A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Development and validation of a mathematics-number sense web-based test battery.

This study aimed to create and validate a web-based battery of tests, age appropriate for 16 year-olds, and designed to assess mathematical skills, general cognitive abilities and number sense. The first stage of the study involved the selection of 11 measures and their administration to a sample of 100 16 year-old students, either in pen and paper format, or on computers. Guided by reliability analyses conducted on the first phase's data, 6 of these tests were selected and implemented online. The new battery revealed to be a reliable tool of assessment showing good internal validity and reliability for all the measures

Impact of geography on prognostic outcomes of 21,509 patients with metastatic colorectal cancer enrolled in clinical trials: an ARCAD database analysis

Impact of geography on prognostic outcomes of 21,509 patients with metastatic colorectal cancer enrolled in clinical trials: an ARCAD database analysis Show less Jun Yin*, Shaheenah Dawood*, Romain Cohen, Jeff Meyers, John Zalcberg, Takayuki Yoshino, Matthew Seymour, Tim Maughan, Leonard Saltz, Eric Van Cutsem, Alan Venook, Hans-Joachim Schmoll, Richard Goldberg, Paulo Hoff, J. Randolph Hecht, Herbert Hurwitz, Cornelis Punt, Eduard Diaz Rubio, Miriam Koopman, Chiara Cremolini, Volker Heinemann, Christophe Tournigard, Carsten Bokemeyer, Charles Fuchs, Niall Tebbutt, John Souglakos, Jean-Yves Doulliard, Fairooz Kabbinavar, Benoist Chibaudel, Aimery de Gramont, Qian Shi, Axel Grothey, Richard AdamsFirst Published June 30, 2021 Research Article https://doi.org/10.1177/17588359211020547 Article information Article has an altmetric score of 7 Open AccessCreative Commons Attribution, Non Commercial 4.0 License Article Information Volume: 13 Article first published online: June 30, 2021; Issue published: January 1, 2021 Received: December 29, 2020; Accepted: May 05, 2021 Jun Yin* Department of Health Sciences Research, Mayo Clinic, 200 First Street, SW Rochester, MN 55905, USA Shaheenah Dawood* Mediclinic City Hospital: North Wing, Dubai Health Care City, Dubai UAE Romain Cohen Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA Jeff Meyers Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA John Zalcberg School of Public Health and Preventative Medicine, Monash University, Melbourne, Australia Takayuki Yoshino Department of Gastrointestinal Oncology, National Cancer Center Hospital East, Chiba, Japan Matthew Seymour NIHR Clinical Research Network, Leeds, UK Tim Maughan CRUK/MRC Oxford Institute for Radiation Oncology, Oxford, UK Leonard Saltz Memory Sloan Kettering Cancer Center, New York, NY, USA Eric Van Cutsem Digestive Oncology, University Hospitals Gasthuisberg Leuven and KU Leuven, Leuven, Belgium Alan Venook Department of Medicine, The University of California San Francisco, San Francisco, CA, USA Hans-Joachim Schmoll Klinik fur Innere Med IV, University Clinic Halle, Saale, Germany Richard Goldberg Department of Oncology, West Virginia University, Morgantown, WV, USA Paulo Hoff Centro de Oncologia de Brasilia do Sirio Libanes: Unidade Lago Sul, Siro Libanes, Brazil J. Randolph Hecht Ronald Reagan UCLA Medical Center, UCLS Medical Center, Santa Monica, CA, USA Herbert Hurwitz Duke Cancer Institute, Duke University, Durham, NC, USA Cornelis Punt Department of Medical Oncology, University of Amsterdam, Amsterdam, The Netherlands Eduard Diaz Rubio Department Oncology, Hospital Clínico San Carlos, Madrid, Spain Miriam Koopman Department of Medical Oncology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands Chiara Cremolini Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy Volker Heinemann Department of Medical Oncology and Comprehensive Cancer Center, University of Munich, Munich, Germany Christophe Tournigard Hopital Henri Mondor, Creteil, France Carsten Bokemeyer Department of Oncology, Hematology and Bone Marrow Transplantation with Section of Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany Charles Fuchs Director of Yale Cancer Center, Boston, MA, USA Niall Tebbutt Sydney Medical School, University of Sydney, Sydney, Australia John Souglakos University of Crete, Heraklion, Greece Jean-Yves Doulliard University of Nantes Medical School, Nantes, France Fairooz Kabbinavar UCLA Medical Center, Santa Monica, CA, USA Benoist Chibaudel Department of Medical Oncology, Franco-British Institute, Levallois-Perret, France Aimery de Gramont Department of Medical Oncology, Franco-British Institute, Levallois-Perret, France Qian Shi Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA Axel Grothey West Cancer Center, Germantown, TN, USA Richard Adams Cardiff University and Velindre Cancer Center, Cardiff, UK Corresponding Author: [email protected] *Co-first authors. https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). Abstract Background: Benchmarking international cancer survival differences is necessary to evaluate and improve healthcare systems. Our aim was to assess the potential regional differences in outcomes among patients with metastatic colorectal cancer (mCRC) participating in international randomized clinical trials (RCTs). Design: Countries were grouped into 11 regions according to the World Health Organization and the EUROCARE model. Meta-analyses based on individual patient data were used to synthesize data across studies and regions and to conduct comparisons for outcomes in a two-stage random-effects model after adjusting for age, sex, performance status, and time period. We used mCRC patients enrolled in the first-line RCTs from the ARCAD database, which provided enrolling country information. There were 21,509 patients in 27 RCTs included across the 11 regions. Results: Main outcomes were overall survival (OS) and progression-free survival (PFS). Compared with other regions, patients from the United Kingdom (UK) and Ireland were proportionaly over-represented, older, with higher performance status, more frequently male, and more commonly not treated with biological therapies. Cohorts from central Europe and the United States (USA) had significantly longer OS compared with those from UK and Ireland (p = 0.0034 and p < 0.001, respectively), with median difference of 3–4 months. The survival deficits in the UK and Ireland cohorts were, at most, 15% at 1 year. No evidence of a regional disparity was observed for PFS. Among those treated without biological therapies, patients from the UK and Ireland had shorter OS than central Europe patients (p < 0.001). Conclusions: Significant international disparities in the OS of cohorts of mCRC patients enrolled in RCTs were found. Survival of mCRC patients included in RCTs was consistently lower in the UK and Ireland regions than in central Europe, southern Europe, and the USA, potentially attributed to greater overall population representation, delayed diagnosis, and reduced availability of therapies

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements