuring ankle dorsiflexion range of motion

PURPOSE/BACKGROUND: A restriction in ankle dorsiflexion range of motion (ROM) has been linked to several clinical manifestations such as metatarsalgia, heel pain, nerve entrapment, ankle joint equinus, patellar and ankle injuries. The purpose of the present study was to examine the validity and reliability of the Leg Motion system for measuring ankle dorsiflexion ROM. STUDY DESIGN: Descriptive repeated-measures study. METHODS: Twenty-six healthy male university students were recruited to test the reliability of the Leg Motion system, which is a portable tool used for assessment of ankle dorsiflexion during the weight-bearing lunge test. The participants were tested two times separated by two weeks and measurements were performed at the same time of the day by the same single rater. To test the validity of the Leg Motion system, other maximal ankle dorsiflexion ROM assessments (goniometer, inclinometer and measuring tape) were measured in a single session (i.e., the first test session) during the weight-bearing lunge position using a standard goniometer, a digital inclinometer and a measuring tape measure with the ability to measure to the nearest 0.1 cm. RESULTS: Paired t-tests showed the absence of significant differences between right and left limb measurements of dorsiflexion in all tests. Mean values ± standard deviations were as follows: Leg Motion test (left 11.6cm±3.9; right 11.9cm ±4.0), tape measure (left 11.6cm±4.0; right 11.8cm±4.2), goniometer (left 40.6º±5.2; right 40.6º±5.2), and digital inclinometer (left 40.0º±5.8; right 39.9º±5.6). The Leg Motion composite values (i.e., average of the two legs) showed a significant (p<0.05) positive correlation with the tape measure (r=0.99), with the goniometer (r=0.66), and with the digital inclinometer (r=0.72). CONCLUSIONS: The results of the present study provide evidence to support the use of the Leg Motion system as a valid, portable, and easy to use alternative to the weight-bearing lunge test to assess ankle dorsiflexion ROM in healthy participants

Effects of multicomponent and power training programs using elastic devices on motor function, body composition, and metabolic, bone and inflammatory profile in older adults

Background: It is needed to understand what type of training strategy can be the most effective for contributing to a healthier, active, and more independent elderly population. Nowadays, there are novel types of training interventions and devices, but only little is known regarding whether these can provoke positive benefits in this target population. Concretely, no evidence has examined the effectiveness of high-speed resistance training and multicomponent training in older adults in respect of not only physical function but also bone, immunity, and metabolic status. Developing an understanding these novel training strategies can ultimately provide a viable alternative to traditional modes of exercise training for a broader range of participants

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

[EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscriptMartinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079S12618 (1)79Thomas, C. C., & Philipson, L. H. (2015). Update on Diabetes Classification. Medical Clinics of North America, 99(1), 1-16. doi:10.1016/j.mcna.2014.08.015Kahn, S. E., Hull, R. L., & Utzschneider, K. M. (2006). Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444(7121), 840-846. doi:10.1038/nature05482Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2), 137-149. doi:10.1016/j.diabres.2013.11.002Beagley, J., Guariguata, L., Weil, C., & Motala, A. A. (2014). Global estimates of undiagnosed diabetes in adults. Diabetes Research and Clinical Practice, 103(2), 150-160. doi:10.1016/j.diabres.2013.11.001Hippisley-Cox, J., Coupland, C., Robson, J., Sheikh, A., & Brindle, P. (2009). Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ, 338(mar17 2), b880-b880. doi:10.1136/bmj.b880Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., … Cupples, L. A. (2008). Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes. New England Journal of Medicine, 359(21), 2208-2219. doi:10.1056/nejmoa0804742Gillies, C. L., Abrams, K. R., Lambert, P. C., Cooper, N. J., Sutton, A. J., Hsu, R. T., & Khunti, K. (2007). Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ, 334(7588), 299. doi:10.1136/bmj.39063.689375.55Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. BMJ, 343(nov28 1), d7163-d7163. doi:10.1136/bmj.d7163Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1), 55. doi:10.7326/m14-0697Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., … Schroter, S. (2013). Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research. PLoS Medicine, 10(2), e1001381. doi:10.1371/journal.pmed.1001381Collins, G. S., & Moons, K. G. M. (2012). Comparing risk prediction models. BMJ, 344(may24 2), e3186-e3186. doi:10.1136/bmj.e3186Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016). External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, i3140. doi:10.1136/bmj.i3140Reilly, B. M., & Evans, A. T. (2006). Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions. Annals of Internal Medicine, 144(3), 201. doi:10.7326/0003-4819-144-3-200602070-00009Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009). Prognosis and prognostic research: validating a prognostic model. BMJ, 338(may28 1), b605-b605. doi:10.1136/bmj.b605Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009). Prognosis and prognostic research: what, why, and how? BMJ, 338(feb23 1), b375-b375. doi:10.1136/bmj.b375Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., … Kattan, M. W. (2010). Assessing the Performance of Prediction Models. Epidemiology, 21(1), 128-138. doi:10.1097/ede.0b013e3181c30fb2Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784-1789. doi:10.1016/j.eswa.2009.07.064Schmidt, M. I., Duncan, B. B., Bang, H., Pankow, J. S., Ballantyne, C. M., … Golden, S. H. (2005). Identifying Individuals at High Risk for Diabetes: The Atherosclerosis Risk in Communities study. Diabetes Care, 28(8), 2013-2018. doi:10.2337/diacare.28.8.2013Talmud, P. J., Hingorani, A. D., Cooper, J. A., Marmot, M. G., Brunner, E. J., Kumari, M., … Humphries, S. E. (2010). Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ, 340(jan14 1), b4838-b4838. doi:10.1136/bmj.b4838Sackett, D. L. (1997). Evidence-based medicine. Seminars in Perinatology, 21(1), 3-5. doi:10.1016/s0146-0005(97)80013-4Segagni, D., Ferrazzi, F., Larizza, C., Tibollo, V., Napolitano, C., Priori, S. G., & Bellazzi, R. (2011). R Engine Cell: integrating R into the i2b2 software infrastructure. Journal of the American Medical Informatics Association, 18(3), 314-317. doi:10.1136/jamia.2010.007914Semantic Webhttp://www.w3.org/2001/sw/Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Murphy, S., Churchill, S., Bry, L., Chueh, H., Weiss, S., Lazarus, R., … Kohane, I. (2009). Instrumenting the health care enterprise for discovery research in the genomic era. Genome Research, 19(9), 1675-1681. doi:10.1101/gr.094615.109Lindstrom, J., & Tuomilehto, J. (2003). The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725-731. doi:10.2337/diacare.26.3.725Alssema, M., Vistisen, D., Heymans, M. W., Nijpels, G., Glümer, C., … Dekker, J. M. (2010). The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia, 54(5), 1004-1012. doi:10.1007/s00125-010-1990-7Mann, D. M., Bertoni, A. G., Shimbo, D., Carnethon, M. R., Chen, H., Jenny, N. S., & Muntner, P. (2010). Comparative Validity of 3 Diabetes Mellitus Risk Prediction Scoring Models in a Multiethnic US Cohort: The Multi-Ethnic Study of Atherosclerosis. American Journal of Epidemiology, 171(9), 980-988. doi:10.1093/aje/kwq030Stern, M. P., Williams, K., & Haffner, S. M. (2002). Identification of Persons at High Risk for Type 2 Diabetes Mellitus: Do We Need the Oral Glucose Tolerance Test? Annals of Internal Medicine, 136(8), 575. doi:10.7326/0003-4819-136-8-200204160-00006Abdul-Ghani, M. A., Abdul-Ghani, T., Stern, M. P., Karavic, J., Tuomi, T., Bo, I., … Groop, L. (2011). Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk. Diabetes Care, 34(9), 2108-2112. doi:10.2337/dc10-2201Rahman, M., Simmons, R. K., Harding, A.-H., Wareham, N. J., & Griffin, S. J. (2008). A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Family Practice, 25(3), 191-196. doi:10.1093/fampra/cmn024Guasch-Ferré, M., Bulló, M., Costa, B., Martínez-Gonzalez, M. Á., Ibarrola-Jurado, N., … Estruch, R. (2012). A Risk Score to Predict Type 2 Diabetes Mellitus in an Elderly Spanish Mediterranean Population at High Cardiovascular Risk. PLoS ONE, 7(3), e33437. doi:10.1371/journal.pone.0033437Wilson, P. W. F. (2007). Prediction of Incident Diabetes Mellitus in Middle-aged Adults. Archives of Internal Medicine, 167(10), 1068. doi:10.1001/archinte.167.10.1068Franzin, A., Sambo, F., & Di Camillo, B. (2016). bnstruct: an R package for Bayesian Network structure learning in the presence of missing data. Bioinformatics, btw807. doi:10.1093/bioinformatics/btw807Rood, B., & Lewis, M. J. (2009). Grid Resource Availability Prediction-Based Scheduling and Task Replication. Journal of Grid Computing, 7(4), 479-500. doi:10.1007/s10723-009-9135-2Ramakrishnan, L., & Reed, D. A. (2009). Predictable quality of service atop degradable distributed systems. Cluster Computing, 16(2), 321-334. doi:10.1007/s10586-009-0078-yKianpisheh, S., Kargahi, M., & Charkari, N. M. (2017). Resource Availability Prediction in Distributed Systems: An Approach for Modeling Non-Stationary Transition Probabilities. IEEE Transactions on Parallel and Distributed Systems, 28(8), 2357-2372. doi:10.1109/tpds.2017.2659746Weber, G. M., Murphy, S. N., McMurry, A. J., MacFadden, D., Nigrin, D. J., Churchill, S., & Kohane, I. S. (2009). The Shared Health Research Information Network (SHRINE): A Prototype Federated Query Tool for Clinical Data Repositories. Journal of the American Medical Informatics Association, 16(5), 624-630. doi:10.1197/jamia.m3191Martinez-Millana, A., Fico, G., Fernández-Llatas, C., & Traver, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical & Biological Engineering & Computing, 53(12), 1295-1303. doi:10.1007/s11517-015-1245-3Foundation for Intelligent Physical Agentshttp://www.pa.org/González-Vélez, H., Mier, M., Julià-Sapé, M., Arvanitis, T. N., García-Gómez, J. M., Robles, M., … Lluch-Ariet, M. (2007). HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis. Applied Intelligence, 30(3), 191-202. doi:10.1007/s10489-007-0085-8Bellazzi, R. (2014). Big Data and Biomedical Informatics: A Challenging Opportunity. Yearbook of Medical Informatics, 23(01), 08-13. doi:10.15265/iy-2014-0024Maximilien, E. M., & Singh, M. P. (2004). A framework and ontology for dynamic Web services selection. IEEE Internet Computing, 8(5), 84-93. doi:10.1109/mic.2004.2

The Effect of Moderate- Versus High-Intensity Resistance Training on Systemic Redox State and DNA Damage in Healthy Older Women

This study investigated effects of a 16-week progressive resistance training program (RTP) with elastic bands at two different intensities on systemic redox state, DNA damage, and physical function in healthy older women. METHODS: Participants were randomly assigned to the high-intensity group (HIGH; n = 39), moderate-intensity group (MOD; n = 31), or control group (CG; n = 23). The exercise groups performed an RTP twice a week with three to four sets of 6 (HIGH) or 15 (MOD) repetitions of six overall body exercises at a perceived exertion rate of 8-9 on the OMNI-Resistance Exercise Scale for use with elastic bands. Thiol redox state was determined by reduced glutathione (GSH), oxidized glutathione (GSSG), and GSSG/GSH in blood mononuclear cells. Degree of DNA damage was assessed by presence of the oxidized DNA base molecule 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-OHdG) in urine. Physical function monitoring was based on the arm curl, chair stand, up and go, and 6-min walk tests. RESULTS: The HIGH group showed a significant increase in 8-OHdG (+71.07%, effect size [ES] = 1.12) and a significant decrease in GSH (-10.91, ES = -0.69), while the MOD group showed a significant decrease in 8-OHdG levels (-25.66%, ES = -0.69) with no changes in thiol redox state. GSH levels differed significantly between the HIGH and CG groups posttest. The exercise groups showed significant improvements in physical function with no differences between groups. CONCLUSION: RTP at a moderate rather than high intensity may be a better strategy to reduce DNA damage in healthy older women while also increasing independence

Desert dust outbreak in the Canary Islands (February 2020): assessment and impacts

World Weather Research Programme (WWRP 2021–1

Una propuesta de estructura de coordinación docente horizontal y vertical para la Universidad Politécnica de Cartagena (UPCT)

[ESP] La UPCT no disponía de una estructura generalizada de coordinación docente hasta el presente curso 2012 -2013. Un equipo docente ha trabajado en el diseño de un modelo completo de coordinación, revisando numerosas publicaciones, incorporando actuaciones que ya funcionan de manera puntual en la UPCT y ensayando nuevos mecanismos de coordinación. El modelo establece órganos de coordinación, metodologías concretas y un nuevo listado reducido de competencias genéricas. La propuesta contempla todos los aspectos relevantes de la coordinación docente y puede adaptarse a las características de cada centro. [ENG] During the course 2012-2013, a team of expe1is from the Technical University of Cartagena (UPCT) has designed and developed a model for vertical and horizontal teaching coordination. The work included compiling and analyzing models from other Universities, selecting some already-working methodologies, and developing new ones. The designed model includes a new list of generic skills and coordination structures and too Is that fít the coordination needs of any UPCT Centre

Izaña Atmospheric Research Center. Activity Report 2019-2020

Editors: Emilio Cuevas, Celia Milford and Oksana Tarasova.[EN]The Izaña Atmospheric Research Center (IARC), which is part of the State Meteorological Agency of Spain (AEMET), is a site of excellence in atmospheric science. It manages four observatories in Tenerife including the high altitude Izaña Atmospheric Observatory. The Izaña Atmospheric Observatory was inaugurated in 1916 and since that date has carried out uninterrupted meteorological and climatological observations, contributing towards a unique 100-year record in 2016. This reports are a summary of the many activities at the Izaña Atmospheric Research Center to the broader community. The combination of operational activities, research and development in state-of-the-art measurement techniques, calibration and validation and international cooperation encompass the vision of WMO to provide world leadership in expertise and international cooperation in weather, climate, hydrology and related environmental issues.[ES]El Centro de Investigación Atmosférica de Izaña (CIAI), que forma parte de la Agencia Estatal de Meteorología de España (AEMET), representa un centro de excelencia en ciencias atmosféricas. Gestiona cuatro observatorios en Tenerife, incluido el Observatorio de Izaña de gran altitud, inaugurado en 1916 y que desde entonces ha realizado observaciones meteorológicas y climatológicas ininterrumpidas y se ha convertido en una estación centenaria de la OMM. Estos informes resumen las múltiples actividades llevadas a cabo por el Centro de Investigación Atmosférica de Izaña. El liderazgo del Centro en materia de investigación y desarrollo con respecto a las técnicas de medición, calibración y validación de última generación, así como la cooperación internacional, le han otorgado una reputación sobresaliente en lo que se refiere al tiempo, el clima, la hidrología y otros temas ambientales afines

Izaña Atmospheric Research Center. Activity Report 2015-2016

This report is a summary of the many activities at the Izaña Atmospheric Research Center to the broader community. The combination of operational activities, research and development in state-of-the-art measurement techniques, calibration and validation and international cooperation encompass the vision of WMO to provide world leadership in expertise and international cooperation in weather, climate, hydrology and related environmental issues