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We can construct a real line bundle arising from the locally conformal Kähler (LCK) structure over an LCK manifold. We study the properties of this line bundle over an LCK solvmanifold whose complex structure is left-invariant. Mainly, we prove that this line bundle LL over an LCK solvmanifold Γ\G\Gamma \backslash G with left-invariant complex structure is flat and GG has a global closed 2-form, which induces an Hermitian structure on the holomorphic tangent bundle twisted by the line bundle LC=L⊗C{L}^{{\mathbb{C}}}=L\otimes {\mathbb{C}} if the Lee form is cohomologous to a left-invariant 1-form on GG.

Hole‐collecting monolayers have greatly advanced the development of positive‐intrinsic‐negative perovskite solar cells (p‐i‐n PSCs). To date, however, most of the anchoring groups in the reported monolayer materials are designed to bind to the transparent conductive oxide (TCO) surface, resulting in less availability for other functions such as tuning the wettability of the monolayer surface. In this work, we developed two anchorable molecules, 4PATTI‐C3 and 4PATTI‐C4, by employing a saddle‐like indole‐fused cyclooctatetraene as a π‐core with four phosphonic acid anchoring groups linked through propyl or butyl chains. Both molecules form monolayers on TCO substrates. Thanks to the saddle shape of a cyclooctatetraene skeleton, two of the four phosphonic acid anchoring groups were found to point upward, resulting in hydrophilic surfaces. Compared to the devices using 4PATTI‐C4 as the hole‐collecting monolayer, 4PATTI‐C3‐based devices exhibit a faster hole‐collection process, leading to higher power conversion efficiencies of up to 21.7 % and 21.4 % for a mini‐cell (0.1 cm2) and a mini‐module (1.62 cm2), respectively, together with good operational stability. This work represents how structural modification of multipodal molecules could substantially modulate the functions of the hole‐collecting monolayers after being adsorbed onto TCO substrates. [ABSTRACT FROM AUTHOR]

Photoluminescence (PL) spectroscopy of individual semiconductor nanocrystals (NCs) is a powerful method for understanding the intrinsic optical properties of these materials. Here, we report the temperature dependence of the PL spectra of single perovskite FAPbBr3 and CsPbBr3 NCs [FA = HC(NH2)2]. The temperature dependences of the PL linewidths were mainly determined by the Fröhlich interaction between excitons and longitudinal optical phonons. For FAPbBr3 NCs, a redshift in the PL peak energy appeared between 100 and 150 K, which was because of the orthorhombic-to-tetragonal phase transition. We found that the phase transition temperature of FAPbBr3 NCs decreases with decreasing NC size. [ABSTRACT FROM AUTHOR]

Introduction: The left ventricular summit (LVS) is the highest point on the epicardial surface of the left ventricle. A part of the LVS that is located between the left coronary arteries (lateral‐LVS) is one of the major sites of idiopathic ventricular arrhythmia (VA) origins. Some idiopathic epicardial VAs can be ablated at endocardial sites adjacent to the epicardial area septal to the lateral‐LVS (septal‐LVS). This study examined the prevalence and electrocardiographic and electrophysiological characteristics of septal‐LVS VAs. Methods: We studied consecutive patients with idiopathic VAs originating from the LVS (67 patients) and aortic root (93 patients). Results: Based on the ablation results, among 67 LVS VAs, 54 were classified as lateral and 13 as septal‐LVS VAs. As compared with the lateral‐LVS VAs, the septal‐LVS VAs were characterized by a greater prevalence of left bundle branch block with left inferior‐axis QRS pattern, later precordial transition, lower R‐wave amplitude ratio in leads III to II, lower Q‐wave amplitude ratio in leads aVL to aVR, and later local ventricular activation time relative to the QRS onset during VAs (V‐QRS) in the great cardiac vein. The electrocardiographic and electrophysiological characteristics of the septal‐LVS VAs were similar to those of the aortic root VAs. However, the V‐QRS at the successful ablation site was significantly later during the septal‐LVS VAs than aortic root VAs (p <.0001). The precordial transition was significantly later during the septal‐LVS VAs than aortic root VAs (p <.05). Conclusions: Septal‐LVS VAs are considered a distinct subgroup of idiopathic VAs originating from the left ventricular outflow tract. [ABSTRACT FROM AUTHOR]

The lower-extremity kinematics associated with forward jump landing after an ankle injury is known to differ for patients with Chronic Ankle Instability (CAI), copers (injured but asymptomatic patients), and healthy individuals. However, the differences in the lower extremity kinematics of these groups associated with a Single-leg Lateral Drop Landing (SLDL) are unknown. The purpose of this study is to characterize the lower limb and foot kinematics during SLDL in CAI patients and to compare these characteristics with those of the copers and healthy individuals. This was a cross-sectional observational study. Nineteen participants, each, were selected from the CAI, Coper, and control groups. The lower-extremity kinematics during SLDL was measured using three-dimensional motion analysis over an interval progressing from 200 ms before landing to 200 ms after landing. Either one-way ANOVA or the Kruskal–Wallis test was used to compare the attributes of the respective groups, with each parameter measured every 10 ms. The maximum values and excursions of the parameters were established over time intervals progressing from 200 ms before landing to 200 ms after landing. Significant observations were subjected to post hoc analysis. Compared to the Coper group, the CAI group exhibited significantly smaller hip adduction angles at 160 ms, ankle dorsiflexion angles in the 110–150 ms interval, and maximum ankle dorsiflexion angles after landing. Compared to the control group, the CAI group exhibited significantly smaller excursions of MH inversion/eversion after landing. Our findings confirm the necessity of focusing on the kinematics of hip adduction/abduction and plantar/dorsiflexion during SLDL in evaluating patients with ankle injuries. [ABSTRACT FROM AUTHOR]

We describe the relation of the carrier lifetime of a light-absorber material determined with pulse-excitation time-resolved techniques to the steady-state carrier density and lifetime in a solar cell under continuous-wave excitation. Our approach constitutes a simple experimental examination of the excitation-fluence-dependent carrier lifetime of absorber materials. It provides the steady-state carrier density and lifetime under 1-sun solar illumination for metal halide perovskite solar cells. The determination of the steady-state carrier responses allows the clarification of optical and photovoltaic properties under 1-sun illumination and thus the identification of loss mechanisms in device performance. Model calculations are also provided to show how the carrier lifetime governs the luminescence quantum yields and open-circuit voltages. The calculations quantify a scaling law between a monomolecular recombination lifetime and an open-circuit voltage as a result of a combination of two density-dependent effects. [ABSTRACT FROM AUTHOR]

Purpose: We sought to develop machine learning models to predict the results of patient‐specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose‐evaluation metrics—including the gamma passing rates (GPRs)—and criteria based on the radiomic features of 3D dose distribution in a phantom. Methods: A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose‐evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance‐to‐agreement in 1‐mm and 2‐mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient‐specific QA. The machine learning regression models for predicting the values of the dose‐evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross‐validation in which four‐fold cross‐validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. Results: The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose‐evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. Conclusions: The developed machine learning models showed high performance for predicting dose‐evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to create and evaluate deep learning‐based models to detect and classify errors of multi‐leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG). Methods: A total of 33 clinical VMAT plans for prostate and head‐and‐neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by ± 10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of σ=0.5$\sigma = 0.5$ and 1.0 were applied to the planar dose maps of the error‐free plan to mimic the measurement dose map, and thus dose difference maps between the error‐free and error plans were obtained. We evaluated 3 deep learning‐based models, created to perform the following detections/classifications: (1) error‐free versus TF error, (2) error‐free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison. Results: The detection and classification of TF and DLG error were feasible for σ=0.5$\sigma = 0.5$; however, a considerable reduction of accuracy was observed for σ=1.0$\sigma = 1.0$ depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for σ=0.5$\sigma = 0.5$ and 1.0. The gamma analysis could not detect errors. Conclusions: We demonstrated that the deep learning‐based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses. [ABSTRACT FROM AUTHOR]