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Topological flat bands — where the kinetic energy of electrons is quenched — provide a platform for investigating the topological properties of correlated systems. Here, we report the observation of a topological flat band formed by polar-distortion-assisted Rashba splitting in the three-dimensional Dirac material ZrTe5. The polar distortion and resulting Rashba splitting on the band are directly detected by torque magnetometry and the anomalous Hall effect, respectively. The local symmetry breaking further flattens the band, on which we observe resistance oscillations beyond the quantum limit. These oscillations follow the temperature dependence of the Lifshitz–Kosevich formula but are evenly distributed in B instead of 1/B at high magnetic fields. Furthermore, the cyclotron mass gets anomalously enhanced about 102 times at fields ~ 20 T. Our results provide an intrinsic platform without invoking moiré or order-stacking engineering, which opens the door for studying topologically correlated phenomena beyond two dimensions. Topological flat bands offer a solid-state platform for studying the interplay between topology and electron correlations. Here, the authors demonstrate that a prototypical 3D Dirac material can host topological flat bands under magnetic fields due to polar-distortion-assisted Rashba splitting. [ABSTRACT FROM AUTHOR]

BACKGROUND: Having short drying time and attractive product quality are important in fruit and vegetable dehydration processing. In this work, tri‐frequency (20, 40 and 60 kHz) ultrasound‐ethanol pretreatment, ultrasound‐water pretreatment and ethanol pretreatment were employed before infrared convection drying (ICD) of scallion stalks, which was aimed at improving the drying process and quality of the end products. The mass transfer, drying characteristics (moisture ratio and drying rate and quality properties of scallion (rehydration, color, flavor, optical microscope image, moisture distribution and microbiological quality) were analyzed. RESULTS: All pretreatments have decreased the drying time by 33.34–83.34% compared to the control, while ultrasound‐ethanol pretreatment provided the highest time reduction (83.34%). The reason is that the volatility of ethanol have replaced air in the tissue, which produced a better osmotic dehydration effect and the cavitation effect of ultrasound changed the cell function of the material, so that the food tissue was rapidly compressed and expanded, resulting in damage to the cell structure. Ultrasonic‐ethanol pretreatment has greatly reduced the water loss and dry matter of fresh scallion, improved the rehydration effect of dried scallion, better retained the color and flavor of scallion and effectively reduced the microbiological quality of the scallion. CONCLUSION: The tri‐frequency ultrasound‐ethanol pretreatment has effectively improved the drying process and quality characteristics of the dried scallion. Therefore, this research has a great contribution to the drying technology, as evident in the remarkable reduction in drying time and the improvement in the quality of the end product. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Background: Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1., Objective: The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early., Methods: Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP)., Results: Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA., Conclusion: The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices., Competing Interests: The authors report no potential conflicts of interest in this work., (© 2024 Pan et al.)