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Moquette-manufacturing industry is one of the textile industries that produces high organic load wastewaters containing polymeric adhesives, especially polyvinyl acetate (PVAc). The generated wastewater cannot be easily treated by commonly used traditional methods. Therefore, in this study, continuous electrocoagulation process (CECP) was applied for pre-treatment of real wastewater from moquette-manufacturing industry. For this purpose, the influences of different operational parameters, such as electrode distance, current density (CD), hydraulic retention time, and electrode type were investigated on the process performance at pilot scale. Under optimal conditions (neutral solution pH, optimum CD of 21 mA/cm2, Charge passed of 0.53 C/cm2 and hydraulic retention time of 25 min), the removal efficiencies of chemical oxygen demand (COD), total suspended solid (TSS), PVAc, total dissolved solid (TDS) and electrical conductivity (EC) using the CECP with Fe–Fe electrode (as the best electrode) were 90.4%, 91.3%, 93.9%, 59.7% and 72.6%, respectively. The results showed the process efficiency increased with decreasing electrode distance, and increasing of CD and retention time. The consumption rate of electrical energy increased with increasing electrode distance, CD and reaction time. Under the optimal conditions, the consumption rates of electrical energy for iron and aluminium anodes were 2.4 and 6.1 kwh/m3, respectively. Also, the electrode consumption for removal of 1 kg of COD using iron and aluminium anodes at the optimum CD was 4.2 and 3.2 Kg/kg CODremoved, respectively. Therefore, it can be concluded that CECP can be used as an effective and economical method for pre-treatment of moquette industry wastewater compared with the traditional methods. [ABSTRACT FROM AUTHOR]

We investigated the role of task constraints on interpersonal interactions. Twenty-one pairs of coworkers performed a finger force production task on force sensors placed at two ends of a seesaw-like apparatus and matched a combined target force of 20 N for 23 s over 10 trials. There were two experimental conditions: 1) FIXED: the seesaw apparatus was mechanically held in place so that the only task constraint was to match the 20 N resultant force, and 2) MOVING: the lever in the apparatus was allowed to rotate freely around its fulcrum, acting like a seesaw, so an additional task constraint to (implicitly) balance the resultant moment was added. We hypothesized that the additional task constraint of moment stabilization imposed on the MOVING condition would deteriorate task performance compared with the FIXED condition; however, this was rejected, as the performance of the force matching task was similar between two conditions. We also hypothesized that the central nervous systems (CNSs) would employ distinct coworking strategies or interpersonal motor synergy (IPMS) between conditions to satisfy different task constraints, which was supported by our results. Negative covariance between coworker’s forces in the FIXED condition suggested a force stabilization strategy, whereas positive covariance in the MOVING condition suggested a moment stabilization strategy, implying that independent CNSs adopt distinct IPMSs depending on task constraints. We speculate that in the absence of a central neural controller, shared visual and mechanical connections between coworkers may suffice to trigger modulations in the cerebellum of each CNS to satisfy competing task constraints. [ABSTRACT FROM AUTHOR]

Computation of muscle force patterns that produce specified movements of muscle-actuated dynamic models is an important and challenging problem. This problem is an undetermined one, and then a proper optimization is required to calculate muscle forces. The purpose of this paper is to develop a general model for calculating all muscle activation and force patterns in an arbitrary human body movement. For this aim, the equations of a multibody system forward dynamics, which is considered for skeletal system of the human body model, is derived using Lagrange–Euler formulation. Next, muscle contraction dynamics is added to this model and forward dynamics of an arbitrary musculoskeletal system is obtained. For optimization purpose, the obtained model is used in computed muscle control algorithm, and a closed-loop system for tracking desired motions is derived. Finally, a popular sport exercise, biceps curl, is simulated by using this algorithm and the validity of the obtained results is evaluated via EMG signals. [ABSTRACT FROM AUTHOR]