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Abstract 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.

Abstract 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 The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer–Lemeshow test (χ2 = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma.

Abstract 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 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.

The aerodynamic performance of wind turbine airfoils is crucial for the efficiency and reliability of wind energy systems, with leading-edge roughness significantly impacting blade performance. This study conducts numerical simulations on the DU 00-W-401 airfoil to investigate the effects of leading-edge roughness. Results reveal that the rough airfoil exhibits a distinctive “N”-shaped lift coefficient curve. The formation mechanism of this nonlinear lift curve is primarily attributed to the development of the trailing-edge separation vortex and variations in the adverse pressure gradient from the maximum thickness position to the trailing-edge confluence. The impact of different roughness heights is further investigated. It is discovered that when the roughness height is higher than 0.3 mm, the boundary layer can be considered fully turbulent, and the lift curve shows the “N” shape stably. When the roughness height is between 0.07 mm and 0.1 mm, a transitional state can be observed, with several saltation points in the lift curve. The main characteristics of different flow regimes based on different lift curve segments are summarized. This research enhances the understanding of the effects of leading-edge roughness on the aerodynamic performance of a thick wind turbine airfoil, and the simulation method for considering the effect of leading-edge roughness is practical to be applied on large-scale wind turbine blade to estimate the aerodynamic performance under rough leading-edge conditions, thereby supporting advancements in wind turbine technology and promoting the broader adoption of renewable energy.

Abstract 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 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.

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 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

Abstract 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.

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.

ObjectiveTo explore the boost effect on ameliorating functional constipation in elderly patients through empowerment-based, healthy dietary behavioral intervention.DesignIn this randomized parallel group study, elderly patients with functional constipation were recruited and assigned to the experimental and control groups at a ratio of 1:1. The control group received routine intervention. The experimental group received 3-month empowerment-based intervention. The results were evaluated based on the Healthy Lifestyle and Personal Control Questionnaire (HLPCQ) and Cleveland Clinic Constipation Score (CCS). GraphPad Prism (Version 9) software was used for the statistical analysis.SettingAs the world's population ages, functional constipation in the elderly has attracted widespread attention. The practical behavioral intervention to ameliorate constipation are worth exploring.ParticipantsSixty elderly patients with functional constipation.ResultsThe study results showed no significant difference in the baseline data between the two groups (P > 0.05). After the intervention, the scores of HLPCQ (77.90 ± 14.57 vs. 61.11 ± 13.64) and CCS (7.48 ± 3.73 vs. 9.70 ± 3.07) in the experimental group were significantly higher than those in the control group (P < 0.05).ConclusionThe results showed that empowerment-based intervention can effectively strengthen the healthy dietary behavior of elderly patients. Through patient empowerment, the subjective initiative and willingness to communicate were boosted in the experimental group. Their symptoms of functional constipation improved considerably better than in the control group.

Septic shock as a subset of sepsis, has a much higher mortality, while the mechanism is still elusive. This study was aimed at identifying core mechanisms associated with septic shock and its high mortality by investigating transcriptome data. We screened 72 septic-shock-associated genes (SSAGs) with differential expression between septic shock and sepsis in the discovery dataset. Further gene set enrichment analysis identified upregulated neutrophil activation and impaired T-cell activation in septic shock. Co-expression analysis revealed nine co-expressed gene modules. In addition, we determined twenty-one prognostic SSAGs using cox regression analysis in an independent dataset. Moreover, protein–protein interaction (PPI) network revealed two clusters. Among these neutrophil activation was enriched in the most positively-related modules and the cluster2 PPI network, while T-cell activation was enriched in both the most negatively-related module and one of the most positively-related modules as well as the cluster1 PPI network. ELANE, LCN2 and IFI44 were identified as hub genes with CytoHubba methods and semantic similarity analysis. Notably, ELANE was the only prognostic gene and was further validated in an external dataset. Blood neutrophil count was demonstrated to increase in septic shock and be a risky factor of prognosis based on clinical data. In conclusions, septic shock is associated with upregulated neutrophil activation and dysregulated T-cell activation. Three hub genes might have potentials as sensitive markers for the further translational research and ELANE could be a robust prognostic biomarker and effective therapeutic target.

BackgroundColorectal polyps are the most common precursors of colorectal cancer (CRC). The close relationship has been observed between colorectal polyps and gut microbiota. However, gut microbiota signatures among sampling sites in patients with colorectal polyps and healthy adults remain elusive.AimsTo learn about gut microbiota signatures in tissues of the colorectal polyp and normal colorectal mucosa, and faeces.MethodsWe performed 16S rRNA gene sequencing and bioinformatic analysis for the microbiota in the normal colorectal mucosa, the colorectal polyps and faeces of adults with colorectal polyps (n = 24) and in faeces and normal mucosa of healthy adults (n = 16) in this preliminary trial.ResultsThe Ace and Chao indexes were higher in the normal colorectal mucosa and polyp tissues compared to faecal samples (P < 0.05). The composition of microbiota based on PCoA and ANOSIM analysis showed the significant differences only between faeces and tissues of the normal mucosa and polyp (P < 0.05). Based on the LEfSe analysis, the abundances of Bacteroides, Prevotella-2 and Agathobacter were higher, whereas the abundances of Haemophilus, Escherichia_Shigella, Fusobacterium and Streptococcus were lower in faeces both in patients with colorectal polyp and healthy individuals, compared with those in the normal mucosa in two groups or polyp tissues. In healthy individuals, the abundance of Fusobacterium was significantly higher in the normal colorectal mucosa than in faeces. Moreover, there was no significant difference in the abundance of Fusobacterium between the normal colorectal mucosa and polyps in patients with colorectal polyps, but it was significantly higher in the mucosa and polyps than in faeces. Remarkably, the abundance of Fusobacterium in the normal colorectal mucosa was significantly higher in healthy individuals than in the polyp group.ConclusionsThe microbial structure in faeces differs from that in tissues of polyp and normal mucusa. Additionally, Fusobacterium may be a normal colonizer in colonic mucosa, and an abnormal increase of Fusobacterium detected in faeces may be related with the injury of the colorectal mucosa. The difference of the faecal microbiota and mucosal microbiota should be carefully considered in studies on gut microbiota in patients with colorectal lesions.

Abstract 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.

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.

As China’s one-child policy is replaced by the two-child policy, young Chinese women and their spouses are increasingly concerned about who will take care of the ‘second child.’ Due to the absence of public childcare services and the rising cost of privatised care services in China, childcare provision mainly relies on families, such that working women’s choices of childbirth, childcare and employment are heavily constrained. To deal with structural barriers, young urban mothers mobilise grandmothers as joint caregivers. Based on interviews with Guangzhou middle-class families, this study examines the impact of childcare policy reform since 1978 on childbirth and childcare choices of women. It illustrates the longstanding contributions and struggles of women, particularly grandmothers, engaged in childcare. It also shows that intergenerational parenting involves a set of practices of intergenerational intimacy embedded in material conditions, practical acts of care, moral values and power dynamics. We argue that the liberation, to some extent, of young Chinese mothers from childcare is at the expense of considerable unpaid care work from grandmothers rather than being driven by increased public care services and improved gender equality in domestic labour. Given the significant stress and seriously constrained choices in later life that childcare imposes, grandmothers now become reluctant to help rear a second grandchild. This situation calls for changes in family policies to increase the supply of affordable and good-quality childcare services, enhance job security in the labour market, provide supportive services to grandmothers and, most importantly, prioritise the wellbeing of women and families over national goals.

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 Numerical weather prediction (NWP) models, which attempt to simulate the full state of the atmosphere, come with many options for dealing with processes that are unable to be explicitly resolved by the model. These model parameterizations are an ongoing area of research in atmospheric science. However, with continuous contributions from the research community and subsequent upgrade of the NWP codes, there are often many options for each unresolved process—leaving the user confronted with potentially thousands of ways to configure the model. We use the weather research and forecasting (WRF) model to forecast global horizontal irradiance (GHI) and undertake the task of narrowing down these options for a location in Qinghai, China. We show that optimizing the configuration of the WRF model based on the type of day (sunny, partly cloudy, or cloudy) is 13.6% better than using a single best configuration for all types of days. We also show that this performance improvement holds true for a longer 3‐month test period (17.8% improvement).

As most global climate models (GCM) suffer from large biases in simulating/predicting summer precipitation over China, it is of great importance to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning (DL) models incorporated. One is the deterministic pathway (DP), in which the bias correction is directly applied to the precipitation forecasts. The other one, namely the probability pathway (PP), corrects the forecasted precipitation anomalies using a conditional probability method before being added to the observational climatology. These two pathways have been applied to correct the precipitation forecasts based on a GCM prediction system Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0). The applications of DL models in the both pathways yield higher resolution of corrected predictions than the uncorrected ones. Both pathways improve summer precipitation predictions at 4-month lead. Moreover, the DP correction shows a better performance in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China. The present results highlight the importance of the application of appropriate correction strategy for different prediction purposes.

In this paper we propose a new type of receiver for underwater acoustic communication. The proposed receiver has the following features: 1) It has a turbo structure with an embedded adaptive decision feedback equalizer (DFE), which is based on the least-symbols-error-rate criterion and is shown to outperform the conventional minimum mean square error (MMSE) equalizers; 2) By exploiting the training symbol at the beginning and at the end of a data frame, the proposed receiver employs time-reversal equalization to suppress the error propagation of the applied DFE, where the time-reversal diversity gain is exploited by combing the output of the bidirectional DFE with an equal-gain combing scheme; and 3) A receiving array is applied, where the output of each hydrophone is individually equalized and then the equalizer outputs are combined with an equal-gain combing scheme. The proposed receiver is extensively evaluated through computer simulation and field experiments, and is also compared with the conventional MMSE based receiver in terms of the bit error rate performance.