| 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. |
- Giordano, Pier Francesco, et al.
(författare)
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Value of Seismic Structural Health Monitoring Information for Management of Civil Structures Under Different Prior Knowledge Scenarios
- 2023
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Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2565 .- 2366-2557. - 9783031391163 ; 433 LNCE, s. 11-20
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Konferensbidrag (refereegranskat)abstract
- Seismic Structural Health Monitoring (S2HM) provides information about the integrity of civil structures and infrastructure in the aftermath of an earthquake. However, quantifying the benefits of S2HM information is crucial to justify the investment in S2HM systems. The benefit of S2HM can be computed through the Value of Information (VoI) from Bayesian decision theory, which compares the expected costs of alternative actions with prior information (without S2HM information) and with S2HM information (before it is available). This paper aims to analyze the VoI from S2HM in civil structures and infrastructure, considering different prior information scenarios regarding seismic action. The theoretical framework of the VoI is adapted to address three prior knowledge scenarios: (i) full information about the earthquake is available (ii) the intensity measure of the seismic motion is obtained using ground motion models, and (iii) no information is available. A numerical case study of a structure in a seismic area is presented, and the effect of different prior information scenarios on the VoI is discussed. The results show that VoI is higher when the prior information is low, indicating that monitoring systems are more valuable when uncertainty about seismic actions is high.
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| 3. |
- Ahmmad, Sheikh Rishad, et al.
(författare)
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Impact of Building Geometry, Window Types, and Materials on Daylighting Performance of Livestock Buildings
- 2024
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Ingår i: 15th International Congress on Agricultural Mechanization and Energy in Agriculture - ANKAgEng 2023. - 2366-2557 .- 2366-2565. - 9783031515781 ; 458 LNCE, s. 262-274
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Konferensbidrag (refereegranskat)abstract
- The availability and quality of natural light in livestock buildings affect the welfare and productivity of animals and their caretakers, while affecting the energy performance of the buildings. This study investigated the impact of different building properties on daylight conditions of livestock buildings. The study was conducted via parametric daylight simulations in Climatestudio. Firstly, building geometries with different facade orientations, width-to-length ratios, and roof pitches were simulated. Secondly, different glazing types, sizes, and locations were considered for their respective daylight performance. Finally, the use of different materials for the facade, floor and roof of the buildings were simulated and comparatively analysed. The study analysed the simulation results to assess daylight availability in relation to different building properties. The results provided insight that can help determine building geometry, window properties, and material selection during the design process. Overall, the study highlighted the importance of further research and development of design guidelines and standards that incorporate these factors to improve the daylighting performance of these buildings, while ensuring animal welfare and productivity.
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| 4. |
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| 5. |
- Bellotti, Francesco, et al.
(författare)
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Alessandro De Gloria-1955-2023
- 2023
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Ingår i: INTERNATIONAL JOURNAL OF SERIOUS GAMES. - : Serious Games Society. - 2384-8766. ; 10:1, s. 2-3
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Capo, Eric, et al.
(författare)
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Lake sedimentary dna research on past terrestrial and aquatic biodiversity: Overview and recommendations
- 2021
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Ingår i: Quaternary. - : MDPI. - 2571-550X. ; 4:1
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Forskningsöversikt (refereegranskat)abstract
- The use of lake sedimentary DNA to track the long-term changes in both terrestrial and aquatic biota is a rapidly advancing field in paleoecological research. Although largely applied nowadays, knowledge gaps remain in this field and there is therefore still research to be conducted to ensure the reliability of the sedimentary DNA signal. Building on the most recent literature and seven original case studies, we synthesize the state-of-the-art analytical procedures for effective sampling, extraction, amplification, quantification and/or generation of DNA inventories from sedimentary ancient DNA (sedaDNA) via high-throughput sequencing technologies. We provide recommendations based on current knowledge and best practises.
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| 7. |
- 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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| 8. |
- Costa, Giancarlo, et al.
(författare)
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Forecasting the Value of Vibration-Based Monitoring Information in Structural Integrity Management
- 2023
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Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2557 .- 2366-2565. - 9783031391163 ; 433 LNCE, s. 21-31
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Konferensbidrag (refereegranskat)abstract
- Structural deterioration and increasing load demand are two main factors that compromise the serviceability and functioning of civil constructions. The vastity of the bridge portfolio and the few resources available require maintenance optimization to provide the required user safety. In this context, vibration-based monitoring may provide information about the structural performance and support decisions in structural integrity management. In this paper, a novel definition of global and local information from a multi-sensor vibration-based system is provided and implemented for the cases of a parallel ductile Daniels system and a serial system. Furthermore, local and global integrity management actions are modeled and analyzed. Vibration-based information is used to optimize the maintenance strategy in terms of optimal action implementation. Decision and value of predicted information analyses are used to drive maintenance optimization. Indeed, each outcome of the monitoring system and maintenance strategy is associated with an expected utility and cost. Optimization is performed by determining the lowest expected cost corresponding to a maintenance strategy.
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| 9. |
- de Rubeis, Tullio, et al.
(författare)
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Learning lighting models for optimal control of lighting system via experimental and numerical approach
- 2021
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Ingår i: Science and Technology for the Built Environment. - : Informa UK Limited. - 2374-4731 .- 2374-474X. ; 27:8, s. 1018-1030
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Tidskriftsartikel (refereegranskat)abstract
- Lighting control systems have been traditionally employed to reduce energy use for lighting by, for example, maximizing daylight harvesting. When highly efficient light sources are installed and for tasks where maintaining target illuminance is particularly important, designers may decide to prioritize the latter together with energy use. In this context, the use of data-driven algorithms is emerging. In this paper different data-driven approaches are proposed as lighting control systems, to maximize daylight harvestingand to optimize energy consumption. The approaches employ experimental data of occupancy and lighting switch on/off events of a private side-lit office in an academic building. The office is later modeled in DIVA4Rhino to provide yearly illuminances and electric lighting dimming profiles. These data are used to implement data-driven optimal controls. Three different approaches have beenemployed: Regression Trees; Random Forests; Least Squares. Different lighting control strategies have been hypothesized based on installed Lighting Power Densities (LPD). Results show that Regression Trees outperforms both Least Squares and Random Forests, in terms of model accuracy and control performance.
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| 10. |
- Figueiredo, Eloi, et al.
(författare)
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Does Climate Change Impact Long-Term Damage Detection in Bridges?
- 2023
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Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2557 .- 2366-2565. - 9783031391163 ; 433 LNCE, s. 432-440
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Konferensbidrag (refereegranskat)abstract
- The effects of operational and environmental variability have been posed as one of the biggest challenges to transit structural health monitoring (SHM) from research to practice. To deal with that, machine learning algorithms have been proposed to learn from experience based on a reference data set. These machine learning algorithms work well based on the premise that the basis of the reference data does not change over time. Meanwhile, climate change has been posed as one of the biggest concerns for the health of bridges. Although the uncertainty associated with the magnitude of the change is large, the fact that our climate is changing is unequivocal. Therefore, it is expected that climate change can be another source of environmental variability, especially the temperature. So, what happens if the mean temperature changes over time? Will it significantly affect the dynamics of bridges? Will the reference data set used for the training algorithms become outdated? Are machine learning algorithms robust enough to deal with those changes? This paper summarizes a preliminary study about the impact of climate change on the long-term damage detection performance of classifiers rooted in machine learning algorithms trained with one-year data from the Z-24 Bridge in Switzerland. The performance will be tested for three climate change scenarios in three future periods centered in 2035, 2060, and 2085.
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