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
Digital transformation is progressing exponentially. Given the importance of this transformation, managerial strategies and practices need to be adapted to meet the new challenges. While countries are on a journey toward a process where human interactions and transactions—with the government, businesses—and consumption of goods, services, and ideas primarily conducted through the use of the internet and internet-based technologies, they are all traveling at different speeds. Based on the theory, drawing from the Global Innovation Index (GII) input-output framework and literature review on innovation, the chapter intends to answer the question: What is the impact of management and strategies for digital enterprise transformation on welfare?

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.

Abstract
Despite decades of research and development in named entity resolution, dealing with name ambiguity is still a challenging issue for many bibliometric-enhanced information retrieval (IR) tasks. As new bibliographic datasets are created as a result of the upward growth of publication records worldwide, more problems arise when considering the effects of errors resulting from missing data fields, duplicate entities, misspellings, extra characters, etc. As these concerns tend to be of large-scale, both the general consistency and the quality of electronic data are largely affected. This paper presents an approach to handle these name ambiguity problems through the use of crowdsourcing as a complementary means to traditional unsupervised approaches. To this end, we present "AuthCrowd", a crowdsourcing system with the ability to decompose named entity disambiguation and entity matching tasks. Experimental results on a real-world dataset of publicly available papers published in peer-reviewed venues demonstrate the potential of our proposed approach for improving author name disambiguation. The findings further highlight the importance of adopting hybrid crowd-algorithm collaboration strategies, especially for handling complexity and quantifying bias when working with large amounts of data.

Abstract
This paper presents a quadratically constrained programming (QCP) model to tackle the optimal power flow (OPF) problem in distribution networks. The proposed model is fast, reliable, and precise enough to be embedded into the multi-emporal power system analysis. The proposed model benefits from a standard QCP to solve the branch active and reactive power flows. The second-order conic programming (SOCP) approach has been applied to address the quadratic constraints. The nonconvex feature of the OPF problem has been relaxed utilizing the McCormick envelopes. To find the minimum current of each branch, the lossless power flow model has been first solved and the obtained results have been considered for solving the OPF problem. The IEEE 33-bus test system has been selected as the benchmark to verify the efficient performance of the proposed OPF model. The simulation study confirms that the McCormick envelopes used in the QCP approach lead to precise results with a very fast convergence time. Overall, the presented model for the OPF can be extended for both planning and operation purposes in distribution system studies.