A Biomechanical Waist Comfort Model for Manual Material Lifting

Low back pain (LBP) is a common disorder that affects the working population worldwide. LBP causes more disability than any other conditions all around the world. Most existing studies focus on the occupational physical factors in association with LBP, while few focus on individual factors, especially the lack of quantitative calculation of waist comfort in biomechanics. Based on the physical statistics of Chinese men, this research used human posture analysis (HPA) to establish the waist strength formula and analyzed the waist strength during a manual material handling. It also explored the influence of weight and height of lifting objects on the L5-S1 spinal load. On this basis, a waist comfort model was proposed in combination with the recommended weight limit (RWL) recommended by NIOSH, and the parameter selection and waist comfort value were verified by Jack simulation software. The results show that pulling force of the Erector Spinae of the waist is closely related to the weight and lifting height of the object. Parameter verification and Jack software simulation results show that the force of L5-S1 is less than 3400 N, which proves that the waist force under this posture is acceptable. The developed waist comfort model can be applied to evaluate work risk, to adjust working intensity and powered exoskeleton design, aiming to decrease the prevalence of LBP.

Adaptive Pairwise Comparison for Educational Measurement

Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Because the scale separation reliability (SSR) may overestimate the benchmark reliability when the ASA is used, caution is required when interpreting the SSR.

Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems

We examine a multiple object tracking problem by jointly optimizing the transmit waveforms used in a multimodal system. Coexisting sensors in this system were assumed to share the same spectrum. Depending on the application, a system can include radars tracking multiple targets or multiuser wireless communications and a radar tracking both multiple messages and a target. The proposed spectral coexistence approach was based on designing all transmit waveforms to have the same time-varying phase function while optimizing desirable performance metrics. Considering the scenario of tracking a target with a pulse–Doppler radar and multiple user messages, two signaling schemes were proposed after selecting the waveform parameters to first minimize multiple access interference. The first scheme is based on system interference minimization, whereas the second scheme explores the multiobjective optimization tradeoff between system interference and object parameter estimation error. Simulations are provided to demonstrate the performance tradeoffs due to different system requirements.

Object–Parameter Approaches to Predicting Unknown Data in an Incomplete Fuzzy Soft Set

The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a new adjustable object-parameter approach is proposed to predict unknown data in incomplete fuzzy soft sets. Data predicting converts an incomplete fuzzy soft set into a complete one, which makes the fuzzy soft set applicable not only to decision making but also to other areas. The compared results elaborated through rate exchange data sets illustrate that both our improved approach and the new adjustable object-parameter one outperform the existing method with respect to forecasting accuracy.