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2025
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- Pub. Date : 2025
- Page : pp.170240-170240
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2025
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- Pub. Date : 2025
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2025
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- Book : 27(1)
- Pub. Date : 2025
- Page : pp.122-127
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2025
Vitreoretinal lymphoma (VRL) remains a diagnostic challenge due to its scarce prevalence, and delayed diagnosis usually results in blindness and even fatal outcomes. Herein, an artificial intelligence (AI) system is developed to identify VRL among 16 retinal diseases and conditions on optical coherence tomography (OCT) images with the cross‐subject meta‐transfer learning (CS‐MTL) algorithm. Extensive experiments of few‐shot VRL recognition tasks prove the robustness of our model on 1‐, 3‐, and 5‐shot scenarios, achieving an F1 score of 0.8697 to 0.9367. The superiority of the model is shown with a higher F1 score (0.9310) compared with other state‐of‐the‐art algorithms (0.5487–0.9018) and three doctors whose clinical experiences range between 3 to 10 years without the help of the CS‐MTL (0.7773–0.8949). AI assistance significantly improves the F1 scores of doctors by 6.16–14.46% (p < 0.001). Moreover, the F1 scores of AI‐assisted senior doctor and retinal specialist (0.9414 and 0.9500), but not the junior doctor (0.8897), exceed that of the CS‐MTL (0.9310). This study presents a promising approach for aiding in the diagnosis of VRL on retinal OCT images and may provide a novel insight into the collaboration of doctors with AI techniques, resulting in reducing the risk of diagnostic delays of rare diseases.- Book : ()
- Pub. Date : 2025
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2025
- Book : ()
- Pub. Date : 2025
- Page : pp.101728-101728
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2025
Precise control of individual particles is crucial for biomedical diagnostics, i.e., single-cell analysis and drug delivery. Acoustic tweezers have shown promise as a precise method for capturing microscale objects, but the spatial selectivity remains challenging to achieve with traditional electroacoustic transducers. An alternative to traditional ultrasound generation is to use photoacoustic conversion from laser pulses, allowing easy customization of acoustic field using optical light patterns, but this approach suffers from low efficiency of photoacoustic conversion. Here, we propose a field-hybridization technique to combine a hologram-generated photoacoustic field with a high-power featureless electroacoustic field. Theoretical and experimental validation show that while the force remains limited to the low pN range, the hybridization amplifies the photoacoustic radiation force by a factor of 80. By adjusting phase difference between electroacoustic and photoacoustic sources, the direction of particle motion can be reversed. The maximum trapping force is reached within 40 μm from the laser spot, which suggests that the method could enable highly precise and selective manipulation.
Published by the American Physical Society
2025
- Book : 23(1)
- Pub. Date : 2025
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2025
A method based on Long Short-Term Memory (LSTM) networks is proposed to forecast hourly energy consumption. Using an office building in Shanghai as a case study, hourly data on occupancy, weather, and energy consumption were collected. Daily energy consumption was analyzed using single-link clustering, and days were classified into three types. The key input variables significantly influencing energy consumption, solar radiation, occupancy, and outdoor dry bulb temperature are identified by the Pearson correlation coefficient. By comparing five algorithms, it was found that the LSTM model performed the best. After considering the occupancy, the hourly MAPE was reduced from 11% to 9%. Accuracy improvements for each day type were noted as 1% for weekdays, 4% for Saturday, and 7% for Sunday. Further analysis indicated that the model started to predict the time (1:00) and commute time (7:00 and 17:00) with large errors. The model was optimized by varying the time step. For the times 1:00, 7:00, and 17:00, the best optimization of the model was achieved when the time step values were set to 6 h, 24 h, and 18 h with an MAPE of 3%, 6%, and 5%, respectively. As the model time step increased (≤2 weeks), the accuracy of the model decreased to 6%.- Book : 15(3)
- Pub. Date : 2025
- Page : pp.404-404
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2025
The STAR collaboration observed a significant global spin alignment (ρ00) signal for ϕ-mesons in Au+Au collisions using the data from the BES-I [1] which cannot be explained by conventional mechanisms, but may be attributable to the influence of a ϕ-meson force field [2–6]. In this talk, we present differential measurements of ϕ-meson global spin alignment using the STAR detector in Au+Au collisions at √sNN = 14.6 and 19.6 GeV from the second phase of the Beam Energy Scan at RHIC (BES-II). The first rapidity (y) dependent ρ00 results for ϕ-mesons will be shown, alongside new centrality and transverse momentum (pT) dependent measurements. The results presented in these proceedings will help understand the potential link of global spin alignment to vector meson fields and their roles in the evolution of nuclear matter.- Book : 316()
- Pub. Date : 2025
- Page : pp.06014-06014
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2025
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2025
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