Sampling Importance Resampling Algorithm with Nonignorable Missing Response Variable Based on Smoothed Quantile Regression

The presence of nonignorable missing response variables often leads to complex conditional distribution patterns that cannot be effectively captured through mean regression. In contrast, quantile regression offers valuable insights into the conditional distribution. Consequently, this article places emphasis on the quantile regression approach to address nonrandom missing data. Taking inspiration from fractional imputation, this paper proposes a novel smoothed quantile regression estimation equation based on a sampling importance resampling (SIR) algorithm instead of nonparametric kernel regression methods. Additionally, we present an augmented inverse probability weighting (AIPW) smoothed quantile regression estimation equation to reduce the influence of potential misspecification in a working model. The consistency and asymptotic normality of the empirical likelihood estimators corresponding to the above estimating equations are proven under the assumption of a correctly specified parameter working model. Furthermore, we demonstrate that the AIPW estimation equation converges to an IPW estimation equation when a parameter working model is misspecified, thus illustrating the robustness of the AIPW estimation approach. Through numerical simulations, we examine the finite sample properties of the proposed method when the working models are both correctly specified and misspecified. Furthermore, we apply the proposed method to analyze HIV—CD4 data, thereby exploring variations in treatment effects and the influence of other covariates across different quantiles.Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of ChinaPeer Reviewe

ASTRAGALUS MONGHOLICUS REGULATE THE TOLL-LIKE-RECEPTOR 4 MEDITATED SIGNAL TRANSDUCTION OF DENDRITIC CELLS TO RESTRAIN STOMACH CANCER CELLS

Backgroud: According to the traditional view, we depend on three methods to treat tumors; surgery, chemotherapy and radiotherapy. However, these methods have its own limitations in application. Traditional Chinese Medicine (TCM) is one of the oldest healing systems. Astragalus mongholicus (AMs) that is the common herbal medicine, the biggest part of TCM, have been proved to be effective in treating cancers from lots of clinical cases. However, we have not fully understood the anti-tumor mechanism of AMs, and this has lead to some doubt for some Western-Medicine scholars and restricts its wide use. The main objective of this research is to discuss the effect and mechanism of AMs to human stomach cancer. Materials and Methods: To observe the effect and mechanism of tumor treatment by AMs, we have done the research from three major aspects, the influence of DCs, the inhibition of tumor in vitro as well as the animal studies in vivo after treatment. First, we culture the mouse dendritic cells (DCs) from bone marrow of mouse hind legs according to the method using Interleukin-4(IL-4) and Granulocyte-macrophage colony stimulating factor (GM-CSF), which refer to the way established by Inaba (Inaba K, 1992). And then we investigate the growth-rate of the DCs co-cultured with AMs injection. We analyze the expression of the Toll-like-receptor 4(TLR4), with SYBR-Green I Real-time PCR and the I-kappa-B-alpha (IκB-α) with Western-Blot, the main regulatory protein to control nuclear factor NFκB-p65 nuclear translocation. Second, we choose the human gastric cancer cell lines MKN 45 as the target cell, which was co-cultured with DCs,T cells from spleen of mouse and AMs injection, and use MTT assay to judge the amount of cell lines and Immnunoflurescene to analyze the expression of anti-active caspase 3 pAb anti-PARP P85 fragment pAb, the mark of apoptosis of cells. Third, we have conducted the animal studies beside the basic experiment in vitro. The nude mouse developed stomach cancer, due to intra-preritoneal injection with MKN45 have been divided into two groups: the treatment group challenged with AMs injection and the control group with saline injection. We took the average of the diameter of each group as the y axis and the days after administered with AMs as x axis. After 40 days, all animals were killed by detruncation, and the tumor were removed and measured. We compare the diameter (40 days) of the tumor as well as the survival days between different groups to investigate the effect of inhibition of cancer. Results: All results show that AMs is effective in treating human stomach cancer and the mechanism might be regulated by TLR4 mediated signal transduction of DCs. The results are briefly introduced as follows:First, we succeed in culturing the DCs induced by IL-4 and GM-CSF and find the positive rate of CD11c expression, the mark of DCs,is beyond 90% (Fig-1). We detect AMs can precipitate DCs maturation by upregulating TLR4 in SYBR-Green I Real-time PCR (Fig-2) and suppressing IκB-αby Western-Blot (Fig-3). Second, after the MKN45 co-cultured with DCs,T cells and AMs injection, the result show that AMs can great reduce the amount of cell lines by MTT assay (Fig-4) and induce apoptosis with Immunofluorescence (Fig-5). Finally, we have conducted animal studies beside the experiment in vitro, and the result in vivo show that AMs can delay tumor development from the diameter and weight of the tumor (Fig-6, Fig-7), prolong life-span and improve life-quality. Conclusion: Ams Can play a great role in treating human stomach cancers as a good Chinese herbal medicine by precipitating DCs maturation, which is probably due to its effects by regulating the TLR4 mediated signal transduction

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the &lsquo Electric Vehicles&ndash Power Grid&ndash Traffic Network&rsquo fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners&rsquo charging behavior. Document type: Articl

Analyzing Dynamic Changes of Laboratory Indexes in Patients with Acute Heart Failure Based on Retrospective Study

Background. Changes of N-terminal probrain natriuretic peptide (NT-proBNP) have been studied whether in the long term or the short term in patients of acute heart failure (AHF); however, changes of NT-proBNP in the first five days and their association with other factors have not been investigated. Aims. To describe the dynamic changes of relevant laboratory indexes in the first five days between different outcomes of AHF patients and their associations. Methods and Results. 284 AHF with dynamic values recorded were analyzed. Changes of NT-proBNP, troponin T, and C-reactive protein were different between patients with different outcomes, with higher values in adverse group than in control group at the same time points ( < 0.05). Then, prognostic use and risk stratification of NT-proBNP were assessed by receiver-operating characteristic curve and logistic regression. NT-proBNP levels at day 3 showed the best prognostic power (area under the curve = 0.730, 95% confidence interval (CI): 0.657 to 0.794) and was an independent risk factor for adverse outcome (odds ratio, OR: 2.185, 95% CI: 1.584-3.015). Classified changes of NT-proBNP may be predictive for adverse outcomes in AHF patients. Conclusions. Sequential monitoring of laboratory indexes within the first 5 days may be helpful for management of AHF patients

Does Environmental Regulation Improve an Enterprise’s Productivity?—Evidence from China’s Carbon Reduction Policy

Theoretical research finds that environmental regulation has both positive and negative effects on enterprise productivity. Based on the Energy Conservation and Carbon Reduction policy implemented by the Chinese government, this study empirically analyzed the policy treatment level boundary condition of the positive and negative effects that dominated and its moderating factors. The generalized propensity score matching model (GPSM) was used to analyze the net effect of the policy on the total factor productivity (TFP) of Chinese manufacturing enterprises. The results showed that: (1) the low treatment level of the policy can promote the growth of the enterprises’ TFP; (2) in contrast, the treatment level of the policy exceeded a certain threshold, which gradually led to an inhibitory effect on the enterprises’ TFP; (3) the mechanism used to enforce the policy caused the enterprises to innovate, which enhanced their TFP, but took time to gradually release; (4) the enterprises with a stronger capacity for innovation or a larger size found it easier to transform the pressure from the policy into an innovative impetus to improve their TFP; (5) however, the government subsidies distorted the forced-innovation effect of the policy on the enterprises’ innovation, which restrained the positive effect of the policy on the TFP

Variation and Dynamics of Soil Nematode Communities in Greenhouses with Different Continuous Cropping Periods

Continuous cropping in greenhouses can result in root-knot nematode outbreaks resulting from imbalances in the soil nematode community. However, the changes in soil nematode communities in greenhouses with continuous crop production are unclear. We compared soil nematode communities in greenhouses after 2 years (2-yr) and 10 years (10-yr) of continuous crop production by 18S rDNA high-throughput sequencing. Compared with the 2-yr greenhouse, soil in the 10-yr greenhouse showed acidification, nutrient accumulation and salinization. Bacterial-feeding nematodes (BF) were dominant in the 2-yr greenhouse over the whole growing season, but plant-parasitic nematodes (PP) were the dominant group in the 10-yr greenhouse during the late growing season. Meloidogyne gradually became the dominant group and had a relative abundance of 70.9% (maximum) in the 10-yr greenhouse. Rhabditidae, with relative abundance ranging from 99.8% to 26.8%, was the predominant group in the 2-year greenhouse. For β-diversity, hierarchical clustering analysis, unweighted UniFrac principal component analysis (PCA) and principal co-ordinates analysis (PCoA) all revealed that soil nematode communities in the two types of greenhouses were significantly different. Redundancy analysis (RDA) showed that soil nematode communities in the 10-yr greenhouse were related to high soil organic material, total nitrogen, electrical conductivity and disease index of root-knot nematodes. Fisher's exact test and Pearson's correlation coefficients revealed that Meloidogyne contributed to the main differences in soil nematode communities between the two types of greenhouses. Population dynamics of Meloidogyne were divided into dormant phase, low-level increasing phase and exponential phase during the whole season. The soil nematode communities within the 2-yr and 10-yr greenhouses had significant variation and different dynamics. This work contributes to a deeper understanding of changes in the soil nematode community in greenhouses with different continuous cropping duration

Analysis of the Noise Reduction Characteristics of a Diffuser with Fizeau Interferometry

To solve the coherent noise problem of an interference image, the method of a rotating diffuser was adopted to change the coherence of a beam to reduce the noise of the interference system. The relationship between the speed of the diffuser and the signal-to-noise ratio (SNR) of the fringe contrast system was simulated to obtain the diffuser control parameters needed for the best interference fringe state. The fringe contrast of each image and the SNR of the system were analyzed. The results showed that the increased speed of the diffuser reduced the contrast of the interference image to a certain extent, but the increased speed also effectively improved the SNR and facilitated the subsequent interference image processing. Due to the coherent noise in the interferometric system, the method of the rotated diffuser reduced the coherence of the light beam to suppress the noise of the interference image. By analyzing the coherent noise reduction characteristics of the rotated diffuser with different surface roughnesses, the relationship between the surface roughness and the noise contrast for different rotation speeds was simulated, and the effective roughness range with the noise reduction effect was selected. A noise reduction system was built based on Fizeau interference, and the noise contrast of the interference image was collected and calculated. The effective range of σh/λ was 0.2–0.5 when the rotation speed was 10 r/s, while the effective range of σh/λ was 0.4–0.6 when the rotation speed was 100 r/s. The experimental results showed that the surface roughness and wavelength ratio σh/λ of the rotated diffuser increased when the noise contrast tended toward 1, but the effective range of the surface roughness decreased with the increase of the rotational speed of the diffuser

Design and Performance Analysis of Probabilistically Shaped QAM Signals for Coherent FSO Systems with Gamma-Gamma Turbulence Channels

Probabilistic shaping (PS) is a promising technique to approach the Shannon limit. In this paper, we design a practical coded modulation scheme based on PS to improve the capacity of coherent free-space optical (FSO) links with quadrature amplitude modulation (QAM), where the fading channel follows the Gamma-Gamma distribution. The aim of this paper is to optimize the probability mass function (PMF) of the QAM signal points to achieve the maximum channel capacity. Due to the complexity of the objective function, the heuristic algorithm was employed to solve the optimization problem. To the best of the authors’ knowledge, the closed-form pairwise error probability (PEP) is first derived with the non-uniform signals under the turbulence channel. In addition, we measure the average symbol error rate (SER) and post-FEC bit error rate (BER) by the Monte Carlo simulation method. The numerical simulation results of both capacity and BER show that the proposed PS scheme is better than the uniform distribution. The post-FEC BER results show that the proposed PS scheme provides significant gains compared with the uniform scheme

Multi-Criteria Evaluation of Distributed Energy System Based on Order Relation-Anti-Entropy Weight Method

Distributed Energy System (DES), a comprehensive energy utilization system distributed on user side, has been recognized as a promising energy utilization method that can improve energy efficiency, reduce greenhouse gas emissions, and achieve sustainable development. However, the DES is usually driven by various energy sources, and it is a complex issue to decide the composition of the system. To improve the incompleteness of a single subjective or objective assessment. So, it is urgent to find a comprehensive and efficient decision-making method for different systems. This paper states a total of 23 indicators in 4 criterion group: technology, economy, environment, and society. Based on the combination of the order relation analysis method (G1) and the anti-entropy weighting method (a-EWM), a comprehensive evaluation model, order relation-anti-entropy weight model (G1-aEWM), of distributed energy is established. This comprehensive evaluation model is used to analyze a hospital in Henan and find the final solution for the distributed energy system of the hospital. The empirical analysis results verify the rationality of the comprehensive evaluation model and provide an evaluation basis for the establishment of distributed energy systems in the future