| 1. |
- de Rubeis, Tullio, et al.
(författare)
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A novel method for daylight harvesting optimization based on lighting simulation and data-driven optimal control
- 2020
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Ingår i: Proceedings of Building Simulation 2019: 16th Conference of IBPSA. - : IBPSA. - 9781775052012 ; 16, s. 1036-1043
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Konferensbidrag (refereegranskat)abstract
- To date, the best daylighting assessment technique is provided by climate-based simulation tools, which require remarkable efforts to create and calibrate realistic models. The data-driven approaches represent an interesting opportunity to support the physics-based modelling. This work proposes a novel method aimed at the optimization of energy use and luminous environment for a set of lighting control system solutions. The method processes experimental data of occupancy and lighting switch on/off events of an individual side-lit office in an academic building at high latitude via DIVA4Rhino; then, the climate-based simulation results provide the data necessary for the data-driven static optimal control that allow different control strategies of the lighting systems according to their lighting power density. The control allows optimal strategies giving priority to either energy saving or luminous environment improvement, depending on the energy efficiency of the lighting installation, while guaranteeing comfort base level. The results show that the method allows to achieve energy savings up to 18.6% by maintaining high visual comfort levels.
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| 2. |
- Condoluci, Adalgisa, et al.
(författare)
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International prognostic score for asymptomatic early-stage chronic lymphocytic leukemia
- 2020
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Ingår i: Blood. - : American Society of Hematology. - 0006-4971 .- 1528-0020. ; 135:21, s. 1859-1869
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Tidskriftsartikel (refereegranskat)abstract
- Most patients with chronic lymphocytic leukemia (CLL) are diagnosed with early-stage disease and managed with active surveillance. The individual course of patients with early-stage CLL is heterogeneous, and their probability of needing treatment is hardly anticipated at diagnosis. We aimed at developing an international prognostic score to predict time to first treatment (TTFT) in patients with CLL with early, asymptomatic disease (International Prognostic Score for Early-stage CLL [IPS-E]). Individual patient data from 11 international cohorts of patients with early-stage CLL (n = 4933) were analyzed to build and validate the prognostic score. Three covariates were consistently and independently correlated with TTFT: unmutated immunoglobulin heavy variable gene (IGHV), absolute lymphocyte count higher than 15 x 10(9)/L, and presence of palpable lymph nodes. The IPS-E was the sum of the covariates (1 point each), and separated low-risk (score 0), intermediate-risk (score 1), and high-risk (score 2-3) patients showing a distinct TTFT. The score accuracy was validated in 9 cohorts staged by the Binet system and 1 cohort staged by the Rai system. The C-index was 0.74 in the training series and 0.70 in the aggregate of validation series. By meta-analysis of the training and validation cohorts, the 5-year cumulative risk for treatment start was 8.4%, 28.4%, and 61.2% among low-risk, intermediate-risk, and high-risk patients, respectively. The IPS-E is a simple and robust prognostic model that predicts the likelihood of treatment requirement in patients with early-stage CLL. The IPS-E can be useful in clinical management and in the design of early intervention clinical trials.
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| 3. |
- Gentile, Francesco, et al.
(författare)
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Deep Docking : A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
- 2020
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Ingår i: ACS central science. - : American Chemical Society (ACS). - 2374-7943 .- 2374-7951. ; 6:6, s. 939-949
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Tidskriftsartikel (refereegranskat)abstract
- Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule from bench to a bedside. While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure-activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.
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| 4. |
- Gentile, Niko, et al.
(författare)
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Teaching Building Performance Simulations to students with a diverse background by using a Control Method
- 2020
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Ingår i: Proceedings of Building Simulation 2019: 16th Conference of IBPSA. - : IBPSA. - 2522-2708. - 9781775052012 ; 3, s. 1579-1586
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Konferensbidrag (refereegranskat)abstract
- Performing advanced and reliable Building Performance Simulations (BPS) in order to study, for example, the energy use of future buildings is an important ability to gain as a future energy specialist. Learning and understanding BPS software and results may be arduous, notably for groups with disparate knowledge. Frustration may arise among students, making learning even more difficult. In this paper, we use questionnaires to evaluate the introduction of a so-called “control method” in the first BPS teaching module of a Master Programme attended by students with diverse backgrounds. The control method verifies – or controls - the results of a basic energy simulation of a traditional shoebox model with those obtained via an Excel sheet based on building code. Through a smoother and guided introduction of BPS to novices, the method aims to increase the level of confidence in BPS tools and more independence in the work. The questionnaires’ answers suggest that the method fulfils its goals to a reasonable extent.
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