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Abstract Xiaoyankangjun tablet (XYKJP) is a traditional Chinese medicine formulation used to treat intestinal disorders in clinical practice. However, the specific therapeutic mechanism of action of XYKJP in colitis has not yet been elucidated. This study aimed to reveal the multifaceted mechanisms of action of XYKJP in treating colitis. The model established based on DSS-induced colitis in C57BL/6 mice was employed to estimate the effect of XYKJP on colitis, which was then followed by histological assessment, 16S rRNA sequencing, RT-qPCR, ELISA, and Western blot. XYKJP alleviated the symptoms of DSS-induced colitis mainly by reducing oxidative stress, inflammatory responses, and intestinal mucosal repair in colitis tissues. In addition, XYKJP regulated the intestinal flora by increasing the relative abundance of Akkermansia and Bifidobacterium and reducing the relative abundance of Coriobacteriaceae_UCG-002. Mechanistically, XYKJP increased the content of short-chain fatty acids (SCFAs) in the feces, particularly propanoic acid and butyric acid, activated their specific receptor GPR43/41, furthermore activated the Nrf2/HO-1 pathway, and suppressed the JAK2/STAT3 pathway. XYKJP significantly alleviated the symptoms of experimental colitis and functioned synergistically by regulating the intestinal flora, increasing the production of SCFAs, and activating their specific receptors, thereby repressing oxidative stress and inflammation. Graphical Abstract

Abstract The impact of aging on diabetic retinopathy (DR) remains underestimated. The current study aimed to investigate the association between biological aging and DR, in contrast to chronological age (CA). Using the National Health and Nutrition Survey data from 2005 to 2008. Biological aging was evaluated through the biological age (BA) and phenotypic age (PA), which were calculated from clinical markers. DR was identified in participants with diabetes mellitus (DM) when they exhibited one or more retinal microaneurysms or retinal blot hemorrhages under retinal imaging, with or without the presence of more severe lesions. Survey-weighted multivariable logistic regression was performed, and the regression model was further fitted using restricted cubic splines. The discriminatory capability and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Based on weighted analyses, of the 3100 participants included in this study, of which 162 had DR. In the adjusted model, BA (odds ratio [OR] = 1.12, 95% CI, 1.06–1.18) and PA (OR = 1.11, 95% CI, 1.07–1.14) were associated with DR, while CA was not significantly (OR = 1.01, 95% CI, 0.99–1.03). Narrowing the analysis to DM participants and adjusting for factors like insulin showed similar results. ROC and DCA analyses indicate that BA/PA predicted DR better than CA and offer greater clinical utility. The positive association between BA/PA and DR was consistent across subgroups despite potential interactions. Biological aging heightens DR risk, with BA/PA showing a stronger association than CA. Our findings underscored the importance of timely anti-aging interventions for preventing DR.

Abstract Background Droplet digital PCR (ddPCR) is widely applied to monitor measurable residual disease (MRD). However, there are limited studies on the feasibility of ddPCR-MRD monitoring after allogeneic hematopoietic stem cell transplantation (allo-HSCT), especially targeting multiple molecular markers simultaneously. Methods Our study collected samples from patients with acute myeloid leukemia (AML) or high-risk myelodysplastic syndrome (MDS) in complete remission after allo-HSCT between January 2018 and August 2021 to evaluate whether posttransplant ddPCR-MRD monitoring can identify patients at high risk of relapse. Results Of 152 patients, 58 (38.2%) were MRD positive by ddPCR within 4 months posttransplant, with a median variant allele frequency of 0.198%. The detectable DTA mutations (DNMT3A, TET2, and ASXL1 mutations) after allo-HSCT were not associated with an increased risk of relapse. After excluding DTA mutations, patients with ddPCR-MRD positivity had a significantly higher cumulative incidence of relapse (CIR, 38.7% vs. 9.7%, P

Posttransplant lymphoproliferative disorder (PTLD) is a rare lymphoid and/or plasmocytic proliferation that occurs after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to identify the pathologic features and clinical outcomes of T-cell PTLD, an extremely rare subtype of PTLD, after allo-HSCT. In this study, six allo-HSCT recipients with T-cell PTLD from five transplant centers in China were enrolled. All the T-cell PTLD were donor-derived, and three patients were with monomorphic and three with polymorphic types, respectively. All patients received cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP)-based chemotherapy. Five patients achieved complete response (CR), and one experienced progressive disease (PD). The median time from HSCT to onset was 4 (range: 0.6-72) months, analyzed in combination with the other 16 patients with T-cell PTLD identified from previous reports. About 56.3% of the T-cell samples (9/16) were positive for in situ hybridization with an Epstein–Barr virus (EBV)-encoded small nuclear early region (EBER ISH). CHOP-based chemotherapy might be the optimal strategy for patients who showed no response to empiric therapy with a CR rate of 87.5%. In conclusion, our study observed that T-cell PTLD has distinct clinical manifestations and morphological features, which characterized by less relation to EBV, later occurrence, and poorer prognosis when compared with B-cell PTLD.

BackgroundExposure to a mixture of environmental chemicals may cause gallstone, but the evidence remains equivocal. The current study aims to investigate the association between phthalate metabolites and gallstones, and to explore their mediators.MethodsData from the National Health and Nutrition Examination Survey 2017–2018 on U.S. adults (≥20 years) were analyzed to explore the association between phthalate metabolites and gallstones by employed survey-weighted logistic regression, restricted cubic spline (RCS), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR). Mediation analyses examined the role of oxidative stress markers, inflammatory markers, metabolic syndrome, body composition, diabetes, and insulin.ResultsThe current study included 1,384 participants, representing 200.6 million U.S. adults. Our results indicated a significant association between phthalate metabolites, particularly high molecular weight metabolites such as Di(2-ethylhexyl) phthalate (DEHP) and 1,2-Cyclohexane dicarboxylic acid diisononyl ester (DINCH), and gallstones. Furthermore, mediation analyses indicated that phthalate metabolites may play a role in the development of gallstones by influencing insulin secretion. Subgroup analyses did not reveal significant interaction.ConclusionThe association between exposure to phthalates and the occurrence of gallstones, potentially mediated by hyperinsulinemia from a nationally representative epidemiological perspective. These insights contribute to a better understanding of the potential health implications of plasticizers, emphasizing the need for proactive management measures.

Data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients at early stage of immune reconstitution after hematopoietic stem cell transplantation (HSCT) are limited. In the present study, we retrospectively investigated the incidence and clinical features of SARS-CoV-2 infection in patients who underwent HSCT in 2022. Patients (allo-HSCT, n = 80; auto-HSCT, n = 37) were consecutively included in the study. The SARS-CoV-2 infection rate was 59.8%, and the median interval of HSCT to coronavirus disease 2019 (COVID-19) was 4.8 (range: 0.5–12) months. Most patients were categorized as mild (41.4%) or moderate (38.6%), and 20% as severe/critical. No deaths were attributable to COVID-19. Further analysis showed that lower circulating CD8+ T-cell counts and calcineurin inhibitor administration increased the risk of SARS-CoV-2 infection. Exposure to rituximab significantly increased the probability of severe or critical COVID-19 compared with that of mild/moderate illness (P < .001). In the multivariate analysis, rituximab use was associated with severe COVID-19. Additionally, COVID-19 had no significant effect on immune reconstitution. Furthermore, it was found that Epstein–Barr virus infection and rituximab administration possibly increase the risk of developing severe illness. Our study provides preliminary insights into the effect of SARS-CoV-2 on immune reconstitution and the outcomes of allo-HSCT recipients.

This study aims to start from the macroscale of the megacity, explore a top-down operational technical path from the identification of community regeneration units as the evaluation objects to the BID priority intervention evaluation, and then propose differentiated community regeneration strategies based on the BID model. In the post-epidemic era, it is necessary for global megacities to take new measures in urban regeneration to respond to worldwide changes and challenges. As an innovative tool to promote central city revitalization, the BID model has played an important role in community transformation. In the context of the continuous decentralization of population and industry in China’s megacities, it is urgent to explore the technical path to apply the BID model to local community regeneration. Given the shortcomings of existing studies in the method to identify the scope of BID implementation and evaluate intervention priorities, this study takes Tianjin, a megacity in China, as an example and uses DBSCAN (density-based spatial clustering of applications with noise) and service area analysis to define the community regeneration units with commercial agglomeration characteristics as the objects. Then, the BID priority intervention evaluation system is constructed from the two measurement aspects of the intervention potential and the necessity of community regeneration to classify the BID intervention priorities of community regeneration units. The main conclusions are as follows: 1. When the DBSCAN analysis parameters take the minimum number of elements as 30 and the search distance as 120 m, the result is most suitable for identifying community units with commercial agglomeration of the study area; 2. Population vitality, especially working and residential population density, is the key factor affecting BID intervention potential, while road network density is an important indicator for determining the necessity of community regeneration; 3. Community regeneration units with high BID priority levels need to develop differentiated regeneration strategies combining their BID intervention potential, regeneration necessity characteristics, and location attributes. These conclusions can provide references for the governments of megacities to screen and establish BIDs.

Abstract N6-methyladenosine(m6A) is one of the most abundant modifications of mammalian cellular RNAs. m6A regulates various biological functions in epitranscriptomic ways, including RNA stability, decay, splicing, translation and nuclear export. Recent studies have indicated the growing importance of m6A modification in precancerous disease, influencing viral replication, immune escape, and carcinogenesis. Here, we review the role of m6A modification in HBV/HCV infection, NAFLD and liver fibrosis, and its function in liver disease pathogenesis. Our review will provide a new sight for the innovative treatment strategy for precancerous liver disease.

SiCp/Al composites offer the advantages of lightweight construction, high strength, and corrosion resistance, rendering them extensively applicable across various domains such as aerospace and precision instrumentation. Nonetheless, the interfacial reaction between SiC and Al under high temperatures leads to degradation in material properties. In this study, the interface segregation energy and interface binding energy subsequent to the inclusion of alloying elements were computed through a first-principle methodology, serving as a dataset for machine learning. Feature descriptors for machine learning undergo refinement via feature engineering. Leveraging the theory of machine-learning-accelerated first-principle computation, six machine learning models—RBF, SVM, BPNN, ENS, ANN, and RF—were developed to train the dataset, with the ANN model selected based on R2 and MSE metrics. Through this model, the accelerated computation of interface segregation energy and interface binding energy was achieved for 89 elements. The results indicate that elements including B, Si, Fe, Co, Ni, Cu, Zn, Ga, and Ge exhibit dual functionality, inhibiting interfacial reactions while bolstering interfacial binding. Furthermore, the atomic-scale mechanism elucidates the interfacial modulation of these elements. This investigation furnishes a theoretical framework for the compositional design of SiCp/Al composites.

In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element–graphene interactions.

The intermittent and random nature of wind brings great challenges to the accurate prediction of wind power; a single model is insufficient to meet the requirements of ultra-short-term wind power prediction. Although ensemble empirical mode decomposition (EEMD) can be used to extract the time series features of the original wind power data, the number of its modes will increase with the complexity of the original data. Too many modes are unnecessary, making the prediction model constructed based on the sub-models too complex. An entropy ensemble empirical mode decomposition (eEEMD) method based on information entropy is proposed in this work. Fewer components with significant feature differences are obtained using information entropy to reconstruct sub-sequences. The long short-term memory (LSTM) model is suitable for prediction after the decomposition of time series. All the modes are trained with the same deep learning framework LSTM. In view of the different features of each mode, models should be trained differentially for each mode; a rule is designed to determine the training error of each mode according to its average value. In this way, the model prediction accuracy and efficiency can make better tradeoffs. The predictions of different modes are reconstructed to obtain the final prediction results. The test results from a wind power unit show that the proposed eEEMD-LSTM has higher prediction accuracy compared with single LSTM and EEMD-LSTM, and the results based on Bayesian ridge regression (BR) and support vector regression (SVR) are the same; eEEMD-LSTM exhibits better performance.

The accurate detection of wind power outliers plays a crucial role in wind power forecasting, while the inherited strong randomness and high fluctuations bring great challenges to this issue. This work investigates the way to improve the outlier detection accuracy based on support vector machine (SVM). Although SVM can achieve good results for outlier detection in theory, its performance is heavily dependent on the hyper-parameters. Parameter optimization is not an easy task due to its complex nonlinear multi-optimum nature; an improved Harris hawk optimization (IHHO) is proposed to optimize the parameters of SVM for more accurate outlier detection. HHO takes the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce and can effectively search the optimal one in large parameter space, but it tends to fall into local optimum. To solve this issue, an improved Harris hawk optimization algorithm (IHHO) was proposed to obtain the optimal parameters of SVM. First, Hammersley sequence initialization is carried out to acquire good initial solutions. Then, a nonlinear factor control mode and an adaptive Gaussian–Cauchy mutation perturbation strategy are proposed to avoid getting trapped in local optima. In this way, a novel wind power outlier detection method named IHHO-SVM was constructed. The results on several wind power data with outliers show that IHHO-SVM outperforms SVM and HHO-SVM, which achieves the highest average F1 score of 96.63% and exhibits the smallest standard deviation. Compared to commonly used models for detecting outliers in wind power, such as isolation forest (IF), local outlier factor (LOF), SVM with grey wolf optimization (GWO-SVM), and SVM with particle swarm optimization (PSO-SVM), the proposed IHHO-SVM model shows the best overall performance with precision, recall, and F1 scores of 95.76%, 96.94%, and 96.35%, respectively.

Three-level NPC inverters have been widely used in grid-connected systems due to their superior performance compared with two-level inverters, but more switches lead to high fault probability. Meanwhile, the neutral point potential (NPP) fluctuation of the DC link is an inherent problem of three-level NPC inverters. To keep the three-level NPC inverter running stably after single-arm failure, a fault-tolerant control strategy based on an optimised space vector pulse width modulation (SVPWM) is proposed in this paper. Firstly, the common-mode voltage (CMV) of the postfault three-level NPC inverter is analysed and then the preliminary synthesis principles of the reference voltage vector are determined. Then, in order to ensure the NPP balance and the quality of the grid-connected currents, the reference voltage vector synthesis rules are optimised, a low-pass filter (LPF) and a hysteresis comparator are designed, respectively, to ensure the quality of grid-connected currents and effectively decrease the DC link NPP deviation. Finally, the simulation results show that the proposed fault-tolerant control strategy can realize the stable and reliable operation of the grid-connected three-level NPC inverter after single-arm failure, and the CMV can be reduced significantly, the quality of grid-connected currents is also improved. The proposed fault-tolerant strategy also shows good performance when the grid-connected currents change.

Aerial TDI cameras can obtain images with a high sensitivity, high resolution, and wide dynamic range under low-illumination conditions. Under the condition of low illumination, exposure is an important parameter that affects the image quality of the camera. Auto-exposure aims to obtain the information of target scene in advance and uses it as the basis for determining exposure parameters, which can avoid the loss of image information caused by overexposure or underexposure. The auto-exposure of TDI CCD is usually difficult, as the shooting mode of TDI CCD is a push and sweep mode, which can only take one image of the target scene, and the output image is not repetitive, so it is difficult to obtain an image of the target scene in advance. At present, the common method is to add an additional sensor to collect the feature information of the target scene in advance; however, this increases the complexity of the system. Therefore, this paper proposes that the camera uses TDI CCD to collect the first N rows of data of the target scene in advance as the basis, and then adjusts and determines the exposure parameters with the median gray value as the target; thus, without adding additional sensors, the auto-exposure of TDI CCD can be realized. To evaluate the effect of auto-exposure, three methods of image power spectrum variance, image histogram, and image information entropy are used. The test results show that, after auto-exposure, the variance in the image power spectrum increased by 0.4362, the entropy of image information increased by 1.7064, and the distribution of the image histogram was more uniform than that before auto-exposure. This shows that the effect of auto-exposure is good, and it has better scene adaptation, allowing for it to meet the requirements of auto-exposure imaging under low-illumination conditions in aerial TDI cameras.

In order to provide guidance for furthering the balance of strength and toughness of AerMet 100 steel through tempering treatment, the effects of the tempering time on microstructure and mechanical properties are investigated. The microstructure evolution, especially M2C precipitates and austenite in AerMet 100 tempered at 482 °C for 1~20 h, was characterized, and its influences on the mechanical properties were studied. The tensile strength decreases gradually, the yield strength increases first and then decreases, and the fracture toughness KIC increases gradually with an increasing tempering time. The strength and toughness matching of AerMet 100 steel is achieved by tempering at 482 °C for 5~7 h. Without considering the martensitic size effect, the influence of the dislocation density on the tensile strength is more significant during tempering at 482 °C. The precipitation strengthening mechanism plays a dominant role in the yield strength when tempering for 5 h or less, and the combined influence of carbide coarsening and a sharp decrease in the dislocation density resulted in a significant decrease in tensile strength when tempering for 8 h or more. The fracture toughness KIC is primarily influenced by the reverted austenite, so that KIC increases gradually with the prolongation of the tempering time. However, a significant decrease in the dislocation density resulting from long-term tempering has a certain impact on KIC, giving rise to a decrease in the rising amplitude in KIC after tempering for 8 h or more.

In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys.

Addressing the exceptional properties of aluminium alloy composites reinforced with graphene, this study presents an interpretable machine learning approach to aid in the rapid and efficient design of such materials. Initially, data on these composites were gathered and optimised in order to create a dataset of composition/process-property. Several machine learning algorithms were used to train various models. The SHAP method was used to interpret and select the best performing model, which happened to be the CatBoost model. The model achieved accurate predictions of hardness and tensile strength, with coefficients of determination of 0.9597 and 0.9882, respectively, and average relative errors of 6.02% and 5.01%, respectively. The results obtained from the SHAP method unveiled the correlation between the composition, process and properties of aluminium alloy composites reinforced with graphene. By comparing the predicted and experimental data in this study, all machine learning models exhibited prediction errors within 10%, confirming their ability to generalise. This study offers valuable insights and support for designing high-performance aluminium matrix composites reinforced with graphene and showcases the implementation of machine learning in materials science.

In the field-weakening region, the traditional field-weakening method for induction motor drives based on model predictive control (MPC) is to take a no-load operation as the premise and adjust the flux reference in the cost function proportional to the inverse of the rotor speed, which leads to poor torque output. This paper presents a novel field-weakening method for IM drives based on MPC. Considering the induction motor field-weakening limiting conditions and according to the speed adaptive field-weakening strategy with a voltage closed-loop, the speed adaptive field-weakening controllers were designed to optimize the references of the excitation current and torque current. In the rotor field-orientation d–q coordinate system, the stator flux amplitude and torque reference values were optimized by the optimal distribution current. Then, according to the dead-beat control principle, they were converted into an equivalent stator flux vector reference. Moreover, the stator voltage vector reference can be obtained. For an induction motor fed by a three-level neutral point clamped (3L-NPC) inverter, the cost function was constructed by combining all the constraints, including the voltage vector, the neutral potential balance, and the switching frequency. In this way, the high-performance field-weakening operation for the induction motor based on a model predictive control can be realized. The simulation and experiment results show that the proposed method can increase the torque output by 22% in the field-weakening region; at the same time, the steady characteristics and the dynamic response performance can be maintained well.

BackgroundThe main purpose of this study was to explore the relationship between depression literacy (D-Lit) and the development and progression of depressive mood.MethodsThis longitudinal study with multiple cross-sectional analyses used data from a nationwide online questionnaire administered via the Wen Juan Xing survey platform. Eligible participants were 18 years or older and had subjectively experienced mild depressive moods at the time of their initial enrollment in the study. The follow-up time was 3 months. Spearman's rank correlation test was used to analyze the predictive role of D-Lit on the later development of depressive mood.ResultsWe included 488 individuals with mild depressive moods. No statistically significant correlation between D-Lit and Zung Self-rating Depression Scale (SDS) at baseline was observed (adjusted rho = 0.001, p = 0.974). However, after 1 month (adjusted rho = −0.449, p < 0.001) and after 3 months (adjusted rho = −0.759, p < 0.001), D-Lit was significantly and negatively correlated with SDS.LimitationsThe targeted subjects were limited to the Chinese adult social media users; meanwhile, China's current management policies for COVID-19 differ from most of the other countries, limiting the generalizability of this study.ConclusionDespite the limitations, our study provided novel evidence supporting that low depression literacy may be associated with exacerbated development and progression of depressive mood, which, if not appropriately and promptly controlled, may ultimately lead to depression. In the future, we encourage further research to explore the practical and efficient ways to enhance public depression literacy.

In this paper, the interfacial behavior and the atom diffusion behavior of an Al4Si alloy were systematically investigated by means of first-principles calculations. The K-points and cutoff energy of the computational system were determined by convergence tests, and the surface energies for five different surfaces of Al4Si alloys were investigated. Among the five surfaces investigated for Al4Si, it was found that the (111) surface was the surface with the lowest surface energy. Subsequently, we investigated the interfacial stability of the (111) surface and found that there were two types of interfaces, the Al/Al interface and the Al/Si interface. The fracture energies and theoretical strengths of the two interfaces were calculated; the results show that the Al/Al interface had the highest interfacial strength, and the calculation of their electronic results explained the above phenomenon. Subsequently, we investigated the diffusion and migration behavior of Si atoms in the alloy system, mainly in the form of vacancies. We considered the diffusion of Si atoms in vacancies of Al and Si atoms, respectively; the results showed that Si atoms are more susceptible to diffusive migration to Al atomic vacancies than to Si atomic vacancies. The results of the calculations on the micromechanics of aluminum alloys, as well as the diffusion migration behavior, provide a theoretical basis for the further development of new aluminum alloys.

The creep life prediction of austenitic heat-resistant steel is necessary to guarantee the safe operation of the high-temperature components in thermal power plants. This work presents a machine learning model that can be applied to predict the creep life of austenitic steels, offering a novel method and approach for such predictions. In this paper, creep life data from six typical austenitic heat-resistant steels are used to predict their creep life using various machine learning models. Moreover, the dissimilarities between the machine learning model and the conventional lifetime prediction method are compared. Finally, the influence of different input characteristics on creep life is discussed. The results demonstrate that the prediction accuracy of machine learning depends on both the model and the dataset used. The Gaussian model based on the second dataset achieves the highest level of prediction accuracy. Additionally, the accuracy and the generalization ability of the machine learning model prediction are significantly better than those of the traditional model. Lastly, the effect of the input characteristics on creep life is generally consistent with experimental observations and theoretical analyses.

With the rapid increase in wind power, its strong randomness has brought great challenges to power system operation. Accurate and timely ultra-short-term wind power prediction is essential for the stable operation of power systems. In this paper, an LsAdam–LSTM model is proposed for ultra-short-term wind power prediction, which is obtained by accelerating the long short-term memory (LSTM) network using an improved Adam optimizer with loss shrinkage (LsAdam). For a specific network topology, training progress heavily depends on the learning rate. To make the training loss of LSTM shrink faster with standard Adam, we use the past training loss-changing information to finely tune the next learning rate. Therefore, we design a gain coefficient according to the loss change to adjust the global learning rate in every epoch. In this way, the loss change in the training process can be incorporated into the learning progress and a closed-loop adaptive learning rate tuning mechanism can be constructed. Drastic changes in network parameters will deteriorate learning progress and even make the model non-converging, so the gain coefficient is designed based on the arctangent function with self-limiting properties. Because the learning rate is iteratively tuned with past loss-changing information, the trained model will have better performance. The test results on a wind turbine show that the LsAdam–LSTM model can obtain higher prediction accuracy with much fewer training epochs compared with Adam–LSTM, and the prediction accuracy has significant improvements compared with BP and SVR models.

AbstractAbrupt changes in aperture (sudden expansion and contraction) are commonly seen in naturally occurred or artificial single fractures. The relevant research mainly focuses on the changes in fluid properties caused by the sudden expansion of the aperture in smooth parallel fractures. To investigate the effects of roughness on the nonlinear flow properties in a single rough fracture with abruptly aperture change (SF-AC), the flow characteristics of the fractures under different Reynolds numbers Re (50~2000) are simulated by the turbulence k-ε steady-state modulus with the Naiver-Stokes equation. The results show that, in a rough SF-AC, the growth of the eddy and the flow path deflection of the mainstream zone are more obvious than those in a smooth SF-AC, and the discrepancies between the rough and smooth SF-ACs become even more obvious when the relative roughness and/or Re values become greater. The increase of the fracture roughness leads to the generation of more local eddies on the rough SF-ACs and enhances the flow path deflection in the sudden expansion fracture. The number of eddies increases with Re, and the size of eddy area increases linearly with Re at first. When Re reaches a value of 300-500, the growth rate of the eddy size slows down and then stabilizes. Groundwater flow in a rough SF-AC follows a clearly visible nonlinear (or non-Darcy) flow law other than the linear Darcy’s law. The Forchheimer equation fits the hydraulic gradient-velocity (J-v) better than the linear Darcy’s law. The corresponding critical Re value at which the nonlinear flow starts to dominate in a rough SF-AC is around 300~500.

Graphene has become an ideal reinforcement for reinforced metal matrix composites due to its excellent mechanical properties. However, the theory of graphene reinforcement in graphene/aluminum matrix composites is not yet well developed. In this paper, the effect of different temperatures on the mechanical properties of the metal matrix is investigated using a classical molecular dynamics approach, and the effects of the configuration and distribution of graphene in the metal matrix on the mechanical properties of the composites are also described in detail. It is shown that in the case of a monolayer graphene-reinforced aluminum matrix, the simulated stretching process does not break the graphene as the strain increases, but rather, the graphene and the aluminum matrix have a shearing behavior, and thus, the graphene “pulls out" from the aluminum matrix. In the parallel stretching direction, the tensile stress tends to increase with the increase of the graphene area ratio. In the vertical stretching direction, the tensile stress tends to decrease as the percentage of graphene area increases. In the parallel stretching direction, the tensile stress of the system tends to decrease as the angle between graphene and the stretching direction increases. It is important to investigate the effect of a different graphene distribution in the aluminum matrix on the mechanical properties of the composites for the design of high-strength graphene/metal matrix composites.

(1) Background: Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide with limited treatment satisfaction. Finding new therapeutic targets has remained a major challenge. Ferroptosis is an iron-dependent cell death program that plays a regulatory role in HBV infection and HCC development. It is necessary to classify the roles of ferroptosis or ferroptosis-related genes (FRGs) in HBV-related HCC progression. (2) Methods: We conducted a matched case–control study from the TCGA database, retrospectively collecting demographic data and common clinical indicators from all subjects. The Kaplan–Meier curve, univariate and multivariate cox regression analysis of the FRGs were used to explore the risk factors for HBV-related HCC. The CIBERSORT algorithm and TIDE algorithm were executed to evaluate the functions of FRGs in the tumor-immune environment. (3) Results: A total of 145 HBV-positive HCC patients and 266 HBV-negative HCC patients were enrolled in our study. Four ferroptosis related genes (FANCD2, CS, CISD1 and SLC1A5) were positively correlated with the progression of HBV-related HCC. Among them, SLC1A5 was an independent risk factor for HBV-related HCC, and correlated with poor prognosis, advanced progression and an immunosuppression microenvironment. (4) Conclusions: Here, we revealed that a ferroptosis-related gene, SLC1A5, may be an excellent predictor of HBV-related HCC and may provide insight into the development of innovative possible therapeutic techniques.

Abstract Background The SARS-CoV-2 RNA was detected positive again after discharged from hospital in some COVID-19 patients, with or without clinical symptoms such as fever or dry cough. Methods 1008 severe COVID-19 patients, with SARS-CoV-2 RNA positive detected with the mixed specimen of nasopharyngeal swab and oropharyngeal swab by real-time fluorescence quantitative PCR (RT-qPCR), were selected to monitor SARS-CoV-2 RNA with the 12 types of specimens by RT-qPCR during hospitalization. All of 20 discharged cases with COVID-19 were selected to detect SARS-CoV-2 RNA in isolation period with 7 types of specimens by RT-qPCR before releasing the isolation period. Results Of the enrolled 1008 severe patients, the nasopharyngeal swab specimens showed the highest positive rate of SARS-CoV-2 RNA (71.06%), followed by alveolar lavage fluid (66.67%), oropharyngeal swab (30.77%), sputum (28.53%), urine (16.30%), blood (12.5%), stool (12.21%), anal swab (11.22%) and corneal secretion (2.99%), and SARS-CoV-2 RNA couldn’t be detected in other types of specimen in this study. Of the 20 discharged cases during the isolation period, the positive rate of SARS-CoV-2 RNA was 30% (6/20): 2 cases were positive in sputum at the eighth and ninth day after discharge, respectively, 1 case was positive in nasopharynx swab at the sixth day after discharge, 1 case was positive in anal swab at the eighth day after discharge, and 1 case was positive in 3 specimens (nasopharynx swab, oropharynx swab and sputum) simultaneously at the fourth day after discharge, and no positive SARS-CoV-2 RNA was detected in other specimens including stool, urine and blood at the discharged patients. Conclusions SARS-CoV-2 RNA should be detected in multiple specimens, such as nasopharynx swab, oropharynx swab, sputum, and if necessary, stool and anal swab specimens should be performed simultaneously at discharge when the patients were considered for clinical cure and before releasing the isolation period.

In order to meet the pressing demand for wide-area communication required by the Global Energy Interconnection (GEI), accelerating the construction of satellite-terrestrial integrated networks that can achieve network extension and seamless global coverage has become the focus of power communication technology development. In this study, we propose a satellite-terrestrial integrated network model that can support interconnection and interoperation on the IP layer between the satellite system and the terrestrial segment of the existing power communication system. First, the composition and function of the satellite-terrestrial collaborative network are explained. Then, the IP-based protocol stack is described, and a typical application experiment is conducted to illustrate the particular process of this protocol stack. Finally, a use case of IP interconnection that depends on GEO satellite communication is detailed. The experimental study has showed that the satellite-terrestrial collaborative network can efficiently support various IP applications for the GEI. Keywords: Global Energy Interconnection (GEI), Satellite-terrestrial integrated network, IP network

Objectives: The longitudinal characterization and risk of poor outcomes related to cytokine overproduction in critical coronavirus disease 2019 (COVID-19) patients with hyperinflammation in bronchoalveolar lavage requires further investigation.Methods: We enrolled two critically ill patients with comorbidities diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detected by RT-PCR during hospitalization. Clinical characteristics, longitudinal immunological, and biochemical parameters of each critical COVID-19 case were collected.Main Results: The clinical characteristics and laboratory results of each case demonstrated critical symptoms of COVID-19 with poor outcomes. Both nasopharyngeal swabs and bronchoalveolar lavage fluid (BALF) samples tested positive for SARS-CoV-2. Two patients received targeted treatments against pathogen infection and inflammation in addition to interventional therapies, except for Patient 2, who received an additional artificial liver system treatment. Hyperinflammation with a dominantly high level of IL-6 was observed in BALF samples from both critical cases with decreased T cell populations. High levels of cytokines and pathological parameters were successively maintained in Patient 1, but rapidly reduced at the late treatment stage in Patient 2. The outcome of Patient 1 is death, whereas the outcome of Patient 2 is recovery.Conclusions: This case report suggests that a high risk of poor outcomes was related to a heavily hyperinflammatory milieu in both the blood and lungs of critical COVID-19 patients. The artificial liver intervention on cytokines overproduction might be beneficial for the recovery of critical COVID-19 patients as a reliable therapy that can be coordinated with targeted treatments, which ought to be further tested in adequately designed and powered clinical trials.

Twinning is a powerful approach to engineering the physicochemical properties of metallic nanomaterials. Here, the authors discover unusual non-close-packed twinning planes in 4H-phase gold nanokites and show that they can be used as templates to grow 4H-phase twinned nanostructures of other noble metals.

This paper proposes an approach to design the length, width, and bundlewidth dimensions of rectangular ironless coils for magnetically levitated planar motors. Firstly, a model for the propulsion and levitation forces generated from a single coil in Halbach permanent magnetic field by using Lorenz force volume integral method is developed. Then, a dimensionless factor named coil width dependent factor, Kc, which can define the amplitudes of propulsion and levitation forces, is formulated. In order to maximize the value of Kc, the optimal coils number, n, of a square forcer is derived to be m/2 + 1, where m is the multiples of coil length Lc to magnetic pole pitch τ. Thereafter, the coil width Wc and bundlewidth Bc are determined considering the given magnetic pole pitch τ. The comparison between the proposed approach and other formulations verifies that if such designed coils is utilized in a forcer, the average amplitude and the quadratic sum of all coil currents are both minimal when the forcer is desired to generate the identical driving forces. The proposed approach can be used to design the dimensions of a single coil and also to configure the high power density forcers or coil arrays for magnetically levitated planar motors.

Abstract Background Stent migration is a common complication in treating trachea stenosis. There is no report concerning suture fixation of tracheal stent. The aim of this study was to investigate whether suture fixation of tracheal stent could avoid stent migration in patients with upper trachea stenosis. The complications were further investigated. Methods The patients with upper trachea stenosis who underwent tracheal stent placement for benign/malignant conditions in our hospital between May 2016 and April 2018 were retrospectively reviewed. Clinical data were collected for each patient, including age, gender, co-morbid diseases, site of tracheal obstruction, degree of tracheal obstruction, success of stent placement, impact on patient’s symptoms, complications, etc. Results Eleven patients (8 males and 3 females; range of age: 17–85, and average age of 63) were enrolled into this study. Six silicone stents and five membrane-covered metal stents were used. The surgery was successfully performed in all the cases. The postoperative recovery was uneventful. All symptoms of the patients were relieved. No complications occurred. The average follow-up for patients was 5 months (range of 1–13 months). During the follow-up, no stent migration was observed according to CT and bronchoscope. Conclusion The results suggested that suture fixation of stents could avoid stent migration in treating upper trachea stenosis with metal stent or silicone stent. This method seemed to be effective without operation complications.

This study focuses on detailed analysis of disturbance for three-phase uninterruptible power supply (UPS) in reality, as well as design of active-disturbance rejection controller (ADRC) and simulation of the corresponding model with multi-disturbances. Parameters of LC-filter will change with UPS operation. Also, UPS not only has pure resistive load, but also resistive–inductive load, and sometimes even non-linear load. The authors take the above disturbance of uninterruptible power supply into consideration at the beginning. Then, according to the above analysis of disturbances, a mathematical model with disturbance term is established. After, the active-disturbance rejection controller is designed to depress the disturbances from internal part and external part. Finally, the corresponding simulation is conducted in order to verify the precision of modelling and the effectiveness of ADRC control strategy.

Abstract Background Tracheal stenosis caused by tracheotomy and intubation is considered intractable. Although the segmental tracheal resection and endoscopic intervention are available, they usually result in great operation injury or are difficult to perform. Case presentation A patient with acquired tracheal stenosis was treated with tracheotomy-coblation. The patient was followed up by bronchoscopy every 2 months. After 6-month follow-up, the symptoms of dyspnea and hoarseness disappeared and no tracheal stenosis was observed. Conclusions The present technique, tracheotomy-coblation, is advantageous with less injury and easy to perform.

The electronic structure and magnetic properties of 3d transition metal (Cr, Co)-codoped 4H–SiC were studied by density functional theory within GGA methods. The results show that all doped magnetic atoms have high magnetic properties in both Cr-doped and Co-doped 4H–SiC, resulting in the net magnetic moments of 3.03, 3.02 μ B for Si 35 CrC 36 and Si 35 CoC 36 . The electronic density of states reaches the peak at Fermi level, which is beneficial to the electronic transitions, indicating that Cr-doped 4H–SiC is a semi-metallic material. In addition, the magnetic properties of (Cr, Co)-codoped 4H–SiC were also calculated. The results show that the (Cr, Co)-codoped 4H–SiC system has more stable ferromagnetic properties with ΔE F M of −244.3 meV, and we estimated T C of about 470.8 K for the (Cr, Co)-codoped 4H–SiC system. The (Cr, Co)-codoped 4H–SiC can be ferromagnetic through some mechanism based on hybridization between local Cr:3d, Co:3d and C:2p states. These interesting discoveries will help promote the use of excellent SiC-based nanomaterials in spintronics and multi-function nanodevices in the near future.

With the rapid development of wind power, the installed capacity of wind power is also growing continuously. The intermittency and uncertainty of wind power may pose danger on the safety of the power system, thus Research of short-term load forecasting has important practical application value in the field of power network dispatching. The keys of wind power forecasting are the forecasting model selection and model optimization. In this paper, the least squares support vector machine (LSSVM) is chosen as the wind speed and the wind power prediction model and quantum-behaved particle swarm optimization (QPSO) algorithm is used to optimize the most important parameters which influence the least squares support vector machine regression model. In the proposed QPSO-LSSVM, the kernel parameter

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper proposes a convex quadratic optimization commutation method to raise the equalization degree of power loss distribution of coil array for magnetic levitation planar motors. Starting with the modeling of electromagnetic forces/torques and commutation of coil array, the global power loss and the local power losses of coil array are analyzed, and the power loss equalizing degree is defined to evaluate the power loss distribution of coil array being commutated dynamically. Then, in consideration of the fact that the global power loss is the quadratic function of commutated coil currents, the convex quadratic function optimization with equality constraint and boundary constraints is applied to commutate the coil array, and the power loss equalizing degree is raised by decreasing the boundary constraints of optimization. Taking the magnetically levitated planar motor under investigation as examples and using quadprog routine in Matlab Optimization Toolbox, which is a dedicated quadratic optimization routine, it is verified that the power loss equalizing the degree of coil array is raised gradually and the power loss distribution of coil array becomes more uniform along with decrease of the boundary constraints. The convex quadratic optimization commutation is verified experimentally on a constructed multi-dimension force/torque measurement platform. Using the convex quadratic optimization commutation can not only improve the power loss distribution of coil array of magnetically levitated planar motors, but also make it possible to select lower capacity power amplifiers to produce the identical desired electromagnetic forces and torques.

Lignin is recognized as a good sustainable material because of its great abundance and potential applications. At present, lignin hydrogenolysis is considered as a potential but challenging way to produce low-molecular-mass aromatic chemicals. The most common linkage between the structural units of lignin polymer is the β-O-4 aryl ether, which are primary or even only target chemical bonds for many degradation processes. Herein, a Pd-Fe3O4 composite was synthesized for catalytic hydrogenolysis of β-O-4 bond in lignin. The synthesized catalyst was characterized by XRD, XPS, and SEM and the lignin depolymerization products were analyzed by GC-MS. The catalyst showed good catalytic performance during the hydrogenolysis process, lignin dimer was degraded into monomers completely and a high yield of monomers was obtained by the hydrogenolysis of bagasse lignin. More importantly, the magnetic catalyst was separated conveniently by magnet after reaction and remained highly catalytically efficient after being reused for five times. This work has demonstrated an efficient & recyclable catalyst for the cleavage of the β-O-4 bond in lignin providing an alternative way to make better use of lignins.