The Analysis and Comparison of All Kinds of Buried Pipeline Model Based on Seismic Effect

The problem of seismic response of buried pipeline aimed at the interaction of soil around the pipeline and the complicated calculation model was considered, and the various simplified finite element model was calculated, and it was analyzed. Firstly, the ADINA finite element analysis software was used. The four nodes in shell unit were used by tube. The spring unit was used by soil spring. The analysis model of buried pipeline finite element numerical based on tension and compression spring was established. Seismic wave was input. The response to the simple boundary, viscoelastic boundary earthquake were calculated and analyzed by the finite element numerical simulation. The pipeline’s earthquake ground motion response was obtained, and was compared with the real soil model, and the most suitable simplified calculation model for numerical analysis of buried pipeline was found, which was the numerical analysis model of buried pipeline pressure spring tension finite element based on the viscoelastic boundary, and the theory basis for the seismic design of pipeline was provided.

Association Between Homocysteine Level and Methylenetetrahydrofolate Reductase Gene Polymorphisms in Type 2 Diabetes Accompanied by Dyslipidemia

Objective To investigate the association between total homocysteine(tHcy)level in plasma and methylenetetrahydrofblate reductase(MTHFR)C677T and A1298C genetic polymorphisms in a Chinese Han nationality population with type 2 diabetes mellitus(T2DM)accompanied by dyslipidemia.Methods This case-control study enrolled T2DM patients with dyslipidemia and without dyslipidemia respectively.Sanger dideoxy-mediated chain-termination method was used to detect the gene polymorphisms of MTHFR C677T and A1298C.Plasma tHcy and lipid levels were measured as well.The genotype frequency and allele frequency between the dyslipidemia and non-dyslipidemia groups were compared by using Chi-square test.Plasma tHcy level ofT2DM patients who carried the different genotypes was compared by Student's t test.Results Finally,82 T2DM patients with dyslipidemia and 94 ones without dyslipidemia were included in this study.There was a significant correlation between tHcy level and MTHFR C677T gene polymorphism inT2DM patients(t=2.27,P=0.02).Moreover,the plasma tHcy level in the dyslipidemia patients who carried MTHFR 677TT genotype was significantly higher than that in those with CT+CC genotype(13.62+6.97 vs.10.95+3.62pmol/L,t=2.2O,P=0.03);while for patients without dyslipidemia,comparison of the tHcy level between those who carried the above two alleles showed no significantly difference(13.34±6.03 vs.12.04±5.09μmol/L,t=1.08,P=0.29).Conclusion MTHFR 677TT genotype might associate with higher tHcy level in T2DM patients with dyslipidemia.

In situ growth of minimal Ir-incorporated CoxNi1-xO nanowire arrays on Ni foam with improved electrocatalytic activity for overall water splitting

Exploration of cost-effective electrocatalysts for boosting the overall water-splitting efficiency is vitally important for obtaining renewable fuels such as hydrogen.Here,earth-abundant CoxNi1-xO nanowire arrays were used as a structural framework to dilute Ir incorporation for fabricating electrocatalysts for water splitting.Minimal Ir-incorporated CoxNi1-xO nanowire arrays were synthesized through the facile hydrothermal method with subsequent calcination by using Ni foam(NF)as both the substrate and source of Ni.The electrocatalytic water-splitting performance was found to crucially depend on the Ir content of the parent CoxNi1-xO nanowire arrays.As a result,for a minimal Ir content,as low as 0.57 wt%,the obtained Ir-CoxNi1-xO/NF electrodes exhibited optimal catalytic activity in terms of a low overpotential of 260 mV for the oxygen evolution reaction and 53 mV for the hydrogen evolution reaction at 10 mA cm?2 in 1 mol L–1 KOH.When used as bifunctional electrodes in water splitting,the current density of 10 mA cm–2 was obtained at a low cell voltage of 1.55 V.Density functional theory calculations revealed that the Ir-doped CoxNi1-xO arrays exhibited enhanced electrical conductivity and low Gibbs free energy,which contributed to the improved electrocatalytic activity.The present study presents a new strategy for the development of transition metal oxide electrocatalysts with low levels of Ir incorporation for efficient water splitting.

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.

S-Nitrosoglutathion Reductase Activity Modulates the Thermotolerance of Seeds Germination by Controlling ABI5 Stability under High Temperature

Seed germination or dormancy status is strictly controlled by endogenous phytohormone and exogenous environment signals.Abscisic acid(ABA)is the important phytohormone to suppress seed germination.Ambient high temperature(HT)also suppressed seed germination,or called as secondary seed dormancy,through upregulating ABI5,the essential component of ABA signal pathway.Previous result shows that appropriate nitric oxide(NO)breaks seed dormancy through triggering S-nitrosoglutathion reductase(GSNOR1)-dependent S-nitrosylation modification of ABI5 protein,subsequently inducing the degradation of ABI5.Here we found that HT induced the degradation of GSNOR1 protein and reduced its activity,thus accumulated more reactive nitrogen species(RNS)to damage seeds viability.Furthermore,HT increased the S-nitrosylation modification of GSNOR1 protein,and triggered the degradation of GSNOR1,therefore stabilizing ABI5 to suppress seed germination.Consistently,the ABI5 protein abundance was lower in the transgenic line overexpressing GSNOR1,but higher in the gsnor mutant after HT stress.Genetic analysis showed that GSNOR1 affected seeds germination through ABI5 under HT.Taken together,our data reveals a new mechanism by which HT triggers the degradation of GSNOR1,and thus stabilizing ABI5 to suppress seed germination,such mechanism provides the possibility to enhance seed germination tolerance to HT through genetic modification of GNSOR1.

Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

Stabilization of Networked Control Systems Using a Mixed-Mode Based Switched Delay System Method

The phenomenon of mixed-mode is one of the most important characteristics of switched delay systems. If a networked control system(NCS) with network induced delays and packet dropouts(NIDs & PDs) is recast as a switched delay system, it is imperative to consider the effects of mixed-modes in the stability analysis for an NCS. In this paper, with the help of the interpolatory quadrature formula and the average dwell time method, stabilization of NCSs using a mixed-mode based switched delay system method is investigated based on a novel constructed Lyapunov-Krasovskii functional. With the Finsler's lemma, new exponential stabilizability conditions with less conservativeness are given for the NCS. Finally, an illustrative example is provided to verify the effectiveness of the developed results.

Research on optimal intelligent routing algorithm for IoV with machine learning and smart contract

The huge increase in the communication network rate has made the application fields and scenarios for vehicular ad hoc networks more abundant and diversified and proposed more requirements for the efficiency and quality of data transmission.To improve the limited communication distance and poor communication quality of the Internet of Vehicles(IoV),an optimal intelligent routing algorithm is proposed in this paper.Combined multiweight decision algorithm with the greedy perimeter stateless routing protocol,designed and evaluated standardized function for link stability.Linear additive weighting is used to optimize link stability and distance to improve the packet delivery rate of the IoV.The blockchain system is used as the storage structure for relay data,and the smart contract incentive algorithm based on machine learning is used to encourage relay vehicles to provide more communication bandwidth for data packet transmission.The proposed scheme is simulated and analyzed under different scenarios and different parameters.The experimental results demonstrate that the proposed scheme can effectively reduce the packet loss rate and improve system performance.

The Color Calibration across Multi-Projector Display

Large plane displays are often achieved by combining tiled imaging elements, which give the appearance of a single, seamless display. However, this is difficult to realize in practical use, since differences in color will make the tiles dis-tinct even if the geometric calibration is perfect. The paper presents an approach of color calibration using cubic B- spline. According to the theory of cubic B-spline, we construct the color transfer function of each projector. This ap-proach reduces the color and photometric non-uniformities to achieve seamless tiled multi-projector display well. Finally, we propose the difference between blending region and overlapping region which can help to achieve a better effect of alpha blending.

Mesoscale Analysis of a Heavy Rainstorm in Yulin City of Northern Shaanxi

[Objective] The study aims at analyzing a regional rainstorm in Yulin City of northern Shaanxi. [Method] Based on conventional meteorological data, satellite cloud pictures and ncep 1 ° ×1 ° reanalysis products, a regional rainstorm occurring during July 1 -3, 2011 in Yulin City of northern Shaanxi was discussed from the aspects of circulation pattem, mesoscale system, water vapor and dynamical conditions.[Result] There was a small closed high-pressure center near Fujian, around which southwest air flow existed constantly, providing water vapor for rainstorm areas for a long time; plateau trough moved eastwards, which provided dynamical field for rainstorm areas; the convergence between northeast cold air and low-level jet flow triggered this rainstorm; rainstorm areas were closely related to mesoscale low-pressure, and the storm appeared in the lowlevel jet flow in front of mesoscale low pressure; upper-trough cloud system and low-level jet cloud system combined, increased and generated rainstorm cloud cluster, while brightness temperature on the top of clouds was below -90 ℃ [ Conclusion] The research could provide theoretical foundation for forecast of rainstorms in future.

Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment(MCI)in T2DM.To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM,six entropies combining empirical mode decomposition(EMD),including Approximate entropy(ApEn),Sample entropy(SaEn),Fuzzy entropy(FEn),Permutation entropy(PEn),Power spectrum entropy(PsEn)and Wavelet entropy(WEn)were used in the study.A feature extraction technique based on maximization of the area under the curve(AUC)and a support vector machine(SVM)were subsequently used to for features selection and classi¯cation.Finally,Pearson's linear correlation was employed to study associations between these entropies and cognitive functions.Compared to other entropies,FEn had a higher classification accuracy,sensitivity and specificity of 68%,67.1% and 71.9%,respectively.Top 43 salient features achieved classification accuracy,sensitivity and speci¯city of 73.8%,72.3% and 77.9%,respectively.P4,T4 and C4 were the highest ranking salient electrodes.Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7,F8 and P4,and PsEn based on EMD was positively correlated to Montreal cognitive assessment(MoCA)and memory at electrode T4.In sum,FEn based on EMD in righttemporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.

Dioscin from Polygonatum sibiricum induces apoptosis and autophagy in Ishikawa human endometrial cancer cell and in vivo

With an aim to comprehend the precise regulatory mechanism of dioscin against endometrial carcinoma(EC), we firstly extracted the components from Polygonatum sibiricum followed by identification and structural characterization. The anti-EC activity of dioscin was initially determined based on the inhibition of Ishikawa cell proliferation and tumor growth. The high-throughput sequencing data indicated that dioscin not only promoted apoptosis, including decrease of poly ADP-ribose polymerase(PARP) and B-cell lymphoma-2(Bcl-2) and increase of c-PARP and Bcl-2-associcated agonist of cell death(Bad), but also induced autophagy, including increase of autophagic lysosomes and LC3Ⅱ/LC3Ⅰ ratio. Mechanistic exploration suggested that dioscin induced autophagy and apoptosis through inhibition of PI3K/AKT/mTOR signaling pathway. Besides, the dioscin-regulated p53 pathway was mainly involved in autophagy induction. Furthermore, inhibition of Ishikawa cell autophagy was linked to dioscin-induced apoptosis. Our data suggest the immense potential of dioscin for the development of functional food for EC and related medical application.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Logistic Regression Analysis of Catheter Fixation Defects and Their Influencing Factors

[Objectives] To analyze the influencing factors of fixed defects in patients with catheter fixation in clinical nursing work, in order to provide the best catheter fixation nursing plan for patients.[Methods] 176 inpatients with indwelling catheter from surgical system of Taihe Hospital in Shiyan City from August 2022 to March 2023 were selected. Using a retrospective analysis method, the influencing factors of catheter fixation defects in the study subjects were divided into two categories based on objective characteristics: type I non modifiable influencing factors and type II modifiable influencing factors. Using the standard for catheter fixation defects, whether the patient had catheter fixation defects was determined. After classified and statistically analyzed item by item, binary Logistic multiple regression analysis was used to identify the influencing factors.[Results] The occurrence of catheter fixation defects in patients with catheter fixation was related to factors such as whether the patient was evaluated before fixation, whether the fixation method was standardized and systematic, whether there was sufficient communication between nurses and patients, and the patient s knowledge of catheter fixation. It was also influenced by factors such as the patient s age, catheterization site, catheterization number, catheterization duration, where there was a consciousness disorder, educational level, and external environmental temperature.[Conclusions] Early attention to the key factors affecting patients with catheter fixation defects can effectively prevent adverse factors and provide patients with the best catheter fixation nursing plan to improve nursing quality.

医用聚乳酸材料性能及加工方法研究进展

与天然降解高分子材料相比,人工合成高分子材料具有更好的力学性能、可控的降解性能及良好的生物相容性,因此广泛用于生物材料制品。聚乳酸是研究最多的人工合成高分子材料之一。文中围绕医用聚乳酸材料的性能及加工方法,首先介绍了聚乳酸体内降解规律及生物安全性的研究现状,其次综述了组织工程支架领域多种加工方法(熔融沉积3D打印、静电纺丝、气体发泡、相分离、冷冻干燥等)的技术特点,并概述了药物传输微球常用加工方法(喷雾干燥法、溶剂挥发法、其他方法)的研究进展,最后对聚乳酸材料性质、加工方法及在生物医学领域的应用前景进行了展望。

光致固液转变型偶氮苯聚合物的合成与光响应性能

偶氮苯聚合物由于其独特的光响应性能在光分子开关、信息存储等领域具有潜在的应用,但偶氮苯顺反异构化转变及光致固液转变的影响因素仍需要更深入的研究。文中设计并合成了4种偶氮苯聚合物,对比了偶氮苯相连的柔性链对其光响应性能的影响。研究发现,引入柔性链段使偶氮苯异构化时间从20 min降到100 s,同时含柔性链段的偶氮苯聚合物具有光致固液转变的能力,而不含柔性链段的偶氮苯聚合物不能发生光致固液转变。

Methylenetetrahydrofolate Reductase Gene Polymorphism C677T is Associated with Increased Risk of Coronary Heart Disease in Chinese Type 2 Diabetic Patients

Objective Chronic cardiovascular diseases induced by long-term poor blood glucose control are the main cause of death in patients with type 2 diabetes mellitus(T2DM).Previous researches report that methylenetetrahydrofolate reductase gene(MTHFR)polymorphisms might influence the occurrence of coronary heart disease(CHD)in T2DM patients.The purpose of this study was to evaluate whether MTHFR C677T and A1298C mutations are associated with the risk of CHD inT2DM patients.Methods A total of 197 subjects with T2DM were studied,of which 95 patients with CHD.The genotypes of MTHFR C677T and A1298C were analyzed by using dideoxy chain-termination method,and compared between patients with CHD and those without CHD.Results We found that the frequency of the 677T allele was significantly higher in T2DM patients with CHD than those without CHD(P=0.011).However,there was no significant difference in any of the examined haplotypes between T2DM patients with and without CHD.Furthermore,the 677T allele was associated with a higher risk of CHD development in diabetic patients with lower homocysteine(Hey)levels(≤15μmol/L)(P=0.006),while no effect of MTHFR gene polymorphism on the incidence of CHD was found in patients with higher Hey levels(>15 μmol/L)(P=0.491).Conclusion The MTHFR C677T gene polymorphism is associated with the risk of CHD of diabetic patients and could be used as an effective marker for CHD in Chinese diabetic populations with normal Hey levels.

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

Data cleaning method for the process of acid production with flue gas based on improved random forest

Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the operating environment of acid production with flue gas is complex and there is much equipment.The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers.Therefore,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is proposed.Firstly,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset.Secondly,an improved random forest regression model is established.Genetic algorithm is used to optimize the hyperparameters of the random forest regression model.Then the optimal parameter combination is found in the search space and the trend of data is predicted.Finally,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized.Results show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue gas.The method improves the accuracy of compensation for missing data.With the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature control.The conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.

Development of low-coherence high-power laser drivers for inertial confinement fusion

The use of low-coherence light is expected to be one of the effective ways to suppress or even eliminate the laser–plasma instabilities that arise in attempts to achieve inertial confinement fusion.In this paper,a review of low-coherence high-power laser drivers and related key techniques is first presented.Work at typical low-coherence laser facilities,including Gekko XII,PHEBUS,Pharos III,and Kanal-2 is described.The many key techniques that are used in the research and development of low-coherence laser drivers are described and analyzed,including low-coherence source generation,amplification,harmonic conversion,and beam smoothing of low-coherence light.Then,recent progress achieved by our group in research on a broadband low-coherence laser driver is presented.During the development of our low-coherence high-power laser facility,we have proposed and implemented many key techniques for working with low-coherence light,including source generation,efficient amplification and propagation,harmonic conversion,beam smoothing,and precise beam control.Based on a series of technological breakthroughs,a kilojoule low-coherence laser driver named Kunwu with a coherence time of only 300 fs has been built,and the first round of physical experiments has been completed.This high-power laser facility provides not only a demonstration and verification platform for key techniques and system integration of a low-coherence laser driver,but also a new type of experimental platform for research into,for example,high-energy-density physics and,in particular,laser–plasma interactions.