| 1. |
- Gonzalez, Maria Camila, et al.
(författare)
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Cognitive and motor decline in dementia with lewy bodies and Parkinson's disease dementia
- 2023
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Ingår i: Movement Disorders Clinical Practice. - : John Wiley & Sons. - 2330-1619. ; 10:6, s. 980-986
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is a need to better understand the rate of cognitive and motor decline of Dementia with Lewy bodies (DLB) and Parkinson's disease Dementia (PDD).Objectives: To compare the rate of cognitive and motor decline in patients with DLB and PDD from the E-DLB Consortium and the Parkinson's Incidence Cohorts Collaboration (PICC) Cohorts.Methods: The annual change in MMSE and MDS-UPDRS part III was estimated using linear mixed regression models in patients with at least one follow-up (DLB n = 837 and PDD n = 157).Results: When adjusting for confounders, we found no difference in the annual change in MMSE between DLB and PDD (−1.8 [95% CI −2.3, −1.3] vs. −1.9 [95% CI −2.6, −1.2] [P = 0.74]). MDS-UPDRS part III showed nearly identical annual changes (DLB 4.8 [95% CI 2.1, 7.5]) (PDD 4.8 [95% CI 2.7, 6.9], [P = 0.98]).Conclusions: DLB and PDD showed similar rates of cognitive and motor decline. This is relevant for future clinical trial designs.
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| 2. |
- Rezaie, Amir, et al.
(författare)
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Comparison of crack segmentation using digital image correlation measurements and deep learning
- 2020
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Ingår i: Construction and Building Materials. - : Elsevier Ltd. - 0950-0618 .- 1879-0526. ; 261
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Tidskriftsartikel (refereegranskat)abstract
- Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on discontinuous fields. Secondly, detected crack patterns can serve as inputs for predictive empirical models to obtain the level of damage to a body. The aim of this paper is to compare the performance of two approaches for crack segmentation on grayscale images acquired from two experimental campaigns on stone masonry walls. In the first approach, a threshold is applied to the maximum principal strain map calculated using post-processed DIC results. In the second approach, a deep convolutional neural network is used. The two methods are compared in terms of standard segmentation criteria, namely precision, dice coefficient and sensitivity. It is shown that the precision and dice coefficient obtained from the deep learning approach are much higher than those obtained from the threshold method (by almost 47% and 34%, respectively). However, the sensitivity computed from the deep learning method is slightly (~4%) lower than the threshold method. These results show that the deep learning method can bet-ter preserve the geometry of detected crack patterns, and the prediction in terms of pixels belonging to a crack is finally more accurate than the threshold method.
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| 3. |
- Rezaie, Amir, et al.
(författare)
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Investigating the cracking of plastered stone masonry walls under shear–compression loading
- 2021
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Ingår i: Construction and Building Materials. - : Elsevier Ltd. - 0950-0618 .- 1879-0526. ; 306
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Tidskriftsartikel (refereegranskat)abstract
- Cracks are the most important source of information about the damage that occurs to unreinforced masonry piers under seismic actions. To predict the structural state of unreinforced masonry piers after an earthquake, research models have been developed to quantify important features of crack patterns. One of the most used crack features is the width, but this can be influenced by several parameters such as the axial load ratio, the shear span ratio, and the loading protocol, which have not been fully studied in previous research studies. In this study, we use experimental data to investigate the evolution of cracking in stone masonry piers during the application of cyclic shear–compression loading. The data consists of gray-scale images taken during quasi-static shear–compression tests performed on six plastered rubble-stone masonry walls subjected to constant axial force and cycles of increasing drift demand. Through the combined use of digital image correlation and a pre-trained deep learning model, crack pixels are identified, post-processed, and quantified based on their width. The dependency of the crack width on the axial load ratio, the shear span ratio, and the loading protocol at the peak force and ultimate drift limit states of the piers is clarified by a displacement vector field analysis, histogram of the crack width, and the concentration of deformation in the cracks. We show that, as opposed to flexural cracks, diagonal shear cracks do not fully close when moving from the applied drift demand to the residual drift measured upon removal of the lateral load. Furthermore, we provide the maximum residual crack width at peak force and ultimate drift limit states. This study will improve the decision making abilities of future models used to quantify earthquake-induced damage to stone masonry buildings.
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| 4. |
- Rezaie, Amir, et al.
(författare)
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Machine-learning for damage assessment of rubble stone masonry piers based on crack patterns
- 2022
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Ingår i: Automation in Construction. - : Elsevier BV. - 0926-5805 .- 1872-7891. ; 140
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Tidskriftsartikel (refereegranskat)abstract
- Under seismic actions, stone masonry buildings are prone to damage. To assess the severity of damaged masonry buildings and their failure modes, engineers connect these problems to surface crack features, such as the crack width and the extent of cracking. We aim to further these assessments in this study, wherein we propose using simple machine learning models to predict: 1) three ratios encoding the degradation of stiffness, strength, and displacement capacity of damaged rubble stone masonry piers as a function of the observed crack features and the applied axial load and shear span ratio; and 2) the pre-peak vs. post-peak regime, based on the crack features. When predicting the stiffness, force, and drift ratios, the prediction error is significantly reduced when the axial load and shear span ratio are included in the feature vector. Furthermore, when predicting the pre-peak vs. post-peak regime, simple machine learning models such as the k-nearest neighbor and the logistic regression result in remarkable accuracy. The obtained results have significant implications on the automated post-earthquake assessment of masonry buildings using image data. It is shown based on documented laboratory test data, that, by selecting proper crack features and incorporating information about the kinematic and static boundary conditions, even simple machine learning models can predict accurately the damage level caused to a rubble masonry pier. The three crack features used in this study are the maximum crack width, length density, and complexity dimension. The pipeline developed in this paper is general enough and is applicable to other masonry typologies and elements upon new evaluation of crack features and image data.
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| 5. |
- Schwarze, Martin, et al.
(författare)
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Band structure engineering in organic semiconductors
- 2016
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Ingår i: Science. - : AMER ASSOC ADVANCEMENT SCIENCE. - 0036-8075 .- 1095-9203. ; 352:6292, s. 1446-1449
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Tidskriftsartikel (refereegranskat)abstract
- A key breakthrough in modern electronics was the introduction of band structure engineering, the design of almost arbitrary electronic potential structures by alloying different semiconductors to continuously tune the band gap and band-edge energies. Implementation of this approach in organic semiconductors has been hindered by strong localization of the electronic states in these materials. We show that the influence of so far largely ignored long-range Coulomb interactions provides a workaround. Photoelectron spectroscopy confirms that the ionization energies of crystalline organic semiconductors can be continuously tuned over a wide range by blending them with their halogenated derivatives. Correspondingly, the photovoltaic gap and open-circuit voltage of organic solar cells can be continuously tuned by the blending ratio of these donors.
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