Relationship between primary caregivers’social support function,anxiety,and depression after interventional therapy for acute myocardial infarction patients

BACKGROUND An acute myocardial infarction(AMI)is often treated with direct coronary intervention and requires home-based rehabilitation.Caregivers of patients with AMI need adequate social support to maintain high-quality care;however,their social support function is low,and relevant indicators for intervention must be identified.AIM To analyze the correlation between social support for primary caregivers,their anxiety,and depression,when caring for patients with AMI after interventional therapy.METHODS Using convenience sampling,we selected 300 primary caregivers of patients with AMI who had undergone interventional therapy.The Social Support Rating Scale(SSRS),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)were used to assess the primary caregivers.A Pearson’s correlation analysis was used to analyze the correlations between the SSRS,SAS,and SDS,and a multiple logistic regression analysis was used to analyze the factors influencing the low social support function of primary caregivers.The receiver operating characteristic curve and area under the curve(AUC)were used to evaluate the predictive ability of the SAS and SDS for low social support function in primary caregivers.RESULTS Considering the norm among Chinese people,AMI caregivers’objective support,subjective support,support utilization,and SSRS scores were lower,while their SAS and SDS scores were higher.The SSRS scores of female caregivers were higher than those of the male caregivers(t=2.123,P=0.035).The Pearson correlation analysis showed that objective support,subjective support,support utilization,and SSRS total scores were significantly correlated with both SAS(r=-0.414,-0.460,-0.416,-0.535)and SDS scores(r=-0.463,-0.379,-0.349,-0.472).Among the 300 AMI caregivers,56 cases(18.67%)had a low level of support function(SSRS≤22 points).Logistic regression model analysis showed that SAS and SDS were independent risk factors for low social support function of AMI caregivers,regardless of adjustment for other variables(P<0.05).SAS and SDS predicted that the AUC of AMI caregivers with low support function was 0.84,sensitivity was 67.9 and 71.4,and specificity was 84.0 and 70.9,respectively.CONCLUSION The social support function of the primary caregiver of patients with AMI after interventional therapy was lower and negatively correlated with anxiety and depression in the primary caregiver.

Smulyan Lemma and Differentiability of the Support Function

The purpose of this paper is to verify the Smulyan lemma for the support function, and also the Gateaux differentiability of the support function is studied on its domain. Moreover, we provide a characterization of Frechet differentiability of the support function on the extremal points.

THE UNIT BALL OF C-ALGEBRA AND DIFFERENTIABILITY OF SUPPORT FUNCTION

In this paper we show that the unit ball of an infinite dimensional commutative C-algebra lacks strongly exposed points, so they have no predual. Also in the second part, we use the concept of strongly exposed points in the Frechet differentiability of support convex functions.

A Note on the Selection Expectation and Support Function

In this paper, we prove the relationship between selection expectation and support function by a new method.

Duality for a Control Problem Involving Support Functions

Mond-Weir type duality for control problem with support functions is investigated under generalized convexity conditions. Special cases are derived. A relationship between our results and those of nonlinear programming problem containing support functions is outlined.

On Continuous Programming with Support Functions

A second-order Mond-Weir type dual problem is formulated for a class of continuous programming problems in which both objective and constraint functions contain support functions;hence it is nondifferentiable. Under second-order strict pseudoinvexity, second-order pseudoinvexity and second-order quasi-invexity assumptions on functionals, weak, strong, strict converse and converse duality theorems are established for this pair of dual continuous programming problems. Special cases are deduced and a pair of dual continuous problems with natural boundary values is constructed. A close relationship between the duality results of our problems and those of the corresponding (static) nonlinear programming problem with support functions is briefly outlined.

Second-Order Duality for Continuous Programming Containing Support Functions

A second-order dual problem is formulated for a class of continuous programming problem in which both objective and constrained functions contain support functions, hence it is nondifferentiable. Under second-order invexity and second-order pseudoinvexity, weak, strong and converse duality theorems are established for this pair of dual problems. Special cases are deduced and a pair of dual continuous problems with natural boundary values is constructed. A close relationship between duality results of our problems and those of the corresponding (static) nonlinear programming problem with support functions is briefly outlined.

Duality Relations for a Class of a Multiobjective Fractional Programming Problem Involving Support Functions

In this article, for a differentiable function , we introduce the definition of the higher-order -invexity. Three duality models for a multiobjective fractional programming problem involving nondifferentiability in terms of support functions have been formulated and usual duality relations have been established under the higher-order -invex assumptions.

On a Control Problem Containing Support Functions

A control problem containing support functions in the integrand of the objective of the functional as well as in the inequality constraint function is considered. For this problem, Fritz John and Karush-Kuhn-Tucker type necessary optimality conditions are derived. Using Karush-Kuhn-Tucker type optimality conditions, Wolfe type dual is formulated and usual duality theorems are established under generalized convexity conditions. Special cases are generated. It is also shown that our duality results have linkage with those of nonlinear programming problems involving support functions.

Adjustable entropy function method for support vector machine

Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.

The Function,Future and Support Elements of Farmer Cooperative Economy Organization

The nature of farmer cooperative economy organization( known as FCEO) determines the fact that the economic effects of farmer cooperative economy organization are as important as its social effects. Many experts,however,now would only focus on its economic function, and either neglect or weaken its social influence. Therefore,this paper introduces the theoretical foundation of the farmer cooperative economy organization,and studies the nature of cooperative economics. Based on those typical cases,the future of cooperative organization and four supporting elements were put forward in this paper.

Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models

Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.

Nutritional Support under the Neuman Systems Model for Stroke Patients

Objective: To explore nutritional support under the Neuman systems model in treating dysphagia in stroke patients. Methods: In this retrospective study, we enrolled 97 patients with dysphagia after stroke admitted to our hospital, and randomly divided them into the Neuman group (n = 51) given nursing intervention based on Neuman systems model and a control group (n = 46) given routine nursing intervention. Both groups received nutritional support for 3 months. Nutritional indexes (serum total protein, plasma albumin, serum albumin, hemoglobin and transferrin levels) and immune indexes (immunoglobulin (Ig) A, IgG, IgM and total lymphocyte count (TLC) in both groups were recorded and compared. Pulmonary function recovery, video fluoroscopic swallowing study score, water swallowing test score, complication rate, and health knowledge mastery level were also compared between the two groups. Results: After the intervention, the Neuman group showed less decrease in the nutritional and immune index scores (serum total protein, plasma albumin, hemoglobin, serum albumin;IgA, IgG, IgM, and TLC;all P Conclusion: For patients with stroke and dysphagia, comprehensive nursing intervention (e.g., nutritional support) under theNeuman systems model can promote the recovery of immune, swallowing, and pulmonary function, reduce complication incidence and facilitate comprehensive rehabilitation, ensuring adequate nutritional intake.

Supporting Functions and The Differentiabilities of the Norms on Banach Spaces

In this paper, we defined a new continuity of supporting function and studyed a new convexity, smoothness of Banach Space and from this we studyed the relations among such convexity, smoothness and differentiability of the norm of Banach Space.

Fuzzy smooth support vector machine with different smooth functions

Smooth support vector machine （SSVM） changs the normal support vector machine （SVM） into the unconstrained op- timization by using the smooth sigmoid function. The method can be solved under the Broyden-Fletcher-Goldfarb-Shanno （BFGS） algorithm and the Newdon-Armijio （NA） algorithm easily, however the accuracy of sigmoid function is not as good as that of polyno- mial smooth function. Furthermore, the method cannot reduce the influence of outliers or noise in dataset. A fuzzy smooth support vector machine （FSSVM） with fuzzy membership and polynomial smooth functions is introduced into the SVM. The fuzzy member- ship considers the contribution rate of each sample to the optimal separating hyperplane and makes the optimization problem more accurate at the inflection point. Those changes play a positive role on trials. The results of the experiments show that those FSSVMs can obtain a better accuracy and consume the shorter time than SSVM and lagrange support vector machine （LSVM）.

Bolt support mechanism based on elastic theory

Mechanical model of anchorage surrounding rock considering tray effect was established based on elastic theory,in order to study the mechanism of bolt supporting.Elastic solutions of normal force at point in the interior of a semi-infnite solid were obtained by means of classical displacement function method in elasticity.The factors which influence stress of bolted surrounding rock,such as the length of bolt and tray effect,were analyzed.The absolute value of stress along bolt axes decreased rapidly with an increase in radical distance and the maximum appeared near ends of bolt.With increasing radical distance,the value of radical stress changed from positive to negative roughly and then increased to zero,with maximum at the middle of bolt.The evolution of hoop stress as radical distance increasing was similar with stress along bolt axes.With an increase in depth,the radical effect ranges of all normal stress components were reduced.These suggest that the effect from tray on stress along bolt axes of bolted surrounding rock could be neglected,except near surface of surrounding rock.

SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS

Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM） and the Monte Carlo simulation （MCS）. As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method （RSM） and the artificial neural network （ANN）, which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the ＂curse of dimensionality＂. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis （usually finite element analysis）, and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.

Tactile and Ultrasound Image Fusion for Functional Assessment of the Female Pelvic Floor

Introduction: The true etiology of pelvic organ prolapse and urinary incontinence and variations observed among individuals are not entirely understood. Tactile (stress) and ultrasound (anatomy, strain) image fusion may furnish new insights into the female pelvic floor conditions. This study aimed to explore imaging performance and clinical value of vaginal tactile and ultrasound image fusion for characterization of the female pelvic floor. Methods: A novel probe with 96 tactile and 192 ultrasound transducers was designed. Women scheduled for a urogynecological visit were considered eligible for enrollment to observational study. Intravaginal tactile and ultrasound images were acquired for vaginal wall deformations at probe insertion, elevation, rotation, Valsalva maneuver, voluntary contractions, involuntary relaxation, and reflex pelvic muscle contractions. Biomechanical mapping has included tactile/ultrasound imaging and functional imaging. Results: Twenty women were successfully studied with the probe. Tactile and ultrasound images for tissues deformation as well as functional images were recorded. Tactile (stress) and ultrasound (strain) images allowed creation of stress-strain maps for the tissues of interest in absolute scale. Functional images allowed identification of active pelvic structures and their biomechanical characterization (anatomical measurements, contractive mobility and strength). Fusion of the modalities has allowed recognition and characterization of levator ani muscles (pubococcygeal, puborectal, iliococcygeal), perineum, urethral and anorectal complexes critical in prolapse and/or incontinence development. Conclusions: Vaginal tactile and ultrasound image fusion provides unique data for biomechanical characterization of the female pelvic floor. Bringing novel biomechanical characterization for critical soft tissues/structures may provide extended scientific knowledge and improve clinical practice.

ANALYTICAL SOLUTION OF A SIMPLY SUPPORTED PIEZOELECTRIC BEAM SUBJECTED TO A UNIFORMLY DISTRIBUTED LOADING

Using the inverse method, the analytical solution of a simply supported piezoelectric beam subjected to a uniformly distributed loading has been studied.First,the polynomials of stress function and induction function are given.Then, considering the gradient properties of the elastic parameter and the potential funciton as well as the piezoelectric parameter,the analytical solution of a simply supported beam subjected to a uniformly distributed loading is obtained and discussed.