Data-driven approach for modeling Reynolds stress tensor with invariance preservation

The present study represents a data-driven turbulent model with Galilean invariance preservation based on machine learning algorithm. The fully connected neural network (FCNN) and tensor basis neural network (TBNN) [Ling et al. (2016)] are established. The models are trained based on five kinds of flow cases with Reynolds Averaged Navier-Stokes (RANS) and high-fidelity data. The mappings between two invariant sets, mean strain rate tensor and mean rotation rate tensor as well as additional consideration of invariants of turbulent kinetic energy gradients, and the Reynolds stress anisotropy tensor are trained. The prediction of the Reynolds stress anisotropy tensor is treated as user's defined RANS turbulent model with a modified turbulent kinetic energy transport equation. The results show that both FCNN and TBNN models can provide more accurate predictions of the anisotropy tensor and turbulent state in square duct flow and periodic flow cases compared to the RANS model. The machine learning based turbulent model with turbulent kinetic energy gradient related invariants can improve the prediction precision compared with only mean strain rate tensor and mean rotation rate tensor based models. The TBNN model is able to predict a better flow velocity profile compared with FCNN model due to a prior physical knowledge.Comment: 23 page

SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation

Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to-Image (L2I) generation, aiming to generate realistic and complex scene images from user-specified layouts, has risen to prominence. However, existing methods transform layout information into tokens or RGB images for conditional control in the generative process, leading to insufficient spatial and semantic controllability of individual instances. To address these limitations, we propose a novel Spatial-Semantic Map Guided (SSMG) diffusion model that adopts the feature map, derived from the layout, as guidance. Owing to rich spatial and semantic information encapsulated in well-designed feature maps, SSMG achieves superior generation quality with sufficient spatial and semantic controllability compared to previous works. Additionally, we propose the Relation-Sensitive Attention (RSA) and Location-Sensitive Attention (LSA) mechanisms. The former aims to model the relationships among multiple objects within scenes while the latter is designed to heighten the model's sensitivity to the spatial information embedded in the guidance. Extensive experiments demonstrate that SSMG achieves highly promising results, setting a new state-of-the-art across a range of metrics encompassing fidelity, diversity, and controllability

Evaluating the quantum optimal biased bound in a unitary evolution process

Seeking the available precision limit of unknown parameters is a significant task in quantum parameter estimation. One often resorts to the widely utilized quantum Cramer-Rao bound (QCRB) based on unbiased estimators to finish this task. Nevertheless, most actual estimators are usually biased in the limited number of trials. For this reason, we introduce two effective error bounds for biased estimators based on a unitary evolution process in the framework of the quantum optimal biased bound. Furthermore, we show their estimation performance by two specific examples of the unitary evolution process, including the phase encoding and the SU(2) interferometer process. Our findings will provide an useful guidance for finding the precision limit of unknown parameters.Comment: 11 pages, 3 figures, welcome comment

Analysis of factors influencing the efficacy of vagus nerve stimulation for the treatment of drug-resistant epilepsy in children and prediction model for efficacy evaluation

ObjectiveVagus nerve stimulation (VNS) has been widely used in the treatment of drug-resistant epilepsy (DRE) in children. We aimed to explore the efficacy and safety of VNS, focusing on factors that can influence the efficacy of VNS, and construct a prediction model for the efficacy of VNS in the treatment of DRE children.MethodsRetrospectively analyzed 45 DRE children who underwent VNS at Qilu Hospital of Shandong University from June 2016 to November 2022. A ≥50% reduction in seizure frequency was defined as responder, logistic regression analyses were performed to analyze factors affecting the efficacy of VNS, and a predictive model was constructed. The predictive model was evaluated by receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA).ResultsA total of 45 DRE children were included in this study, and the frequency of seizures was significantly reduced after VNS treatment, with 25 responders (55.6%), of whom 6 (13.3%) achieved seizure freedom. There was a significant improvement in the Quality of Life in Childhood Epilepsy Questionnaire (15.5%) and Seizure Severity Score (46.2%). 16 potential factors affecting the efficacy of VNS were included, and three statistically significant positive predictors were ultimately screened: shorter seizure duration, focal seizure, and absence of intellectual disability. We developed a nomogram for predicting the efficacy of VNS in the treatment of DRE children. The ROC curve confirmed that the predictive model has good diagnostic performance (AUC = 0.864, P < 0.05), and the nomogram can be further validated by bootstrapping for 1,000 repetitions, with a C-index of 0.837. Besides, this model showed good fitting and calibration and positive net benefits in decision curve analysis.ConclusionVNS is a safe and effective treatment for DRE children. We developed a predictive nomogram for the efficacy of VNS, which provides a basis for more accurate selection of VNS patients

Tracing children's vocabulary development from preschool through the school‐age years: an 8‐year longitudinal study

In this 8‐year longitudinal study, we traced the vocabulary growth of Chinese children, explored potential precursors of vocabulary knowledge, and investigated how vocabulary growth predicted future reading skills. Two hundred and sixty‐four (264) native Chinese children from Beijing were measured on a variety of reading and language tasks over 8 years. Between the ages of 4 to 10 years, they were administered tasks of vocabulary and related cognitive skills. At age 11, comprehensive reading skills, including character recognition, reading fluency, and reading comprehension were examined. Individual differences in vocabulary developmental profiles were estimated using the intercept‐slope cluster method. Vocabulary development was then examined in relation to later reading outcomes. Three subgroups of lexical growth were classified, namely high‐high (with a large initial vocabulary size and a fast growth rate), low‐high (with a small initial vocabulary size and a fast growth rate) and low‐low (with a small initial vocabulary size and a slow growth rate) groups. Low‐high and low‐low groups were distinguishable mostly through phonological skills, morphological skills and other reading‐related cognitive skills. Childhood vocabulary development (using intercept and slope) explained subsequent reading skills. Findings suggest that language‐related and reading‐related cognitive skills differ among groups with different developmental trajectories of vocabulary, and the initial size and growth rate of vocabulary may be two predictors for later reading development. “In this 8‐year longitudinal study, we traced the vocabulary growth of Chinese children, explored potential precursors of vocabulary knowledge, and investigated how vocabulary growth predicted future reading skills. Three subgroups of lexical growth were classified, namely high‐high (with a large initial vocabulary size and a fast growth rate), low‐high (with a small initial vocabulary size and a fast growth rate) and low‐low (with a small initial vocabulary size and a slow growth rate) groups. Low‐high and low‐low groups were distinguishable mostly through phonological skills, morphological skills and other reading‐related cognitive skills. Childhood vocabulary development (using intercept and slope) explained subsequent reading skills. Findings suggest that language‐related and reading‐related cognitive skills differ among groups with different developmental trajectories of vocabulary, and the initial size and growth rate of vocabulary may be two predictors for later reading development.”Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109871/1/desc12190.pd

Patterns in use and transplant outcomes among adult recipients of kidneys from deceased donors with COVID-19

IMPORTANCE: While the COVID-19 pandemic enters a new phase and the proportion of individuals with a previous COVID-19 diagnosis increases, the national patterns in kidney use and medium-term kidney transplant (KT) outcomes among patients receiving kidneys from active or resolved COVID-19-positive donors remain unknown. OBJECTIVE: To evaluate the patterns in kidney use and KT outcomes among adult recipients of kidneys from deceased donors with active or resolved COVID-19. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study was conducted using national US transplant registry data from 35 851 deceased donors (71 334 kidneys) and 45 912 adult patients who received KTs from March 1, 2020, to March 30, 2023. EXPOSURE: The exposure was donor SARS-CoV-2 nucleic acid amplification test (NAT) results, with positive NAT results within 7 days before procurement defined as active COVID-19 and positive NAT results 1 week (\u3e7 days) before procurement defined as resolved COVID-19. MAIN OUTCOMES AND MEASURES: Primary outcomes were kidney nonuse, all-cause kidney graft failure, and all-cause patient death. Secondary outcomes were acute rejection (ie, rejection in the first 6 months after KT), transplant hospitalization length of stay (LOS), and delayed graft function (DGF). Multivariable logistic regression analyses were performed for kidney nonuse, rejection, and DGF; multivariable linear regression analyses were performed for LOS; and multivariable Cox regression analyses were performed for graft failure and all-cause death. All models were adjusted for inverse probability treatment weighting. RESULTS: Among 35 851 deceased donors, the mean (SD) age was 42.5 (15.3) years; 22 319 (62.3%) were men and 23 992 (66.9%) were White. Among 45 912 recipients, the mean (SD) age was 54.3 (13.2) years; 27 952 (60.9%) were men and 15 349 (33.4%) were Black. The likelihood of nonuse of kidneys from active or resolved COVID-19-positive donors decreased over time. Overall, kidneys from active COVID-19-positive donors (adjusted odds ratio [AOR], 1.55; 95% CI, 1.38-1.76) and kidneys from resolved COVID-19-positive donors (AOR, 1.31; 95% CI, 1.16-1.48) had a higher likelihood of nonuse compared with kidneys from COVID-19-negative donors. From 2020 to 2022, kidneys from active COVID-19-positive donors (2020: AOR, 11.26 [95% CI, 2.29-55.38]; 2021: AOR, 2.09 [95% CI, 1.58-2.79]; 2022: AOR, 1.47 [95% CI, 1.28-1.70]) had a higher likelihood of nonuse compared with kidneys from donors without COVID-19. Kidneys from resolved COVID-19-positive donors had a higher likelihood of nonuse in 2020 (AOR, 3.87; 95% CI, 1.26-11.90) and 2021 (AOR, 1.94; 95% CI, 1.54-2.45) but not in 2022 (AOR, 1.09; 95% CI, 0.94-1.28). In 2023, kidneys from both active COVID-19-positive donors (AOR, 1.07; 95% CI, 0.75-1.63) and resolved COVID-19-positive donors (AOR, 1.18; 95% CI, 0.80-1.73) were not associated with higher odds of nonuse. No higher risk of graft failure or death was found in patients receiving kidneys from active COVID-19-positive donors (graft failure: adjusted hazard ratio [AHR], 1.03 [95% CI, 0.78-1.37]; patient death: AHR, 1.17 [95% CI, 0.84-1.66]) or resolved COVID-19-positive donors (graft failure: AHR, 1.10 [95% CI, 0.88-1.39]; patient death: AHR, 0.95 [95% CI, 0.70-1.28]). Donor COVID-19 positivity was not associated with longer LOS, higher risk of acute rejection, or higher risk of DGF. CONCLUSIONS AND RELEVANCE: In this cohort study, the likelihood of nonuse of kidneys from COVID-19-positive donors decreased over time, and donor COVID-19 positivity was not associated with worse KT outcomes within 2 years after transplant. These findings suggest that the use of kidneys from donors with active or resolved COVID-19 is safe in the medium term; further research is needed to assess longer-term transplant outcomes

Survival after simultaneous pancreas-kidney transplantation in type 1 diabetes: The critical role of early pancreas allograft function

Simultaneous pancreas-kidney transplantation (SPK) carries about a 7%-22% risk of technical failure, but the impact of early pancreas allograft loss on subsequent kidney graft and patient survival is not well-defined. We examined national transplant registry data for type 1 diabetic patients who received SPK between 2000 and 2021. Associations of transplant type (i.e., SPK, deceased-donor kidney transplant [DDKA], living-donor kidney transplant [LDKA]) with kidney graft failure and patient survival were estimated by multivariable inverse probability of treatment-weighted accelerated failure-time models. Compared to SPK recipients with a functioning pancreas graft 3 months posttransplant (SPK,P+), LDKA had 18% (Time Ratio [TR] 0.82, 95%CI: 0.70-0.95) less graft survival time and 18% (TR 0.82, 95%CI: 0.68-0.97) less patient survival time, DDKA had 23% (TR 0.77, 95%CI: 0.68-0.87) less graft survival time and 29% (TR 0.71, 95%CI: 0.62-0.81) less patient survival time, and SPK with early pancreas graft loss had 34% (TR 0.66, 95%CI: 0.56-0.78) less graft survival time and 34% (TR 0.66, 95%CI: 0.55-0.79) less patient survival time. In conclusion, SPK,P+ recipients have better kidney allograft and patient survival compared with LDKA and DDKA. Early pancreas graft failure results in inferior kidney and patient survival time compared to kidney transplant alone

Minimising efficiency roll-off in high-brightness perovskite light-emitting diodes.

Efficiency roll-off is a major issue for most types of light-emitting diodes (LEDs), and its origins remain controversial. Here we present investigations of the efficiency roll-off in perovskite LEDs based on two-dimensional layered perovskites. By simultaneously measuring electroluminescence and photoluminescence on a working device, supported by transient photoluminescence decay measurements, we conclude that the efficiency roll-off in perovskite LEDs is mainly due to luminescence quenching which is likely caused by non-radiative Auger recombination. This detrimental effect can be suppressed by increasing the width of quantum wells, which can be easily realized in the layered perovskites by tuning the ratio of large and small organic cations in the precursor solution. This approach leads to the realization of a perovskite LED with a record external quantum efficiency of 12.7%, and the efficiency remains to be high, at approximately 10%, under a high current density of 500 mA cm-2