Search results for: deep brain stimulation

Authors: Nam Chol Yu, GyongIl Chu, HoJong Ri

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Using DLTS (Deep-level transient spectroscopy) measurement techniques, we determined the dominant recombination center levels (defects of both A and B) in gold and platinum doped n-type silicon. Also, the injection and temperature dependence of the Shockley-Read-Hall (SRH) carrier lifetime was studied under low-level injection and high-level injection. Here measurements show that the dominant level under low-level injection located at EC-0.25 eV (A) correlated to the Pt+G1 and the dominant level under high-level injection located at EC-0.54 eV (B) correlated to the Au+G4. Finally, A and B are the same dominant levels for controlling the lifetime in gold-platinum doped n-silicon.

Keywords: recombination center level, lifetime, carrier lifetime control, Gold, Platinum, Silicon

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Authors: Pawan Kumar, Dhananjoy Shaw, Manoj Kumar Rathi

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Purpose of the study were to compare between (a) frequencies among the four quartiles of percentile norms of autonomic variables from power events and (b) frequencies among the four quartiles percentile norms of autonomic variables from aerobic events of Indian sportspersons withdrawn from competitive games and sports in regard to number of samples falling in each quartile. The study was conducted on 430 males of 30 to 35 years of age. Based on the nature of game/sports the retired sportspersons were classified into power events (throwers, judo players, wrestlers, short distance swimmers, cricket fast bowlers and power lifters) and aerobic events (long distance runners, long distance swimmers, water polo players). Date was collected using ECG polygraphs. Data were processed and extracted using frequency domain analysis and time domain analysis. Collected data were computed with frequency, percentage of each quartile and finally the frequencies were compared with the chi square analysis. The finding pertaining to norm reference comparison of frequencies among the four quartiles of Indian sportspersons withdrawn from competitive games and sports from (a) power events suggests that frequency distribution in four quartile namely Q1, Q2, Q3, and Q4 are significantly different at .05 level in regard to variables namely, SDNN, Total Power (Absolute Power), HF (Absolute Power), LF (Normalized Power), HF (Normalized Power), LF/HF ratio, deep breathing test, expiratory respiratory ratio, valsalva manoeuvre, hand grip test, cold pressor test and lying to standing test, whereas, insignificantly different at .05 level in regard to variables namely, SDSD, RMSSD, SDANN, NN50 Count, pNN50 Count, LF (Absolute Power) and 30: 15 Ratio (b) aerobic events suggests that frequency distribution in four quartile are significantly different at .05 level in regard to variables namely, SDNN, LF (Normalized Power), HF (Normalized Power), LF/HF ratio, deep breathing test, expiratory respiratory ratio, hand grip test, cold pressor test, lying to standing test and 30: 15 ratio, whereas, insignificantly different at .05 level in regard to variables namely, SDSD, RMSSD. SDANN, NN50 count, pNN50 count, Total Power (Absolute Power), LF(Absolute Power) HF(Absolute Power), and valsalva manoeuvre. The study concluded that comparison of frequencies among the four quartiles of Indian retired sportspersons from power events and aerobic events are different in four quartiles in regard to selected autonomic functions, hence the developed percentile norms are not homogenously distributed across the percentile scale; hence strengthen the percentage distribution towards normal distribution.

Keywords: power, aerobic, absolute power, normalized power

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: Humberto Foyaca Sibat, Lourdes de Fátima Ibañez Valdés

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Background Cysticercosis (Ct) is a preventable and eradicable zoonotic parasitic disease secondary to a cestode infection by the larva form of pig tapeworm Taenia solium (Ts), mainly seen in people living in developing countries. When the cysticercus is in the brain parenchymal, intraventricular system, subarachnoid space (SAS), cerebellum, brainstem, optic nerve, or spinal cord, then it has named neurocysticercosis (NCC), and the often-clinical manifestations are headache and epileptic seizures/epilepsy among other less frequent symptoms and signs. In this study, we look for a manuscript related to the role played by oligodendrocytes in the pathogenesis of NCC. We review this issue and formulate some hypotheses regarding its role and the role played in the pathogenesis of calcified NCC and epileptic seizures, and secondary epilepsy. Method: We searched the medical literature comprehensively, looking for published medical subject heading (MeSH) terms like "neurocysticercosis", "pathogenesis of neurocysticercosis", "comorbidity in NCC"; OR "oligodendrocytes"; OR "oligodendrocyte precursor cells(OPC/NG2)"; OR "epileptic seizures(ES)/Epilepsy(Ep)/NCC" OR "oligodendrocytes(OLG)/ES/Ep”; OR "calcified NCC/OLG"; OR “OLG Ca2+.” Results: All selected manuscripts were peer-reviewed, and we did not find publications related to OLG/NCC.

Keywords: oligodendrocytes, neurocysticercosis, oligodendrocytes, oligodendrocyte precursor cell, KG2, calcified neurocysticercosis, cellular calcium influx.

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Authors: I. Mahmood

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This survey was conducted to explore the hypothesis that excessive screen exposure combined with a subsequent decrease in parent-child interaction during infancy might be associated with autism. The main questions being asked are: Were children with autism exposed to long hours of screen time during the first 2 years of life? And what was the reason(s) for exposure at such an early age? Other variables were also addressed in this survey. An Arabic questionnaire was administered online (June 2019) via a Facebook page, relatively well-known in Arab countries. 1725 parents of children diagnosed with autism participated in this survey. Results show that 80.9% of children surveyed who were diagnosed with autism had been exposed to screens for long periods of time during the first 2 years of life. It can be inferred from the results of this survey that over-exposure to screens disrupt the parent-child interaction which is shown to be associated with ASD. The results of this survey highlight the harmful effects of screen exposure during infancy and the importance of parent-child interaction during the critical period of brain development. This paper attempts to further explore the connection between parent-child interaction and ASD, as well as serve as a call for further research and investigation of the relation between screens and parent-child interactions during infancy and Autism.

Keywords: attachment disorder, autism, screen exposure, virtual autism

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Authors: Mohamad Farhad Bakhtiyariyan

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One of the manner to earning profit and having a high income, is investing in an acceptable market. Every the thinker brain knows, investing in the business world today, maybe, have a manifold profit or lead to failure. So, before entering in the investment market, we must have a comprehensive and sufficient awareness, know markets, acquainted with the main industrial activities, know the rules and regulation and consider the conditions of society. Qatar, as a one of the richest countries in the world, can be a good destination for investment. The inflation rate, taxes, easiness of the importing, company registration, ease of exporting process, profitable and appropriate markets, simple and applicable rules, all of this has made Qatar, one of the best and gainful investment countries. Above all, Qatar 2022 world cup event, has led of investment in this country efficiently and profitable method. In this paper, first, we have introduced the Qatar and its location, also looked at the countries international markets during the world cup and we have described the impact of the world cup on business, and then the laws and regulations of the Qatar in the field of investment, company registration, ownership by foreigners, obtaining residency by investors, export and import process in second part its examined, and in third part, major investment markets, principal industrial activities in Qatar, markets affected by the world cup and the main needs of this country in various fields during the world cup, have been investigated.

Keywords: investment, Qatar, markets, world cup

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: Saeed Delara, Kendra MacKay

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Varying methodologies are considered for pile design for both Russian and Western approaches. Although both approaches rely on toe and side frictional resistances, different calculation methods are proposed to estimate pile capacity. The Western approach relies on compactness (internal friction angle) of soil for cohesionless soils and undrained shear strength for cohesive soils. The Russian approach relies on grain size for cohesionless soils and liquidity index for cohesive soils. Though most recommended methods in the Western approaches are relatively simple methods to predict pile settlement, the Russian approach provides a detailed method to estimate single pile and pile group settlement. Details to calculate pile axial capacity and settlement using the Russian and Western approaches are discussed and compared against field test results.

Keywords: pile capacity, pile settlement, Russian approach, western approach

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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Authors: Thayalan Nall, Harry Poulos

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Piling is the most widely used foundation method for heavy structures in poor soil conditions. The geotechnical engineer can choose among a variety of piling methods, but in most cases, driving piles by impact hammer is the most cost-effective alternative. Under unfavourable conditions, driving piles can cause environmental problems, such as noise, ground movements and vibrations, with the risk of ground disturbance leading to potential damage to proposed structures. In one of the project sites in which the authors were involved, three offshore container terminals, namely CT1, CT2 and CT3, were constructed over thick compressible marine mud. The seabed was around 6m deep and the soft clay thickness within the project site varied between 9m and 20m. CT2 and CT3 were connected together and rectangular in shape and were 2600mx800m in size. CT1 was 400m x 800m in size and was located on south opposite of CT2 towards its eastern end. CT1 was constructed first and due to time and environmental limitations, it was supported on a “forest” of large diameter driven piles. CT2 and CT3 are now under construction and are being carried out using a traditional dredging and reclamation approach with ground improvement by surcharging with vertical drains. A few months after the installation of the CT1 piles, a 2600m long sand bund to 2m above mean sea level was constructed along the southern perimeter of CT2 and CT3 to contain the dredged mud that was expected to be pumped. The sand bund was constructed by sand spraying and pumping using a dredging vessel. About 2000m length of the sand bund in the west section was constructed without any major stability issues or any noticeable distress. However, as the sand bund approached the section parallel to CT1, it underwent a series of deep seated failures leading the displaced soft clay materials to heave above the standing water level. The crest of the sand bund was about 100m away from the last row of piles. There were no plausible geological reasons to conclude that the marine mud only across the CT1 region was weaker than over the rest of the site. Hence it was suspected that the pile driving by impact hammer may have caused ground movements and vibrations, leading to generation of excess pore pressures and cyclic softening of the marine mud. This paper investigates the probable cause of failure by reviewing: (1) All ground investigation data within the region; (2) Soil displacement caused by pile driving, using theories similar to spherical cavity expansion; (3) Transfer of stresses and vibrations through the entire system, including vibrations transmitted from the hammer to the pile, and the dynamic properties of the soil; and (4) Generation of excess pore pressure due to ground vibration and resulting cyclic softening. The evidence suggests that the problems encountered at the site were primarily caused by the “side effects” of the pile driving operations.

Keywords: pile driving, ground vibration, excess pore pressure, cyclic softening

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Authors: Eng Lye, Ooi

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The Old Man and The Sea is a novella written by Ernest Hemingway that depicts an old fisherman’ journey out into the deep sea in his pursuit to catch a big fish. Through this novella, Hemingway creates a world for his protagonist, Santiago who is portrayed as an old man who has gone eighty-four days without catching a fish, at last hooks an eighteen-foot marlin, the largest he ever known. The old man endures pain and struggles to bring back to shore. Looking through the lens of gerotranscendence, we can see that the old man has his dreams, and goals in life. In his pursuit for happiness, he has fought tirelessly to ward off the shark attacks and finally he won even though only half of his fish is left. Hemingway has portrayed Santiago as an old man as a transcendent self leaping from the dimension of “The Self” to the cosmic dimension with the personal and social relationship dimension in tow. The Old Man and The Sea offers a glimpse of the struggles of an old man, who is old and gaunt but spiritually undefeated in his battle out in the sea. He is surprisingly strong and powerful despite his old age, he respects the sea, the birds. the turtles, the sharks and the fish. He can endure suffering and is focussed on achieving his goals. This is what Hemingway has portrayed Santiago to be a gerotranscendent in the eyes of the gerotranscendental approach in respect of the changes and development as seen in Santiago, the protagonist in this novella.

Keywords: gerotranscendence, gerotranscendenatal, old man, the sea, hemingway

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Authors: Ali Babaei

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There is a significant relationship between Soul Faculties and human relationships. Man has at least three levels of relationship according to three levels of his Faculties: individual (with himself), dual (with another) and collective (with others). Since all human actions are organized by the type of use of their internal faculties, their "hierarchy of relations" is related to the "hierarchy of their Faculties." In the final explanation based on the ontology of Islamic wisdom, one can consider the hierarchy of human Faculties in three levels: 1. senses, 2. intellect and heart, and 3. Soul. The best relationship, in the individual one is that every human being, with healthy senses, achieves both the intellectual growth and the perfection of the heart, which we call "Clear-headed" and "Good-hearted.” The result of human evolution in this two aspects will lead to the development of a powerful personality which can be interpreted as "spiritual prosperity"; having a great soul is the result of such evolution. A smart brain without a "Good-heart"ince can lead to criminality; and mere "Good-heart"ince" without "Clear-head"ince leads to "naivety". “clear-head”ince is achieved through thoughtfulness and study, and "Good-heart"ince through love and worship. So the best way to achieve perfection in a personal relationship is to have a dependable appearance, a coherent thinking

Keywords: Ontology , good-heartince, wisdom, relationship, clear-head”ince, criminality, naivety

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Authors: Jae Soo Do, Kyoung-Seok Kim

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Radical changes in the industrial environment, and spectacular developments of IT have changed the current of managements from people-centered to technology- or IT-centered. Interpersonal emotion exchanges have long become insipid and interactive services have also come as mechanical reactions. This study offers various concepts for the emotional labor based on traditional studies on emotional labor. Especially the present day, on which human emotions are subject to being served as machinized thing, is the time when the study on human emotions comes momentous. Precedent researches on emotional labors commonly and basically dealt with the relationship between the active group who performs actions and the passive group who is done with the action. This study focuses on the passive group and tries to offer a new perspective of 'liquid emotion' as a defence mechanism for the passive group from the external environment. Especially, this addresses a concrete discussion on directions of following studies on the liquid labor as a newly suggested perspective.

Keywords: emotion labor, surface acting, deep acting, liquid emotion

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Authors: Asia Majeed

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The learning of mathematics in science, engineering and social science programs can be enhanced through practical problem-solving techniques. The instructors can design their lessons with some strategies to improve students’ educational needs and accomplishments in mathematics classrooms. The use of technology in class problem solving and application sessions can enhance deep understanding of mathematics among students. As mathematician, we believe in subject specific and content-driven teaching methods. Through technology the relationship between the physical problems and the mathematical models can be analyzed. This paper is about selective use of technology in mathematics classrooms and helpful to others mathematics instructors who wishes to improve their traditional teaching techniques to improve students’ attitude in learning mathematics. These techniques corpus can be used in teaching large mathematics classes in science, technology, engineering, and social science.

Keywords: attitude in learning mathematics, mathematics, non-mathematics undergraduate students, technology

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Safwat E. Musa, Nuha E. Mohamed, Nuha A. Bagi

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In this study, several crossplots of the P-impedance with the lithology logs (gamma ray, neutron porosity, deep resistivity, water saturation and Vp/Vs curves) were made in three available wells, which were drilled in central part of the Blue Nile basin in depths varies from 1460 m to 1600 m. These crossplots were successful to discriminate between sand and shale when using P-Impedance values, and between the wet sand and the pay sand when using both P-impedance and Vp/Vs together. Also, some impedance sections were converted to porosity sections using linear formula to characterize the reservoir in terms of porosity. The used crossplots were created on log resolution, while the seismic resolution can identify only the reservoir, unless a 3D seismic angle stacks were available; then it would be easier to identify the pay sand with great confidence; through high resolution seismic inversion and geostatistical approach when using P-impedance and Vp/Vs volumes.

Keywords: basin, Blue Nile, inversion, seismic

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Authors: Surita Maini

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There are many perceived advantages of microwave ablation have driven researchers to develop innovative antennas to effectively treat deep-seated, non-resectable hepatic tumors. In this paper a coaxial antenna with a miniaturized sleeve choke has been discussed for microwave interstitial ablation therapy, in order to reduce backward heating effects irrespective of the insertion depth into the tissue. Two dimensional Finite Element Method (FEM) is used to simulate and measure the results of miniaturized sleeve choke antenna. This paper emphasizes the importance of factors that can affect simulation accuracy, which include mesh resolution, surface heating and reflection coefficient. Quarter wavelength choke effectiveness has been discussed by comparing it with the unchoked antenna with same dimensions.

Keywords: microwave ablation, tumor, finite element method, coaxial slot antenna, coaxial dipole antenna

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Authors: Ron Dick, P. S. D. V. Prasadarao, Glenn Coltman

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Agenesis of the corpus callosum (ACC) is a failure to develop corpus callosum - the large bundle of fibers of the brain that connects the two cerebral hemispheres. It can occur as a partial or complete absence of the corpus callosum. In the general population, its estimated prevalence rate is 1 in 4000 and a wide range of genetic, infectious, vascular, and toxic causes have been attributed to this heterogeneous condition. The diagnosis of ACC is often achieved by neuroimaging procedures. Though persons with ACC can perform normally on intelligence tests they generally present with a range of neuropsychological and social deficits. The deficit profile is characterized by poor coordination of motor movements, slow reaction time, processing speed and, poor memory. Socially, they present with deficits in communication, language processing, the theory of mind, and interpersonal relationships. The present paper illustrates the role of neuropsychological assessment with implications to psychosocial management in a case of agenesis of the corpus callosum. Method: A 27-year old left handed Caucasian male with a history of ACC was self-referred for a neuropsychological assessment to assist him in his employment options. Parents noted significant difficulties with coordination and balance at an early age of 2-3 years and he was diagnosed with dyspraxia at the age of 14 years. History also indicated visual impairment, hypotonia, poor muscle coordination, and delayed development of motor milestones. MRI scan indicated agenesis of the corpus callosum with ventricular morphology, widely spaced parallel lateral ventricles and mild dilatation of the posterior horns; it also showed colpocephaly—a disproportionate enlargement of the occipital horns of the lateral ventricles which might be affecting his motor abilities and visual defects. The MRI scan ruled out other structural abnormalities or neonatal brain injury. At the time of assessment, the subject presented with such problems as poor coordination, slowed processing speed, poor organizational skills and time management, and difficulty with social cues and facial expressions. A comprehensive neuropsychological assessment was planned and conducted to assist in identifying the current neuropsychological profile to facilitate the formulation of a psychosocial and occupational rehabilitation programme. Results: General intellectual functioning was within the average range and his performance on memory-related tasks was adequate. Significant visuospatial and visuoconstructional deficits were evident across tests; constructional difficulties were seen in tasks such as copying a complex figure, building a tower and manipulating blocks. Poor visual scanning ability and visual motor speed were evident. Socially, the subject reported heightened social anxiety, difficulty in responding to cues in the social environment, and difficulty in developing intimate relationships. Conclusion: Persons with ACC are known to present with specific cognitive deficits and problems in social situations. Findings from the current neuropsychological assessment indicated significant visuospatial difficulties, poor visual scanning and problems in social interactions. His general intellectual functioning was within the average range. Based on the findings from the comprehensive neuropsychological assessment, a structured psychosocial rehabilitation programme was developed and recommended.

Keywords: agenesis, callosum, corpus, neuropsychology, psychosocial, rehabilitation

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Maria L. G. Lourenço, Keylla H. N. P. Pereira, Viviane Y. Hibaru, Fabiana F. Souza, Joao C. P. Ferreira, Simone B. Chiacchio, Luiz H. A. Machado

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Anasarca is generalized congenital edema that is often lethal. The condition is transmitted hereditarily and is autosomal dominant, with a racial predisposition in French Bulldogs and English Bulldogs. This study aims at reporting a case of anasarca treatment in neonates. The fetuses of a one year and six months old, primiparous English Bulldog mother were diagnosed with anasarca during an ultrasound examination performed at the 55th day of pregnancy and, therefore, an elective cesarean section was scheduled to prevent fetal dystocia. At birth, all puppies presented anasarca, and one of the six was stillborn. The newborns presented cyanosis, dyspnea, bradycardia, absent reflexes, low vitality scores (3/10), and hypothermia ( < 32ºC). The weight of the puppies at the time of birth varied between 347 and 373 grams, about 100 grams above the average weight estimated for the breed. Immediate neonatal care was applied with oxygen therapy via a mask, aminophylline (0.2 ml/100 g/PV/sublingual), and slow heating. After 10 minutes, there was a significant improvement in the neonatal parameters. The anasarca was treated with the drug furosemide, administered subcutaneously, at a dose of 0.2 mg per 100 grams of weight, every three hours. The stimulation for urination of newborns was performed every 30 minutes, and weight loss was monitored every 30 minutes. Five grams of potassium chloride were administered orally for every 30 grams of weight loss to counterbalance the loss of potassium caused by the diuretic medication. After 15 hours, the neonates reached the ideal weight for the breed, around 209 to 230 grams. In total, four neonates received five doses of furosemide, while one received six doses. The puppies are currently ten months old, healthy and neutered. Anasarca should not be ignored and is considered potentially lethal and an indication for euthanasia in all cases. Early intervention is of utmost importance for the survival of these patients.

Keywords: Walrus syndrome, congenital edema, water puppy syndrome, puppies

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Authors: Naim Izet Kajtazi

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Introduction: There are few reported cases of ipsilateral weakness following ischemic or hemorrhagic stroke. In these rare cases, ipsilateral weakness is typically the result of damage to uncrossed components of the corticospinal tract (CST), which were recruited in response to previous CST injury. Patients and Methods: We report a series of six cases of acute ipsilateral weakness or numbness following a hemorrhagic or ischemic stroke from three medical institutions in Saudi Arabia. Results: Three of these patients presented with right-sided weakness caused by an ipsilateral right hemispheric stroke, while two exhibited left-sided symptoms and one had only left-sided numbness. In all six cases, the ipsilateral corona radiata, internal capsule, basal ganglia, insula, and thalamus were involved. No concomitant opposite hemisphere or brainstem lesion in none of the patients was evident. Two patients had previous strokes affecting the brainstem and left corona radiata, respectively. A complete stroke workup to reveal the cause of the stroke was carried out, however, no functional MRI was performed. Conclusion: Ischemic or hemorrhagic stroke may indeed result in ipsilateral weakness or numbness, though in very rare cases. We assume that the most likely mechanism of their ipsilateral weakness subsequent to the ipsilateral stroke was a functional reorganization favoring CST pathways within the ipsilateral hemisphere.

Keywords: stroke, weakness, MRI brain, stroke unit

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Rabia Göçmen, Gülşah Kanbur, Sinan Sefa Parlat

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In this study, in the breeding Japanese quails (coturnix coturnix japonica), it was aimed to study the effects of cellular insulin receptor stimulation on the performance, some blood parameters, and hatchability features. In the study, a total of 84 breeding quails were used, which are in 6 weeks age, and whose 24 are male and 60 female. In the trial, rations which contain 2900 kcal/kg metabolic energy; crude protein of 20%, and water whose pH is calibrated to 7.45 were administered as ad-libitum, to the animals, as metformin source, metformin-HCl was used and as chrome resource, Chromium Picolinate. Trial groups were formed as control group (basal ration), metformin group (basal ration, added metformin at the level of fodder of 20 mg/kg), and chromium picolinate group (basal ration, added fodder of 1500 ppb Cr. When regarded to the results of performance at the end of trial, it is seen that live weight gain, fodder consumption, egg weight, fodder evaluation coefficient, and egg production were affected at the significant level (p < 0.05). When the results are evaluated in terms of incubation features at the end of trial, it was identified that incubation yield and hatchability are not affected by the treatments but in the groups, in which metformin and chromium picolinate are added to ration, that fertility rose at the significant level compared to control group (p < 0,05). According to the results of blood parameters and hormone at the end of the trial, while the level of plasma glucose level was not affected by treatments (p > 0.05), with the addition of metformin and chromium picolinate to ration, plasma, total control, cholesterol, HDL, LDL, and triglyceride levels were significantly affected from insulin receptor stimulators added to ration (p<0,05). Hormone level of Plasma T3 and T4 were also affected at the significant level from insulin receptor stimulators added to ration (p < 0,05).

Keywords: cholesterol, chromium picolinate, hormone, metformin, performance, quail

Procedia PDF Downloads 191

Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

Procedia PDF Downloads 112

Authors: Song Qingfeng

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This research aims to seek the relationship between social media usage and overtourism from the perspective of tourists based on the theory of Maslow’s hierarchy needs. A questionnaire is formulated to collect data from 400 tourists who have visited the Hangzhou city in China in the last 12 months. Structural Equation Model (SEM) is employed to analysis data. The finding is that social media usage aggravates overtourism. Specifically, social media is used by tourists to information-seeking, entertainment, self-presentation, and socialization for traveling. These roles of social media would evoke the traveling intention to a specific destination at a certain time, which further influences the tourist flow. When the tourist flow concentrate, the overtourism would be aggravated. This study contributes to the destination managers to deep-understand the cause-effect relationship between social media and overtourism in order to address this problem.

Keywords: social media, overtourism, tourist flow, SEM, Maslow’s hierarchy of needs, Hangzhou

Procedia PDF Downloads 128

Authors: Lizhi Ma, Chengcheng Hu, Fuxian Wong

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Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines.

Keywords: collective communication, decentralized distributed training, machine learning, P-Reduce

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Authors: Athena Johnson

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Special education is an important field but often under-researched, particularly for the cause of learning deficiencies. Often times special education looks at the symptoms rather than the cause, and this can lead to many misdiagnoses. Student trauma, as measured by the Adverse Childhood Experiences (ACE) test, is extremely common, often resulting in Post Traumatic Stress Disorder (PTSD). PTSD affects the brain's ability to learn properly, making students have a much more difficult time with auditory learning and memory due to always being in flight or fight mode, and due to this, students with PTSD are often misdiagnosed with Attention Deficit and Hyperactivity Disorder (ADHD). This can lead to them getting the wrong support, with PTSD students needing more counseling than anything else. Through these research papers' methodologies, a literature review on article research from the perspectives of students who were misdiagnosed, and imperial research, the major findings of this study were the importance of trauma-informed care in schools. Trauma-informed care in the school system is crucial for helping the many students who experience traumatic life events and struggle in school due to it. It is important to support students with PTSD so that they are able to integrate and learn better in society and school with trauma-informed school care.

Keywords: ACE test, ADHD, misdiagnoses, special education, trauma, trauma-informed care, PTSD

Procedia PDF Downloads 97

Authors: Shumaila Shakoor

Abstract:

C. tubulosa is a rare parasitic plant. It is found to be endangered and it acquires nutrition by penetrating roots deep in host roots. It has momentous potential to fulfill local and national health needs. This specie became endangered due to its parasitic mode of life and lack of awareness. Investigation of distribution and habitat conditions of C. tubulosa from District Pakpattan is the objective of this study. To explore its habitat conditions and community ecology phytosociological survey of C. tubulosa in different habitats i.e roadsides and graveyards was carried out. It was found that C. tubulosa occurs successfully in different habitats like graveyards and roadsides with specific neighboring species. Soil analysis was carried out by taking soil samples from seven sites. Soil was analyzed for pH, EC, soil texture, OM, N %age, Ca, Mg, P and K, which shows that soil of C. tubulosa is rich in all these nutrients.

Keywords: organic matter, potassium, phosphorus, magnesium

Procedia PDF Downloads 185

Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

Procedia PDF Downloads 220