Abstract
Cardiac auscultation is a key screening tool used for cardiovascular evaluation. When used properly, it speeds up treatment and thus improving the patient's life quality. However, the analysis and interpretation of the heart sound signals is subjective and dependent of the physician's experience and domain knowledge. A computer assistant decision (CAD) system that automatically analyse heart sound signals, can not only support physicians in their clinical decisions but also release human resources to other tasks. In this paper, and to the best of our knowledge, for the first time a SMOTE strategy is used to boost a Convolutional Neural Network performance on the detection of murmur waves. Using the SMOTE strategy, a CNN achieved an overall of 88.43%.

Abstract
Bidirectional electric vehicle supply equipment and charging stations (EVSE) offer new business models and can provide services to the electrical grid. The smart grid lab in Vienna gives unique testing possibilities of future smart grids, as different type of electrical equipment can be operated at a reconstructed, well-known distribution grid. In this work the harmonic and supraharmonic emissions of a bidirectional EVSE are measured according to IEC61000-4-7 and IEC61000-4-30 Ed3 standard as well as the high-frequency grid impedance. In addition, the efficiency and the power factor are determined at various operating points. Although THDi at nominal power (10 kW) is very low and the efficiency and power factor is very high, at low power levels the opposite situation arise. Supraharmonic emissions remain stable independent of the charging/discharging power, and both wideband and narrowband emissions occur. The additional capacitance when connecting the EVSE impacts the high-frequency grid impedance substantially and generates resonance points.

Abstract
Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.

Abstract
Background: Approximately 62% of patients with breast cancer with a pathogenic variant (BRCA1 or BRCA2) undergo primary breast-conserving therapy. Objective: The study aims to develop a personalized risk management decision support tool for carriers of a pathogenic variant (BRCA1 or BRCA2) who underwent breast-conserving therapy for unilateral early-stage breast cancer. Methods: We developed a Bayesian network model of a hypothetical cohort of carriers of BRCA1 or BRCA2 diagnosed with stage I/II unilateral breast cancer and treated with breast-conserving treatment who underwent subsequent second primary cancer risk-reducing strategies. Using event dependencies structured according to expert knowledge and conditional probabilities obtained from published evidence, we predicted the 40-year overall survival rate of different risk-reducing strategies for 144 cohorts of women defined by the type of pathogenic variants (BRCA1 or BRCA2), age at primary breast cancer diagnosis, breast cancer subtype, stage of primary breast cancer, and presence or absence of adjuvant chemotherapy. Results: Absence of adjuvant chemotherapy was the most powerful factor that was linked to a dramatic decline in survival. There was a negligible decline in the mortality in patients with triple-negative breast cancer, who received no chemotherapy and underwent any secondary risk-reducing strategy, compared with surveillance. The potential survival benefit from any risk-reducing strategy was more modest in patients with triple-negative breast cancer who received chemotherapy compared with patients with luminal breast cancer. However, most patients with triple-negative breast cancer in stage I benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy or just risk-reducing salpingo-oophorectomy. Most patients with luminal stage I/II unilateral breast cancer benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy. The impact of risk-reducing salpingo-oophorectomy in patients with luminal breast cancer in stage I/II increased with age. Most older patients with the BRCA1 and BRCA2 pathogenic variants in exons 12-24/25 with luminal breast cancer may gain a similar survival benefit from other risk-reducing strategies or surveillance. Conclusions: Our study showed that it is mandatory to consider the complex interplay between the types of BRCA1 and BRCA2 pathogenic variants, age at primary breast cancer diagnosis, breast cancer subtype and stage, and received systemic treatment. As no prospective study results are available at the moment, our simulation model, which will integrate a decision support system in the near future, could facilitate the conversation between the health care provider and patient and help to weigh all the options for risk-reducing strategies leading to a more balanced decision.

Abstract

Abstract
Unmanned Aerial Vehicles (UAVs) are being deployed in different applications due to their reduced time execution to perform tasks, more extensive coverage area, and more risk minimization to humans. In the Oil & Gas industry, its use for inspection activities is even more attractive due to the large structures in these facilities. Therefore, this research proposes deploying an autonomous UAV system to inspect unburied pipelines of onshore O&G facilities. The proposed UAV guiding system is based on image processing techniques Canny edge detection and Hough Transform to detect the line and on a path follower algorithm to generate the trajectory. The proposed strategy was developed in Robot Operating System (ROS) and tested in a simulated environment considering the practical oper-ational. The same controller was tested on a physical UAV to validate the results obtained in previous simulations. The results demonstrated the effectiveness and feasibility of deploying the proposed strategy for this specific task and the cost reduction potential for real-life operations, as well as reduced potential risks to the physical integrity of the workers.