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Cable resistance training machines are showing resurgent popularity and allow greater number of degrees of freedom than typical selectorized equipment. Given that specific kinetic chains are used during distinct activities of daily living (ADL), cable machines may provide more effective interventions for some ADL, whereas others may be best addressed using selectorized equipment. This study examined differences in activity levels (root mean square of the EMG [rmsEMG]) of 6 major muscles (pectoralis major, PM; anterior deltoid, AD; biceps brachii, BB; rectus abdominis, RA; external obliques, EO; and triceps brachii, TB) and kinematics of multiple joints between a cable and standard selectorized machines during the biceps curl, the chest press, and the overhead press performed at 1.5 seconds per contractile stage. Fifteen individuals (9 men, 6 women; mean age ± SD, 24.33 ± 4.88 years) participated. Machine order was randomized. Significant differences favoring cable training were seen for PM and AD during biceps curl; BB, AD, and EO for chest press; and BB and EO during overhead press (p ≤ 0.05). Greater starting and ending angles were seen for the elbow and shoulder joints during selectorized biceps curl, whereas hip and knee starting and ending angles were greater for cable machine during chest and overhead presses (p < 0.0001). Greater range of motion (ROM) favoring the cable machine was also evident (p < 0.0001). These results indicate that utilization patterns of selected muscles, joint angles, and ROMs can be varied because of machine application even when similar exercises are used, and therefore, these machines can be used selectively in training programs requiring specific motor or biomechanical patterns. [ABSTRACT FROM AUTHOR]

The power output capacity of a local electrical utility is dictated by its customers' cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility's bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs), provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA), linear regression, and multivariate adaptive regression splines (MARSplines) and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE) of 3.9% as compared to the SMA, linear regression, and MARSplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME) of 8.2% as compared to the SMA, linear regression, and MARSplines AMEs of 26.2%, 45.1%, and 22.5% respectively. [ABSTRACT FROM AUTHOR]

Background/aims: Loading during concurrent bending and compression associated with deadlift, hang clean and hang snatch lifts carries the potential for injury to the intervertebral discs, muscles and ligaments. This study examined the capacity of a newly developed spinal model to compute shear and compressive forces, and bending moments in lumbar spine for each lift., Methods: Five male subjects participated in the study. The spine was modeled as a chain of rigid bodies (vertebrae) connected via the intervertebral discs. Each vertebral reference frame was centered in the center of mass of the vertebral body, and its principal directions were axial, anterior-posterior, and medial-lateral., Results: The results demonstrated the capacity of this spinal model to assess forces and bending moments at and about the lumbar vertebrae by showing the variations among these variables with different lifting techniques., Conclusion: These results show the model's potential as a diagnostic tool.