An exploratory evaluation of the family meal intervention for adolescent anorexia nervosa

Although weight restoration is a crucial factor in the recovery of anorexia nervosa (AN), there is scarce evidence regarding which components of treatment promote it. In this paper, the author reports on an effort to utilize research methods in her own practice, with the goal of evaluating if the family meal intervention (FMI) had a positive effect on increasing weight gain or on improving other general outcome measures. Twenty-three AN adolescents aged 12-20 years were randomly assigned to two forms of outpatient family therapy (with [FTFM] and without [FT]) using the FMI, and treated for a 6-month duration. Their outcome was compared at the end of treatment (EOT) and at a 6-month posttreatment follow-up (FU). The main outcome measure was weight recovery; secondary outcome measures were the Morgan Russell Global Assessment Schedule (MRHAS), amenorrhea, general psychological symptoms, and eating disorder symptoms. The majority of the patients in both groups improved significantly at EOT, and these changes were sustained through FU. Given its primarily clinical nature, findings of this investigation project preclude any conclusion. Although the FMI did not appear to convey specific benefits in causing weight gain, clinical observation suggests the value of a flexible stance in implementation of the FMI for the severely undernourished patient with greater psychopathology.

Stability analysis of a general family of nonlinear positive discrete time-delay systems

In this paper, we propose a new approach to analyse the stability of a general family of nonlinear positive discrete time-delay systems. First, we introduce a new class of nonlinear positive discrete time-delay systems, which generalises some existing discrete time-delay systems. Second, through a new technique that relies on the comparison and mathematical induction method, we establish explicit criteria for stability and instability of the systems. Three numerical examples are given to illustrate the feasibility of the obtained results.

Full converter based wind turbine generator system generic modelling: Variations and applicability

Wind energy system integration can lead to adverse effects on modern electric grid so it is imperative toassess their dynamic performance before actual plant startup. Transmission system operators all over theworld stress the need for a proper wind turbine generator model for dynamic performance as well asancillary service assessments. Due to the bulk power system assessment requirements, developmentof suitable generic modeling has gained high priority. Generic modeling of type 4 full converter wind turbinegenerator system for application in frequency ancillary service investigations under varying windspeed and varying reference power has been presented in this study. Prevalent generic model, manufacturerspecific proprietary generic model along with detailed wind turbine model with synchronous generatoris also provided to highlight various modelling framework difference. Descriptions of individualsub models of proposed generic model are presented in detail and performance results are comparedand validated with GE’s proprietary generic model and detailed WTG model by means of simulationsin the MATLAB Power System Block set.

A general collaborative framework for modeling and perceiving distributed network behavior

Collaborative Anomaly Detection (CAD) is an emerging field of network security in both academia and industry. It has attracted a lot of attention, due to the limitations of traditional fortress-style defense modes. Even though a number of pioneer studies have been conducted in this area, few of them concern about the universality issue. This work focuses on two aspects of it. First, a unified collaborative detection framework is developed based on network virtualization technology. Its purpose is to provide a generic approach that can be applied to designing specific schemes for various application scenarios and objectives. Second, a general behavior perception model is proposed for the unified framework based on hidden Markov random field. Spatial Markovianity is introduced to model the spatial context of distributed network behavior and stochastic interaction among interconnected nodes. Algorithms are derived for parameter estimation, forward prediction, backward smooth, and the normality evaluation of both global network situation and local behavior. Numerical experiments using extensive simulations and several real datasets are presented to validate the proposed solution. Performance-related issues and comparison with related works are discussed.