Search

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research. This paper introduces FuXi-S2S, a machine-learning model that outperforms conventional numerical weather prediction models at subseasonal timescales globally, extending the skillful Madden–Julian Oscillation prediction form 30 days to 36 days. [ABSTRACT FROM AUTHOR]

In the face of an aging population and declining birth rates, urban Chinese families are grappling with a new challenge of simultaneously caring for both elderly parents and children. This study employs a three-generational analytical framework and a mixed research method to examine the intergenerational distribution of care resources and its consequences for urban Chinese families. The findings reveal intergenerational competition for care resources within families, with a general tendency to prioritize children and only respond to crisis situations for elderly parents; this leads to a care deficit for older individuals. Furthermore, social care resources are ineffective in bridging this care gap, thereby inducing anxiety within families as they strive to balance the care needs of both elderly parents and children. The study argues that insufficient support from care policies exacerbates the resource squeeze on the elderly side by the children's side within families, which calls for enhancing the policy support system to help families balance eldercare, childcare, and work commitments. [ABSTRACT FROM AUTHOR]

Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a "gray zone", where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

This article studies the intricate dynamics of family resilience within the Chinese productivist regime over the course of seven decades, emphasizing the instrumental role of public policy in shaping this resilience. Drawing on a dynamic approach, the research demonstrates how, across distinct periods of central planning and economic reform, policies have persistently harnessed families to advance economic objectives. Showing that the productivist regime's double-edged role on family resilience emerged while public policies bolstered short-term family resilience in times of sustained economic growth and favorable demographics, and signs of family sector downturn—declining fertility rates, family dysfunctions—became evident, the current article underscores the challenges of a model that primarily perceives families as economic instruments. Advocating for a policy paradigm shift that harmonizes economic ambitions with family well-being, this study offers valuable insights for policy-makers and lays the groundwork for future research in comparative welfare systems. [ABSTRACT FROM AUTHOR]

NaTi2(PO4)3 (NTP) is considered a promising anode material for sodium-ion batteries due to its open and stable framework structure. However, its poor conductivity prevents its direct application as an anode material. In this paper, a dual strategy of La3+ doping and carbon coating is proposed to improve the conductivity of NTP. The results show that the NTP-La0.06@C electrode doped with 6.00% La and 5.00 wt% of carbon coating has excellent electrochemical performance, and the coulombic efficiency is up to 95.31% in the first cycle. The initial specific capacity is 103.05 mAh·g−1 with a capacity retention of 97.30% after 1000 cycles at 10C rate. Even after 2000 cycles, there is still a specific capacity of 98.62 mAh·g−1 with a capacity retention of 92.71%, and as well as the NTP-La0.06@C has a higher Na+-insert diffusion coefficient of 3.19×10−11 cm2·s−1 than the pristine NTP@C of 2.35×10−11 cm2·s−1 during charging. Therefore, the La3+ doping and carbon coating strategy shows great potential in improving the performance of NaTi2(PO4)3 anode materials. [ABSTRACT FROM AUTHOR]

A solar heat pump based on the photovoltaic photothermal (PV/T) module is a new technology that can improve the photovoltaic efficiency and recovery of waste heat in photovoltaic conversion. The comprehensive efficiency of a system can thus be greatly improved. At present, there is little research on the simulation of a solar heat pump based on the Simulink 2018 software. In this paper, PV/T modules are modeled and simulated using the Simulink software based on the typical meteorological parameters in Beijing city during summer and winter conditions. Considering the system characteristics of a simultaneous operation, the models of all components such as the compressor, condenser, evaporator, expansion valve, and heat storage tanks contained in a system are established based on the system's working principle, energy conservation equations, and some empirical formulas and then simulated. As PV/T modules are the key influencing factor for system performance, the model and simulation process are introduced in detail in this paper. The results show that the surface temperature of PV/T modules under different operating conditions are all significantly lower than that of ordinary photovoltaic panels. The average temperature of PV/T modules is only 15.2 °C, which is 20.2 °C lower than that of ordinary panels under summer conditions, and the average temperature of PV/T modules is only 0.5 °C, 17.8 °C lower than that of ordinary panels under winter conditions. The average photoelectric efficiency can be improved by 15.4% and 8.9%. And, the temperature change amplitude of PV/T modules is lower, which weakens the temperature fluctuation of the modules. The photovoltaic efficiency is therefore further increased. As a result, the power generation and photovoltaic efficiency are both improved significantly. [ABSTRACT FROM AUTHOR]

Energy storage is considered a crucial unit in utilizing renewable energy sources, and compressed air energy storage (CAES) provides a cost-effective solution. It offers the benefits of zero pollution, a long lifespan, low maintenance costs, and minimal environmental impact. In order to increase the possibilities of compressed air energy storage for vehicle power, the performance of the expander needs to be studied. First, a CAES unit test bench is established. Then, the volumetric flow rate, rotational speed, torque, and output power are examined. Additionally, the isentropic exhaust temperature, pressure, and gas consumption rate of the scroll expander are analyzed. Finally, analyzing the economic feasibility of the CAES unit entails running the unit under varied driving conditions. Results reveal that the pressure of the input expander is high, which will lead to greater torque, greater peak power, and a greater temperature drop, but the gas in the air tank will also run out quickly. The peak power of the scroll expander does not occur at the maximum volume flow rate, rotation speed, and torque. The basic investment of the CAES unit mainly depends on the peak output power. [ABSTRACT FROM AUTHOR]

With the increasing demand for logistics in modern society, how to achieve low-cost and efficient logistics delivery has become an urgent research topic. A hybrid evolutionary JAYA algorithm (H-JAYA) based on global optimization was designed to address the complex path planning problem of electric vehicles. This algorithm introduces a reverse learning mechanism to calculate the current optimal and worst individuals, while using differential perturbation mechanism and sine cosine operator to update the individual's position. In addition, the study used the H-JAYA algorithm to construct a corresponding mathematical model for the optimization problem of electric vehicle paths. The results showed that in the three examples, the H-JAYA algorithm tested the optimal curve convergence speed, and it tended to stabilize after about 30 iterations. Meanwhile, in the RCDP5001 example, the total cost of the H-JAYA algorithm reached the lowest value of 623 yuan. The H-JAYA algorithm has significant advantages in solving the distribution path problem of electric vehicles, and can be well applied to practical logistics distribution, providing effective technical support for modern e-commerce logistics planning. [ABSTRACT FROM AUTHOR]

High‐resolution and accurate prediction of near‐surface weather parameters based on numerical weather prediction (NWP) models is essential for many downstream and real‐world applications. Traditional dynamical or statistical downscaling methods are insufficient to derive high‐resolution data from operational NWP forecasts, making it essential to devise new approaches. In recent years, an increasing number of researchers have explored the implementations of deep learning (DL) based models for spatial downscaling, motivated by the similarity between the super‐resolution (SR) problem in computer vision (CV) and downscaling. Furthermore, while transformer‐based models have become state‐of‐the‐art models for many SR tasks, they are rarely applied for downscaling of weather forecasts or climate projections. This study adapted transformer‐based models such as SwinIR and Uformer to downscale the temperature at 2 m (T2m$$ {T}_{2\mathrm{m}} $$) and wind speed at 10 m (WS10m$$ W{S}_{10\mathrm{m}} $$) over Eastern Inner Mongolia, encompassing the area from 39.6–46°N latitude and 111.6–118°E longitude. We used high‐resolution forecast (HRES) data from the European Centre for Medium‐range Weather Forecast (ECMWF) with a spatial resolution of 0.1° as the input and gridded observation data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) at a spatial resolution of 0.01° as the target. Given that the models use observation data rather than a coarse‐grained version of forecast data as the target, they accomplish both bias correction and spatial downscaling. The results demonstrate that the performance of SwinIR and Uformer is superior to that of two convolutional neural network (CNN) based models (UNet and RCAN). Additionally, we introduced a novel module to extract features of varying resolution from the high‐resolution topography data and applied a multiscale feature fusion module to merge features of different scales, contributing to further enhancement of Uformer's performance. [ABSTRACT FROM AUTHOR]

Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES), which is widely considered as the world's best physics-based weather forecasting system. Specifically, ML models have outperformed HRES in 10-day forecasts with a spatial resolution of 0.25∘. However, the challenge remains in mitigating the accumulation of forecast errors for longer effective forecasts, such as achieving comparable performance to the ECMWF ensemble in 15-day forecasts. Despite various efforts to reduce accumulation errors, such as implementing autoregressive multi-time step loss, relying on a single model has been found to be insufficient for achieving optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts at a temporal resolution of 6 hours and a spatial resolution of 0.25∘. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation demonstrates that FuXi has forecast performance comparable to ECMWF ensemble mean (EM) in 15-day forecasts. FuXi surpasses the skillful forecast lead time achieved by ECMWF HRES by extending the lead time for Z500 from 9.25 to 10.5 days and for T2M from 10 to 14.5 days. Moreover, the FuXi ensemble is created by perturbing initial conditions and model parameters, enabling it to provide forecast uncertainty and demonstrating promising results when compared to the ECMWF ensemble. [ABSTRACT FROM AUTHOR]

Warm-sector heavy rainfall often occurs along the coast of South China, and it is usually localized and long-lasting, making it challenging to predict. High-resolution numerical weather prediction (NWP) models are increasingly used to better resolve topographic features and forecast such high-impact weather events. However, when the grid spacing becomes comparable to the length scales of convection, known as the gray zone, the turbulent eddies in the atmospheric boundary layer are only partially resolved and parameterized to some extent. Whether using a convection parameterization (CP) scheme in the gray zone remains controversial. Scale-aware CP schemes are developed to enhance the representation of convective transport within the gray zone. The multi-scale Kain-Fritsch (MSKF) scheme includes modifications that allow for its effective implementation at a grid resolution as high as 2 km. In recent years, there has been an increasing application of machine learning (ML) models to various domains of atmospheric sciences, including the replacement of physical parameterizations with ML models. This work proposes a multi-output bidirectional long short-term memory (Bi-LSTM) model as a replace the scale-aware MSKF CP scheme. The Weather Research and Forecast (WRF) model is used to generate training and testing data over South China at a horizontal resolution of 5 km. Furthermore, the WRF model is coupled with the ML based CP scheme and compared with WRF simulations with original MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the potential use of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients. [ABSTRACT FROM AUTHOR]

The conventional full-scale wind turbine blade testing method is unable to study the detailed structural response of blades' key subcomponents under complex loads. Therefore, developing an efficient and reliable blade subcomponent testing method is of great significance for investigating the structural characteristics of blades. This paper develops an innovative blade subcomponent test method framework, which is capable of studying the structural response under combined bending and torsion load conditions. A high-fidelity finite element model corresponding to the framework that considers nonlinear factors is established. This study further investigated the influence of boundary conditions on the results. The results showed that the test method proposed in this paper is reasonable, and the experiment based on this method is successfully conducted. The contact nonlinearity and size effects will affect the accuracy of the test results, which can be avoided by adjusting the size of the clamp. [ABSTRACT FROM AUTHOR]

Developing non‐noble metal catalysts for the oxygen evolution reaction (OER) is one of the main challenges in the production of hydrogen energy by water splitting reaction. Herein, taking advantage of the fact that microorganisms have high element contents and metabolic activities, we used them to recycle Ni and Fe from electroplating sludge. Based on the above, we designed a highly efficient oxygen evolution catalyst (Fe‐Ni2P@NPC) through the in situ synthesis of Fe‐doped Ni2P nanoparticles and N, P dual‐doped carbon materials coupled together. The material exhibited excellent catalytic activity, with a low overpotential of 282 mV at the current density of 10 mA cm−2 along with a fast reaction kinetics, with a small Tafel slope of 57.1 mV dec−1. This outstanding OER performance is mainly attributed to the in situ doping of Fe in Ni2P nanoparticles, resulting in a strong synergistic effect between them. Besides, the strong coupling interaction between Fe‐Ni2P and Fe‐NiOOH formed during the OER can adjust the electron distribution and optimize the adsorption capacity of the intermediates. This work provides a reference for the recycling of valuable Ni and Fe metals from electroplating sludge, as well as new insight into the preparation of low‐cost and high‐performance OER catalysts. [ABSTRACT FROM AUTHOR]

Ni-rich layered cathode materials LiNixCoyMn(1-x–y)O2 (x ≥ 0.8) suffer from capacity decay due to structural deterioration during electrochemical cycling. To overcome these problems, the fast-ion conductor Li0.33La0.56TiO3 (LLTO) is coated on the surface of LiNi0.83Co0.12Mn0.05O2 (NCM83) cathodes through the sol–gel method, which can stabilize the electrode/electrolyte interface of cathode materials and facilitate Li+ transport and charge transfer. As a result, the LLTO-coated NCM83 exhibits better cycle stability and higher rate performance. The NCM83 modified by 5 wt% LLTO has the discharge capacity of 185.9 mAh g−1 at 1C rate after 100 cycles and with higher capacity retention of 92.9% than the pristine NCM83 of 86.2%, and as well as the 5 wt% LLTO-coated NCM83 has a higher Li+-insert diffusion coefficient of 1.143 × 10−11 cm2 s−1 than the pristine NCM83 of 8.757 × 10−12 cm2 s−1. Therefore, the fast-ion conductor LLTO coating strategy shows great potential in improving the performance of Ni-rich layered cathode materials. [ABSTRACT FROM AUTHOR]

The atmospheric radiative transfer calculations are among the most time‐consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL‐based parameterizations. Therefore, we propose a physics‐incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic‐incorporated layer. In addition, we trained and compared various types of DL model architectures, including fully connected (FC) neural networks (NNs), convolutional‐based NNs (CNNs), bidirectional recurrent‐based NNs (RNNs), transformer‐based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer‐based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere. Plain Language Summary: Numerical weather prediction models require a lot of computational resources and time to run. Calculating the atmospheric radiative transfer processes is one of the most computationally expensive parts of the NWP model. One alternative is to model the radiative transfer using deep learning (DL) models, but the DL models do not involve physical laws and may have physically inconsistent outputs. This paper proposes a DL model framework to ensure the thermal equilibrium between fluxes and heating rates, which are outputs of radiative transfer models. Also, the accuracy of DL‐based radiative transfer prediction is improved when using the framework. Various DL models have been trained and compared. The results demonstrate that model structures with global receptive fields work best for emulating radiative transfer calculations. Key Points: A physics‐incorporated deep learning (DL) framework for parameterization of atmospheric radiative transfer is proposedThe DL model structures with global receptive fields are more suitable for the radiative transfer problem [ABSTRACT FROM AUTHOR]

In modern large-scale wind farms, power loss caused by the wake effect is more than 30%, and active yaw control can greatly reduce the influence of the wake effect by deflecting the wind turbine's wake. The yawed wind turbine's wake characteristics are complex, and a deep comprehension of a yawed turbine's wake is necessary. The actuator line method combined with URANS (unsteady Reynold-averaged Navier–Stokes equations) is used to study the yawed wind turbine's wake characteristics in this paper. Compared with an un-yawed wind turbine, a yawed one has two main characteristics, deflection and deformation. With an increasing yaw angle, turbine wake shows an increasing deflection. The results indicated that deflection at different height was different, the wake profile showed the biggest deflection at about the hub height, while the smallest deflection existed at the top and bottom of the yawed turbine's wake. This can be visually demonstrated by the evolution of a kidney-shape velocity distribution at the vertical cross-section. Two-dimensional and three-dimensional presentations of velocity deficit distributions are presented in this paper. The evolution of an irregular kidney-shape distribution is discussed in this paper. It is formed by the momentum exchange caused by the counter-rotating vortex pair. The results indicated that the counter-rotating vortex pair was composed of the streamwise vortex flux brought by the tip vortex. Furthermore, when the wind turbine rotated clockwise and yawed clockwise, the negative vorticity of counter-rotating vortex first appeared in the upper left position. [ABSTRACT FROM AUTHOR]

Background: To investigate the ameliorative effects of glucosamine (GS), chondroitin sulphate (CS) and glucosamine plus chondroitin sulphate (GC) on rheumatoid arthritis (RA) in rats, and to explore the mechanism of GS, CS and GC in improving RA based on the gut microbiota. Methods: RA rat models were effectively developed 14 days after CFA injection, and then garaged with GS, CS and GC. Body weight and paw volume of rats were monitored at multiple time points at the beginning of CFA injection. Until D36, serum and ankle tissue specimens were used to measure levels of circulating inflammatory factors (TNF-α, IL-1β, MMP-3, NO and PGE2) and local inflammatory indicators (TLR-4 and NF-κB). On D18, D25, and D36, intergroup gut microbiota was compared using 16S rRNA gene sequencing and bioinformatics analysis. We also performed the correlation analysis of gut bacteria, joint swelling and inflammatory indicators. Results: GC, rather than GS and CS, could reduce right paw volumes, levels of TLR-4 and NF-κB in synovial tissues. In addition, enriched genera in RA model rats screened out by LEfSe analysis could be inhibited by GC intervention, including potential LPS-producing bacteria (Enterobacter, Bacteroides, Erysipelotrichaceae_unclassified and Erysipelotrichaceae_uncultured) and some other opportunistic pathogens (Esherichia_Shigella, Nosocomiicoccus, NK4A214_group, Odoribacter, Corynebacterium and Candidatus_Saccharimonas.etc.) that positively correlated with pro-inflammatory cytokines, right paw volume, and pathology scores. Furthermore, the gut microbiota dysbiosis was observed to recover before alleviating joint swelling after interventions. Conclusions: GC could inhibit potential LPS-producing bacteria and the activation of TLR-4/NF-κB pathway in RA rats, thus alleviating RA-induced joint injury. [ABSTRACT FROM AUTHOR]

Introduction: Given the new ideas on wound care offered by the eCASH (early Comfort using Analgesia, minimal Sedatives, and maximal Humane care) and the substantial differences in clinical treatment between acute and chronic wounds, we aimed to investigate the effect of comfort therapy under the eCASH concept on analgesic sedation and accelerated wound healing in patients with acute or chronic wounds. Methods: This randomized clinical study was conducted in two parts: acute wounds and chronic wounds. Patients with acute wounds were allocated into the acute wound control group (AWCG) and the acute wound experimental group (AWEG). Patients with chronic wounds were allocated into the chronic wound control group (CWCG) and two experimental groups, in which they received intermittent negative pressure therapy (IPTEG) and continuous negative pressure therapy (CPTEG). On the basis of the standard treatment for patients in the control group, eCASH therapy was used in the experimental groups. In addition, pain intensity and procedural anxiety were evaluated using the visual analogue score (VAS) and the Hamilton Anxiety Scale (HAM-A). In addition, clinical effects were assessed on the basis of the size of the surface area, rate of healing, and concentration of pro-inflammatory factors (IL-1, IL-6, TNF-α) and growth factors (VEGF, bFGF, TGF-β1). Results: Compared with the control group, the VAS score and HAM-A score in the experimental groups were significantly decreased after intervention (P < 0.05). After intervention, the levels of IL-1β, IL-6, and TNF-α in AWEG, IPTEG, and CPTEG were significantly lower than those in AWCG. In addition, the levels of VEGF, bFGF, and TGF-β1 in IPTEG and CPTEG were significantly higher than those in CWCG (P < 0.05). Conclusion: These results indicated that comfort therapy under the eCASH concept has a significant effect on ameliorating the pain and anxiety of patients, reducing the inflammatory reaction during the period of wound healing in the treatment of acute and chronic wounds. Clinical Trial Registry: The trial has been registered in the Chinese Clinical Trial Registry (ChiCTR2200057981). [ABSTRACT FROM AUTHOR]

In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that machine learning (ML) parameterizations of subgrid physics have the potential to accelerate and even outperform conventional physics-based schemes. However, as the ML models are commonly implemented using the ML libraries written in Python, very few ML-based parameterizations have been successfully integrated with NWP models due to the difficulty of embedding Python functions into Fortran-based NWP models. To address this issue, we developed a coupler to allow the ML-based parameterizations to be coupled with a widely used NWP model, i.e., the Weather Research and Forecasting (WRF) model. Similar to the WRF I/O methodologies, the coupler provides the options to run the ML model inference with exclusive processors or the same processors for WRF calculations. In addition, to demonstrate the effectiveness of the coupler, the ML-based radiation emulators are trained and coupled with the WRF model successfully. [ABSTRACT FROM AUTHOR]

In this study, graphene is used as a photothermal material, which is added to the SiO2 superhydrophobic solution treated with fluorine silane, and then sprayed on the copper plate surface to prepare a new type of photothermal superhydrophobic surface with contact angles up to 160.5° and 159.8°. Under the conditions of natural convection, the effects of photothermal superhydrophobic surfaces on droplet condensation, freezing, and frost growth are investigated in different environments. The results show that the photothermal superhydrophobic surface can not only delay the freezing of surface droplets, prolong the freezing time of droplets, and reduce the thickness of the frost layer, but also allow for the rapid removal of droplets under near-infrared (NIR) irradiation. If the droplet is irradiated by an infrared laser emitter while the cooling system is still turned on, the internal temperature of the droplet will always be higher than the crystallization temperature under the illumination intensity of 2 W/cm2, and the droplets will not freeze. With the extension of irradiation time, the droplet will evaporate, and the volume of the droplet will decrease. On the basis of summarizing and evaluating the study on the anti-icing performance of superhydrophobic surfaces and the properties of photothermal materials, a new research direction regarding the anti-icing of fan blade surfaces was established. This kind of surface combines the photothermal capabilities of light absorption materials with the micro- and nanostructure of the superhydrophobic surface to improve the anti-icing capability of wind turbine blade surfaces in difficult conditions. [ABSTRACT FROM AUTHOR]

In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that deep learning (DL) parameterizations of subgrid physics have the potential to accelerate and even outperform conventional physic-based schemes. However, as the DL models are commonly implemented using the DL libraries written in Python, very few DL-based parameterizations have been successfully integrated with NWP models due to the difficulty of embedding Python functions into Fortran-based NWP models. To address this issue, we developed a coupler to allow the DL-based parameterizations to be coupled with a widely used NWP model, i.e., the Weather and Research Forecasting (WRF) model. Similar to the WRF I/O methodologies, the coupler provides the options to run the DL model inference with exclusive processors or the same processors for WRF calculations. In addition, to demonstrate the effectiveness of the coupler, the DL-based radiation emulators are trained and coupled with the WRF model successfully. [ABSTRACT FROM AUTHOR]

Despite a rapid increase in people with Parkinson's disease (PD) in China, studies focusing on caregiver burden remain scarce. Challenges faced by caregivers in other Asian societies are also underexplored in the PD‐related research field. To addresses this gap, this study drew on the theory of resilience and examined the caregiving experiences of Chinese family caregivers for older people with PD. We conducted participant observation and semi‐structured interviews with 15 family caregivers of older people with PD in Shanghai in 2018. We used thematic analysis to identify key patterns of stressors and factors affecting the capacity of caregivers to address burdens. Our analysis shows that stressors faced by Chinese PD caregivers included physical limitations, psychological and emotional problems, reduction in social connections, financial burden and lack of information and knowledge. Three levels of factors—personal, social and structural—played different roles in resilience among Chinese PD caregivers. However, resilience was unevenly developed among PD caregivers across gender, kinship, residential status and generation. Our study provides novel empirical evidence about the burden on Chinese PD caregivers. It can inform policymakers and social workers as they develop a holistic understanding of promoting resilience building in this rapidly growing group. Our study also enriches cross‐cultural knowledge about factors contributing to caregivers' resilience building in Asian societies. [ABSTRACT FROM AUTHOR]

Background: The rice gall midge (RGM, Orseolia oryzae, Wood-Mason), an important stem-feeding pest worldwide, has caused serious production losses over the past decades. Rice production practices indicate that the most reliable method for managing RGM is the deployment of cultivars that incorporate host resistance. However, the conventional phenotypic screening method of rice resistance to RGM suggested by the International Rice Research Institute (IRRI) has been used for approximately 30 years, and only 12 rice varieties/lines (including controls) can be evaluated in one tray. It is not suitable for high-throughput phenotyping of rice germplasm. Moreover, a suitable method to prepare samples for molecular biological studies of rice resistance against RGM is imperative with the rapid development of modern molecular techniques. Results: The proper density of seedlings/RGM was determined for four seeding arrangements. A high-throughput phenotyping method (HTPM) for 60 lines/varieties infested with 36 female RGM adults in one tray, as described by method 4–3 (seeded 60 lines/varieties), was developed and verified using mutant screening. Furthermore, one RGM resistance gene flanked by markers 12RM28346 and 12RM28739 on chromosome 12 was simultaneously detected using method 2–2 (seeded 30 lines/varieties in one tray) treated with 24 RGM and analyzed using conventional and simplified grading systems. Genetic analysis of the RGM resistance gene was confirmed using a method identical to that suggested by IRRI. Finally, one bucket with 24 seedlings treated with at least five female RGM adults was efficacious and could offer adequate samples for insect development observation or molecular biological studies. Conclusion: A highly efficient and reliable procedure for evaluation of resistance in rice to RGM was developed and improved, and was verified through mutant screening, gene mapping, genetic analysis, and insect growth and development observations. [ABSTRACT FROM AUTHOR]

Photocatalytic reduction of CO2 holds tremendous promise for alleviating the energy crisis. Despite the progress that has been made, there are still some challenges to overcome, such as the realization under real sunlight rather than the simulation condition. In this work, ultrathin Ni2(OH)(PO4) nanotubes (NTs) prepared through hydrothermal route are applied as a novel catalyst for photocatalytic reduction of CO2 under real sunlight. The prepared Ni2(OH)(PO4) NTs exhibit a 11.3 µmol·h−1 CO production rate with 96.1% CO selectivity. Interestingly, Ni2(OH)(PO4) NTs have a positive impact on the facilitation of photoreduction in diluted CO2. Notably, when the system is performed under real sunlight, Ni2(OH)(PO4) NTs afford an accumulated CO of ca. 26.8 µmol with 96.9% CO selectivity, exceeding most previous inorganic catalysts under simulated irradiation in the laboratory. Our experimental results demonstrate that the multisynergetic effects induced by surface-OH and the lattice strain serve as highly active sites for CO2 molecular adsorption and activation as well as electron transfer, hence enhancing photoreduction activity. Therefore, this work provides experimental basis that CO2 photocatalysis can be put into practical use. [ABSTRACT FROM AUTHOR]

Existing literature has examined the contextual effects on individual environmental activism, but little research has been conducted to investigate how local-level governance factors affect individual environmental activism, particularly in developing countries. This paper investigates the moderating effects of local government transparency and responsiveness on the motivation-activism relationship in China. We compiled a provincial-level database capturing local government transparency and responsiveness and combined this with data from the China General Social Survey 2013. Multilevel modeling analysis revealed that motivation factors at the individual level have stronger effects on individual engagement in environmental activism when government transparency and responsiveness are more encouraging at the provincial level. The results not only point to local governance as a contextual factor for understanding individual environmental activism in China but also highlight the need to consider the interactions of individuals in the political context to understand how individual factors translate into environmental activism in many developing countries. [ABSTRACT FROM AUTHOR]

This article examines the role of information technologies in helping small companies hire disabled people. It draws upon the data from a single-case study of a car-washing start-up business in China. I argue that technologies have allowed even micro-sized companies and entrepreneurs in NGOs to become more involved in disability employment. This calls for greater policy support specific to the needs of small companies and more respect for the autonomy of those NGOs. Information technology should also be included in disability employment support services so that the disabled are more likely to take advantages of the new technology economy. [ABSTRACT FROM AUTHOR]

A new-type multiple-source heat pump cycle with two-stage compression was established on the basis of the problems of similarly existing heat pumps. The equivalent temperature levels of typical evaporators are applied to the different heat sources of the proposed cycle, and the high-temperature heat sources are shown to enhance vapor injection. Then, the mathematical model and prototype are developed, and the results from experimental simulation and validation showed that the solar collector can improve the heating performance of the proposed heat pump system. In the middle-temperature heating period, the outdoor temperature is less than −25°C, and the average coefficient of performance (COP) value of the proposed heat pump was 4.2, which was greater than the COPs of conventional ground source heat pumps. [ABSTRACT FROM AUTHOR]

Poor charge transfer and separation rate are the major bottlenecks for the activity and stability of BiVO4 photoanode. Here, we introduced oxygen vacancies into MoO3/BiVO4 heterojunction film by post-annealing the film in argon-saturated environment for improving its photoelectrochemical (PEC) water oxidation activity and stability. In comparison with the normal MoO3/BiVO4 film, the MoO3/BiVO4 film with oxygen vacancies is of better PEC water oxidation performance. Specifically, a higher photocurrent density of 4.1 mA/cm2 in 0.1 M Na2SO4 at 1.1 V versus SCE was achieved on the MoO3/BiVO4 film with oxygen vacancies, which is about 200% improved over the normal MoO3/BiVO4 film (1.83 mA cm−2, at 1.1 V versus SCE). In addition, the MoO3/BiVO4 film with oxygen vacancies shows more stable activity and faster kinetics for water oxidation, without significant activity loss for 5 h reaction at 1.23 V versus RHE. The enhanced performance on such a MoO3/BiVO4 film photoanode can be attributed to that the oxygen vacancies accelerate the charge transfer and separation rate between film/electrolyte interface, and thus improve the water oxidation activity and restrain the anodic photocorrosion simultaneously. [ABSTRACT FROM AUTHOR]

BiVO4 is a promising photoanode material for photoelectrochemical water splitting, but its actual activity is hindered by the high energy surface states. Here, we report that In3+ can be used as a dopant to substitute the partial sites of Bi3+ in BiVO4 for modifying the surface states and improving the water oxidation activity of a BiVO4 nanoflake film. Among the In3+-doped BiVO4 film photoanodes, the 7% In3+-doped BiVO4 film shows optimal photoelectrochemical water oxidation activity. At 1.23 V vs. RHE, the 7% In3+-doped BiVO4 photoanode exhibits a photocurrent density of 1.56 mA cm−2 in 0.1 M Na2SO4, which is over 200% greater than that of the undoped BiVO4 photoanode. In3+-doping did not change the morphology, phase and band gap of BiVO4 obviously, but resulted in a positive shift of the flat band position and higher surface charge separation efficiency for water oxidation. Density functional theory calculations indicate that the surface energy of BiVO4 decreased after In3+-doping that involved more unsaturated electrons of the Bi atom in the Bi–O bonds, thus reducing the amount of exposed unsaturated Bi atoms and broken Bi–O bonds. Therefore, the enhanced water oxidation activity on the In3+-doped BiVO4 photoanode can be ascribed to In3+-doping that passivated the surface states of BiVO4 and thus inhibited the surface charge recombination. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s13555-022-00852-5. B Correction to: Dermatol Ther (Heidelb) (2023) 13:299-314 b https://doi.org/10.1007/s13555-022-00852-5 The authors would like to emphasize that Minlie Yang is the corresponding author and Lihong Zhu is the co-corresponding author. [Extracted from the article]

This article examines the process in which urban families in China renegotiate intergenerational contracts in order to secure old age care and, in particular, emotional support. We argue that as the filial piety tradition, which stresses both caring for and obeying one’s parents, has eroded, parents sense that, unlike in the past, there is no guarantee that their children will care for them. Instead of waiting passively to lose their power over their children, Chinese parents use their resources to reinforce the caring part, but not the ‘obedience’ part, of the traditional family relationship. Based on in-depth interviews with eight families in Guangzhou over a 5-year period, we found that parents and children have negotiated new arrangements concerning emotional communication, practical assistance, spatial proximity and long-term care arrangements. [ABSTRACT FROM PUBLISHER]

Cubic PtCu nanocages (NCs) were successfully synthesized through a redox reaction using cuprous oxide (CuO) as a sacrificial template and reducing agent. The porous PtCu NCs were composed of amounts of PtCu nanograins with an average particle size of 2.9 nm. The electrocatalytic performance of the PtCu NC electrode towards HO was studied by cyclic voltammetry (CV) and chronoamperometry. The prepared PtCu NC electrode exhibited excellent electrocatalytic activity towards HO, with a wide liner range from 5 μM to 22.25 mM, a relatively high sensitivity of 295.3 μA mM cm, and a low detection limit of 5 μM (S/N = 3). The hollow porous nanostructure has potential applications in biosensors. [ABSTRACT FROM AUTHOR]

A miniature vapor compression refrigeration equipment prototype was developed for a range of microclimate control applications, including man portable cooling and distributed space conditioning. The miniature refrigeration equipment has dimensions of 260 mm × 250 mm × 120 mm, and weighs approximately 2.85 kg. The optimal motor and its transmission ratio, the optimal dimension of capillary tube and the optimal quantity of refrigerant charge were obtained by matching and operating performance experimental study. Experimental results show that the miniature refrigeration equipment can provide 300 W cooling, and its COPc is above 2.0. [ABSTRACT FROM AUTHOR]

A polymerase chain reaction (PCR) targeted to the central portion of the bovine herpesvirus 1 (BHV1) genome, and overlapping the 3′ untranslated end of the gI glycoprotein, was used to amplify BHV1 genomic sequences. PCR products generated from cell cultures infected with BHV1.1 were consistently smaller than the corresponding products from cells infected with BHV1.2. The nature of the sequence differences between these isolates within the target region was found to be a consequence of variable numbers of small GC rich repeats, particularly the sequence 5′-G(A/T)CC-3′, present in the region downstream of the gI coding region. Based on these differences a modified PCR protocol which readily discriminated between several BHV1.1 and BHV1.2 strains was devised. [ABSTRACT FROM AUTHOR]