Abstract
Objective: to analyze the factorial structure of the Perception Scale of Nursing Activities that Contribute to the Quality of Care. Method: a methodological study with 3,451 nurses from 36 Portuguese hospitals. In addition to carrying out confirmatory factorial analysis, Cronbach’s alpha and composite reliability were used to assess the reliability of the obtained factorial model. Results: the factorial weights of the solution found were mostly high; the values of the model’s adjustment indexes were reasonable; Cronbach’s alpha was elevated for the entire scale and five dimensions, being acceptable in only one dimension. The composite reliability was also high in five dimensions, except for one, considered acceptable. All activities showed high individual reliability. Conclusion: Compared to the original scale, the identified factorial model contemplates six dimensions and not seven, producing a reliable and valid scale, which can be applied in the hospital context.

Abstract
In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated and compared for k-nearest neighbours imputation. In this work, we study the effect of several heterogeneous distances on k-nearest neighbours imputation on a large benchmark of publicly-available datasets.

Abstract
An important scheduling function of manufacturing systems is controlled order release. While there exists a broad literature on order release, reported release procedures typically use simple sequencing rules and greedy heuristics to determine which jobs to select for release. While this is appealing due to its simplicity, its adequateness has recently been questioned. In response, this study uses an integer linear programming model to select orders for release to the shop floor. Using simulation, we show that optimisation has the potential to improve performance compared to 'classical' release based on pool sequencing rules. However, in order to also outperform more powerful pool sequencing rules, load balancing and timing must be considered at release. Existing optimisation-based release methods emphasise load balancing in periods when jobs are on time. In line with recent advances in Workload Control theory, we show that a better percentage tardy performance can be achieved by only emphasising load balancing when many jobs are urgent. However, counterintuitively, emphasising urgency in underload periods leads to higher mean tardiness. Compared to previous literature we further highlight that continuous optimisation-based release outperforms periodic optimisation-based release. This has important implications on how optimised-based release should be designed.

Abstract
Based on the characteristics of ferrofluids, a monolithic optofluidic device for magnetic field sensing is proposed and demonstrated. The device consists of a Fabry-Perot interferometer, composed by an optical waveguide orthogonal to a microfluidic channel, which was fabricated inside a fused silica substrate through femtosecond laser micromachining. The interferometer was first optimized by studying the influence of the waveguide writing parameters on its spectral properties. Waveguides written at higher pulse energies led to a decrease of the signal-to-noise ratio, due to an enhancement of micrometer sized defects associated with Mie scattering. Fringe visibility was also maximized for waveguides written at lower scanning speeds. Making use of the tunable refractive index property exhibited by magnetic fluids, the interferometer was then tested as a magnetic field sensor by injecting a ferrofluid inside the microfluidic channel. A linear sensitivity of -0.25 nm/mT was obtained in the 9.0-30.5 mT range with the external field parallel to the waveguide axis.

Abstract
Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed. Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with $$K=6$$. The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging. © Springer Nature Switzerland AG 2020.

Abstract
Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for trajectory data mining is segmentation. This task can be seen as a pre-processing step in which a trajectory is divided into several meaningful consecutive sub-sequences. This process is necessary because trajectory patterns may not hold in the entire trajectory but on trajectory parts. In this work we propose a supervised trajectory segmentation algorithm, called Wise Sliding Window Segmentation (WS-II). It processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal that is further used to train a binary classifier for segmenting trajectory data. This algorithm is flexible and can be used in different domains. We evaluate our method over three real datasets from different domains (meteorology, fishing, and individuals movements), and compare it with four other trajectory segmentation algorithms: OWS, GRASP-UTS, CB-SMoT, and SPD. We observed that the proposed algorithm achieves the highest performance for all datasets with statistically significant differences in terms of the harmonic mean of purity and coverage. © Springer Nature Switzerland AG 2020.