Abstract
The Box-Cox (BC) transformation is widely used in data analysis for achieving approximate normality in the transformed scale. The transformation is only possible for non-negative data. This positiveness requirement implies a truncation to the distribution on the transformed scale and the distribution in the transformed scale is truncated normal. This fact has consequences for the estimation of the parameters specially if the truncated probability is high. In the seminal paper Box and Cox proposed to estimate parameters using the normal distribution which in practice means to ignore any consequences of the truncation on the estimation process. In this work we present the framework for exact likelihood estimation on the PN distribution to which we call method m(1) and how to calculate the parameters estimates using consistent estimators. We also present a pseudo-Likelihood function for the same model not taking into account truncation and allowing to replace parameters mu and sigma for their estimates. We call m(2) to this estimation method. We conclude that for cases where the truncated probability is low both methods give good estimation results. However for larger values of the truncated probability the m(2) method does not present the same efficiency.

Abstract

Abstract
Setting labor standards is an important topic to operational and strategic planning which requires the time studies establishment. This paper applies the statistical method for the definition of a sample size in order to define a reliable cycle time for a real industrial process. For the case study it is considered a welding process performed by a single operator that does the load and unload of components in 4 different welding machines. In order to perform the time studies, it is necessary to collect continuously data in the production line by measuring the time taken for the operator to perform the task. In order to facilitate the measurements, the task is divided into small elements with visible start and end points, called Measurement Points, in which the measurement process is applied. Afterwards, the statistical method enables to determine the sample size of observations to calculate the reliable cycle time. For the welding process presented, it is stated that the sample size defined through the statistical method is 20. Thus, these time observations of the task are continuously collected in order to obtain a reliable cycle time for this welding process. This time study can be implemented in similar way in other industrial processes.

Abstract
The automotive industry deals with complex processes. Becoming aware of the importance of agile management they are contributing to a creative fusion known as leagile. However, this concept needs further study and investigation. This work presents a systematic literature review on Lean Agile implementation in the automotive industry. Thirty-three publications were reviewed and characterized according to the year of publication, country of origin, industrial sector, used tools and their contributions to the automotive sector. The results show that 50% of the articles were published after 2018. The countries with the most publications are India, Portugal, and United Kingdom. The most cited tools are Value Stream Mapping (VSM), Just in Time (JIT) and 5S (23%). This study confirms the growing use of leagile in the automotive industry and the growing potential for research development in the area.

Abstract
Diabetes management data is composed of diverse factors and glycaemia indicators. Glycaemia predictive models tend to focus solely on glycaemia values. A comprehensive understanding of diabetes management requires the consideration of several aspects of diabetes management, beyond glycaemia. However, the inclusion of every aspect of diabetes management can create an overly high-dimensional data set. Excessive feature spaces increase computational complexity and may introduce over-fitting. Additionally, the inclusion of inconsequential features introduces noise that hinders a model's performance. Feature importance is a process that evaluates a feature's value, and can be used to identify optimal feature sub-sets. Depending on the context, multiple methods can be used. The drop feature method, in the literature, is considered to be the best approach to evaluate individual feature importance. To reach an optimal set, the best approach is branch and bound, albeit its heavy computational cost. This overhead can be addressed through a trade-off between the feature set's optimisation level and the process' computational feasibility. The improvement of the feature space has implications on the effectiveness of data balancing approaches. Whilst, in this study, the observed impact was not substantial, it warrants the need to reconsider the balancing approach given a superior feature space.

Abstract
Radiographic canine hip dysplasia (CHD) diagnosis is crucial for breeding selection and disease management, delaying progression and alleviating the associated pain. Radiography is the primary imaging modality for CHD diagnosis, and visual assessment of radiographic features is sometimes used for accurate diagnosis. Specifically, alterations in femoral neck shape are crucial radiographic signs, with existing literature suggesting that dysplastic hips have a greater femoral neck thickness (FNT). In this study we aimed to develop a three-stage deep learning-based system that can automatically identify and quantify a femoral neck thickness index (FNTi) as a key metric to improve CHD diagnosis. Our system trained a keypoint detection model and a segmentation model to determine landmark and boundary coordinates of the femur and acetabulum, respectively. We then executed a series of mathematical operations to calculate the FNTi. The keypoint detection model achieved a mean absolute error (MAE) of 0.013 during training, while the femur segmentation results achieved a dice score (DS) of 0.978. Our three-stage deep learning-based system achieved an intraclass correlation coefficient of 0.86 (95% confidence interval) and showed no significant differences in paired t-test compared to a specialist (p > 0.05). As far as we know, this is the initial study to thoroughly measure FNTi by applying computer vision and deep learning-based approaches, which can provide reliable support in CHD diagnosis. © The Author(s) 2024.