Structural phase transitions of optical patterns in atomic gases with microwave-controlled Rydberg interactions

Spontaneous symmetry breaking and formation of self-organized structures in nonlinear systems are intriguing and important phenomena in nature. Advancing such research to new nonlinear optical regimes is of much interest for both fundamental physics and practical applications. Here we propose a scheme to realize optical pattern formation in a cold Rydberg atomic gas via electromagnetically induced transparency. We show that, by coupling two Rydberg states with a microwave field (microwave dressing), the nonlocal Kerr nonlinearity of the Rydberg gas can be enhanced significantly and may be tuned actively. Based on such nonlocal Kerr nonlinearity, we demonstrate that a plane-wave state of probe laser field can undergo a modulation instability (MI) and hence spontaneous symmetry breaking, which may result in the emergence of various self-organized optical patterns. Especially, we find that a hexagonal lattice pattern (which is the only optical pattern when the microwave dressing is absent) may develop into several types of square lattice ones when the microwave dressing is applied; moreover, as a outcome of the MI the formation of nonlocal optical solitons is also possible in the system. Different from earlier studies, the optical patterns and non-local optical solitons found here can be flexibly manipulated by adjusting the effective probe-field intensity, nonlocality degree of the Kerr nonlinearity, and the strength of the microwave field. Our work opens a route for versatile controls of self-organizations and structural phase transitions of laser light, which may have potential applications in optical information processing and transmission

ℓ_1-Based Construction of Polycube Maps from Complex Shapes

Polycube maps of triangle meshes have proved useful in a wide range of applications, including texture mapping and hexahedral mesh generation. However, constructing either fully automatically or with limited user control a low-distortion polycube from a detailed surface remains challenging in practice. We propose a variational method for deforming an input triangle mesh into a polycube shape through minimization of the ℓ_1-norm of the mesh normals, regularized via an as-rigid-as-possible volumetric distortion energy. Unlike previous work, our approach makes no assumption on the orientation, or on the presence of features in the input model. User-guided control over the resulting polycube map is also offered to increase design flexibility. We demonstrate the robustness, efficiency, and controllability of our method on a variety of examples, and explore applications in hexahedral remeshing and quadrangulation

Symmetry and Orbit Detection via Lie-Algebra Voting

In this paper, we formulate an automatic approach to the detection of partial, local, and global symmetries and orbits in arbitrary 3D datasets. We improve upon existing voting-based symmetry detection techniques by leveraging the Lie group structure of geometric transformations. In particular, we introduce a logarithmic mapping that ensures that orbits are mapped to linear subspaces, hence unifying and extending many existing mappings in a single Lie-algebra voting formulation. Compared to previous work, our resulting method offers significantly improved robustness as it guarantees that our symmetry detection of an input model is frame, scale, and reflection invariant. As a consequence, we demonstrate that our approach efficiently and reliably discovers symmetries and orbits of geometric datasets without requiring heavy parameter tuning

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies.Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability.Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models.Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness

Mediating Effect of Physical Activity in the Association between Low 25-Hydroxyvitamin D and Frailty Trajectories: The English Longitudinal Study of Ageing

BACKGROUND: Frailty is associated with adverse health outcomes, and vitamin D (VD) deficiency may be a risk factor. We aimed to identify frailty trajectories and examine the mediating effect of physical activity (PA) on the association between VD deficiency and frailty trajectories. METHODS: We included 2997 participants aged 60 to 85 years from ELSA. VD was measured using serum 25-hydroxyvitamin D [25(OH)D] (sufficient: >50; insufficient: 30–50; deficient: <30 nmol/L). Frailty was assessed by a 60-item frailty index, and PA was measured on the basis of total energy expenditure. Frailty trajectories were identified using group-based trajectory modeling, and the mediation effect of PA was tested using causal mediation analysis. RESULTS: Three distinct frailty trajectories emerged: “Non-frail” (66.48%), “Pre-frail to frail” (25.67%) and “Frail to severely frail” (7.85%). VD deficiency was associated with the “Pre-frail to frail” (OR = 1.51, 95% CI: 1.14, 1.98) and “Frail to severely frail” trajectories (OR = 2.29, 95% CI: 1.45, 3.62). PA only mediated 48.4% (95% CI: 17.1%–270.8%) of the association between VD deficiency and the “Pre-frail to frail” trajectory. CONCLUSIONS: Vitamin D deficiency is associated with the onset and worsening of frailty in older adults, and reduced PA may mediate its impact on the transition from pre-frailty to frailty

Optical Pattern Formation in a Rydberg-Dressed Atomic Gas with Non-Hermitian Potentials

Spontaneous pattern formation from a spatially homogeneous background of nonlinear systems driven out of equilibrium is a widespread phenomenon in nature. However, similar phenomena and their physical realization in nonlinear systems with external potentials of gain and loss remain a challenge. We propose a scheme to realize a new type of spatial pattern formation through the self-organization of laser light in a Rydberg-dressed atomic gas with self-defocusing Kerr nonlinearity as well as non-Hermitian optical potentials. We show that by a suitable design of control and assistant laser fields, non-Hermitian optical potentials with or without parity-time (PT) symmetry for the probe laser field can be created. We find that through the nonlocal Kerr nonlinearity contributed by the long-range atom–atom interaction, a constant-intensity wave (CIW) may undergo modulation instability and induce spontaneous symmetry breaking, resulting in the emergence of various self-organized optical structures, which can be actively manipulated by tuning the nonlocality degree of the Kerr nonlinearity and by designing the non-Hermitian optical potentials. The results reported here open a door for developing non-Hermitian nonlinear optics

Optical Pattern Formation in a Rydberg-Dressed Atomic Gas with Non-Hermitian Potentials

Spontaneous pattern formation from a spatially homogeneous background of nonlinear systems driven out of equilibrium is a widespread phenomenon in nature. However, similar phenomena and their physical realization in nonlinear systems with external potentials of gain and loss remain a challenge. We propose a scheme to realize a new type of spatial pattern formation through the self-organization of laser light in a Rydberg-dressed atomic gas with self-defocusing Kerr nonlinearity as well as non-Hermitian optical potentials. We show that by a suitable design of control and assistant laser fields, non-Hermitian optical potentials with or without parity-time (PT) symmetry for the probe laser field can be created. We find that through the nonlocal Kerr nonlinearity contributed by the long-range atom&ndash;atom interaction, a constant-intensity wave (CIW) may undergo modulation instability and induce spontaneous symmetry breaking, resulting in the emergence of various self-organized optical structures, which can be actively manipulated by tuning the nonlocality degree of the Kerr nonlinearity and by designing the non-Hermitian optical potentials. The results reported here open a door for developing non-Hermitian nonlinear optics

Symmetry Based Material Optimization

Many man-made or natural objects are composed of symmetric parts and possess symmetric physical behavior. Although its shape can exactly follow a symmetry in the designing or modeling stage, its discretized mesh in the analysis stage may be asymmetric because generating a mesh exactly following the symmetry is usually costly. As a consequence, the expected symmetric physical behavior may not be faithfully reproduced due to the asymmetry of the mesh. To solve this problem, we propose to optimize the material parameters of the mesh for static and kinematic symmetry behavior. Specifically, under the situation of static equilibrium, Young’s modulus is properly scaled so that a symmetric force field leads to symmetric displacement. For kinematics, the mass is optimized to reproduce symmetric acceleration under a symmetric force field. To efficiently measure the deviation from symmetry, we formulate a linear operator whose kernel contains all the symmetric vector fields, which helps to characterize the asymmetry error via a simple ℓ2 norm. To make the resulting material suitable for the general situation, the symmetric training force fields are derived from modal analysis in the above kernel space. Results show that our optimized material significantly reduces the asymmetric error on an asymmetric mesh in both static and dynamic simulations

Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model

As a raw material for beer, barley seeds play a critical role in producing beers with various flavors. Unexcepted mixed varieties of barley seeds make malt quality uncontrollable and can even destroy beer flavors. To ensure the quality and flavor of malts and beers, beer brewers will strictly check the appropriate varieties of barley seeds during the malting process. There are wide varieties of barley seeds with small sizes and similar features. Professionals can visually distinguish these varieties, which can be tedious and time-consuming and have high misjudgment rates. However, biological testing requires professional equipment, reagents, and laboratories, which are expensive. This study aims to build an automatic artificial intelligence detection method to achieve high performance in multi-barley seed datasets. There are nine varieties of barley seeds (CDC Copeland, AC Metcalfe, Hockett, Scarlett, Expedition, AAC Synergy, Celebration, Legacy, and Tradition). We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. The multi-barley seed dataset had random amounts and varieties of barley seeds in each image. The single-barley seed dataset had one barley seed in each image. Data augmentation can reduce overfitting and maximize model performance and accuracy. Multi-variety barley seed recognition deploys an efficient data augmentation method to effectively expand the barley dataset. After adjusting the hyperparameters of the networks and analyzing and augmenting the datasets, the YOLOv5 series network was the most effective in training the two barley seed datasets and achieved the highest performance. The YOLOv5x6 network achieved the second highest performance. The mAP (mean Average Precision) of the trained YOLOv5x6 was 97.5%; precision was 98.4%; recall was 98.1%; the average speed of image detection reached 0.024 s. YOLOv5x6 only trained the multi-barley seed dataset; the trained performance was greater than that of the YOLOv5 series. The two datasets had 39.5% higher precision, 27.1% higher recall, and 40.1% higher mAP than when just using the original multi-barley seed dataset. The multi-barley seed detection results showed high performance, robustness, and speed. Therefore, malting and brewing industries can assess the original barley seed quality with the assistance of fast, intelligent, and detected multi-barley seed images

DataSheet5_Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study.docx

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies.Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability.Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models.Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.</p