Manuel Oviedo de la Fuente

This paper proposes a new nonlinear approach for additive functional regression with functional response based on kernel methods along with some slight reformulation and implementation of the linear regression and the spectral additive model. The latter methods have in common that the covariates and the response are represented in a basis and so, can only be applied when the response and the covariates belong to a Hilbert space, while the proposed method only uses the distances among data and thus can be applied to those situations where any of the covariates or the response is not Hilbertian typically normed or even metric spaces with a real vector structure. A comparison of these methods with other procedures readily available in R is performed in a simulation study and in real datasets showing the results the advantages of the nonlinear proposals and the small loss of efficiency when the simulation scenario is truly linear. The comparison is done in the Hilbertian case as it is the only scenario where all the procedures can be compared. Finally, the supplementary material provides a visualization tool for checking the linearity of the relationship between a single covariate and the response and a link to a GitHub repository where the code and data are available.} %and an example considering that the response is not Hilbertian.

Objectives: To determine the extent of career-long and 12-month exposure to sexual, physical, and psychological/verbal violence committed by patients or their companions among physical therapists in Spain. Additionally, to identify the factors associated with such exposure. Methods: This study employed an observational cross-sectional approach. Initially, a questionnaire was developed and validated using a convenience sample. Subsequently, it was distributed via email to all physical therapists registered in Spain in the first quarter of 2022. Individual risk models were created for each type of violence experienced within the past 12 months. Results: The prevalence of violence encountered by physical therapists throughout their careers was 47.9% for sexual violence, 42.7% for psychological/verbal abuse, and 17.6% for physical abuse. Lower values were observed within the last 12 months (13.4%, 15.8%, and 5.2%, respectively). Statistical risk modeling for each type of violence experienced in the past 12 months indicated that the common precipitating factor for all forms of violence was working with patients with cognitive impairment. Working part-time appeared to be a protective factor. Other factors, such as the practitioners’ gender, practice setting, or clinic location showed variations among the diverse types of violence. Conclusions: The exposure to type II workplace violence within the last 12 months among physical therapists in Spain (Europe) is not so high as in some other world regions. Various individual, clinical, and professional/organizational risk factors have been identified in connection with type II workplace violence. Further research is warranted to compare the violence experienced once the COVID pandemic has subsided.

Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a number of handcrafted features representing the local geometry around it are calculated at different scales, that is, varying the spatial extension of the local analysis. Calculating the scales at small intervals allows each feature to be accurately approximated using a smooth function and, for the problem of semantic segmentation, to be tackled using functional data analysis. We also present a step-wise method to select the optimal features to include in the model based on the calculation of the distance correlation between each feature and the response variable. The algorithm showed promising results when applied to simulated data. When applied to the semantic segmentation of a point cloud of a forested plot, the results proved better than when using a standard multiscale semantic segmentation method. The comparison with two popular deep learning models showed that our proposal requires smaller training samples sizes and that it can compete with these methods in terms of prediction.

Supervised classification of 3D point clouds using machine learning algorithms and handcrafted local features as covariates frequently depends on the size of the neighborhood (scale) around each point used to determine those features. It is therefore crucial to estimate the scale or scales providing the best classification results. In this work, we propose three methods to estimate said scales, all of them based on calculating the maximum values of the distance correlation (DC) functions between the features and the label assigned to each point. The performance of the methods was tested using simulated data, and the method presenting the best results was applied to a benchmark data set for point cloud classification. This method consists of detecting the local maximums of DC functions previously smoothed to avoid choosing scales that are very close to each other. Five different classifiers were used: linear discriminant analysis, support vector machines, random forest, multinomial logistic regression and multilayer perceptron neural network. The results obtained were compared with those from other strategies available in the literature, being favorable to our approach.