Dynamic Simulation and Test Verifcation of Hydraulic Automatic Tensioner for an Engine Timing Chain Drive System

As a fundamental component of an automobile engine’s timing chain drive system, the hydraulic automatic tensioner signifcantly enhances fuel economy while minimizing system vibrations and noise. However, there is a noticeable lack of research on automatic hydraulic tensioners. This study presents a comprehensive calculation approach for the principal parameters of a hydraulic automatic tensioner. An efective method, grounded in hydraulics and multibody dynamics, was introduced for estimating the dynamic response of such a tensioner. The simulation model developed for the hydraulic tensioner is characterized by its rapid dynamic response, consistent operation, and high accuracy. The dynamic behavior of the tensioner was analyzed under varying boundary conditions, using sinusoidal signal excitation. It was observed that the maximum damping force increases with a decreasing leakage gap. Conversely, an increase in oil temperature or air content leads to a decrease in the maximum damping force. The reaction forces derived from these calculations align well with experimental results. This calculation and simulation approach ofers considerable value for the design of innovative hydraulic tensioners. It not only streamlines the design phase, minimizes the number of trials, and reduces product costs, but also provides robust insights for evaluating the performance of hydraulic tensioners.

Detection of the Pandemic H1N1/2009 Influenza A Virus by a Highly Sensitive Quantitative Real-time Reverse-transcription Polymerase Chain Reaction Assay

A quantitative real time reverse-transcription polymerase chain reaction （qRT-PCR） assay with specific primers recommended by the World Health Organization （WHO） has been widely used successfully for detection and monitoring of the pandemic H1N1/2009 influenza A virus. In this study, we report the design and characterization of a novel set of primers to be used in a qRT-PCR assay for detecting the pandemic H1N1/2009 virus. The newly designed primers target three regions that are highly conserved among the hemagglutinin （HA） genes of the pandemic HlN1/2009 viruses and are different from those targeted by the WHO-recommended primers. The qRT-PCR assays with the newly designed primers are highly specific, and as specific as the WHO-recommended primers for detecting pandemic H1N1/2009 viruses and other influenza viruses including influenza B viruses and influenza A viruses of human, swine, and raccoon dog origin. Furthermore, the qRT-PCR assays with the newly designed primers appeared to be at least 10-fold more sensitive than those with the WHO-recommended primers as the detection limits of the assays with our primers and the WHO-recommended primers were 2.5 and 25 copies of target RNA per reaction, respectively. When tested with 83 clinical samples, 32 were detected to be positive using the qRT-PCR assays with our designed primers, while only 25 were positive by the assays with the WHO-recommended primers. These results suggest that the qRT-PCR system with the newly designed primers represent a highly sensitive assay for diagnosis of the pandemic HIN1/2009 virus infection.

An Improved Inventory Coordination Model in a Multi-stage Multi-customer Supply Chain

To solve the inventory coordination model in a multi-stage, multi-customer supply chain, this paper first analyzes the third model （integer powers of two multipliers at each firm） studied by Moutaz Khouja （2003）, and the authors take a numerical example to prove that the third model is irrational to miss feasible solution. Then this paper puts up a new improved model （integer multiplier at each firm）, and takes the example to prove it gives better results than the integer powers of two multipliers at each firm.

Formal management-specifying approach for model-based safety assessment

In the field of model-based system assessment,mathematical models are used to interpret the system behaviors.However,the industrial systems in this intelligent era will be more manageable.Various management operations will be dynamically set,and the system will be no longer static as it is initially designed.Thus,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the dependability.There mainly exists three problems.Complex:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same.Motivated by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be re-suable.Finally,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Reliability Analysis of Satellite Turntable System under Multiple Operation Modes Based on Multi-Valued Decision Diagrams

As a payload support system deployed on satellites,the turntable system is often switched among different working modes during the on-orbit operation,which can experience great state changes.In each mode,the missions to be completed are different,consecutive and non-over-lapping,from which the turntable system can be considered to be a phased-mission system(PMS).Reliability analysis for PMS has been widely studied.However,the system mode cycle characteristic has not been taken into account before.In this paper,reliability analysis method of the satellite turntable system is proposed considering its multiple operation modes and mode cycle characteristic.Firstly,the multi-valued decision diagrams(MDD)manipulation rules between two adjacent mission cycles are proposed.On this basis,MDD models for the turntable system in different states are established and the reliability is calculated using the continuous time Markov chains(CTMC)method.Finally,the comparative study is carried out to show the effectiveness of our proposed method.

Increased expression of tumor necrosis factor-a is associated with advanced colorectal cancer stages

AIM:To detect the expression of tumor necrosis factor-a(TNF-a)in colorectal cancer(CRC)cells among Saudi patients,and correlate its expression with clinical stages of cancer.METHODS:Archival tissue specimens were collected from 30 patients with CRC who had undergone surgical intervention at King Khalid University Hospital.Patient demographic information,including age and gender,tumor sites,and histological type of CRC,was recorded.To measure TNF-a m RNA expression in CRC,total RNA was extracted from tumor formalin-fixed,paraffinembedded,and adjacent normal tissues.Reverse transcription and reverse transcription polymerase chain reaction were performed.Colorectal tissue microarrays were constructed to investigate the protein expression of TNF-a by immunohistochemistry.RESULTS:The relative expression of TNF-a m RNA in colorectal cancer was significantly higher than that seen in adjacent normal colorectal tissue.High TNF-a gene expression was associated with StageⅢandⅣneoplasms when compared with earlier tumor stages(P=0.004).Eighty-three percent of patients(25/30)showed strong TNF-a positive staining,while only 10%(n=3/30)of patients showed weak staining,and 7%(n=2/30)were negative.We showed the presence of elevated TNF-a gene expression in cancer cells,which strongly correlated with advanced stages of tumor.CONCLUSION:High levels of TNF-a expression could be an independent diagnostic indicator of colorectal cancer,and targeting TNF-a might be a promising prognostic tool by assessment of the clinical stages of CRC.

Recent advances in molecular diagnostics of hepatitis Bvirus

Hepatitis B virus(HBV)is one of the important global health problems today.Infection with HBV can lead to a variety of clinical manifestations including severe hepatic complications like liver cirrhosis and hepatocellular carcinoma.Presently,routine HBV screening and diagnosis is primarily based on the immuno-detection of HBV surface antigen(HBsAg).However,identification of HBV DNA positive cases,who do not have detectable HBsAg has greatly encouraged the use of nucleic acid amplification based assays,that are highly sensitive,specific and are to some extent tolerant to sequence variation.In the last few years,the field of HBV molecular diagnostics has evolved rapidly with advancements in the molecular biology tools,such as polymerase chain reaction(PCR)and real-time PCR.Recently,apart of PCR based amplification methods,a number of isothermal amplification assays,such as loop mediated isothermal amplification,transcription mediated amplification,ligase chain reaction,and rolling circle amplification have been utilized for HBV diag-nosis.These assays also offer options for real time detection and integration into biosensing devices.In this manuscript,we review the molecular technologies that are presently available for HBV diagnostics,with special emphasis on isothermal amplification based technologies.We have also included the recent trends in the development of biosensors and use of next generation sequencing technologies for HBV.

Survivin isoforms and clinicopathological characteristics in colorectal adenocarcinomas using real-time qPCR

AIM:To investigate three isoforms of survivin in colorectal adenocarcinomas.METHODS:We used the LightCycler Technology(Roche),along with a common forward primer and reverse primers specific for the splice variants and two common hybridization probes labeled with fluorescein and LightCycler-Red fluorophore(LC-Red 640).Real time quantitative polymerase chain reaction(PCR) was performed on cDNAs from 52 tumor specimens from colorectal cancer patients and 10 unrelated normal colorectal tissues.In the patients group,carcinoembryonic antigen(CEA) and CA19-9 tumor markers were also measured immunochemically.RESULTS:Wild type survivin mRNA isoform was expressed in 48%of the 52 tumor samples,survivin-2b in 38%and survivin-ΔΕx3 in 29%,while no expression was found in normal tissues.The mRNA expression of wild type survivin presented a significant correlation with the expression of the ratio of survivin-2b,survivin-ΔΕx3,survivin-2b/wild type survivin and survivin-ΔΕx3/wild type survivin(P<0.001).The mRNA expression of wildsurvivin and survivin-ΔΕx3 was related with tumor size and invasion(P=0.006 and P<0.005,respectively).A significant difference was found between survivin-2b and morphologic cancer type.Also,the ratio of survivin-ΔEx3/ wild-survivin was significantly associated with prognosis.No association was observed between the three isoforms and grade,metastasis,Dukes stage and gender.The three isoforms were not correlated with CEA and CA19-9.CONCLUSION:Survivin isoforms may play a role in cell apoptosis and their quantification could provide information about clinical management of patients suffering from colorectal cancer.

Analyzing Effect of Demand Rate on Safety of Systems with Periodic Proof-tests

Quantitative safety assessment of safety systems plays an important role in decision making at all stages of system lifecycle, i.e., design, deployment and phase out. Most safety assessment methods consider only system parameters, such as configuration, hazard rate, coverage, repair rate, etc. along with periodic proof-tests （or inspection）. Not considering demand rate will give a pessimistic safety estimate for an application with low demand rate such as nuclear power plants, chemical plants, etc. In this paper, a basic model of IEC 61508 is used. The basic model is extended to incorporate process demand and behavior of electronic- and/or computer-based system following diagnosis or proof-test. A new safety index, probability of failure on actual demand （PFAD） based on extended model and demand rate is proposed. Periodic proof-test makes the model semi-Markovian, so a piece-wise continuous time Markov chain （CTMC） based method is used to derive mean state probabilities of elementary or aggregated state. Method to determine probability of failure on demand （PFD） （IEC 61508） and PFAD based on these state probabilities are described. In example, safety indices of PFD and PFAD are compared.

Connective tissue growth factor expression hints at aggressive nature of colorectal cancer

BACKGROUND Connective tissue growth factor(CTGF)is a mediator of transforming growth factor-beta signaling and plays a key role in connective tissue remodeling,inflammatory processes and fibrosis in various illnesses including cancer.AIM To investigate the role of CTGF in colorectal cancer(CRC)progression and to compare the CTGF expression with different clinicopathological parameters.METHODS Real-time polymerase chain reaction,immunohistochemistry and Western blotting was performed to evaluate the CTGF expression and the results were statistically analyzed against the clinicopathological variables of patient data using STATA software version 16.RESULTS CTGF expression levels in tumor specimens were significantly higher than their paired normal specimens.The higher protein expression levels showed a significant association with smoking,staging,tumor grade,invasion depth,necrosis of tumor tissue,and both lymphovascular and perineural invasion.As per the cox regression model and classification tree analysis,tumor-nodemetastasis stage and perineural invasion were important predictors for CTGF expression and prognosis of CRC patients.Survival analysis indicated that CTGF overexpression was associated with poorer overall and disease-free survival.CONCLUSION Expression of CTGF was increased in CRC and was linked with poor overall and disease-free survival of CRC patients.These findings support prior observations and thus CTGF may be a possible prognostic marker in CRC.

Steady state speed distribution analysis for a combined cellular automaton traffic model

Cellular Automaton （CA） based traffic flow models have been extensively studied due to their effectiveness and simplicity in recent years. This paper develops a discrete time Markov chain （DTMC） analytical framework for a Nagel-Schreckenberg and Fukui Ishibashi combined CA model （W^2H traffic flow model） from microscopic point of view to capture the macroscopic steady state speed distributions. The inter-vehicle spacing Maxkov chain and the steady state speed Markov chain are proved to be irreducible and ergodic. The theoretical speed probability distributions depending on the traffic density and stochastic delay probability are in good accordance with numerical simulations. The derived fundamental diagram of the average speed from theoretical speed distributions is equivalent to the results in the previous work.

DTMC-based Modeling and Analysis of Obstacle Ad hoc Networks

One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.

Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

We propose a probabilistic approach to modelling the propagation of the coronavirus disease 2019 (COVID-19) in Madagascar, with all its specificities. With the strategy of the Malagasy state, which consists of isolating all suspected cases and hospitalized confirmed case, we get an epidemic model with seven compartments: susceptible (S), Exposed (E), Infected (I), Asymptomatic (A), Hospitalized (H), Cured (C) and Death (D). In addition to the classical deterministic models used in epidemiology, the stochastic model offers a natural representation of the evolution of the COVID-19 epidemic. We inferred the models with the official data provided by the COVID-19 Command Center (CCO) of Madagascar, between March and August 2020. The basic reproduction number R0 and the other parameters were estimated with a Bayesian approach. We developed an algorithm that allows having a temporal estimate of this number with confidence intervals. The estimated values are slightly lower than the international references. Generally, we were able to obtain a simple but effective model to describe the spread of the disease.

Modelling Problems of Hybrid Event Systems

This paper discusses the mathematical description of hybrid event systems, consisting of continuous, discrete, even logic-judgment and inference-decision event systems. The description takes events as its core, and information interconnection as its tie. In this paper, an event, instead of a process, is regarded as an analyzed element. The hybrid event systems are described in mathematical models with the following tools: chains of time, generalized space and interconnecting information.

AVAILABILITY MODEL FOR SELF TEST AND REPAIR IN FAULT TOLERANT FPGA-BASED SYSTEMS

Dynamically reconfigurable Field Programmable Gate Array(dr-FPGA) based electronic systems on board mission-critical systems are highly susceptible to radiation induced hazards that may lead to faults in the logic or in the configuration memory. The aim of our research is to characterize self-test and repair processes in Fault Tolerant(FT) dr-FPGA systems in the presence of environmental faults and explore their interrelationships. We develop a Continuous Time Markov Chain(CTMC) model that captures the high level fail-repair processes on a dr-FPGA with periodic online Built-In Self-Test(BIST) and scrubbing to detect and repair faults with minimum latency. Simulation results reveal that given an average fault interval of 36 s, an optimum self-test interval of 48.3 s drives the system to spend 13% of its time in self-tests, remain in safe working states for 76% of its time and face risky fault-prone states for only 7% of its time. Further, we demonstrate that a well-tuned repair strategy boosts overall system availability, minimizes the occurrence of unsafe states, and accommodates a larger range of fault rates within which the system availability remains stable within 10% of its maximum level.

A Stochastic SIVS Epidemic Model Based on Birth and Death Process

A new stochastic epidemic model, that is, a general continuous time birth and death chain model, is formulated based on a deterministic model including vaccination. We use continuous time Markov chain to construct the birth and death process. Through the Kolmogorov forward equation and the theory of moment generating function, the corresponding population expectations are studied. The theoretical result of the stochastic model and deterministic version is also given. Finally, numerical simulations are carried out to substantiate the theoretical results of random walk.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Influences of bracket bonding on mutans streptococcus in plaque detected by real time fluorescence-quantitative polymerase chain reaction

Background Enamel demineralization occurs frequently during orthodontic treatment. In this study, we evaluated the changes of the density of mutans streptococcus （MS） in plaque after bracket bonding and using fluoride adhesive on maxillary incisors by real time fluorescence-quantitative polymerase chain reaction （RT-FQ PCR）.Methods The study was designed as a self-paired test. Brackets were bonded with fluoride adhesive on the left side, while non-fluoride adhesive on the right side for each patient. Plaque samples were taken from the surfaces around the brackets of four maxillary incisors before brackets bonding and after the bonding 4 weeks later. The amount of MS was measured by RT-FQ PCR. The data obtained were analyzed statistically using the SPSS 11.5 version and the alpha level was set at 0. 05 （ 2-tailed）.Results The amount of MS in plaque increased significantly after bracket bonding （ P 〈 0.01 ）, whereas no significant differences were observed among four maxillary incisors both before and after brackets bonding （P 〉 0. 05 ）, and among the incisors using and not using fluoride adhesive （ P 〉 0. 05 ）.Conclusions The increase of the density of MS in plaque after bracket bonding is one of the etiological factors for enamel demineralization in orthodontic patients. The result of this study did not support what we observed clinically that the incidence of enamel demineralization for lateral incisors was higher than that for central incisors. Using fluoride adhesive for bonding did not affect the amount of MS in plaque in our study. Further study is needed.

Siegmund Duality for Continuous Time Markov Chains on Z_+~d

For the continuous time Markov chain with transition function P（t） on Z d ＋ , we give the necessary and sufficient conditions for the existence of its Siegmund dual with transition function P - （t）. If Q, the q-matrix of P（t）, is uniformly bounded, we show that the Siegmund dual relation can be expressed directly in terms of q-matrices, and a sufficient condition under which the Q-function is the Siegnmnd dual of some Q-function is also given.

A Markov chain model of mixing kinetics for ternary mixture of dissimilar particulate solids

This paper presents a simple but informative mathematical model to describe the mixing of three dissimilar components of particulate solids that have the tendency to segregate within one another. A nonlinear Markov chain model is proposed to describe the process. At each time step, the exchange of particulate solids between the cells of the chain is divided into two virtual stages. The first is pure stochastic mixing accompanied by downward segregation. Upon the completion of this stage, some of the cells appear to be overfilled with the mixture, while others appear to have a void space. The second stage is related to upward segregation. Components from the overfilled cells fill the upper cells （those with the void space） according to the proposed algorithm. The degree of non-homogeneity in the mixture （the standard deviation） is calculated at each time step, which allows the mixing kinetics to be described. The optimum mixing time is found to provide the maximum homogeneity in the ternary mixture. However, this “common” time differs from the optimum mixing times for individual components. The model is verified using a lab-scale vibration vessel, and a reasonable correlation between the calculated and experimental data is obtained