An EMG-based model to estimate lumbar muscle forces and spinal loads during complex, high-effort tasks: Development and application to residential construction using prefabricated walls

Residential building construction is moving toward more industrialized construction methods (e.g., use of prefabricated wall or “panels”), yet remains one of highest risk sectors for work-related musculoskeletal disorders. The centralized design process inherent in the use of wall panels offers the potential for proactive control of musculoskeletal risks, consistent with the prevention-through-design (or PtD) philosophy. As part of an ongoing effort to incorporate ergonomics into panel design, estimates of low back loading and injury risk were needed over a range of tasks performed during panelized construction. Here, a free-dynamic, three-dimensional, electromyography-based model was developed to provide such estimates, which was a modification of an earlier, relatively coarser model. Specific modifications included a more detailed representation of lumbar muscle anatomy and contraction dynamics, high-pass filtering of electromyograms to better represent muscle activation levels, and an enhanced calibration procedure through which five model parameters are specified on an individual basis and used to estimate lumbar muscle forces. With these enhancements, the predictive ability of the model was assessed over a wide range of simulated panel erection tasks. Predicted model parameters corresponded well with values reported earlier. Reasonable levels of correspondence were found between measured and predicted lumbosacral moments, though predictive ability varied between tasks and rotation planes.

Relevance to industry

Wall panels are representative of the current trend toward increasing use of industrialized methods in residential construction. Model-based estimates can be used as part of a larger project to facilitate proactive design of residential construction using panelized walls in order to reduce musculoskeletal exposures and spine injury risks.     

Prediction of hip contact forces and muscle activations during walking at different speeds

The validation of musculoskeletal models is a challenging task necessary to obtain confidence in the numerical predictions they can provide. In this paper, a musculoskeletal model of the lower limb is used to predict the hip contact forces and muscle activations resulting from walking at different speeds for three total hip replacement patients implanted with instrumented prostheses (Bergmann et?al., J.?Biomech. 34:859?C871, 2001). The developed model is shown to estimate the magnitude of hip contact forces with encouraging accuracy in terms of relative peak error (on average within 22% of the experimental value) and global prediction error measurements. Hip contact force predictions were found to be generally more accurate for a slow walking speed. The static optimization technique adopted to estimate muscle activation profiles reproduced for the majority of muscles the modulation and variation in activation patterns documented in the literature for different walking speeds.     

A new approach to estimate fractal dimensions of corrosion images

A new approach is proposed based on box-counting method to estimate the fractal dimensions, D_2d and D_3d of corrosion images. They denote two-dimensional (2D) and three-dimensional (3D) characteristics of corroded surface morphology. The results have been tested through dealing with classical fractal figures, pit morphology of moon surface and practical corroded samples etc. to show that this new method is computationally simple, convenient and accurate. More specially, D_2d and D_3d have definite corrosion expressions, which respectively denote the fractal dimensions for pit diameter distribution and pit depth distribution on real corroded surface.     

A simple approach to the control of locomotion in self-reconfigurable robots

In this paper we present role-based control which is a general bottom-up approach to the control of locomotion in self-reconfigurable robots. We use role-based control to implement a caterpillar, a sidewinder, and a rolling track gait in the CONRO self-reconfigurable robot consisting of eight modules. Based on our experiments and discussion we conclude that control systems based on role-based control are minimal, robust to communication errors, and robust to reconfiguration.     

A new data mining approach to estimate causal effects of policy interventions

This paper presents a data driven approach that enables one to obtain a measure of comparability between-groups in the presence of observational data.The main idea lies in the use of the general framework of conditional multiple correspondences analysis as a tool for investigating the dependence relationship between a set of observable categorical covariates X and an assignment-to-treatment indicator variable T, in order to obtain a global measure of comparability between-groups according to their dependence structure. Then, we propose a strategy that enables one to find treatment groups, directly comparable with respect to pre-treatment characteristics, on which estimate local causal effects.     

A Bayesian approach to estimate the marginal loss distributions in operational risk management

One of the main problems in operational risk management is the lack of loss data, which affects the parameter estimates of the marginal distributions of the losses. The principal reason is that financial institutions only started to collect operational loss data a few years ago, due to the relatively recent definition of this type of risk. Considering this drawback, the employment of Bayesian methods and simulation tools could be a natural solution to the problem. The use of Bayesian methods allows us to integrate the scarce and, sometimes, inaccurate quantitative data collected by the bank with prior information provided by experts. An original proposal is a Bayesian approach for modelling operational risk and for calculating the capital required to cover the estimated risks. Besides this methodological innovation a computational scheme, based on Markov chain Monte Carlo simulations, is required. In particular, the application of the MCMC method to estimate the parameters of the marginals shows advantages in terms of a reduction of capital charge according to different choices of the marginal loss distributions.     

A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

A methodology is developed here to model evapotranspiration (λE_c ) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where λE_c is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground flux (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resistance, a model is developed to scale the transpiration estimates from the full canopy to give an area averaged estimate from the mean NDVI of the study area. The model was tested using data collected from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), and the results show that the modelled values of total surface evapotranspiration from the soil and canopy layers vary from the ground measurements by less than 9 per cent.     

A bootstrap approach to assess parameter uncertainty in simple catchment models

Catchment models simulate water and solute dynamics at catchment scales and are invaluable tools for natural resource management. Parameters for catchment models can provide useful information about the importance of the hydrological processes involved. We propose and demonstrate a bootstrap approach to assess parameter uncertainty in dynamic catchment models. This approach, which is non-Bayesian and essentially non-parametric, requires no distributional assumptions about parameters and only weak assumptions about the distributional form of the model residuals. It is able to handle autocorrelated model errors which are very common in the application of dynamic hydrological models at catchment scales. The ability of our bootstrap approach to assess parameter uncertainty is demonstrated using numerical experiments with the abc hydrological model and an application of a conceptual model of salt load from an irrigated catchment in southeastern Australia.     

A novel approach to estimate proximity in a random forest: An exploratory study

A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel.     

A simple approach to ranking a group of aggregated fuzzy utilities

When ranking a large quantity of fuzzy numbers, the efficiency, accuracy, and effectiveness of the ranking process is critical. The paper considers the application of "alpha-cut" and "fuzzy arithmetic operations" to the fuzzy weighted average (FWA) method which can be used to rank aggregated fuzzy utilities (or generalized fuzzy numbers). The purpose of this application is to make the method easier to program and the data easier to manipulate, which results in a more practical method for fuzzy decisions.     

A simple approach to stabilization of discrete-time systems by output feedback

We present a simple cost-function approach to the stabilizability of linear discrete-time systems by output feedback. It makes use of new stability theorems involving the coefficients of the characteristic polynomial.     

A probabilistic approach to estimate the mean waiting times in the earliest deadline first polling

In queueing system, the mean waiting times of messages are important measures to characterize the quality of service (QoS) under various requirements. In a time-critical system, message transactions which cannot meet deadline constraints might lead to catastrophic consequences. Currently, the waiting time estimations using the first-come-first-served (FCFS) and priority (PRI) strategies are already well developed. However, in the case of multi-queue dynamic environments, these quantities are more difficult to analyze due to multiple classes of messages are considered. In this paper, we aim to consider a polling system consisting of a number of parallel infinite-capacity single-server queues. We propose a probabilistic approach to derive the waiting times for different classes of messages by using non-preemptive earliest deadline first (EDF) polling policy. The resulting formula can also lead to the FCFS polling and PRI polling by altering the relative deadlines. Moreover, the bounds of waiting times are discussed. The accuracy of the proposed algorithm is established by comparisons with simulation results. The runtime results are in very good convergence with the theoretical predictions made by our formulas, in terms of prediction accuracies of waiting times and untimely service ratios of messages under various scenarios and timing constraints.     

A simple model to estimate the daily value of the regional maximum evapotranspiration from satellite temperature and albedo images

Abstract

We have tried an adaptation of the radiation model proposed by FAO, applicable in any area, for the estimation of the regional maximum evapotranspiration, ET, from temperature and albedo images obtained from a satellite. This model is based on the relationships

ET_m = k _cET_o

ET_o = A + BR _g + CR _g T_a ^max

where k _c is the crop coefficient, ET_o is the maximum evapotranspiration of the reference crop (green grass), R_g is the global radiation obtained from satellite albedo images, T _a ^max is the maximum temperature of the air obtained from the near-midday satellite temperature and A, B and C are the empirical coefficients characteristic of each zone calculated for different intervals of wind velocity and relative humidity of the air. By applying this model to the Valentian Region (Spain) we have obtained an estimation of the maximum daily evapotranspiration to an accuracy of 20 per cent.     

CompAS: A new approach to commonality and variability analysis with applications in computer assisted orthopaedic surgery

In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.     

A new approach to estimate the interpolation error of fuzzy data using similarity measures of fuzzy numbers

In this article, new error and similarity indexes to determine the accuracy of interpolation of fuzzy data by cubic spline functions are presented. The measures introduced are based on the similarity measure of fuzzy numbers. Through experimental simulations with different examples, we verify the homogeneity of the error and similarity indexes, which provides a criterion for determining the accuracy of the interpolation method with fuzzy data. The development of a criterion or an error or similarity index represents an important advancement, because of the lack of qualitative measures to estimate the interpolation error using fuzzy numbers in order to compare the results from distinct fuzzy data sets.     

A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems

This paper presents a new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems which is based on the idea of adaptive learning approach and simulated annealing. The proposed approach uses a weight parameter to perturb task priorities of a solution to obtain improved solutions. The weight parameters are then modified using a learning strategy. The maximization of line efficiency (i.e., the minimization of the number of stations) and the equalization of workloads among stations (i.e., the minimization of the smoothness index or the minimization of the variation of workloads) are considered as the performance criteria. In order to clarify the proposed solution methodology, a well known problem taken from literature is solved. A computational study is conducted by solving a large number of benchmark problems available in the literature to compare the performance of the proposed approach to the existing methods such as simulated annealing and genetic algorithms. Some test instances taken from literature are also solved by the proposed approach. The results of the computational study show that the proposed approach performs quite effectively. It also yields optimal solutions for all test problems within a short computational time.     

A simple approach to characterizing the driving force of polysilicon laterally driven thermal microactuators

Based on the elastic analysis of structures, a simple approach to calculating the driving force for polysilicon laterally driven thermal microactuators is presented by using their deflection. The driving force obtained through the deflection is compared with available results measured by force testers fabricated on the same substrate as the microactuators. Reasonable agreement has been achieved. The approach allows one to predict the driving force for the microactuators as their deflection is designed.     

A simple approach to find optimal topology of a continuum with tension-only or compression-only material

The material-replacement method is presented to improve the efficiency of topology optimization of a continuum structure with tension-only or compression-only material. Traditionally, a structure with tension-only or compression-only material should be considered as nonlinear in finite element analysis and many times of reanalysis are required to obtain the accurate physical fields for the update of the design variables in optimization. To improve the efficiency of structural optimization, the material-replacement method is proposed, in which the original tension/compression only material is replaced with an isotropic material with the same effective elasticity. The method contents two major ideas. One is the structural reanalysis for nonlinearity of material is put into the global iteration of optimization. The other is that the local stiffness will be modified step by step according to the local stress state. Numerical results show the validity of the method.     

A simple approach to fabricate multi-layer glass microfluidic chips based on laser processing and thermocompression bonding

Microfluidics and Nanofluidics - We study the influence of variations of cross-section on the drying dynamics by pervaporation in microfluidic channels surrounded by water-permeable...