Ensuring an effective voltage regime in the electrical network when powering ships of the technical fleet and floating objects from the shore

The article deals with topical issues of reducing the asymmetry of linear voltages in the electrical networks of water transport enterprises. Asymmetric modes in the electrical network are researched, which defined the need for automated determination of conductive electromagnetic disturbance. A mathematical description of the occurrence process and strategy for determining the conductive low-frequency electromagnetic disturbance by the voltage asymmetry coefficient in the reverse sequence are presented. The parameters of this coefficient are described: mathematical expectation, standard deviation, probability of occurrence during the calculation period. A mathematical model is obtained that represents a conductive electromagnetic disturbance and explains the probability of its occurrence. A method of automated determination of conductive electromagnetic disturbance has been developed. A software product is presented (certificate of registration No. 2016661752), which allows processing the voltage coefficients obtained as a result of measurements in the reverse sequence. The conductive disturbance is determined for the purpose of suppression and ensuring electromagnetic compatibility. To ensure an effective mode by powering ships from the shore and to increase the efficiency of power transmission, a strategy for voltage symmetry in the electrical network is presented.

Automated determination of interfacial tension and contact angle using computer vision for oil field applications

Contact angle and surface tension are the two most widely used surface analysis approaches for reservoir fluid characterization in petroleum industries. The pendant drop method has among the most widely used techniques for the estimation of surface tension. The present work utilizes a python-based computer program to automatically determine interfacial tension (IFT) and contact angle from the pendant drop image acquired from a typical pendant drop apparatus. The proposed program uses python-based image processing libraries for the analysis of the pendant drop image. Also, the program is tested on images acquired from the standard solutions for the IFT and contact angle calculation showing promising results with a standard deviation of less than 1.7 mN/m.

ArcDrain: A GIS Add-In for Automated Determination of Surface Runoff in Urban Catchments

Surface runoff determination in urban areas is crucial to facilitate ex ante water planning, especially in the context of climate and land cover changes, which are increasing the frequency of floods, due to a combination of violent storms and increased imperviousness. To this end, the spatial identification of urban areas prone to runoff accumulation is essential, to guarantee effective water management in the future. Under these premises, this work sought to produce a tool for automated determination of urban surface runoff using a geographic information systems (GIS). This tool, which was designed as an ArcGIS add-in called ArcDrain, consists of the discretization of urban areas into subcatchments and the subsequent application of the rational method for runoff depth estimation. The formulation of this method directly depends on land cover type and soil permeability, thereby enabling the identification of areas with a low infiltration capacity. ArcDrain was tested using the city of Santander (northern Spain) as a case study. The results achieved demonstrated the accuracy of the tool for detecting high runoff rates and how the inclusion of mitigation measures in the form of sustainable drainage systems (SuDS) and green infrastructure (GI) can help reduce flood hazards in critical zones.

Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients

The gold standard to diagnose intracerebral lesions after traumatic brain injury (TBI) is computed tomography (CT) scan, and due to its accessibility and improved quality of images, the global burden of CT scan for TBI patients is increasing. The recent developments of automated determination of traumatic brain lesions and medical-decision process using artificial intelligence (AI) represent opportunities to help clinicians in screening more patients, identifying the nature and volume of lesions and estimating the patient outcome. This short review will summarize what is ongoing with the use of AI and CT scan for patients with TBI.