Background: Although intensive insulin therapy has been adopted for several years, the exact target of glucose control remained controversial, especially for neurologic critically ill patients. In this study, we aimed to investigate the optimal range of glucose which can predict outcomes and present a possible window of moderate glucose control. Methods: Patients meeting inclusion criteria were enrolled and demographic data were collected for comparison. After 90 days follow-up, logistic regression analysis, Kaplan-Meier survival analysis and receiver operating characteristic analysis were used to identify the relationship between admission glucose level and clinical outcomes. Results: From analysis based on a total of 207 patients, admission higher glucose level can predict the worsen outcome, which also had significant correlation with APACHEⅡ, SAPSⅡ. Receiver Operating Characteristic analyses indicated that glucose cut-off value of 140mg/dl for admission was the best predictor of mortality. Additionally, patients with 140-200mg/dl glucose band and >200mg/dl glucose band had significant increase in the odds of dying when compared with <110mg/dl glucose band. Kaplan-Meier survival analysis confirmed the previous results. Conclusions: Results from our study indicated that 140 mg/dl may be a useful cut-off point which can predict the worsen outcome and also present a possible therapeutic window of insulin therapy aimed to 110-140 mg/dl for critically ill neurologic patients.

In geophysical processes electronic sensors are required to measure and automate different tasks types. Throughout history, people have created multiple types of sensors, but the acoustic devices have an important application such as the content form description in deep wells, watersheds, lakes, caves, among others. The acoustic signal is capable of reflecting where other types of signals cannot operate, either by drawbacks or where fluid is displaced.\nThis paper presents a mathematical model in state space described as a basic acoustic sensor; the objective is to adjust the parameters allowing the acoustic device to describe a signal in its trajectory, representing in geophysical manner the cavity form. Therefore, the control is performed on the response of the acoustic sensor model with the parameter estimation adjusted with a process technique. The simulation results allow viewing counts convergence.

Today increasing population necessitated supplying their food and pressure for existent resources that can endanger the future life generation that resulted more attention of countries in the world to sustainable development. Shortage of water resources as one of the most important obstacles of agricultural development, increasing agricultural productions and creating nutrition health are factors that are factors of lack of access to sustainable development. Purpose of this research is establishing a structure for evaluating projects of supplying agricultural water according to the indexes of sustainable development using multi-criteria decision making criteria and testing them in the form of applying a case study for optimal achievement of agricultural water. The most important feature of this evaluation is specifying a series of indexes to analyze sustainability and preparing possibility of beneficiaries’ cooperation in the process of evaluating processes and for this reason a number of projects of supplying agricultural water in Poldasht catchment in West Azerbaijan is selected as case study. Multi-variable decision making technique is similar to the ideal item (TOPSIS) as a selected method of evaluating different items. Results shows efficiency of multi-criteria decision-making techniques in evaluating projects based on sustainable development criteria.

This paper aims to develop a novel method for assessing students’ learning level in a more sophisticated way. It also presents the basic grading philosophies: the norm-referenced systems, and criterion-referenced systems. Assessment of learning is one of the most important and difficult task in the learning and teaching processes. Assessment consists of several components, each with a number of judgments often based on imprecise and vague data. The impression and vagueness arise from human interpretation, evaluation and assessment. This study presents the statistical analysis of learning level and two other advanced approaches based on fuzzy inference system for assessing the outcomes of students. As a result, statistical outcomes of learning level were calculated, evaluated and compared with the outcomes of the fuzzy methods. The proposed approaches incorporate the educators, families and educational institutions to have a better understanding on the assessment criteria. It also aims at encouraging students to participate in the whole learning process and proving a fair environment for student assessment.

A new method for defuzzification based on the radius of gyration point is proposed and some of its desirable properties are studied. This point of each fuzzy number plays an important role in fuzzy sets and systems, specifically in physics, mathematics and dynamics. Also, the researcher explains the development of a decision- making model using the neural network with an error back propagation learning algorithm and a fuzzy numerical process

The parabolic stochastic filter estimation based on a first differences order and second grade model with uncorrelated innovation conditions with two auxiliary inequalities bounding the spatial operation region allowing an approximation to real parameters set with a sufficient convergence rate in a probability sense. The stochastic first differences order second grade with uncorrelated parameters and stationary conditions through the parabolic surface with bounded drift around the mean value was considered. The recursive parameters estimation was built without lost generality. Adaptive identification state resulted in a high convergence condition, observed in the functional error. Simulated results considering the recursive filter achieved and observed the great convergence with respect to the reference model. Finally, the advantage that it has to traditional parameters estimation is viewed in the functional error figure.

【Abstract】 Objectuve It has been shown that Inflammation plays an important role in the initiation and progression of acute cardiovascular events，especially the AMI. There is a possible association between ventricular dysfunction following acute myocardial infarction and high Sensitivity C-reactive protein (HS-CRP) and Mannose-binding lectin (MBL). Methods In this study we assessed the relationship between HS-CRP and MBL with LVEF and Killip Class in patients with acute myocardial infarction (AMI). In a cross sectional study, 188 patients(63 females and 125 males) with AMI (STEMI) who were admitted in CCU ward in the Affiliated Hospital of Qingdao University, and The Qingdao Hiser Medical Center, were entered. MBL and HS-CRP were measured within first day of admission. We measured ejection fraction (LVEF) and used Killip classification system. Results The mean age of patients was 60.4±9.2 years. The mean of MBL was 5.9±1.6, 6.6±2.1, 7.1±2.1 and 9.4±1.3 in patients with Killip Class I, II, III and IV, respectively (P=0.005). The mean of HS-CRP was 1.9±1.4, 14.2±10.9, 12.2±10.9 and 15.7±6.7 in patients with Killip Class I, II, III and IV, respectively (P=0.005). There was a relationship between HS-CRP and LVEF (Correlation coefficient= -0.788, P<0.001), but there was not between MBL and LVEF (Correlation coefficient= -0.111, P=0.129), The plasma concentration of C-reactive protein correlated with LVEF and Killip Class in patients with AMI but serum MBL was just correlated with Killip Class IV. Conclusion It seems that plasma concentrations of HS-CRP and MBL are useful for prediction of development of heart failure in AMI patients. More future studies are necessary for final judgment.

The identification process based on a reference system commonly known as a black box scheme considering the transition function, the gain and the innovation process describes the internal dynamic states. Unfortunately the exponential transition function is based on the unknown internal parameters. In this paper presents estimation for a single input - single output (SISO) model with two delays known as a second order system, in differences. It is a special technique which describes the black box internal gains, actualizing the transition function affecting the identification evolution. The estimator built on the second probability moment is expressed in recursive form and has an adaptive strategy allowing a sufficient convergence rate. The filter is integrated with two actions: adaptive estimation and internal states description, seeking a good convergence level. The theoretical result demonstrates the filter strategies.

Although tourist cards and cultural routes have been interesting innovations when they were launched on the international tourism market, they are rarely implemented in emerging tourism destinations, especially in an integrated manner. This paper presents the results of a quantitative research among tourists visiting an emerging destination, aiming to pre-test tourists’ interest in city cards and cultural routes. Using univariate, bivariate and multivariate methods this study provides useful information to the Destination Management Organization concerning: tourists’ interest to purchase a city card, preferred validity period, distribution channels, as well as their interest in cultural routes.