A mathematical model of neuromuscular adaptation to resistance training and its application in a computer simulation of accommodating loads

A large corpus of data obtained by means of empirical study of neuromuscular adaptation is currently of limited use to athletes and their coaches. One of the reasons lies in the unclear direct practical utility of many individual trials. This paper introduces a mathematical model of adaptation to resistance training, which derives its elements from physiological fundamentals on the one side, and empirical findings on the other. The key element of the proposed model is what is here termed the athlete’s capability profile. This is a generalization of length and velocity dependent force production characteristics of individual muscles, to an exercise with arbitrary biomechanics. The capability profile, a two-dimensional function over the capability plane, plays the central role in the proposed model of the training-adaptation feedback loop. Together with a dynamic model of resistance the capability profile is used in the model’s predictive stage when exercise performance is simulated using a numerical approximation of differential equations of motion. Simulation results are used to infer the adaptational stimulus, which manifests itself through a fed back modification of the capability profile. It is shown how empirical evidence of exercise specificity can be formulated mathematically and integrated in this framework. A detailed description of the proposed model is followed by examples of its application—new insights into the effects of accommodating loading for powerlifting are demonstrated. This is followed by a discussion of the limitations of the proposed model and an overview of avenues for future work.

A new social-family model for eating disorders: A European multicentre project using a case-control design

OBJECTIVE: To examine a new socio-family risk model of Eating Disorders (EDs) using path-analyses. METHOD: The sample comprised 1264 (ED patients = 653; Healthy Controls = 611) participants, recruited into a multicentre European project. Socio-family factors assessed included: perceived maternal and parental parenting styles, family, peer and media influences, and body dissatisfaction. Two types of path-analyses were run to assess the socio-family model: 1.) a multinomial logistic path-model including ED sub-types [Anorexia Nervosa-Restrictive (AN-R), AN-Binge-Purging (AN-BP), Bulimia Nervosa (BN) and EDNOS)] as the key polychotomous categorical outcome and 2.) a path-model assessing whether the socio-family model differed across ED sub-types and healthy controls using body dissatisfaction as the outcome variable. RESULTS: The first path-analyses suggested that family and media (but not peers) were directly and indirectly associated (through body dissatisfaction) with all ED sub-types. There was a weak effect of perceived parenting directly on ED sub-types and indirectly through family influences and body dissatisfaction. For the second path-analyses, the socio-family model varied substantially across ED sub-types. Family and media influences were related to body dissatisfaction in the EDNOS and control sample, whereas perceived abusive parenting was related to AN-BP and BN. DISCUSSION: This is the first study providing support for this new socio-family model, which differed across ED sub-types. This suggests that prevention and early intervention might need to be tailored to diagnosis-specific ED profiles.

Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines.

An analytical model for web-warping in variable width flexible roll forming

The development of ultra/advanced high strength steels (U/AHSS) has challenged traditional forming methods due to their higher strength and reduced formability. An alternative method is flexible roll forming, which allows the manufacture of sheet metal of high strength and limited ductility into complex and weight-optimized components. However, one major problem in flexible roll forming is the web-warping defect, which is the deviation in height of the web over the length of the profile. The authors’ previous work developed an analytical model to predict the magnitude of web-warping. That model was purely geometric and neglected the effect of material properties. This work develops an analytical solution for the prediction of web-warping that considers both geometric and material parameters. The model results were validated by comparison with numerical and experimental results. The impact of this new model will be the ability to provide a rapid initial design assessment before an intensive numerical analysis of flexible roll forming is conducted.

Stability analysis of two-dimensional Markovian jump state-delayed systems in the Roesser model with uncertain transition probabilities

This paper is concerned with the problem of stochastic stability analysis of discrete-time two-dimensional (2-D) Markovian jump systems (MJSs) described by the Roesser model with interval time-varying delays. The transition probabilities of the jumping process/Markov chain are assumed to be uncertain, that is, they are not exactly known but can be estimated. A Lyapunov-like scheme is first extended to 2-D MJSs with delays. Based on some novel 2-D summation inequalities proposed in this paper, delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs) which can be computationally solved by various convex optimization algorithms. Finally, two numerical examples are given to illustrate the effectiveness of the obtained results.