irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

Most wheelchairs have low directional stability due to their wheel-drive system configurations. Hence, when a wheelchair experiences excessive slip, it will create an uncontrollable moment that changes its heading direction, endangering the safety of the wheelchair user. This paper presents an approach to longitudinal slip reduction (or traction control) for an Electric Powered Wheelchair that uses a variable reference model, which is a mass-damper system. The position of a joystick is mapped to force and torque values, which are the input to this model. The output is the desired trajectory, which is fed to a trajectory tracking controller. The key idea is that if slip occurs, the applied torque to the wheels is reduced by decreasing the desired acceleration, which is achieved by changing the parameters of the reference model. In this study, the mass of the reference model is changed. The proposed slip reduction approach is validated via experiments.

In this paper, we cluster a unlabeled human wrist pulse signal data set via a multiple criteria decision making (MCDM) framework to mine useful information for further study. First, a preprocessing scheme is performed and spatial features are extracted to represent a pulse signal. Then, a list of clustering algorithms are initialized to generate a number of clustering alternatives. The goodness of these clustering alternatives are sequentially comprehensively evaluated by 11 criteria, including ten internal cluster validation indices and an ad-hoc index, the robustness to noise, which is proposed for assessing the clustering alternatives of the pulse data set with spatial features. Taking the evaluation results as inputs, the technique for order preference by similarity to ideal solution (TOPSIS) method is employed to solve the resulting MCDM model. According to the TOPSIS rank, clustering the data set into thirteen clusters via k-means is optimal. Samples drawn from each cluster have similar patterns, corresponding to specific pulse type in traditional Chinese pulse diagnosis. The thirteen clusters are segregated into two groups, namely the healthy and the unhealthy, which can be further applied for unhealthy pulse detection.

The ability to perform activities of daily living and mobility-related activities of daily living are substantial indicators of one's ability to live at home and to participate in one's community. Technologies to assist with mobility and manipulation are among the most important tools that clinicians can provide to people with disabilities to promote independence and community participation. For people with severe disabilities involving both the upper and lower extremities, there are few systems that provide practical and coordinated assistance with mobility and manipulation tasks. The personal mobility and manipulation appliance (PerMMA) was created in response to goals set forth by a team of clinicians and people with disabilities.

In this paper, we investigate the enhanced ability of manipulation with the newly developed Personal Mobility and Manipulation Appliance (PerMMA). PerMMA is a new assistive device that integrates bimanual manipulation with smart mobility to assist people with severe physical disabilities and enhance their quality of lives. Different from the fixed mounting method used in most existing systems, a novel mounting system was designed on PerMMA to enhance its capability of manipulation assistance. With a workspace characterized by essential daily living tasks, we evaluated PerMMA's performance of manipulation using a comparative study between PerMMA and classic design, such as single arm and fixed mounting, used in most existing systems. Simulation results demonstrate significant improvements with PerMMA in both of its reachability and manipulability.