The purpose of this study was to identify the factors affecting family functioning of stroke patients. A descriptive survey research was conducted in which 65 stroke patients and their primary caregivers were conveniently sampled. Data were collected from July to September, 1998 through interviewing using a structured questionnare. The measuring instruments used were Barthel Index by Mahoney and Barthel(15 items), Quality of Relationship Scale by Archbold and Stewart(15 Items), Role Stress of Caregiver Scale by Yang(14 items), Situational Definition Scale by Lee(9 items), Family Hardiness Index by McCubbin, McCubbin. and Thompson(20 items), and Family Adaptability Cohesion Evaluation Scale(FACES-III) by Olson, Portner, and Lavee(20 items). The obtained data were analyzed using percentage, t-test, ANOVA, Duncan test, and Pearson coefficients correlation by SAS/PC program. The results were as follows; 1. Role Stress of Caregiver was not severe and Quality of Relationship was moderate. The level of Situational Definition of primary caregivers was not high but Family Hardiness and Family Functioning were rather high. 2. The following relationships between research variables and demographic characteristics of the primary caregivers of stroke patients were significantly different; occupation of caregiver between Quality of Relationship, occupation of caregiver between Situational Definition, family type between Role Stress, caregiving duration between Family Hardiness, caregiving duration between Family Functioning, and hospitalization days between Family Functioning. 3. The correlations between research variables were as follows; There was positively correlated between patient's ADL and Quality of Relationship. The relationship of the patient's ADL between Role Stress was negatively correlated. Quality of Relationship between Situational Definition, Family Hardiness, and Family Functioning were significantly correlated. The correlation of Situational Definition between Family Hardiness, and Situational Definition between Family Functioning were very high. As a result of these findings, Quality of Relationship, Role Stress, Situational Definition. and Family Hardiness were useful variables for identifying Family Functioning of stroke patients. It is important for the rehabilitation nurse to be knowledgeable about family functioning of stroke patients to promote rehabilitation process.
One of the reasons students have difficulty in studying probability is that they do not understand the meaning of mathematical terms precisely. One such term is a continuous random variable. Students tend not to think of the accurate definition of continuous random variables but to understand the definition of continuity of functions and the meaning of continuity in probability as equal. In this study, we try to explore the degree of pre-service teachers' understanding on the concept of continuation of functions and continuous random variables. To do this, the questionnaire items related to continuous random variables and continuity of functions were developed by experts and examined by pre-service teachers. Based on this, we make suggestions on implications for teaching and learning about continuous random variables.
To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.
One of the possible alternatives of simulation-based time-dependent reliability assessment of pre-stressed biconcave and biconvex cable trusses, the Monte Carlo method, is applied in this paper. The influence of an excessive deflection of cable truss (caused by creep of cables and rheologic changes) on its time-dependent serviceability is investigated. Attention is given to the definition of the basic random variables and their statistical functions (basic, mutually dependent random variables such as the pre-stressing forces of the bottom and top cable, structural geometry, the Young's modulus of elasticity of the cables, and the independent variables, such as permanent load, wind, snow and thermal actions). Then, the determination of the response of the cable truss to the loading effects, and the definition of the limiting values considering serviceability of the structure are performed. The potential of the method, using direct Monte Carlo technique for simulation-based time-dependent reliability assessment as a powerful tool, is emphasized. Results obtained by the First order reliability method (FORM) are compared with those obtained by the Monte Carlo simulation technique.
Park, Dong-Hoon;Kim, Min-Soo;Kim, Jong-Rip;Jeon, Jae-Young
Journal of Mechanical Science and Technology
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제15권9호
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pp.1257-1267
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2001
For efficient mechanical system optimization, a new two-point approximation method is presented. Unlike the conventional two-point approximation methods such as TPEA, TANA, TANA-1, TANA-2 and TANA-3, this introduces the shifting level into each exponential intervening variable to avoid the lack of definition of the conventional exponential intervening variables due to zero-or negative-valued design variables. Then a new quadratic approximation whose Hessian matrix has only diagonal elements of different values is proposed in terms of these shifted exponential intervening variables. These diagonal elements are determined in a closed form that corrects the typical error in the approximate gradient of the TANA series due to the lack of definition of exponential type intervening variables and their incomplete second-order terms. Also, a correction coefficient is multiplied to the pre-determined quadratic term to match the value of approximate function with that of the previous point. Finally, in order to show the numerical performance of the proposed method, a sequential approximate optimizer is developed and applied to solve six typical design problems. These optimization results are compared with those of TANA-3. These comparisons show that the proposed method gives more efficient and reliable results than TANA-3.
This paper presents a new two-point approximation method based on the exponential intervening variable. To avoid the lack of definition of the conventional exponential intervening variables due to zero- or negative-valued design variables the shifting level into each exponential intervening variable is introduced. Then a new quadratic approximation, whose Hessian matrix has only diagonal elements of different values, is proposed in terms of these intervening variables. These diagonal elements are computed in a closed form, which correct the typical error in the approximate gradient of the TANA series due to the lack of definition of exponential type intervening variables and their incomplete second-order terms. Also, a correction coefficient is multiplied to the pre-determined quadratic term to match the value of approximate function with that of the original function at the previous point. Finally, the authors developed a sequential approximate optimizer, solved several typical design problems used in the literature and compared these optimization results with those of TANA-3. These comparisons show that the proposed method gives more efficient and reliable results than TANA-3.
Objectives : In this paper, we proposed a method for selecting reliable variables from various vocal features such as frequency derivative features, frequency band ratios, intensities of 5 vowels and an intensity of a sentence, since some features are sensitive to the variation of a subject's utterance. Methods : To obtain the reliable voice variables, the coefficient of variation (CV) was used as the index to evaluate the level of reliability. Since the distributions of a few features are not Gaussian, but are instead skewed to the right or left, we transformed the features by taking the log or square root. Moreover, the definition of the variables that are suitable to represent the vocal property was explained and analyzed. Results : At first, we recorded the vowels and the sentence five times both in the morning and afternoon of the same day, totally ten recordings from each of six subjects (three males and three females). We then analyzed the CVs of each subject's voice to obtain the stable features with a sufficient repeatability. The features having less than 20% CVs for all six subjects were selected. As a result, 92 stable variables from the 222 features were extracted, which included all the transformed variables. Conclusions : Voice can be widely used to classify the four constitution types and to recognize one's health condition from extracting meaningful features as physical quantity in traditional Korean medicine or Western medicine. Therefore, stable voice variables can be useful in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.
Purpose: This study was undertaken to obtain the basic data on the spouses‘ stress and adaptation caring their wives affected by gynecological cancer. In addition, moderating variables for adaptation, family resource and situational definition, were also investigated. Method: This study was descriptive survey research work by questionnaire on the 71 spouses hospitalized in an university hospital located on Gwangju from Sep. 2001 to Oct. 2002. Results: The mean score of stress was 2.27, and the mean score of adaptation was 3.12. The correlation between stress and adaptation was significant (r = -.692, p = .000). The adaptation score of high stress group with high family resource was higher (p = .041) than the group with low family resource. The adaptation score of high stress group with high situational definition was higher (p = .033) than the group with low situational definition. Conclusion: This study showed that the spouses experienced a similar level of stress compared with the level of families with other type of cancer. This study also showed that the stress and adaptation levels were different depending upon the family resource and situational definition. This aspect should be considered for the development of nursing intervention program for the spouse of patients with gynecological cancer.
The prediction of the inelastic behavior of the structure is an essential part of reliability assessment procedure, because most of the failures are induced by the inelastic deformation, such as creep and plastic deformation. During decades, there has been much progress in understanding of the inelastic behavior of the materials and a lot of inelastic constitutive equations have been developed. These equations consist of the definition of inelastic strain and the evolution of the state variables introduced to quantify the irreversible processes occurred in the material. With respect to the definition of the inelastic strain, the inelastic constitutive models can be categorized into elastoplastic model, unified viscoplastic model and separated viscoplastic model and the different integration methods have been applied to each category. In the present investigation, the generalized integration method applicable for various types of constitutive equations is developed and implemented into ABAQUS by means of UMAT subroutine. The solution of the non-linear system of algebraic equations arising from time discretization with the generalized midpoint rule is determined using line-search technique in combination with Newton method. The strategy to control the time increment for the improvement of the accuracy of the numerical integration is proposed. Several numerical examples are considered to demonstrate the efficiency and applicability of the present method. The prediction of the inelastic behavior of the structure is an essential part of reliability assessment procedure, because most of the failures are induced by the inelastic deformation, such as creep and plastic deformation. During decades, there has been much progress in understanding of the inelastic behavior of the materials and a lot of inelastic constitutive equations have been developed. These equations consist of the definition of inelastic strain and the evolution of the state variables introduced to quantify the irreversible processes occurred in the material. With respect to the definition of the inelastic strain, the inelastic constitutive models can be categorized into elastoplastic model, unified viscoplastic model and separated viscoplastic model and the different integration methods have been applied to each category. In the present investigation, the generalized integration method applicable for various types of constitutive equations is developed and implemented into ABAQUS by means of UMAT subroutine. The solution of the non-linear system of algebraic equations arising from time discretization with the generalized midpoint rule is determined using line-search technique in combination with Newton method. The strategy to control the time increment for the improvement of the accuracy of the numerical integration is proposed. Several numerical examples are considered to demonstrate the efficiency and applicability of the present method.
Journal of the Korean Data and Information Science Society
/
제26권2호
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pp.505-516
/
2015
We consider sparse high-dimensional linear regression models. Penalized regressions have been used as effective methods for variable selection and estimation in high-dimensional models. In penalized regressions, it is common practice to standardize variables before fitting a penalized model and then fit a penalized model with standardized variables. Finally, the estimated coefficients from a penalized model are recovered to the scale on original variables. However, these procedures produce a slightly different solution compared to the corresponding original penalized problem. In this paper, we investigate issues on the standardization of variables in penalized regressions and formulate the definition of the standardized penalized estimator. In addition, we compare the original penalized estimator with the standardized penalized estimator through simulation studies and real data analysis.
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