Abstract

Abstract
Many archival collections have been recently digitized and made available to a wide public. The contained documents however tend to have limited attractiveness for ordinary users, since content may appear obsolete and uninteresting. Archival document collections can become more attractive for users if suitable content can be recommended to them. The purpose of this research is to propose a new research direction of Archival Content Suggestion to discover interesting content from long-term document archives that preserve information on society history and heritage. To realize this objective, we propose two unsupervised approaches for automatically discovering interesting sentences from news article archives. Our methods detect interesting content by comparing the information written in the past with one created in the present to make use of a surprise effect. Experiments on New York Times corpus show that our approaches effectively retrieve interesting content.

Abstract
Data is a major asset in today's healthcare scenery. Hospitals are one of the primary producers of healthcare-related data and the value this data can provide is enormous. However, to use this to improve healthcare practice and push science forward, it is necessary to safeguard the patient's privacy and the ethical use of the data. The ethical and legal requirements are vast and complex. Synthetic data appears as a tool to overcome these hurdles and provide fast and reliable access to data without compromising utility nor privacy. Even though Generative Adversarial Networks (GANs) are receiving a lot of attention lately, the application of most common models and architectures are not suited to tabular data - the most prevalent healthcare-related data. This study surveys the current GAN implementations tailored to this scenario. The analysis was focused mainly on the models employed, datasets used, and metrics reported regarding the quality of the generated data in terms of utility, privacy and how they compare among themselves. We aim to help institutions and investigators get a grasp of the tools to facilitate access to healthcare data, as well as recommendations for testing data synthesizers with privacy concerns.

Abstract
In hydroponic cultivation, monitoring and quantification of nutrients is of paramount importance. Precision agriculture has an urgent need for measuring fertilization and plant nutrient uptake. Reliable, robust and accurate sensors for measuring nitrogen (N), phosphorus (P) and potassium (K) are regarded as critical in this process. It is vital to understand nutrients’ interference; thusly, a Hoagland fertilizer solution-based orthogonal experimental design was deployed. Concentration ranges were varied in a target analyte-independent style, as follows: [N] = [103.17–554.85] ppm; [P] = [15.06–515.35] ppm; [K] = [113.78–516.45] ppm, by dilution from individual stock solutions. Quantitative results for N and K, and qualitative results for P were obtained.

Abstract
Current biophotonics methods cover the entire optical spectrum from the deep ultraviolet to the terahertz. To optimize such methods for diagnostic and therapeutic applications, the need to obtain the wideband dispersion of tissues is high. The pancreas is a very important organ in the human body, since it produces insulin and its malfunction may induce diabetes. A reduced number of biophotonics publications regarding the pancreas is available, meaning that studies to determine its optical properties and their variation during optical clearing treatments are necessary. Considering this fact, we used the total internal reflection method to measure the refractive index of the rabbit pancreas for wavelengths between 400 and 850 nm. The experimental results allowed to calculate the pancreas dispersion with the Cauchy, Conrady and Cornu equations. It was observed that all those equations provided good data fitting in the spectral range of the measurements, but differences were observed outside these limits. Considering the wavelength of 633 nm, the mean value from the three dispersions was 1.3521, while the one published for porcine pancreas is 1.3517. The dispersion calculated with the Conrady equation does not present a fast decreasing behavior for shorter wavelengths as the ones calculated with the Cauchy and Cornu equations, but comparing these curves with a dispersion for a tissue-like material, all seem to have good agreement.

Abstract
Poly(vinyl alcohol) (PVA) in multifilament and braided yarns (BY) forms presents great potential for the design of numerous applications. However, such solutions fail to accomplish their requirements if the chemical and thermomechanical behaviour is not sufficiently known. Hence, a comprehensive characterisation of PVA multifilament and three BY architectures (6, 8, and 10 yarns) was performed involving the application of several techniques to evaluate the morphological, chemical, thermal, and mechanical features of those structures. Scanning electron microscopy (SEM) was used to reveal structural and morphological information. Differential thermal analysis (DTA) pointed out the glass transition temperature of PVA at 76 & DEG;C and the corresponding crystalline melting point at 210 & DEG;C. PVA BY exhibited higher tensile strength under monotonic quasi-static loading in comparison to their multifilament forms. Creep tests demonstrated that 6BY structures present the most deformable behaviour, while 8BY structures are the least deformable. Relaxation tests showed that 8BY architecture presents a more expressive variation of tensile stress, while 10BY offered the least. Dynamic mechanical analysis (DMA) revealed storage and loss moduli curves with similar transition peaks for the tested structures, except for the 10BY. Storage modulus is always four to six times higher than the loss modulus.