A simple and accurate approach to solve the power flow for balanced islanded microgrids

Power flow studies are very important in the planning or expansion of power system. With the integration of distributed generation (DG), micro-grids are becoming attractive. So, it is important to study the power flow of micro-grids. In grid connected mode, the power flow of the system can be solved in a conventional manner. In islanded mode, the conventional method (like Gauss Seidel) cannot be applied to solve power flow analysis. Hence some modifications are required to implement the conventional Gauss Seidel method to islanded micro-grids. This paper proposes a Modified Gauss Seidel (MGS) method, which is an extension of the conventional Gauss Seidel (GS) method. The proposed method is simple, easy to implement and accurate in solving the power flow analysis for islanded microgrids. The MGS algorithm is implemented on a 6 bus test system. The results are compared against the simulations results obtained from PSCAD/EMTDC which proves the accuracy of the proposed MGS algorithm

COVID-19 Crowd Detection

Object detection was introduced by researchers for face detection. Researchers explain how the detected face is divided into minor frames to be recognized by the algorithm. Due to COVID-19 and government regulations, many people face problems going to shopping centers and shop safely. It has been very hard for both the government and the people to manage social distancing. In our study, we developed a system using Raspberry Pi-4 that will detect the distance between people along with counting the number of distance and mask violations. An error message will appear on the screen in red, showing the total number of distance and mask violations, which could later be used by the customer as statistical evidence for better safety precautions

A novel approach to solve power flow for islanded microgrids using modified Newton Raphson with droop control of DG

The study of power flow analysis for microgrids has gained importance where several methods have been proposed to solve these problems. However, these schemes are complic ated and not easy to implement due to the absence of a slack bus as well as the dependence of the power on frequency as a result of the droop characteristics. This paper proposes simple and e ffec- tive modifications to the conventional method (Newton Raphs on) to compute the power flow for microgrids. The presented metho d provides a simple, easy to implement, and accurate approach to solve the power flow equations for microgrids. The propose d method is applied to two test systems: a 6-bus system and a 38- bus system. The results are compared against simulation result s from PSCAD/EMTDC which validate the effectiveness of the develo ped method. The proposed technique can be easily integrated in current commercially available power system software and c an be applied for power system studies method is applied to two test systems: a 6-bus system and a 38-bus system. The results are compared against simulation results from PSCAD/EMTDC which validate the effectiveness of the developed method. The proposed technique can be easily integrated in current commercially available power system software and can be applied for power system studies

iRegNet: Non-rigid Registration of MRI to Interventional US for Brain-Shift Compensation using Convolutional Neural Networks

Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance

Explainability of deep neural networks for MRI analysis of brain tumors

Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. Methods In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. Results NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. Conclusion Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI

Mapping and Assessment of Evapotranspiration over Different Land-Use/Land-Cover Types in Arid Ecosystem

Evapotranspiration (ET) is an essential process for defining the mass and energy relationship between soil, crop and atmosphere. This study was conducted in the Eastern Region of Saudi Arabia, to estimate the actual daily, monthly and annual evapotranspiration (ETa) for different land-use systems using Landsat-8 satellite data during the year 2017/2018. Initially, six land-use and land-cover (LULC) types were identified, namely: date palm, cropland, bare land, urban land, aquatic vegetation, and open water bodies. The Surface Energy Balance Algorithm for Land (SEBAL) supported by climate data was used to compute the ETa. The SEBAL model outputs were validated using the FAO Penman-Monteith (FAO P-M) method coupled with field observation. The results showed that the annual ETa values varied between 800 and 1400 mm.year−1 for date palm, 2000 mm.year−1 for open water and 800 mm.year−1 for croplands. The validation measure showed a significant agreement level between the SEBAL model and the FAO P-M method with RMSE of 0.84, 0.98 and 1.38 mm.day−1 for date palm, open water and cropland respectively. The study concludes that the ETa produced from the satellite data and the SEBAL model is useful for water resource management under arid ecosystem of the study area

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

New insights from animal models of colon cancer: inflammation control as a new facet on the tumor suppressor APC gem

Maged Zeineldin, Kristi L Neufeld Department of Molecular Biosciences, University of Kansas, Lawrence, KS, USA Abstract: Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths worldwide. As with other cancers, CRC is a genetic disease, however, several risk factors including diet and chronic colitis predispose to the disease. Mutations in the tumor suppressor adenomatous polyposis coli (APC) initiate most cases of CRC. Recent data from mouse models suggest that APC mutations and colitis are not completely independent factors in colorectal carcinogenesis. Here, we review the evidence supporting an interaction between APC mutations and chronic colitis. We will also discuss possible pathophysiologic mechanisms behind this interaction. Keywords: rodent model, colon cancer, adenomatous polyposis coli, APC, tumor suppressor, inflammatory bowel disease&nbsp