The objective of this study was to collect and interpret three-axis acceleration, temperature, and relative humidity data from six locations within commercial transport trailers shipping market-weight pigs. Transport was observed in Kansas (n = 15) and North Carolina (n = 20). Prior to loading, three-axis accelerometers were affixed to six locations on the trailers: top fore (TF), top center (TC), top aft (TA), bottom fore (BF), bottom center (BC), and bottom aft (BA) compartments. Data were post-processed to calculate root-mean-square (RMS) accelerations and vibration dose values (VDV) in the vertical direction and the horizontal plane. These values were compared with exposure action values (EAV) and exposure limit values (ELV), vibration levels deemed uncomfortable and potentially dangerous to humans. Additionally, RMS and VDV were compared among the trailer compartments. The vertical RMS accelerations for all compartments exceeded the EAV for loads measured in Kansas, and for the majority of the compartments measured in North Carolina. Many compartments, specifically the BA compartment from all trips, exceeded the vertical ELV. Regardless of where the data were collected, fewer compartments exceeded the EAV in the horizontal orientation. Only BA compartments exceeded the ELV in the horizontal orientation. There were Area × Level interactions for vertical and horizontal RMS and VDV (P < 0.01). The BF compartment had a greater vertical RMS value than the TF, TC, and BC (P < 0.02) compartments, but did not differ (P = 0.06) from the TA compartment. The vertical RMS of the TA compartment did not differ from the TF, TC, and BC compartments (P > 0.13). The BF compartment had a greater (P = 0.02) vertical VDV value than the TC location, but did not differ from the other locations (P > 0.16). All other locations did not differ in vertical VDV (P > 0.12). The BF compartment had greater horizontal RMS than the TC and TA compartments (P < 0.01), but did not differ from TF and BC compartments (P > 0.12). All other compartments did not differ in horizontal RMS (P > 0.34). All compartments, aside from the BA compartment, did not differ in horizontal VDV (P > 0.19). Vibration analyses indicated the BA compartment had the greatest vertical and horizontal vibrations and a large percentage of the compartments exceed the EAV and ELV, which indicated pigs may have experienced uncomfortable trips that could cause discomfort or fatigue.

In many applications it is desirable to inverse-calculate the distributed loading on a structure using a limited number of sensors. Yet, the calculated loads can be extremely sensitive to the placement of these sensors. In the case of predicting point loading applied at a known location, best results are typically achieved when one sensor is collocated with the force. However, the extension of this rule to distributed loading remains uncertain, and even simple sensor system design scenarios often require the designer to directly optimize the sensor placements using a numerical model. In an effort to provide designers with guidance, we identify optimal sensor configurations for predicting static distributed loads on beams with classical boundary conditions. An influence coefficient method, wherein the strain is related linearly to the static load, is used to estimate the applied forces. The loading distribution on the structure is assumed to be either a piece-wise linearly-distributed load or a uniformly-distributed load, allowing for distributed loads to be estimated using the magnitudes of a small number of control points. Given the simplicity of the beam structure, the equations of the influence coefficient method are derived analytically, which allows for the sensor placement to be specified using continuous optimization methods. The condition number of the influence coefficient matrix is used as a surrogate for error during optimization. ‘Rules of thumb’ for sensor placement are presented based on the optimization results. Results show that the optimal and rule-of-thumb sensor configurations are more resistant to input noise than naïve configurations, with the rule-of-thumb configurations yielding similar force predictions relative to the optimal configurations. We expect the rules of thumb to be useful guidelines for engineers designing tests on beam-like structures such as aircraft wings or marine propellers where the inverse calculation of distributed loads is of interest.

When measuring real-time in vivo muscle fatigue with electromyography (EMG), data collection can be compromised by premature sensor removal or environmental noise; therefore, the objective of this study was to develop a postmortem in vivo methodology to induce muscle fatigue and measure it using EMG. Barrows (N = 20) were stratified by weight and randomly allocated into one of two treatments. The treatments consisted of barrows being subjected to a hog electric stunner super-contraction cycle (ES) or not (CON) postmortem. The right hind limb bicep femoris (BF) and semitendinosus (ST) were selected for ambulatory movement simulation using electronic muscle stimulation (EMS). Muscle workload during EMS was measured with EMG using median power frequency (MdPF) and root mean square (RMS) as indicators of action potential velocity and muscle fiber recruitment. Ambulatory movement was induced and recorded for 20 min with a 4:4 duty cycle at 70 Hz. Muscle biopsies were collected pre- and post-EMS for metabolite analyses to corroborate muscle fatigue onset. There was a TRT × Muscle interaction for normalized RMS percentage (p < 0.01), where BF from CON barrows had greater values (p < 0.01). There were no interactions or TRT main effects for the MdPF normalized value (p ≥ 0.25), but there were Period and muscle effects on MdPF (p < 0.01). Bicep femoris had smaller (p < 0.01) MdPF than ST. The percentage of MdPF decreased (p < 0.01) by Period 5 compared to the other Periods, which did not differ from each other (p ≥ 0.38). There were TRT × Muscle and Muscle × Period interactions for ATP muscle concentration (p ≤ 0.03). The concentration of CON BF ATP was greater (p < 0.01) than that of ES BF and CON and ES ST, which did not differ from each other (p ≥ 0.11), but the APT concentration tended to differ between ES BF and ES ST (p = 0.06). Semitendinosus ATP concentration decreased (p < 0.01) post-EMS compared to ST pre- and BF pre- and post-EMS (p ≥ 0.29), but BF and ST concentration tended to differ pre-EMS (p = 0.07). The data indicated that EMS is a valuable tool for replicating ambulatory movement or physical activity, but super-contraction is not a means to accelerate postmortem muscle fatigue onset. Therefore, further refinement, such as longer EMS stimulation time, should be considered.