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Disturbances associated with Multiple Sclerosis (MS) can be due to the pathologic process of the disease or insufficient physical fitness. The benefits of exercise in improving cardiopulmonary fitness have been demonstrated in animal studies and also clinical trials on cardiovascular patients and healthy human cases; however, its effectiveness in people with MS(pwMS) is still unknown. people with MS often engage in rehabilitation programs for exercise tolerance improvement. Therefore, it is necessary to investigate the effect of this specific intervention on cardiopulmonary fitness of these patients. The present study intended to illustrate the etiology of exercise intolerance in pwMS and also the effects of exercise on these etiological factors.The present interventional study included 21 female patients suffering from Relapsing-Remitting MS (RR-MS) who had a mean age of 35 ± 5.18 years and the Expanded Disability Status Scale (EDSS) scores of 1 to 4. All the participants underwent Cardiopulmonary Exercise Testing (CPET) on an ergometer pre- and post-intervention. The intervention included 18 sessions of endurance training using a stationary bicycle with an intensity of 70% of the peak Heart Rate (HR) or 60% of the peak VOThe present study showed that even a short, 6-week course of aerobic exercise could change the peak HR and VO

Permanent deformation in asphalt pavement is a function of material properties, loading, environmental conditions, and structural design (e.g., thickness of pavement layers). Because of the large number of effective variables and their nonlinear interrelationships, it is not easy to develop a predictive model for permanent deformation. In this study, a laboratory database containing accumulated strain values (output) and material properties (inputs) from several asphalt pavements has been used to develop a predictive model for permanent deformation. We first show that the inputs are highly correlated, then principal component analysis (PCA) is used to compute a set of orthogonal pseudo-inputs. Two predictive models based on the pseudo-inputs were developed using linear regression analysis and artificial neural networks (ANN) and are compared using statistical analysis. Extrapolation using empirical predictive models is highly risky and discouraged by experienced practitioners, so to guard against extrapolation, a method is developed to determine an input hyper-space. The above-developed model, along with an n-dimensional hyper-space, provides sufficient information for supporting an optimization algorithm for finding the minimum accumulated strain. An asphalt pavement design with accumulated strain value of 1772 micro-strain (0.02 in. in a 10-in.-thick asphalt pavement layer) is obtained by solving the optimization problem and the design parameters meet flexible pavement design specifications. The proposed framework is able to generate accurate predictive models when the original inputs are highly correlated and able to map to an optimal point in the fitted input space.

Gantry-crane pavements and foundations are significant assets within intermodal facilities. The performance of these pavements, which are subjected to highly-variable loads, is critical to safe operations. Traffic interruptions and costs associated with maintaining and rehabilitating distressed or failed pavements in associated areas are of particular importance. The purpose of this study was to evaluate structural behavior and improve design procedures for gantry crane way pavement used at intermodal facilities by assessing interactions among pavements, subgrades, and operational loading conditions. The performance of the gantry crane pavements and foundations was assessed using a finite-element (FE) model, while pavement structural response to a crane load was measured using strain gages installed in the field. Measuring material properties using in-situ/laboratory tests was not possible in this work due to the limited resources, restricted access to intermodal facilities, and tight schedule of rail freight transport(i.e. tight schedule of cranes). To verify the materials properties and ensure a consistent behavior of the developed FE model with respect to the field measurements, an inverse design approach was implemented. Because of the significant cost of FE simulation, the gradient-based solver used in this study is based on a trust region algorithm. The verified model was used to predict the critical responses of Portland cement concrete (PCC) layer, base course, and subgrade soil. These parameters were then used to conduct a pavement fatigue damage analysis, parametric analyses of material strength and slab geometry were carried out based on Model Code, and resulting fatigue-life recommendations for improved designs were made. The developed FE model along with the inverse approach create a framework that can be used in further health monitoring problems when a specific parameter cannot be directly observed.

Permanent deformation is a major load-associated distress occurring in flexible pavement systems and increases with load repetitions affecting road roughness, serviceability, and the international roughness index (IRI). Early detection of rutting is necessary for maintenance and rehabilitation activities, but due to the complex behavior of asphalt mixtures, accurately predicting the permanent deformation of asphalt pavement is difficult. Historically, multivariate regression modeling and recently, artificial neural networks (ANNs) are used widely for material properties prediction. The ability to model accurately the response variable is adversely affected when inputs have pairwise correlations. To overcome this barrier, principal component analysis (PCA), as a dimensionality reduction technique, can be used to produce uncorrelated linear combinations of the original inputs as illustrated in this work using 83 (i.e., samples) laboratory compacted specimens from the State of Wisconsin. Asphalt binder, aggregate, and mix properties are obtained and used as the model inputs. The response parameter is the accumulated strain at the corresponding flow number. Using the developed pseudo inputs from PCA, a multivariate regression and an ANN model are generated and were able to fit the test cases with r fit of 0.8 and 0.97 respectively. The developed machine learning-based framework is shown to be a capable tool in estimating the rutting behavior of asphalt mixture.

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems.

Historically, asphalt mixtures in Minnesota have been produced with fine gradations. However, recently more coarse-graded mixtures are being produced as they require less asphalt binder. Thus, it is important that pavement performance for coarse gradations be evaluated. Within this research work, performance evaluation took place with the use of the dynamic modulus test in indirect tension mode on coarse-graded mixtures consisting of field cores from 9 different pavements located in five districts of Minnesota. From each pavement’s surface layer, 3 specimens were tested at three temperatures; 0.4°C, 17.1°C, and 33.8°C each at nine frequencies ranging between 0.1 Hz and 25 Hz. Additional volumetric characterization of the field mixtures was done to determine asphalt content, air voids, and blended aggregate gradations. Asphalt binders were extracted and recovered for use in determining binder shear complex master curves. Through this information the modified Witczak model was used to create |E*| master curves which were then compared against the indirect tension (IDT) test |E*| experimentally created master curves. From the results the modified Witczak model needs to be modified for IDT collected dynamic modulus data.

Summary Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean-variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single-solution and population-based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering-based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.