A computing task ergonomic risk assessment tool for assessing risk factors of work related musculoskeletal disorders

Observation method remains to be the most widely applied method in assessing exposure to risk factors for work-related musculoskeletal disorders (WMSDs) related to office works because it is inexpensive and applicable to wide range of office jobs. However, the existing research that applied this method was mainly focused to a limited range of office components and computer accessories such as seat pan, keyboards, mouse, monitor and telephone. In addition, further testing of reliability and validity of the observational method was less reported. This study was conducted to propose the new office ergonomic risk assessment (OFFERA) method to assess a wide range of office risk factors related to WMSDs, which include office components and office environment where this method covers both right and left side of the body part. The initial development of OFFERA method was divided into two stages, the development of OFFERA system components and psychometric properties of OFFERA method. In reliability testing, the results of inter and intra observer reliability recorded good (K=0.62-0.78) and very good (K=0.81-0.96) agreement among the observers. Meanwhile, in validity testing, the relationship of the final score of OFFERA to the musculoskeletal symptoms statistically shows a significant value for wrists/hands (χ²=7.942; p=0.047), lower back (χ²=13.478; p=0.000), knees (χ²=7.001; p=0.008), and ankle/leg (χ²=5.098; p=0.024). The usability testing shows that the OFFERA method was easy and quick to be used (mean 4.48 ± 0.821) and applicable for wide range of office working activities (mean 4.02 ± 0.952). Based on the results obtained, it can be concluded that the OFFERA method was found to be practically reliable and applicable for wide range of office work-related activities

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling

A black-box model for neurons

We explore the identification of neuronal voltage traces by artificial neural networks based on wavelets (Wavenet). More precisely, we apply a modification in the representation of dynamical systems by Wavenet which decreases the number of used functions; this approach combines localized and global scope functions (unlike Wavenet, which uses localized functions only). As a proof-of-concept, we focus on the identification of voltage traces obtained by simulation of a paradigmatic neuron model, the Morris-Lecar model. We show that, after training our artificial network with biologically plausible input currents, the network is able to identify the neuron's behaviour with high accuracy, thus obtaining a black box that can be then used for predictive goals. Interestingly, the interval of input currents used for training, ranging from stimuli for which the neuron is quiescent to stimuli that elicit spikes, shows the ability of our network to identify abrupt changes in the bifurcation diagram, from almost linear input-output relationships to highly nonlinear ones. These findings open new avenues to investigate the identification of other neuron models and to provide heuristic models for real neurons by stimulating them in closed-loop experiments, that is, using the dynamic-clamp, a well-known electrophysiology technique.Peer ReviewedPostprint (author's final draft

Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework