Search

Highlights Integrating perovskite solar cells onto irregular rough CuIn(Ga)Se2 (CIGS) surfaces remains a challenge; new strategy was explored to develop monolithic perovskite/CIGS tandem solar cell by manipulating the crystallization of perovskite. Surface reconstruction and field-effect passivation are developed synergistically to issue complex interface relationship between perovskite and C60. The champion power conversion efficiency （PCE） of 24.6% realized, providing significant commercial opportunities for all thin-film-based perovskite/CIGS tandem cells.

The combined cooling, heating, and power (CCHP) system has attracted increasing attention due to its potential outstanding performance in thermodynamics, economics, and the environment. However, the conventional CCHP systems are carbon-intensive. To solve this issue, a low-carbon-emission CCHP system (LC-CCHP) is firstly proposed in this work by integrating a sorption-enhanced steam methane reforming (SE-SMR) process. In the LC-CCHP system, CO2 is continuously captured by the calcium loop so that low-carbon energy can be generated. Then, the LC-CCHP system thermodynamic model, mainly consisting of a dual fluidized bed reactor which includes the SE-SMR reactor and a CaCO3 calcination reactor, a hydrogen gas turbine, a CO2 reheater, and a lithium bromide absorption chiller, is built. To prove that the LC-CCHP model is reliable, the system major sub-unit model predictions are compared against data from the literature in terms of thermodynamics and economics. Finally, the effects of reforming temperature (Tref), the steam-to-carbon mole ratio (S/C), the calcium-to-carbon mole ratio (RCC), the equivalent ratio for gas turbine (RAE), and the hydrogen separation ratio (Sfg) on total energy efficiency (ηten), total exergy efficiency (ηtex), and carbon capture capability (Rcm) are detected. It is found that the minimum exergy efficiency of 64.5% exists at the calciner unit, while the maximum exergy efficiency of 78.7% appears at the gas turbine unit. The maximum energy efficiency and coefficient of performance of the absorption chiller are 0.52 and 1.33, respectively. When Tref=600 °C, S/C=4.0, RCC=7.62, RAE=1.20, and Sfg=0.27, the ηten, ηtex, and Rcm of the system can be ~61%, ~68%, and ~96%, and the average specific cost of the system is 0.024 USD/kWh, which is advanced compared with the parallel CCHP systems.

In recent years, the model predictive control (MPC) algorithm has been increasingly applied to the path tracking of self-driving vehicles due to its capacity to deal with dynamic constraints explicitly. The control performance of MPC is highly dependent on the accuracy dynamic model; however, as vehicles are strongly coupled nonlinear systems, the prediction accuracy of the classical mechanism model decreases significantly at high-speed conditions, leading to increased control errors. This paper proposes replacing the classical mechanism model with a recurrent neural network (RNN) for vehicle dynamical state prediction under the framework of MPC to achieve higher control effects under high speed steering processes. The RNN vehicle dynamic model uses historical data of control and state variables to predict future states. Based on this novel model, longitudinal/lateral coupled model predictive control is realized. The differential evolution algorithm is proposed to solve the optimization problem in the controller. Finally, the prediction accuracy of the RNN model is verified on the real vehicle dataset and compared with linear/nonlinear mechanism models. The control algorithm proposed in this paper is compared with classical MPC against low and high speeds (10m/s and 30m/s) on the ADAMS/Python/Simulink joint simulation platform. The results show that the control accuracy and stability of the longitudinal/lateral coupled neural network MPC are higher than classical MPC, especially at high speed.

In this study, an electrochemical method was used to permeate hydrogen through annealed DP590 steel under various pre-strain conditions (0-15%). Stress-strain and internal friction-temperature curves of the dual phase (DP) steel were obtained from slow strain-rate tensile tests and internal friction measurements, respectively. The diffusion of interstitial atoms, formation of Cottrell atmospheres, and embrittlement mechanism of DP steel were investigated under different prestress conditions before and after hydrogen permeation. The results show that the tensile strength of DP steel first decreases and then increases and the elongation sharply decreases with increasing pre-strain. The strength and ductility present similar trends with changes in pre-strain before and after hydrogen charging, however, after hydrogen charging, an obvious increase in tensile strength and decrease in elongation are observed. Furthermore, the γ peak amplitude decreases and the Snoek-Ke-Koster (SKK) peak amplitude increases with increasing internal pre-strain according to the friction-temperature curve. The γ peak and SKK peak exhibit the same trends with increasing pre-strain before and after hydrogen charging and both the γ peak and SKK peak decrease with hydrogen charging. The dislocation density in DP steel increases after hydrogen charging.

Recently with the development of deep learning on data representation and generation, how to sampling on a data manifold becomes a crucial problem for research. In this paper, we propose a method to learn a minimizing geodesic within a data manifold. Along the learned geodesic, our method is able to generate high-quality uniform interpolations with the shortest path between two given data samples. Specifically, we use an autoencoder network to map data samples into the latent space and perform interpolation in the latent space via an interpolation network. We add prior geometric information to regularize our autoencoder for a flat latent embedding. The Riemannian metric on the data manifold is induced by the canonical metric in the Euclidean space in which the data manifold is isometrically immersed. Based on this defined Riemannian metric, we introduce a constant-speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolations along the learned geodesic on the manifold. We provide a theoretical analysis of our model and use image interpolation as an example to demonstrate the effectiveness of our method.

Abstract To study the influence of diesel fuel blended with polyoxymethylene dimethyl ethers (PODEn), a new alternative fuel with a high oxygen content and large cetane number, on the combustion characteristics, fuel economies, and emission characteristics of heavy-duty diesel engines that meet China VI emission standards, relevant tests were conducted on a supercharged intercooled high-pressure common-rail diesel engine. The PODEn were blended with diesel fuel at three different ratios (volume fractions of 10%, 20%, and 30%). The test results showed that the PODEn could optimize the combustion process of diesel engines that met the China VI emission standards, effectively improve the thermal efficiencies of diesel engines, and reduce the emissions of hydrocarbon (HC), carbon monoxide (CO), and soot. With an increase in the PODEn blending ratio, the peak values of the in-cylinder pressure, average in-cylinder temperature, and instantaneous heat release rate gradually decreased, and each peak progressively moved forward. As the start of combustion gradually moved forward, the combustion duration was shortened by 0.7–2.8°CA, the heat release process became more concentrated, and the effective thermal efficiency was increased by up to 2.57%. The effective fuel consumption gradually increased, yet the equivalent effective fuel consumption gradually decreased, with the largest drop being as high as 4.55%. The nitrogen oxides (NO x ) emission increased slightly, and the emissions of HC, CO, and soot gradually decreased. The emissions of CO and soot declined significantly under high-speed and high-load conditions, with the highest reductions reaching 66.2% and 76.3%, respectively.

Steam methane reforming is a major method of hydrogen production. However, this method usually suffers from low energy efficiency and high carbon-emission intensity. To solve this issue, a novel steam-methane-reforming process over a Ni-based catalyst in a pressurized dual fluidized bed reactor is proposed in this work. A three-dimensional computational fluid dynamics (CFD) model for the complex physicochemical process was built to study the reforming characteristics. The model was first validated against the reported data in terms of hydrodynamics and reaction kinetics. Next, the performance of the proposed methane-steam-reforming process was predicted. It was found that the methane-conversion ratio was close to 100%. The mole fraction of H2 in the dry-yield syngas reached 98.8%, the cold gas efficiency reached 98.5%, and the carbon-capture rate reached 96.4%. It is believed that the proposed method can be used for methane reforming with high efficiency and low carbon intensity.

Studying Λc+ decays provides important and complementary information to help understand the effects of non-perturbative quantum chromodynamics. Recently, many interesting results about Λc+ decays have been reported thanks to the high luminosity in Belle and dedicated operation at the Λc+ pair production threshold in BESIII. This paper reviews the recent progress in experimental measurements, containing sections about the hadronic weak decay, semileptonic decay, and decay asymmetry parameters and CP violation.

Background: The association of serum uric acid (SUA) with hypertension has been well established in Caucasian populations. However, its association with hypertension in Chinese remained to be clarified. Methods: Consecutive patients, homogeneous in Chinese Han ethnicity, aged ≥18 years, abstracted from the database, admitted from 1 January 2010 to 31 December 2013, were included for potential analysis. The patients were grouped according to the presence or absence of hypertension. Unconditional logistic regression was performed to estimate the association between SUA and hypertension. Its possible interactions with risk factors on hypertension were also explored. Results: A total of 9587 patients were finally analyzed in the current study, where 5692 were with hypertension and 3895 were without hypertension. Per 100 μmol/L higher SUA concentration was associated with multivariable-adjusted odds ratios (95% CI) of 1.25 (1.08–1.22) in males, 1.10 (1.01–1.20) in females, and 1.19 (1.13–1.24) in total. On a categorical scale, when compared with the first quintile, the multivariable-adjusted odds ratios (95% CI) were 1.40 (1.20–1.64) for the 2nd quintile, 1.48 (1.27–1.74) for the 3rd quintile, 1.55 (1.32–1.82) for the 4th quintile, and 1.92 (1.63–2.26) for the 5th quintile, with a p for trend < 0.01. Conclusions: SUA is associated with hypertension in a dose-response manner among the Chinese hospitalized population. Management of SUA could help to the prevention and control of hypertension.

Data reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having tradeoff in size reduction rate and learning accuracy. In this paper, we propose a fast data reduction approach by using granular computing to label important instances, i.e., instances with higher contributions to the learning task. The original data set is first granulated into K granules by applying K-means to a mapped lower-dimension space. Then, the importance of each instance in every granule is labeled based on its Hausdorff distance. Those instances whose importance values are lower than an experimentally tuned threshold are eliminated. The presented algorithm is applied to k NN classification tasks with eighteen different sizes of data sets from the UCI repository, and its outstanding performance in classification accuracy, size reduction rate, and runtime is illustrated by comparing with seven data reduction methods. The experimental results demonstrate that the proposed algorithm can greatly reduce the computational cost and achieve a higher classification accuracy when the reduction size is the same for all the compared algorithms.

Introduction High-fidelity simulation (HFS) can bridge the gap between theoretical knowledge and nursing practice and improve safety and quality of patient care in baccalaureate nursing education. Although inconsistent assessment instruments or lack of high-quality research designs affect the strength of the evidence and limit the generalisability of the results, quantitative studies generally demonstrate the effectiveness of HFS in baccalaureate nursing education. Synthesis of the existing evidence of baccalaureate nursing students’ experiences with HFS is crucial for the improvement and revision of simulation design and teaching.Methods and analysis A comprehensive search for qualitative studies on baccalaureate nursing students’ experiences with HFS will be conducted in the following databases: PubMed, Embase, CINAHL, ProQuest, Web of Science, PsycINFO, the Cochrane library, China Biology Medicine disc, China National Knowledge Infrastructure and VIP Database for Chinese Technical Periodicals. This review considered studies reported in English or Chinese, and studies that were conducted between January 2000 and December 2019 in view of the launch of International Nursing Association for Clinical Simulation and Learning. The literature search will be conducted by two independent reviewers, and any disagreement will be adjudicated by discussion or with a third reviewer. The two independent reviewers will use the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research to assess the methodological validity, following which the JBI standardised data extraction tools will be used to extract relevant data. The JBI meta-aggregation method will be subsequently used to synthesise the data, eventually forming themes, categories and synthesised findings. The final synthesised findings will establish confidence levels based on the JBI ConQual approach.Ethics and dissemination This review does not require formal ethical review since it is based on available published literature. Findings will be disseminated through publication in a peer-reviewed journal, and, if possible, presented in scientific conferences.

This paper proposes a novel hybrid power system to improve the shift quality of a hybrid electric vehicle (HEV). After selecting a typical shift scheme, the study focused on the motor rotary velocity control algorithm and coordinated control strategy for the motor and clutch. The effects of various control algorithms on different target rotary velocities were analyzed, and a proportional-integral-derivative (PID)–bang-bang–fuzzy compound intelligent algorithm for a motor rotary velocity control system was investigated. In addition, to address the problems of the long synchronizing time required for the rotary velocity and large sliding friction work, which affect the shift quality during the process of engaging the clutch, a coordinated control strategy for the motor rotary velocity and clutch oil pressure was investigated. The research results showed that, compared with a gear shift coordinated control strategy based on a PID control algorithm, the strategy based on the PID–bang-bang–fuzzy compound intelligent control algorithm proposed here reduced the shift time and clutch slipping friction work by 35.7% and 19.2%, respectively.

This paper proposes a double layered multi parameters braking energy recovery control strategy for Hybrid Electric Vehicle, which can combine the mechanical brake system with the motor brake system in the braking process to achieve higher energy utilization efficiency and at the same time ensure that the vehicle has sufficient braking performance and safety performance. The first layer of the control strategy proposed in this paper aims to improve the braking force distribution coefficient of the front axle. On the basis of following the principle of braking force distribution, the braking force of the front axle and the rear axle is reasonably distributed according to the braking strength. The second layer is to obtain the proportional coefficient of regenerative braking, considering the influence of vehicle speed, braking strength, and power battery state of charge (SOC) on the front axle mechanical braking force and motor braking force distribution, and a three-input single-output fuzzy controller is designed to realize the coordinated control of mechanical braking force and motor braking force of the front axle. Finally, the AMESim and Matlab/Simulink co-simulation model was built; the braking energy recovery control strategy proposed in this paper was simulated and analyzed based on standard cycle conditions (the NEDC and WLTC), and the simulation results were compared with regenerative braking control strategies A and B. The research results show that the braking energy recovery rate of the proposed control strategy is respectively 2.42%, 18.08% and 2.56%, 16.91% higher than that of the control strategies A and B, which significantly improves the energy recovery efficiency of the vehicle.

BACKGROUND Myocardial ischemia-reperfusion (I/R) is a serious and irreversible injury. Bone marrow-derived mesenchymal stem cells (MSCs) is considered to be a potential therapy for I/R injury due to the paracrine effects. High-mobility group box 1 (HMGB1) is a novel mediator in MSC and regulates the response of inflammation injury. Signal Transduction and Transcription Activator 3 (STAT3) is a critical transcription factor and important for release of paracrine factors. However, the relationship between HMGB1 and STAT3 in paracrine effect of MSC remains unknown. METHODS In vitro, hypoxia/reoxygenation injury model was established by AnaeroPack System and examined by Annexin V flow cytometry, CCK8 assay and morphology observation. Detection of apoptotic proteins and protein expression of HMGB1 and STAT3 by Western blot. RESULTS The conditioned medium of MSCs with or without LPS pretreatment was cocultured with H9C2 cells for 24 h before hypoxia treatment and MSC showed obvious cardiomyocytes protect role, as evidence by decreased apoptosis rate and improved cells viability, and LPS pretreated MSC exhibited better protect role than untreated MSC. However, such effect was abolished in HMGB1 deficiency group, silencing HMGB1 decreased the secretion of vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), insulin growth factor (IGF), cell viability, and the expression of STAT3. Furthermore, STAT3 silence attenuated the protective effect of LPS in MSC. CONCLUSIONS These findings suggested that LPS improved MSC-mediated cardiomyocytes protection by HMGB1/STAT3 signaling. [ABSTRACT FROM AUTHOR]