| 1. |
- Kim, Minjin, et al.
(författare)
-
Conformal quantum dot-SnO2 layers as electron transporters for efficient perovskite solar cells
- 2022
-
Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 375:6578, s. 302-306
-
Tidskriftsartikel (refereegranskat)abstract
- Improvements to perovskite solar cells (PSCs) have focused on increasing their power conversion efficiency (PCE) and operational stability and maintaining high performance upon scale-up to module sizes. We report that replacing the commonly used mesoporous-titanium dioxide electron transport layer (ETL) with a thin layer of polyacrylic acid-stabilized tin(IV) oxide quantum dots (paa-QD-SnO2) on the compact-titanium dioxide enhanced light capture and largely suppressed nonradiative recombination at the ETL-perovskite interface. The use of paa-QD-SnO2 as electron-selective contact enabled PSCs (0.08 square centimeters) with a PCE of 25.7% (certified 25.4%) and high operational stability and facilitated the scale-up of the PSCs to larger areas. PCEs of 23.3, 21.7, and 20.6% were achieved for PSCs with active areas of 1, 20, and 64 square centimeters, respectively.
|
|
| 2. |
|
|
| 3. |
- Kristanl, Matej, et al.
(författare)
-
The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
-
Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
-
Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
|
|
| 4. |
|
|
| 5. |
- Baumann, Stefan, et al.
(författare)
-
Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
- 2019
-
Ingår i: European Journal of Radiology. - : ELSEVIER IRELAND LTD. - 0720-048X .- 1872-7727. ; 119
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFR mL ) for the detection of lesion-specific ischemia. Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR amp;lt;= 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis amp;gt;= 50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
|
|
| 6. |
- Biswas, Deepnarayan, et al.
(författare)
-
Ultrafast Triggering of Insulator-Metal Transition in Two-Dimensional VSe2
- 2021
-
Ingår i: Nano Letters. - : American Chemical Society (ACS). - 1530-6984 .- 1530-6992. ; 21:5, s. 1968-1975
-
Tidskriftsartikel (refereegranskat)abstract
- The transition-metal dichalcogenide VSe2 exhibits an increased charge density wave transition temperature and an emerging insulating phase when thinned to a single layer. Here, we investigate the interplay of electronic and lattice degrees of freedom that underpin these phases in single-layer VSe2 using ultrafast pump-probe photoemission spectroscopy. In the insulating state, we observe a light-induced closure of the energy gap, which we disentangle from the ensuing hot carrier dynamics by fitting a model spectral function to the time-dependent photoemission intensity. This procedure leads to an estimated time scale of 480 fs for the closure of the gap, which suggests that the phase transition in single-layer VSe2 is driven by electron-lattice interactions rather than by Mott-like electronic effects. The ultrafast optical switching of these interactions in SL VSe2 demonstrates the potential for controlling phase transitions in 2D materials with light.
|
|
| 7. |
- Coenen, Adriaan, et al.
(författare)
-
Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve Result From the MACHINE Consortium
- 2018
-
Ingår i: Circulation Cardiovascular Imaging. - : LIPPINCOTT WILLIAMS & WILKINS. - 1941-9651 .- 1942-0080. ; 11:6
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; Pamp;lt;0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR. Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.
|
|
| 8. |
- de Geer, Jakob, et al.
(författare)
-
Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry
- 2019
-
Ingår i: American Journal of Roentgenology. - : AMER ROENTGEN RAY SOC. - 0361-803X .- 1546-3141. ; 213:2, s. 325-331
-
Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR(ML)). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFR(ML). Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFR(ML). MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFR(ML). CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFR(ML) in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFR(ML) in relation to the invasively measured FFR value was rho = 0.684, rho = 0.622, and rho = 0.669, respectively (p amp;lt; 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p amp;lt; 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p amp;lt; 0.001 for all). CONCLUSION. CCTA-derived cFFR(ML) is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.
|
|
| 9. |
- Nous, Fay M. A., et al.
(författare)
-
Comparison of the Diagnostic Performance of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve in Patients With Versus Without Diabetes Mellitus (from the MACHINE Consortium)
- 2019
-
Ingår i: American Journal of Cardiology. - : EXCERPTA MEDICA INC-ELSEVIER SCIENCE INC. - 0002-9149 .- 1879-1913. ; 123:4, s. 537-543
-
Tidskriftsartikel (refereegranskat)abstract
- Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus with suspected coronary artery disease. The study population included 75 diabetic and 276 nondiabetic patients who were enrolled in the MACHINE consortium. The overall diagnostic performance of coronary CT angiography alone and in combination with CT-FFR were analyzed with direct invasive FFR comparison in 110 coronary vessels of the diabetic group and in 415 coronary vessels of the nondiabetic group. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area under the receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and in nondiabetic patients 75% (p = 0.088), showing improvement over the diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p = 0.223), respectively. In addition, the diagnostic accuracy of CT-FFR was similar between diabetic and nondiabetic patients per stratified CT-FFR group (CT-FFR amp;lt; 0.6, 0.6 to 0.69, 0.7 to 0.79, 0.8 to 0.89, amp;gt;= 0.9). The area under the curves for diabetic and nondiabetic patients were also comparable, 0.88 and 0.82 (p = 0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT angiography and accurately discriminated lesion-specific ischemia in both diabetic and nondiabetic patients suspected of coronary artery disease. (C) 2018 Elsevier Inc. All rights reserved.
|
|
| 10. |
- Tesche, Christian, et al.
(författare)
-
Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR Results From MACHINE Registry
- 2020
-
Ingår i: JACC Cardiovascular Imaging. - : ELSEVIER SCIENCE INC. - 1936-878X .- 1876-7591. ; 13:3, s. 760-770
-
Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVESThis study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR).BACKGROUNDCT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.METHODSA total of 482 vessels from 314 patients (age 62.3 +/- 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and >=$400) on a per-vessel level with invasive FFR as the reference standard.RESULTSThe diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC >= 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC >= 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).CONCLUSIONSMachine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621). (C) 2020 by the American College of Cardiology Foundation.
|
|
