Boosting the efficiency of inverted quantum dot light-emitting diodes by balancing charge densities and suppressing exciton quenching through band alignment.

We report an inverted and multilayer quantum dot light emitting diode (QLED) which boosts high efficiency by tuning the energy band alignment between charge transport and light emitting layers. The electron transport layer (ETL) was ZnO nanoparticles (NPs) with an optimized doping concentration of cesium azide (CsN3) to effectively reduce electron flow and balance charge injection. This is by virtue of a 0.27 eV upshift of the ETL's conduction band edge, which inhibits the quenching of excitons and preserves the superior emissive properties of the quantum dots due to the insulating characteristics of CsN3. The demonstrated QLED exhibits a peak current efficiency, power efficiency and external quantum efficiency of up to 13.5 cd A-1, 10.6 lm W-1 and 13.4% for the red QLED, and correspondingly 43.1 cd A-1, 33.6 lm W-1 and 9.1% for green, and 4.1 cd A-1, 2.0 lm W-1 and 6.6% for the blue counterparts. Compared with QLEDs without optimization, the performance of these modified devices shows drastic improvement by 95.6%, 39.4% and 36.7%, respectively. This novel device architecture with heterogeneous energy levels reported here offers a new design strategy for next-generation high efficiency QLED displays and solid-state lighting technologies

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.</p

Repair of Adult Mammalian Heart After Damages by Oral Intake of Gu Ben Pei Yuan San

Adult mammalian heart repair after myocardial damage is highly inefficient due to the post-mitotic nature of cardiomyocytes. Interestingly, in traditional Chinese medicine (TCM), there are reported effective treatments of myocardial infarction (MI) and heart failure in adult humans by oral intake of a TCM concoction named Gu Ben Pei Yuan San (GBPYS), which is composed of Panax ginseng, velvet antler, Gekko gecko Linnaeus tail, human placenta, Trogopterus dung, Panax notoginseng, and amber. We fed mice with GBPYS after myocardial damages through everyday self-feeding. We then examined the effect of everyday oral intake of GBPYS on improving cardiac function and myocardial repair in adult mice after apical resection or MI. We found that long-term oral intake of GBPYS significantly improved cardiac function after myocardial damages in adult mice. BrdU, phospho-histone 3, and AuroraB staining indicated increased cell proliferation at the border zone of MI after TCM feeding. GBPYS feeding reduced organ inflammation, induced angiogenesis, and is non-toxic to mice after long-term oral intake. Further, serum derived from TCM-fed MI rats promoted division of both neonatal rat cardiomyocytes and human induced pluripotent stem cell (iPSC)-derived cardiomyocytes in vitro. Oral intake of GBPYS improved heart repair after myocardial damages in adult mice. Our results suggest that there are substances present in GBPYS that help improve adult mammalian heart repair after MI. Also, it could be a good choice of non-invasive alternative therapy for myocardial damages and heart failure after rigorous clinical study in the future

EEG microstate changes during hyperbaric oxygen therapy in patients with chronic disorders of consciousness

Hyperbaric oxygen (HBO) therapy is an effective treatment for patients with disorders of consciousness (DOC). In this study, real-time electroencephalogram (EEG) recordings were obtained from patients with DOC during HBO therapy. EEG microstate indicators including mean microstate duration (MMD), ratio of total time covered (RTT), global explained variance (GEV), transition probability, mean occurrence, and mean global field power (GFP) were compared before and during HBO therapy. The results showed that the duration of microstate C in all patients with DOC increased after 20 min of HBO therapy (p &lt; 0.05). Further statistical analysis found that the duration of microstate C was longer in the higher CRS-R group (≥8, 17 cases) than in the lower group (&lt;8, 24 cases) during HBO treatment. In the higher CRS-R group, the transition probabilities from microstate A to microstate C and from microstate C to microstate A also increased significantly compared with the probability before treatment (p &lt; 0.05). Microstate C is generally considered to be related to a salience network; an increase in the transition probability between microstate A and microstate C indicates increased information exchange between the auditory network and the salience network. The results of this study show that HBO therapy has a specific activating effect on attention and cognitive control in patients and causes increased activity in the primary sensory cortex (temporal lobe and occipital lobe). This study demonstrates that real-time EEG detection and analysis during HBO is a clinically feasible method for assessing brain function in patients with DOC. During HBO therapy, some EEG microstate indicators show significant changes related to the state of consciousness in patients with chronic DOC. This will be complementary to important electrophysiological indicators for assessing consciousness and may also provide an objective foundation for the precise treatment of patients with DOC

Survival and morbidity in very preterm infants in Shenzhen: a multi-center study

ObjectiveTo analyze survival and morbidity among very preterm infants (VPIs) in Shenzhen and explore factors associated with survival without major morbidity.MethodsBetween January 2022 and December 2022, 797 infants were admitted to 25 neonatal intensive care units in Shenzhen with gestational age (GA) &lt; 32 weeks, excluded discharged against medical advice, insufficient information, and congenital malformation, 742 VPIs were included. Comparison of maternal and neonate characteristics, morbidities, survival, and survival without major morbidities between groups used Mann Whitney U test and X2 test, multivariate logistic regression was used to analyze of risk factors of survival without major morbidities.ResultsThe median GA was 29.86 weeks (interquartile range [IQR], 28.0–31.04), and the median birth weight was 1,250 g (IQR, 900–1,500). Of the 797 VPIs, 721 (90.46%) survived, 53.52% (38 of 71) at 25 weeks’ or less GA, 86.78% (105 of 121) at 26 to 27 weeks' GA, 91.34% (211 of 230) at 28 to 29 weeks' GA, 97.86% (367 of 375) at 30 to 31 weeks' GA. The incidences of the major morbidities were moderate-to-severe bronchopulmonary dysplasia,16.52% (113 of 671); severe intraventricular hemorrhage and/or periventricular leukomalacia, 2.49% (17 of 671); severe necrotizing enterocolitis, 2.63% (18 of 671); sepsis, 2.34% (16 of 671); and severe retinopathy of prematurity, 4.55% (27 of 593), 65.79% (450 of 671) survived without major morbidities. After adjustment for GA, birth weight, and 5-min Apgar score, antenatal steroid administration (OR = 2.397), antenatal magnesium sulfate administration (OR =  1.554) were the positivity factors to survival without major morbidity of VPIs, however, surfactant therapy (OR = 0.684,), and delivery room resuscitation (OR = 0.626) that were the negativity factors.ConclusionsThe present results indicate that survival and the incidence of survival without major morbidities increased with GA. Further, antenatal administration of steroids and magnesium sulfate, surfactant therapy, and delivery room resuscitation were pronounced determinants of survival without morbidities

Unstructured mesh finite difference/finite element method for the 2D time-space Riesz fractional diffusion equation on irregular convex domains

Fractional differential equations are powerful tools to model the non-locality and spatial heterogeneity evident in many real-world problems. Although numerous numerical methods have been proposed, most of them are limited to regular domains and uniform meshes. For irregular convex domains, the treatment of the space fractional derivative becomes more challenging and the general methods are no longer feasible. In this work, we propose a novel numerical technique based on the Galerkin finite element method (FEM) with an unstructured mesh to deal with the space fractional derivative on arbitrarily shaped convex and non-convex domains, which is the most original and significant contribution of this paper. Moreover, we present a second order finite difference scheme for the temporal fractional derivative. In addition, the stability and convergence of the method are discussed and numerical examples on different irregular convex domains and non-convex domains illustrate the reliability of the method. We also extend the theory and develop a computational model for the case of a multiply-connected domain. Finally, to demonstrate the versatility and applicability of our method, we solve the coupled two-dimensional fractional Bloch-Torrey equation on a human brain-like domain and exhibit the effects of the time and space fractional indices on the behaviour of the transverse magnetization