Search results for: brain tumor classification

Authors: Rajkumar Ghosh

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The Chaura Thrust, located in Himachal Pradesh, India, is a prominent geological feature that exhibits characteristics of an out-of-sequence thrust fault. This paper explores the geological setting of Himachal Pradesh, focusing on the Chaura Thrust's unique characteristics, its classification as an out-of-sequence thrust, and the implications of its presence in the region. The introduction provides background information on thrust faults and out-of-sequence thrusts, emphasizing their significance in understanding the tectonic history and deformation patterns of an area. It also outlines the objectives of the paper, which include examining the Chaura Thrust's geological features, discussing its classification as an out-of-sequence thrust, and assessing its implications for the region. The paper delves into the geological setting of Himachal Pradesh, describing the tectonic framework and providing insights into the formation of thrust faults in the region. Special attention is given to the Chaura Thrust, including its location, extent, and geometry, along with an overview of the associated rock formations and structural characteristics. The concept of out-of-sequence thrusts is introduced, defining their distinctive behavior and highlighting their importance in the understanding of geological processes. The Chaura Thrust is then analyzed in the context of an out-of-sequence thrust, examining the evidence and characteristics that support this classification. Factors contributing to the out-of-sequence behavior of the Chaura Thrust, such as stress interactions and fault interactions, are discussed. The geological implications and significance of the Chaura Thrust are explored, addressing its impact on the regional geology, tectonic evolution, and seismic hazard assessment. The paper also discusses the potential geological hazards associated with the Chaura Thrust and the need for effective mitigation strategies in the region. Future research directions and recommendations are provided, highlighting areas that warrant further investigation, such as detailed structural analyses, geodetic measurements, and geophysical surveys. The importance of continued research in understanding and managing geological hazards related to the Chaura Thrust is emphasized. In conclusion, the Chaura Thrust in Himachal Pradesh represents an out-of-sequence thrust fault that has significant implications for the region's geology and tectonic evolution. By studying the unique characteristics and behavior of the Chaura Thrust, researchers can gain valuable insights into the geological processes occurring in Himachal Pradesh and contribute to a better understanding and mitigation of seismic hazards in the area.

Keywords: chaura thrust, out-of-sequence thrust, himachal pradesh, geological setting, tectonic framework, rock formations, structural characteristics, stress interactions, fault interactions, geological implications, seismic hazard assessment, geological hazards, future research, mitigation strategies.

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

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Authors: Yuanyuan Zhang, Yujuan Zheng, Giuseppina Barutello, Sumako Kameishi, Kungchun Chiu, Katharina Hennig, Martial Balland, Federica Cavallo, Lars Holmgren

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Angiogenesis describes that new blood vessels migrate from pre-existing ones to form 3D lumenized structure and remodeling. During directional migration toward the gradient of pro-angiogenic factors, the endothelial cells, especially the tip cells need filopodia to sense the environment and exert the pulling force. Of particular interest are the integrin proteins, which play an essential role in focal adhesion in the connection between migrating cells and extracellular matrix (ECM). Understanding how these biomechanical complexes orchestrate intrinsic and extrinsic forces is important for our understanding of the underlying mechanisms driving angiogenesis. We have previously identified Angiomotin (Amot), a member of Amot scaffold protein family, as a promoter for endothelial cell migration in vitro and zebrafish models. Hence, we established inducible endothelial-specific Amot knock-out mice to study normal retinal angiogenesis as well as tumor angiogenesis. We found that the migration ratio of the blood vessel network to the edge was significantly decreased in Amotec- retinas at postnatal day 6 (P6). While almost all the Amot defect tip cells lost migration advantages at P7. In consistence with the dramatic morphology defect of tip cells, there was a non-autonomous defect in astrocytes, as well as the disorganized fibronectin expression pattern correspondingly in migration front. Furthermore, the growth of transplanted LLC tumor was inhibited in Amot knockout mice due to fewer vasculature involved. By using MMTV-PyMT transgenic mouse model, there was a significantly longer period before tumors arised when Amot was specifically knocked out in blood vessels. In vitro evidence showed that Amot binded to beta-actin, Integrin beta 1 (ITGB1), Fibronectin, FAK, Vinculin, major focal adhesion molecules, and ITGB1 and stress fibers were distinctly induced by Amot transfection. Via traction force microscopy, the total energy (force indicater) was found significantly decreased in Amot knockdown cells. Taken together, we propose that Amot is a novel partner of the ITGB1/Fibronectin protein complex at focal adhesion and required for exerting force transition between endothelial cell and extracellular matrix.

Keywords: angiogenesis, angiomotin, endothelial cell migration, focal adhesion, integrin beta 1

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Authors: Rai Sachindra Prasad

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Thought is physical force. This has been well recognized but hardly translated visually or otherwise in the sense of its transfer from one individual to another. In the present world of chaos and disorder with yawning gaps between right and wrong thinking individuals, if it is possible to transfer the right thoughts to replace the wrong ones it would indeed be a great achievement in the present situation of the world which is torn with violence with dangerous thoughts of individuals. Moreover, such a possibility would completely remove the barrier of language between two persons, which at times proves to be a great obstacle in realizing a desired purpose. If a proper instrumentation scheme containing appropriate transducers and electronics is designed and implemented to realize this thought ransfer phenomenon, this would prove to be extremely useful when properly used. Considering the advancements already made in recording the nerve impulses in the brain, which are electrical events of very short durations that move along the axon, it is conceivable that this may be used to good effect in implementing the scheme. In such a proposition one shoud consider the roles played by pineal body, pituitary gland and ‘association’ areas. Pioneer students of brain have thought that associations or connections between sensory input and motor output were made in these areas. It is currently believed that rather than being regions of simple sensory-motor connections, the association areas process and integrate sensory information relayed to them from the primary sensory areas of the cortex and from the thalamus, after the information has been processed, it may be sent to motor areas to be acted upon. Again, even though the role played by pineal body is not known fully to neurologists its interconnection with pituitary gland is a matter of great significance to the ‘Rishis’ and; Seers’ s described in Vedas and Puranas- the ancient Holy books of Hindus. If the pineal body is activated through meditation it would control the pituitary gland thereby the individual’s thoughts and acts. Thus, if thoughts can be picked up by special transducers, these can be connected to suitable electronics circuitry to amplify the signals. These signals in the form of electromagnetic waves can then be transmitted using modems for long distance transmission and eventually received by or passed on to a subject of interest through another set of electronics circuit and devices.

Keywords: modems, pituitary gland, pineal body, thought transfer

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Authors: K. Pankaj, R. K. Chaudhary, B. P. Mishra, S. Kochar

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Introduction: Consultation-Liaison psychiatry is a branch of psychiatry that includes clinical service, teaching and research. A consultation-liaison psychiatrist plays a role in having an expert opinion and linking the patients to other medical professionals and the patient’s bio-psycho-social aspects that may be leading to his/her symptoms. Consultation-Liaison psychiatry has been recognised as 'The guardian of the holistic approach to the patient', underlining its pre-eminent role in the management of patients who are admitted in a tertiary care hospital. Aims/ Objectives: The aim of the study was to analyse the utilization of psychiatric services and reasons for referrals in a tertiary care hospital. Materials and Methods: The study was done in a tertiary care hospital. The study included all the cases referred from different Inpatient wards to the psychiatry department for consultation. The study was conducted on 300 patients over a 3 month period. International classification of diseases 10 was used to diagnose the referred cases. Results: The majority of the referral was from the Medical Intensive care unit (22%) followed by general medical wards (18.66%). Majority of the referral was taken for altered sensorium (24.66%), followed by low mood or unexplained medical symptoms (21%). Majority of the referrals had a diagnosis of alcohol withdrawal syndrome (21%) as per International classification of diseases criteria, followed by unipolar Depression and Anxiety disorder (~ 14%), followed by Schizophrenia (5%) and Polysubstance abuse (2.6%). Conclusions: Our study concludes the importance of utilization of consultation-liaison psychiatric services. Also, the study signifies the need for sensitization of our colleagues regarding psychiatric sign and symptoms from time to time and seek psychiatric consult timely to decrease morbidity.

Keywords: consultation-liaison, psychiatry, referral, tertiary care hospital

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Authors: D. Pradhan, G. Tripathy, S. Pradhan

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Objectives: Imperviousness gainst estrogen treatments is a noteworthy reason for infection backslide and mortality in estrogen receptor alpha (ERα)- positive breast diseases. Tamoxifen or estrogen withdrawal builds the reliance of breast malignancy cells on INT3 flagging. Here, we researched the commitment of Quercetin and INT3 motioning in endocrine-safe breast tumor cells. Methods: We utilized two models of endocrine treatments safe (ETR) breast tumor: Tamoxifen-safe (TamR) and long haul estrogen-denied (LTED) MCF7 cells. We assessed the transitory and intrusive limit of these cells by Transwell cells. Articulation of epithelial to mesenchymal move (EMT) controllers and in addition INT3 receptors and targets were assessed by constant PCR and western smudge investigation. Besides, we tried in-vitro hostile to Quercetin monoclonal Antibodies (mAbs) and Gamma Secretase Inhibitors (GSIs) as potential EMT inversion remedial specialists. At last, we created stable Quercetin overexpressing MCF7 cells and assessed their EMT components and reaction to Tamoxifen. Results: We found that ETR cells procured an Epithelial to Mesenchymal move (EMT) phenotype and showed expanded levels of Quercetin and INT3 targets. Interestingly, we distinguished more elevated amount of INT3 however lower levels of INT1 and INT3 proposing a change to motioning through distinctive INT3 receptors after obtaining of resistance. Against Quercetin monoclonal antibodies and the GSI PF03084014 were powerful in obstructing the Quercetin/INT3 pivot and in part repressing the EMT process. As a consequence of this, cell relocation and attack were weakened and the immature microorganism like populace was essentially decreased. Hereditary hushing of Quercetin and INT3 prompted proportionate impacts. At long last, stable overexpression of Quercetin was adequate to make MCF7 lethargic to Tamoxifen by INT3 initiation. Conclusions: ETR cells express abnormal amounts of Quercetin and INT3, whose actuation eventually drives intrusive conduct. Hostile to Quercetin mAbs and GSI PF03084014 lessen articulation of EMT particles decreasing cell obtrusiveness. Quercetin overexpression instigates Tamoxifen resistance connected to obtaining of EMT phenotype. Our discovering propose that focusing on Quercetin and INT3 warrants further clinical Correlation as substantial restorative methodologies in endocrine-safe breast.

Keywords: endocrine, epithelial, mesenchymal, INT3, quercetin, MCF7

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Authors: Y. I. Tretyakova, S. G. Shulkina, T. Y. Kravtsova, A. A. Antipova, N. Y. Kolomeets

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The research aimed to assess the functional significance of tumor necrosis factor-alpha (TNF-α) gene polymorphism at the -308G/A (rs1800629) region and vascular endothelial growth factor A (VEGFA) gene polymorphism at the -634G/C (rs 2010963) region in the development of ulcerative colitis (UC), focusing on patients from the Perm region, Russia. We examined 70 UC patients and 50 healthy donors during the active phase of the disease. Our focus was on TNF-α and VEGF concentration in the blood serum, as well as TNF-α and VEGFA gene polymorphisms at the -308G/А and -634G/C regions, respectively. We found that TNF-α and VEGF levels were significantly higher in patients with severe UC and high endoscopic activity compared to those with milder forms of the disease and low endoscopic activity. These tests could serve as additional non-invasive markers for assessing mucosal damage in the large intestine of UC patients. The frequency of allele variations in the TNF-α gene -308G/A (rs1800629) revealed a significantly higher occurrence of the unfavorable homozygote AA in UC patients compared to donors. Additionally, the major allele G and the allele pair GG were more frequent in patients with mild to moderate disease and 1-2 degree of endoscopic activity than in those with severe UC and 3-4 degree of endoscopic activity (χ2=14.19; p=0.000). We also observed a mutant allele A and the unfavorable homozygote AA associated with severe progressive UC. The occurrence of the mutant allele increased the risk of severe UC by 5 times (OR 5.03; CI 12.07-12.21). We did not find any significant differences in the frequency of the CC homozygote (χ2=1.02; p=0.6; OR=1.32) and the mutant allele C of the VEGFA gene -634G/C (rs 2010963) (χ2=0.01; p=0.913; OR=0.97) between groups of UC patients and healthy individuals. However, we detected that the mutant allele C and the unfavorable homozygote CC of the VEGFA gene were associated with more severe endoscopic changes in the colonic mucosa of UC patients (χ2=25,76; р=0,000; OR=0,15). The presence of the mutant allele increased the risk of severe UC by 6 times (OR 6,78; CI 3,13–14,7). We found a direct correlation between TNF-α and VEGFA gene polymorphisms, increased production of the same factors, disease severity, and endoscopic activity (р=0.000). Therefore, the presence of the mutant allele A and homozygote AA of the TNF-α gene at the -308G/A region and the mutant allele C and homozygote CC of the VEGFA gene at the -634G/C region are associated with risks related to an unfavorable clinical course of UC, frequent recurrences, and rapid progression. These findings should be considered when making prognoses regarding the clinical course of the disease and selecting treatment strategies. The presence of the homozygote AA in the TNF-α gene (rs1800629) is considered a sign of genetic predisposition to UC.

Keywords: gene polymorphism, TNF-α, ulcerative colitis, VEGF

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: Sylwia Baluta, Joanna Cabaj, Karol Malecha

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The evolution of biosensors was driven by the need for faster and more versatile analytical methods for application in important areas including clinical, diagnostics, food analysis or environmental monitoring, with minimum sample pretreatment. Rapid and sensitive neurotransmitters detection is extremely important in modern medicine. These compounds mainly occur in the brain and central nervous system of mammals. Any changes in the neurotransmitters concentration may lead to many diseases, such as Parkinson’s or schizophrenia. Classical techniques of chemical analysis, despite many advantages, do not permit to obtain immediate results or automatization of measurements.

Keywords: adrenaline, biosensor, dopamine, laccase, tyrosinase

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Emhimed Saffor

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In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications.

Keywords: CT images, Matlab, medical images, edge detection

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Authors: G. Martino, F. Silva, E. Marchal

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The control over delivered iron ore blend characteristics is one of the most important aspects of the mining business. The iron ore price is a function of its composition, which is the outcome of the beneficiation process. So, end-to-end integrated planning of mine operations can reduce risks of penalties on the iron ore price. In a standard iron mining company, the production chain is composed of mining, ore beneficiation, and client supply. When mine planning and client supply decisions are made uncoordinated, the beneficiation plant struggles to deliver the best blend possible. Technological improvements in several fields allowed bridging the gap between departments and boosting integrated decision-making processes. Clusterization and classification algorithms over historical production data generate reasonable previsions for quality and volume of iron ore produced for each pile of run-of-mine (ROM) processed. Mathematical modeling can use those deterministic relations to propose iron ore blends that better-fit specifications within a delivery schedule. Additionally, a model capable of representing the whole production chain can clearly compare the overall impact of different decisions in the process. This study shows how flexibilization combined with a planning optimization model between the mine and the ore beneficiation processes can reduce risks of out of specification deliveries. The model capabilities are illustrated on a hypothetical iron ore mine with magnetic separation process. Finally, this study shows ways of cost reduction or profit increase by optimizing process indicators across the production chain and integrating the different plannings with the sales decisions.

Keywords: clusterization and classification algorithms, integrated planning, mathematical modeling, optimization, penalty minimization

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Authors: Ingy Ibrahim El-Darwish, Neveen Youssef Azmy

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The endowment of a healthy environment can be implemented by endorsing sustainable fundamentals. Design of sustainable buildings through recycling of waste, can reduce health problems, provide good environments and contribute to the aesthetically pleasing entourage. Such environments can help in providing energy-saving alternatives to consolidate the principles of sustainability. The poor community awareness and the absence of laws and legislation in Egypt for waste management specifically in residential areas have led to an inability to provide an integrated system for waste management in urban and rural areas. Many problems and environmental challenges face the Egyptian urban environments. From these problems, is the lack of a cohesive vision for waste collection and recycling for energy-saving. The second problem is the lack public awareness of the short term and long term vision of waste management. Bad practices have adversely affected the efficiency of environmental management systems due to lack of urban legislations that codify collection and recycling of residential communities in Egyptian urban environments. Hence, this research tries to address residents on waste management matters to facilitate legislative process on waste collection and classification within residential units and outside them in a preparation phase for recycling in the Egyptian urban environments. In order to achieve this goal, one of the Egyptian communities has been addressed, analyzed and studied. Waste collection, classification, separation and access to recycling places in the urban city are proposed in preparation for a legislation ruling and regulating the process. Hence, sustainable principles are to be achieved.

Keywords: recycling, residential buildings, sustainability, waste

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Authors: Marija Zulić, Vanda Hájková, Nina Brkić-Jovanović, Linda Rathousová, Sanja Tomić

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Cerebral palsy is primarily reflected in the disorder of the development of movement and posture, which may be accompanied by sensory disturbances, disturbances of perception, cognition and communication, behavioural disorders and epilepsy. According to current inclusive attitudes towards people with disabilities implies that full social participation of children with cerebral palsy means inclusion in all activities in family, peer, school and leisure environments in the same scope and to the same extent as is the case with the children of proper development and without physical difficulties. Due to the fact that it has been established that the quality of children's participation in primary school is directly related to their social inclusion in future life, the aim of the paper is to identify predictors of social participation, respectively, and in particular, factors that could to improve the quality of social participation of children with cerebral palsy, in the primary school environment in Czech Republic. The study includes children with cerebral palsy (n = 75) in the Czech Republic, aged between six and 12 years who attend mainstream or special primary schools to the sixth grade. The main instrument used was the first and third part of the School function assessment questionnaire. It will also take into account the type of damage assessed according to a scale the Gross motor function classification system, five–level classification system for cerebral palsy. The research results will provide detailed insight into the degree of social participation of children with cerebral palsy and the factors that would be a potential cause of their levels of participation, in regular and special primary schools, in different socioeconomic environments in Czech Republic.

Keywords: cerebral palsy, Czech republic, social participation, the school function assessment

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Authors: Sagheer Ahmad

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Teaching is a sacred profession whereby students are developed in their mental abilities to cope with the challenges of the remote world. Thinkers have developed plenty of interesting ways to make the learning process quick and absorbing for the students. However, third world countries are still lacking these remote facilities in the institutions, and therefore, teaching is totally dependent upon the skills of the teachers. Skillful teachers use self-devised and stimulating ideas to grab the attention of their students. Most of the time their ideas are based on local grounds with which the students are already familiar. This self-explanatory characteristic is the base of several local ideologies to disseminate scientific knowledge to new generations. Biology is such a subject which largely bases upon hypotheses, and teaching it in an interesting way is needful to create a friendly relationship between teacher and student, and to make a fantastic learning environment. Taxonomic classification if presented as it is, may not be attractive for the secondary school students who just start learning about biology at elementary levels. Presenting this hierarchy by exemplifying Kingdom, Phylum, Class, Order, family, genus and Species as comparatives of our division into continents, countries, cities, towns, villages, homes and finally individuals could be an attention-grabbing approach to make this concept get into bones of students. Similarly, many other interesting approaches have also been adopted to teach students in a fascinating way so that learning science subjects may not be boring for them. Discussing these appealing ways of teaching students can be a valuable stimulus to refine teaching methodologies about science, thereby promoting the concept of friendly learning.

Keywords: biology, innovative approaches, taxonomic classification, teaching

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Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

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Authors: Maximilian Elsen, Frank Tietze

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The development and diffusion of green technologies are crucial for achieving our ambitious climate targets. The Paris Agreement commits its members to develop strategies for achieving net zero greenhouse gas emissions by the second half of the century. Governments, executives, and academics are working on net-zero strategies and the business of rating organisations on their environmental, social and governance (ESG) performance has grown tremendously in its public interest. ESG data is now commonly integrated into traditional investment analysis and an important factor in investment decisions. Creating these metrics, however, is inherently challenging as environmental and social impacts are hard to measure and uniform requirements on ESG reporting are lacking. ESG metrics are often incomplete and inconsistent as they lack fully accepted reporting standards and are often of qualitative nature. This study explores the use of patent data for assessing the environmental performance of companies by focusing on their patented inventions in the space of climate change mitigation and adaptation technologies (CCMAT). The present study builds on the successful identification of CCMAT patents. In this context, the study adopts the Y02 patent classification, a fully cross-sectional tagging scheme that is fully incorporated in the Cooperative Patent Classification (CPC), to identify Climate Change Adaptation Technologies. The Y02 classification was jointly developed by the European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO) and provides means to examine technologies in the field of mitigation and adaptation to climate change across relevant technologies. This paper develops sustainability-related metrics for firm-level patent portfolios. We do so by adopting a three-step approach. First, we identify relevant CCMAT patents based on their classification as Y02 CPC patents. Second, we examine the technological strength of the identified CCMAT patents by including more traditional metrics from the field of patent analytics while considering their relevance in the space of CCMAT. Such metrics include, among others, the number of forward citations a patent receives, as well as the backward citations and the size of the focal patent family. Third, we conduct our analysis on a firm level by sector for a sample of companies from different industries and compare the derived sustainability performance metrics with the firms’ environmental and financial performance based on carbon emissions and revenue data. The main outcome of this research is the development of sustainability-related metrics for firm-level environmental performance based on patent data. This research has the potential to complement existing ESG metrics from an innovation perspective by focusing on the environmental performance of companies and putting them into perspective to conventional financial performance metrics. We further provide insights into the environmental performance of companies on a sector level. This study has implications of both academic and practical nature. Academically, it contributes to the research on eco-innovation and the literature on innovation and intellectual property (IP). Practically, the study has implications for policymakers by deriving meaningful insights into the environmental performance from an innovation and IP perspective. Such metrics are further relevant for investors and potentially complement existing ESG data.

Keywords: climate change mitigation, innovation, patent portfolios, sustainability

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Authors: Nicole Scott, Emily Dwyer, Cara Patrissy, Samantha Bonventre, Lina Begdache

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Across North America, the use and abuse of Attention Deficit Hyperactivity Disorder (ADHD) medication, cannabis, nicotine, and other psychoactive substances across college campuses have become an increasingly prevalent problem. Students frequently use these substances to aid their studying or deal with their mental health issues. However, it is still unknown what psychoactive substances are likely to be abused when college students illicitly use ADHD medication. In addition, it is not clear which psychoactive substance is associated with mental distress. Thus, the purpose of this study is to fill these gaps by assessing the use of different psychoactive substances when illicit ADHD medication is used; and how this association relates to mental stress. A total of 702 undergraduate students from different college campuses in the U.S. completed an anonymous survey distributed online. Data were self-reported on demographics, the use of ADHD medications, cannabis, nicotine, other psychoactive drugs, and mental distress, and feelings and opinions on the use of illicit study drugs were all included in the survey. Mental distress was assessed using the Kessler Psychological Distress 6 Scale. Data were analyzed in SPSS, Version 25.0, using Pearson’s Correlation Coefficient. Our results show that use of ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent), there were both statistically significant positive and negative correlations to specific psychoactive substances and their corresponding frequencies. Along the same lines, ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent) had statistically significant positive and negative correlations to specific mental distress experiences. As these findings are combined, a vicious loop can initiate a cycle where individuals who abuse psychoactive substances may or may not be inclined to use other psychoactive substances. This may later inhibit brain functions in those main areas of the brain stem, amygdala, and prefrontal cortex where this vicious cycle may or may not impact their mental distress. Addressing the impact of study drug abuse and its potential to be associated with further substance abuse may provide an educational framework and support proactive approaches to promote awareness among college students.

Keywords: stimulant, depressant, nicotine, ADHD medication, psychoactive substances, mental health, illicit, ecstasy, adrenochrome

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Authors: Néstor Iván Rojas, Wilson Medina Sierra

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Managing spatial information is described through a Geographic Information System (GIS), for some neighborhoods in the city of Tunja, in relation to the structural typology of the buildings. The use of GIS provides tools that facilitate the capture, processing, analysis and dissemination of cartographic information, product quality evaluation of the classification of buildings. Allows the development of a method that unifies and standardizes processes information. The project aims to generate a geographic database that is useful to the entities responsible for planning and disaster prevention and care for vulnerable populations, also seeks to be a basis for seismic vulnerability studies that can contribute in a study of urban seismic microzonation. The methodology consists in capturing the plat including road naming, neighborhoods, blocks and buildings, to which were added as attributes, the product of the evaluation of each of the housing data such as the number of inhabitants and classification, year of construction, the predominant structural systems, the type of mezzanine board and state of favorability, the presence of geo-technical problems, the type of cover, the use of each building, damage to structural and non-structural elements . The above data are tabulated in a spreadsheet that includes cadastral number, through which are systematically included in the respective building that also has that attribute. Geo-referenced data base is obtained, from which graphical outputs are generated, producing thematic maps for each evaluated data, which clearly show the spatial distribution of the information obtained. Using GIS offers important advantages for spatial information management and facilitates consultation and update. Usefulness of the project is recognized as a basis for studies on issues of planning and prevention.

Keywords: microzonation, buildings, geo-processing, cadastral number

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Authors: Janneth Gonzalez, Andrés Pinzon Velasco, Maria Angarita

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Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatorypathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β‐oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA‐mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation of metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.

Keywords: astrocytes, data integration, palmitic acid, computational model, multi-omics

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Authors: Usman Ahmed Khan

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Urbanization directly affects the existing infrastructure, landscape modification, environmental contamination, and traffic pollution, especially if there is a lack of urban planning. Recently, the rapid urban sprawl has resulted in less developed green areas and has devastating environmental consequences. This study was aimed to study the past urban expansion rates and measure LST from satellite data. The land use land cover (LULC) maps of years 1996, 2010, 2013, and 2017 were generated using landsat satellite images. Four main classes, i.e., water, urban, bare land, and vegetation, were identified using unsupervised classification with iterative self-organizing data analysis (isodata) technique. The LST from satellite thermal data can be derived from different procedures: atmospheric, radiometric calibrations and surface emissivity corrections, classification of spatial changeability in land-cover. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, From 1996-2017, urban areas increased to about a considerable increase of about 48%. Few areas of the city also shown in a reduction in LST from the year 1996-2017 that actually began their transitional phase from rural to urban LULC. The mean temperature of the city increased averagely about 1ºC each year in the month of October. The green and vegetative areas witnessed a decrease in the area while a higher number of pixels increased in urban class.

Keywords: LST, LULC, isodata, urbanization

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Authors: Sara Mosteller, Larissa K. Samuelson, Sobanawartiny Wijeakumar, John P. Spencer

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This study was designed to examine how humans are able to teach and learn semantic information as well as cooperate in order to jointly achieve sophisticated goals. Specifically, we are measuring individual differences in how these abilities develop from foundational building blocks in early childhood. The current study adopts a paradigm for novel noun learning developed by Samuelson, Smith, Perry, and Spencer (2011) to a hyperscanning paradigm [Cui, Bryant and Reiss, 2012]. This project measures coordinated brain activity between a parent and child using simultaneous functional near infrared spectroscopy (fNIRS) in pairs of 2.5, 3.5 and 4.5-year-old children and their parents. We are also separately testing pairs of adult friends. Children and parents, or adult friends, are seated across from one another at a table. The parent (in the developmental study) then teaches their child the names of novel toys. An experimenter then tests the child by presenting the objects in pairs and asking the child to retrieve one object by name. Children are asked to choose from both pairs of familiar objects and pairs of novel objects. In order to explore individual differences in cooperation with the same participants, each dyad plays a cooperative game of Jenga, in which their joint score is based on how many blocks they can remove from the tower as a team. A preliminary analysis of the noun-learning task showed that, when presented with 6 word-object mappings, children learned an average of 3 new words (50%) and that the number of objects learned by each child ranged from 2-4. Adults initially learned all of the new words but were variable in their later retention of the mappings, which ranged from 50-100%. We are currently examining differences in cooperative behavior during the Jenga playing game, including time spent discussing each move before it is made. Ongoing analyses are examining the social dynamics that might underlie the differences between words that were successfully learned and unlearned words for each dyad, as well as the developmental differences observed in the study. Additionally, the Jenga game is being used to better understand individual and developmental differences in social coordination during a cooperative task. At a behavioral level, the analysis maps periods of joint visual attention between participants during the word learning and the Jenga game, using head-mounted eye trackers to assess each participant’s first-person viewpoint during the session. We are also analyzing the coherence in brain activity between participants during novel word-learning and Jenga playing. The first hypothesis is that visual joint attention during the session will be positively correlated with both the number of words learned and with the number of blocks moved during Jenga before the tower falls. The next hypothesis is that successful communication of new words and success in the game will each be positively correlated with synchronized brain activity between the parent and child/the adult friends in cortical regions underlying social cognition, semantic processing, and visual processing. This study probes both the neural and behavioral mechanisms of learning and cooperation in a naturalistic, interactive and developmental context.

Keywords: communication, cooperation, development, interaction, neuroscience

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Authors: Ibrahim A. Adeniran, Rui F. Zhu, Man S. Wong

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Owing to the insufficiency in the spatial representativeness and continuity of in situ temperature measurements from weather stations (WS), the use of temperature measurement from WS for large-range diurnal analysis in heterogenous landscapes has been limited. This has made the accurate estimation of land surface temperature (LST) from remotely sensed data more crucial. Moreover, the study of dynamic interaction between the atmosphere and the physical surface of the Earth could be enhanced at both annual and diurnal scales by using optimal LST data derived from satellite sensors. The tradeoff between the spatial and temporal resolution of LSTs from satellite’s thermal infrared sensors (TIRS) has, however, been a major challenge, especially when high spatiotemporal LST data are recommended. It is well-known from existing literature that polar satellites have the advantage of high spatial resolution, while geostationary satellites have a high temporal resolution. Hence, this study is aimed at designing a framework for the cross-comparison of LST data from polar and geostationary satellites in a heterogeneous landscape. This could help to understand the relationship between the LST estimates from the two satellites and, consequently, their integration in diurnal LST analysis. Landsat-8 satellite data will be used as the representative of the polar satellite due to the availability of its long-term series, while the Himawari-8 satellite will be used as the data source for the geostationary satellite because of its improved TIRS. For the study area, Hong Kong Special Administrative Region (HK SAR) will be selected; this is due to the heterogeneity in the landscape of the region. LST data will be retrieved from both satellites using the Split window algorithm (SWA), and the resulting data will be validated by comparing satellite-derived LST data with temperature data from automatic WS in HK SAR. The LST data from the satellite data will then be separated based on the land use classification in HK SAR using the Global Land Cover by National Mapping Organization version3 (GLCNMO 2013) data. The relationship between LST data from Landsat-8 and Himawari-8 will then be investigated based on the land-use class and over different seasons of the year in order to account for seasonal variation in their relationship. The resulting relationship will be spatially and statistically analyzed and graphically visualized for detailed interpretation. Findings from this study will reveal the relationship between the two satellite data based on the land use classification within the study area and the seasons of the year. While the information provided by this study will help in the optimal combination of LST data from Polar (Landsat-8) and geostationary (Himawari-8) satellites, it will also serve as a roadmap in the annual and diurnal urban heat (UHI) analysis in Hong Kong SAR.

Keywords: automatic weather station, Himawari-8, Landsat-8, land surface temperature, land use classification, split window algorithm, urban heat island

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Authors: Rahul Patel

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Ahmedabad, a city located in western India, is experiencing rapid growth due to urbanization and industrialization. It is projected to become a metropolitan city in the near future, resulting in various construction activities. Soil testing is necessary before construction can commence, requiring construction companies and contractors to periodically conduct soil testing. The focus of this study is on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical (Geo)-database involves three steps: collecting borehole data from reputable sources, verifying the accuracy and redundancy of the data, and standardizing and organizing the geotechnical information for integration into the database. Once the database is complete, it is integrated with GIS, allowing users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. This GIS map enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This study highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers.

Keywords: ArcGIS, borehole data, geographic information system, geo-database, interpolation, SPT N-value, soil classification, Φ-Value, bearing capacity

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Authors: Sylvia Nkiru Ogbuoji

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This paper discussed the consequences of inadequate funding in Nigerian education system. It briefly explained the meaning of education in relation to the context and identified various ways education in Nigeria can be funded. It highlighted some of the consequences of inadequate funding education system to include: Inadequate facilitates for teaching and learning, western brain drain, unemployment, crises of poverty, low staff morale it. Finally, some recommendations were put forward, the government should improve the annual budget allocation to education, in order to achieve educational objective, also government should monitor the utilization of allocated funds to minimize embezzlement.

Keywords: consequences, corruption, education, funding

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Authors: Lev Bertovsky

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The purpose of this study was to determine the reasons for the formation of false testimony from witnesses and make recommendations on the recognition of such cases. During the studies, which were based on the achievements of professionals in the field of psychology, as well as personal investigative practice, the stages of perception of the information were studied, as well as the process of its reclaim from the memory and transmission to the communicator upon request. Based on the principles of the human brain, kinds of conscientious witness mistakes were systematized. Proposals were formulated for the optimization of investigative actions in cases where the witnesses make an honest mistake with respect to the effects previously observed by them.

Keywords: criminology, eyewitness testimony, honest mistake, information, investigator, investigation, questioning

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, seizure, phase correlation, fluctuation, deviation.

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Authors: S. Woźniak, J. Rybka, T. Paszkowski, P. Milart

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A 30-year-old patient, who was pregnant for her second 9 weeks, was admitted to the hospital due to a suspected incomplete miscarriage. A fetal egg was found in the uterine cavity near the mouth of the fallopian tube. The patient was qualified for dilatation and curettage. The histopathological examination revealed fragments of the trophoblast. Two months later, the patient was re-admitted to the hospital due to vaginal bleeding and elevated levels of beta-hCG. Additional tests were performed. An intramural pregnancy was suspected. The patient was qualified for embolization of the uterine arteries and then treatment with methotrexate. Three weeks later, during a routine gynecological examination, a detached tumor 4 cm in diameter was found in the vagina. The material was sent for histopathological examination, which showed the presence of trophoblastic cells.

Keywords: ectopic pregnancy, intramural pregnancy, uterine artery embolization, methotrexate

Procedia PDF Downloads 96