Abstract
Consuming Semantic Web data presents several challenges, from the number of datasets it is composed of, to the (very) large size of some of those datasets and the uncertain availability of querying endpoints. According to its core principles, accessing linked data can be done simply by dereferencing the IRIs of RDF resources. This is a light alternative both for clients and servers when compared to dataset dumps or SPARQL endpoints. The linked data interface does not support complex querying, but using it recursively may suffice to gather information about RDF resources, or to extract the relevant sub-graph which can then be processed and queried using other methods. We present Derzis1, an open source semantic web crawler capable of traversing the linked data cloud starting from a set of seed resources. Derzis maintains information about the paths followed while crawling, which allows to define property path-based restrictions to the crawling frontier.

Abstract

Abstract
Thrombocytosis is a disorder with an excessive number of platelets in the blood, where total platelet counts (TPC) are crucial for diagnosis. This condition predisposes to blood vessels clotting and diseases such as stroke or heart attack. TPC is generally performed at the laboratory by flow cytometry with laser scattering or impedance detection. Due to the limited capacity of automated hematology in performing TPC quantification, a manual microscopy count is a very common quality assurance measure undertaken by clinical pathologists. Monitoring coagulation risk is key in many health conditions, and point-of-care platforms would simplify this procedure by taking platelet counts to the bedside. Spectroscopy has high potential for reagent-less point-of-care miniaturized technologies. However, platelets are difficult to detect in blood by standard spectroscopy analysis, due to their small size, low number when compared to red blood cells, and low spectral contrast to hemoglobin. In this exploratory research, we show that it is possible to perform TPC by advanced spectroscopy analysis, using a new processing methodology based on self-learning artificial intelligence. The results show that TPC can be measured by visible–near-infrared spectroscopy above the standard error limit of 61.19 × 109 cells/L (R2 = 0.7016), tested within the data range of 53 × 109 to 860 × 109 cells/L of dog blood. These results open the possibility for using spectroscopy as a diagnostic technology for the detection of high levels of platelets directly in whole blood, towards the rapid diagnosis of thrombocytosis and stroke prevention.

Abstract
Background: Several guidelines recommend that patients with chronic kidney disease treated by haemodialysis (HD) take care of their own arteriovenous fistula (AVF). The dialysis nurse plays an important role in the development of such self-care behaviours. A very small number of instruments are available to assess self-care behaviours with AVF in Turkey. Objective: Cultural adaptation and psychometric testing of the Turkish version of the scale of assessment of self-care behaviours with arteriovenous fistula in haemodialysis (ASBHD-AVF) patients. Design: Cross-sectional validation study. Participants and Measurements: This study was conducted involving 160 patients in the Bolu region in Turkey. The guidelines provided by Sousa and Rojjanasrirat were taken into account in the scale translation, adaptation and validation process. Validity was analysed through content validity and construct validity. The latter was measured through principal component analysis with varimax rotation, considering only factor loadings of 0.30 or larger. Reliability analysis was based on internal consistency measured by Cronbach's alpha. Results: A two-factor structure was extracted explaining 59.01% of the total variance. Cronbach's alpha was 0.91, 0.85 and 0.84 for the overall scale, the self-care in prevention of complications subscale and the self-care in management of signs and symptoms subscale, respectively. Conclusions: The Turkish version of the scale of ASBHD-AVF patients is a reliable and valid instrument and can therefore be used.

Abstract
This study presents a simulation-based procedure to analyse a production line of a metalworking company. We use a simulation tool, ProModel ® software, to reproduce the existing production line layout of the company best-selling product which represents about 70% of the total sales. Our purpose is to get information about the existing system behaviour, and to find strategies to increase actual production level to meet the market’s demand. Based on an initial simulation model, different production scenarios were proposed and results have shown that it is possible to increase the production level allowing to meet the increasing demand for the product. The following changes in the production system were considered: (i) the use of intermediate stock of work-in-process items, (ii) the introduction of new equipment, and (iii) a mixed strategy where the introduction of new equipment is combined with the use of intermediate stock of work-in-process items. In summary, this research exhibits the flexibility of the simulation technique to address manufacturing problems throughout the creation of different scenarios providing some of the behaviour of the systems allowing the anticipation of final outputs. © 2021, Springer Nature Switzerland AG.

Abstract
This paper presents an effort to timely handle 400+ GBytes of sensor data in order to produce Predictive Maintenance (PdM) models. We follow a data-driven methodology, using state-of-the-art python libraries, such as Dask and Modin, which can handle big data. We use Dynamic Time Warping for sensors behavior description, an anomaly detection method (Matrix Profile) and forecasting methods (AutoRegressive Integrated Moving Average - ARIMA, Holt-Winters and Long Short-Term Memory - LSTM). The data was collected by various sensors in an industrial context and is composed by attributes that define their activity characterizing the environment where they are inserted, e.g. optical, temperature, pollution and working hours. We successfully managed to highlight aspects of all sensors behaviors, and produce forecast models for distinct series of sensors, despite the data dimension. © 2021, Springer Nature Singapore Pte Ltd.