Search results for: computational brain

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: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Roghaye Behroozi

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The development of functionalized metal complexes and nanomaterials has gained significant attention due to their potential in catalyzing selective oxidation and coupling reactions. These catalysts play a crucial role in various industrial and pharmaceutical processes, enhancing the efficiency, selectivity, and sustainability of chemical reactions. This research aims to design and synthesize new functionalized metal complexes and nanomaterials to explore their catalytic applications in the selective oxidation of alcohols and coupling reactions, focusing on improving yield, selectivity, and catalyst reusability. The study involves the synthesis of a nickel Schiff base complex stabilized within 41-MCM as a heterogeneous catalyst. A Schiff base ligand derived from glycine was used to create a tin (IV) metal complex characterized through spectroscopic techniques and computational analysis. Additionally, iron-based magnetic nanoparticles functionalized with melamine were synthesized for catalytic evaluation. Lastly, a palladium (IV) complex was prepared, and its oxidative stability was analyzed. The nickel Schiff base catalyst showed high selectivity in converting primary and secondary alcohols to aldehydes and ketones, with yields ranging from 73% to 90%. The tin (IV) complex demonstrated accurate structural and electronic properties, with consistent results between experimental and computational data. The melamine-functionalized iron nanoparticles exhibited efficient catalytic activity in producing triazoles, with enhanced reaction speed and reusability. The palladium (IV) complex displayed remarkable stability and low reactivity towards C–C bond formation due to its symmetrical structure. The synthesized metal complexes and nanomaterials demonstrated significant potential as efficient, selective, and reusable catalysts for oxidation and coupling reactions. These findings pave the way for developing environmentally friendly and cost-effective catalytic systems for industrial applications.

Keywords: catalysts, Schiff base complexes, metal-organic frameworks, oxidation reactions, nanoparticles, reusability

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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

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Authors: Nisreen Naji Al-Khawaldeh, Lara Ahmad Mansour El-Awar

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The present study investigated the strategies MA translation students used for translating technical terms, the most common obstacles they encountered in translating such terms, and the motives behind using such terms as they are in their original form despite their translatability into Arabic. To achieve these objectives, a translation test was administered to 100 MA students specialising in translation at both Hashemite University and The University of Jordan. It consisted of two parts: (a) 50 English technical terms to be translated (b) two questions to be answered concerning the challenges or problems encountered while translating the previous technical terms and the motives that drive them to use most of the English technical terms as they are despite their translatability into Arabic. The analysis of the results revealed that MA translation students faced problems in translating technical terms, namely the inability to find the equivalent form for the given technical terms, the use of literal translation, and the wider use of loan-words type. Besides, the students used different strategies to translate the technical terms, namely borrowing (i.e., loan- words), paraphrasing, synonymy, naturalization, equivalence, and literal translation. Moreover, it was also revealed that most technical terms were used as they are in the source language despite their translatability into Arabic because these technical terms are easier to use in English rather than in Arabic. Also, when these terms were introduced to the Arab world, they were introduced in English, not in Arabic. So, the brain links these objects to their English terms.

Keywords: arabic, english, technical terms, translation strategies, translation problems

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Authors: Gabriela Rivadeneyra-Romero, Claudia del C. Gutierrez Torres, Sergio A. Martinez-Delgadillo, Victor X. Mendoza-Escamilla, Alejandro Alonzo-Garcia

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Ultrasonic degradation is currently being used in sonochemical reactors to degrade pollutant compounds from aqueous media, as emerging contaminants (e.g. pharmaceuticals, drugs and personal care products.) because they can produce possible ecological impacts on the environment. For this reason, it is important to develop appropriate water and wastewater treatments able to reduce pollution and increase reuse. Pollutants such as textile dyes, aromatic and phenolic compounds, cholorobenzene, bisphenol-A and carboxylic acid and other organic pollutants, can be removed from wastewaters by sonochemical oxidation. The effect on the removal of pollutants depends on the type of the ultrasonic frequency used; however, not much studies have been done related to the behavior of the fluid into the sonoreactors operated at different ultrasonic frequencies. Based on the above, it is necessary to study the hydrodynamic behavior of the liquid generated by the ultrasonic irradiation to design efficient sonoreactors to reduce treatment times and costs. In this work, it was studied the hydrodynamic behavior of the fluid in sonochemical reactors at different frequencies (250 kHz, 500 kHz and 1000 kHz). The performances of the sonoreactors at those frequencies were simulated using computational fluid dynamics (CFD). Due to there is great sound speed gradient between piezoelectric and fluid, k-e models were used. Piezoelectric was defined as a vibration surface, to evaluate the different frequencies effect on the fluid into sonochemical reactor. Structured hexahedral cells were used to mesh the computational liquid domain, and fine triangular cells were used to mesh the piezoelectric transducers. Unsteady state conditions were used in the solver. Estimation of the dissipation rate, flow field velocities, Reynolds stress and turbulent quantities were evaluated by CFD and 2D-PIV measurements. Test results show that there is no necessary correlation between an increase of the ultrasonic frequency and the pollutant degradation, moreover, the reactor geometry and power density are important factors that should be considered in the sonochemical reactor design.

Keywords: CFD, reactor, ultrasound, wastewater

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Authors: Qasim A. El Dwairi, Saleh M. Banihani, Ayat S. Banihani, Ziad M. Bataineh

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Neurons in the spinal trigeminal nucleus of the camel were studied by Golgi impregnation. Neurons were classified based on differences in size and shape of their cell bodies, density of their dendritic trees, morphology and distribution of their appendages. In the spinal trigeminal nucleus of the camel, at least twelve types of neurons were identified. These neurons include, stalked, islets, octubus-like, lobulated, boat-like, pyramidal, multipolar, round, oval and elongated neurons. They have large number of different forms of appendages not only for their dendrites but also for their cell bodies. Neurons with unique large dilatations especially at their dendritic branching points were found. The morphological features of these neurons were described and compared with their counterparts in other species. Finding of large number of neuronal types with different size and shapes and large number of different forms of appendages for cell bodies and dendrites together with the presence of cells with unique features such as large dilated parts for dendrites may indicate to a very complex information processing for pain and temperature at the level of the spinal trigeminal nucleus in the camel that traditionally live in a very hard environment (the desert).

Keywords: camel, golgi, neurons , spinal trigeminal nucleus

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Amira Ben Rabeh, Faouzi Benzarti, Hamid Amiri, Mouna Bouaziz

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Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work; we proposed an application to detect Alzheimer’s diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Keywords: Alzheimer Diseases (AD), Computer Assisted Diagnostic(CAD), hippocampus, Corpus Callosum (CC), cortex, Support Vector Machine (SVM)

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Authors: Nikolas Aasheim, Laura Sakalauskaitė, Julie Dubois, Malina Ploug Larsen, Paul Michael Petersen, Marcus S. Carstensen, Marcus S. Carstensen, Mai Nguyen, Line Katrine Harder Clemmensen, Kamilla Miskowiak, Klaus Martiny

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Background: Major depressive disorder (MDD) is a debilitating condition that affects more than 300 million people worldwide and profoundly impacts well-being and health. Current treatments are based on a trial-and-error approach, and reliable biomarkers are needed for more informed and personalized treatment solutions. One potential biomarker is aberrant gamma-frequency (30-80 Hz) brainwaves, hypothesized to originate from deficiencies in the excitatory-inhibitory interaction between the pyramidal cells and interneurons. An imbalance within this interaction is described as a crucial pathological mechanism in various neuropsychiatric conditions, including MDD, and the modulation of this pathological interaction has been investigated as a potential target. A specific type of steady-state visually evoked potential (SSVEP) in the gamma frequency band, referred to as gamma entrainment using sensory stimuli (GENUS), particularly around the 40Hz spectrum, entrains large scale, fast-spiking PV+ interneurons, facilitating coordinated activity in key brain regions, reduced neuronal and synaptic loss, and enhanced synaptic stability and plasticity. GENUS has shown promise in improving sleep, offering neuroprotective effects in Alzheimer's disease (AD), and reducing pathological markers like Amyloid Beta and TAU proteins, as seen in animal models. In this study, we explore the antidepressant, cognitive, and electrophysiological effects of a novel, non-invasive brain stimulation (NIBS) approach utilizing a 40 Hz invisible spectral flicker to induce gamma activity in patients diagnosed with Major Depressive Disorder (MDD). This non-invasive targeted stimulation of lower gamma band activity (40 Hz) is designed to modulate neural circuits associated with mood and cognitive functions, providing a potential new therapeutic avenue for MDD. Methods and Design: 60 patients with a current diagnosis of a major depressive episode will be enrolled in a randomized, double-blinded, placebo-controlled trial. The active treatment group will receive 40 Hz invisible spectral flickering light stimulation while the control group will receive continuous light matched in colour temperature and brightness. Patients in both groups will get an hour of daily light treatment in their own homes and will attend four follow-up visits to assess depression severity measured by Hamilton Depression Rating Scale (HAM-D₆), several aspects of sleep, cognitive function, quality of life. Additionally, exploratory EEG is conducted to assess spectral changes throughout the protocol. The primary endpoint is the mean change from baseline to week 6 in depression severity (HAM-D₆ subset) between the groups. Current state of affairs/timeline: The FELIX study was initiated in the beginning of 2022, planning to reach stage of publication in December 2025. 21 participants have been enrolled in the protocol thus far, expecting to be finished with trials and recruitment by the end of 2024.

Keywords: major depressive disorder, gamma, neurostimulation, EEG

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Authors: Mostafa Zandi, Ramin Mansouri

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Spillways are one of the most important hydraulic structures of dams that provide the stability of the dam and downstream areas at the time of flood. Morning-Glory spillway is one of the common spillways for discharging the overflow water behind dams, these kinds of spillways are constructed in dams with small reservoirs. In this research, the hydraulic flow characteristics of a morning-glory spillways are investigated with CFD model. Two dimensional unsteady RANS equations were solved numerically using Finite Volume Method. The PISO scheme was applied for the velocity-pressure coupling. The mostly used two-equation turbulence models, k- and k-, were chosen to model Reynolds shear stress term. The power law scheme was used for discretization of momentum, k , and  equations. The VOF method (geometrically reconstruction algorithm) was adopted for interface simulation. The results show that the fine computational grid, the input speed condition for the flow input boundary, and the output pressure for the boundaries that are in contact with the air provide the best possible results. Also, the standard wall function is chosen for the effect of the wall function, and the turbulent model k -ε (Standard) has the most consistent results with experimental results. When the jet is getting closer to end of basin, the computational results increase with the numerical results of their differences. The lower profile of the water jet has less sensitivity to the hydraulic jet profile than the hydraulic jet profile. In the pressure test, it was also found that the results show that the numerical values of the pressure in the lower landing number differ greatly in experimental results. The characteristics of the complex flows over a Morning-Glory spillway were studied numerically using a RANS solver. Grid study showed that numerical results of a 57512-node grid had the best agreement with the experimental values. The desired downstream channel length was preferred to be 1.5 meter, and the standard k-ε turbulence model produced the best results in Morning-Glory spillway. The numerical free-surface profiles followed the theoretical equations very well.

Keywords: morning-glory spillway, CFD model, hydraulic characteristics, wall function

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Authors: Sabiha Sahin, Kursad Bora Carman, Coskun Yarar

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Introduction: Measuring the diameter of the optic nerve sheath is a noninvasive and easy to use imaging technique to predict intracranial pressure in children and adults. The aim was to measure the diameter of the optic nerve sheath in pediatric head trauma. Methods: The study group consisted of 40 children with healthy and 40 patients with head trauma. Transorbital sonographic measurement of the optic nerve sheath diameter was performed. Conclusion: The mean diameters of the optic nerve sheath of right and left eyes were 0.408 ± 0.064 mm and 0.417 ± 0.065 mm, respectively, in the trauma group. These results were higher in patients than in control group. There was a negative correlation between optic nerve sheath diameters and Glasgow Coma Scales in patients with head trauma (p < 0.05). There was a positive correlation between optic nerve sheath diameters and positive CT findings, systolic blood pressure in patients with head trauma. The clinical status of the patients at admission, blood pH and lactate level were related to the optic nerve sheath diameter. Conclusion: Measuring the diameter of the optic nerve sheath is not an invasive technique and can be easily used to predict increased intracranial pressure and to prevent secondary brain injury.

Keywords: head trauma, intracranial pressure, optic nerve, sonography

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: M. O. Olayiwola

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Numerical solutions of the generalized Burger-Fisher are obtained using a Modified Variational Iteration Method (MVIM) with minimal computational efforts. The computed results with this technique have been compared with other results. The present method is seen to be a very reliable alternative method to some existing techniques for such nonlinear problems.

Keywords: burger-fisher, modified variational iteration method, lagrange multiplier, Taylor’s series, partial differential equation

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Authors: Ssembatya Joseph Mary

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Introduction: Cryptococcal infection in the Central Nervous System (CNS) is frequently seen in human immunodeficiency virus (HIV) patients and others with low immunity as an opportunistic fungal infection. However, CNS cryptococcal granuloma (cryptococcoma) in immunocompetent patients is rare. We present a case of CNS cryptococcoma in an immunocompetent patient and review the literature to illustrate the diagnosis and treatment of such lesions. Case presentation: A 62-year-old, HIV-negative, immunocompetent female patient with no known chronic illness presented with 5 months history of a progressive headache associated with on and off episodic generalized tonic-clonic convulsions. She had been to several hospitals before she was referred to our center with a diagnosis of a brain tumor. Before referral and despite a negative CSF analysis result, she had received treatment for bacterial meningitis with no success. At Mbarara Regional Referral Hospital (MRRH), she had surgery with an excision biopsy which showed features consistent with cryptococcosis on histology. The patient had a successful adjuvant treatment with antifungal drugs following surgery. Conclusion: The diagnosis of a parasitic CNS infection, particularly cryptococcal infection mimicking neoplastic lesions in an immunocompetent patient, was unusual. Surgical management of such lesions from different reports has a bad outcome and management remains totally conservative.

Keywords: Cryptococcal meningitis, immunocompetent patient, Uganda, low resource setting

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: G. St-Pierre-Lemieux, E. Askari Mahvelati, D. Groleau, P. Proulx

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Mathematical modeling has become an important tool for the study of foam behavior. Computational Fluid Dynamic (CFD) can be used to investigate the behavior of foam around foam breakers to better understand the mechanisms leading to the ‘destruction’ of foam. The focus of this investigation was the simple cone foam breaker, whose performance has been identified in numerous studies. While the optimal pumping angle is known from the literature, the contribution of pressure drop, shearing, and centrifugal forces to the foam syneresis are subject to speculation. This work provides a screening of those factors against changes in the cone angle and foam rheology. The CFD simulation was made with the open source OpenFOAM toolkits on a full three-dimensional model discretized using hexahedral cells. The geometry was generated using a python script then meshed with blockMesh. The OpenFOAM Volume Of Fluid (VOF) method was used (interFOAM) to obtain a detailed description of the interfacial forces, and the model k-omega SST was used to calculate the turbulence fields. The cone configuration allows the use of a rotating wall boundary condition. In each case, a pair of immiscible fluids, foam/air or water/air was used. The foam was modeled as a shear thinning (Herschel-Buckley) fluid. The results were compared to our measurements and to results found in the literature, first by computing the pumping rate of the cone, and second by the liquid break-up at the exit of the cone. A 3D printed version of the cones submerged in foam (shaving cream or soap solution) and water, at speeds varying between 400 RPM and 1500 RPM, was also used to validate the modeling results by calculating the torque exerted on the shaft. While most of the literature is focusing on cone behavior using Newtonian fluids, this works explore its behavior in shear thinning fluid which better reflects foam apparent rheology. Those simulations bring new light on the cone behavior within the foam and allow the computation of shearing, pressure, and velocity of the fluid, enabling to better evaluate the efficiency of the cones as foam breakers. This study contributes to clarify the mechanisms behind foam breaker performances, at least in part, using modern CFD techniques.

Keywords: bioreactor, CFD, foam breaker, foam mitigation, OpenFOAM

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Authors: Nicolas M. Beleski, Gustavo A. G. Lugo

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Robotic agents are taking more and increasingly important roles in society. In order to make these robots and agents more autonomous and efficient, their systems have grown to be considerably complex and convoluted. This growth in complexity has led recent researchers to investigate forms to explain the AI behavior behind these systems in search for more trustworthy interactions. A current problem in explainable AI is the inner workings with the logic inference process and how to conduct a sensibility analysis of the process of valuation and alteration of beliefs. In a social HRI (human-robot interaction) setup, theory of mind is crucial to ease the intentionality gap and to achieve that we should be able to infer over observed human behaviors, such as cases of cognitive dissonance. One specific case inspired in human cognition is the role emotions play on our belief system and the effects caused when observed behavior does not match the expected outcome. In such scenarios emotions can make a person wrongly assume the antecedent P for an observed consequent Q, and as a result, incorrectly assert that P is true. This form of cognitive dissonance where an unproven cause is taken as truth induces changes in the belief base which can directly affect future decisions and actions. If we aim to be inspired by human thoughts in order to apply levels of theory of mind to these artificial agents, we must find the conditions to replicate these observable cognitive mechanisms. To achieve this, a computational architecture is proposed to model the modulation effect emotions have on the belief system and how it affects logic inference process and consequently the decision making of an agent. To validate the model, an experiment based on the prisoner's dilemma is currently under development. The hypothesis to be tested involves two main points: how emotions, modeled as internal argument strength modulators, can alter inference outcomes, and how can explainable outcomes be produced under specific forms of cognitive dissonance.

Keywords: cognitive architecture, cognitive dissonance, explainable ai, sensitivity analysis, theory of mind

Procedia PDF Downloads 133

Authors: M. Y. Waziri, M. A. Aliyu

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The paper proposes an approach to improve the performance of Broyden’s method for solving systems of nonlinear equations. In this work, we consider the information from two preceding iterates rather than a single preceding iterate to update the Broyden’s matrix that will produce a better approximation of the Jacobian matrix in each iteration. The numerical results verify that the proposed method has clearly enhanced the numerical performance of Broyden’s Method.

Keywords: mulit-step Broyden, nonlinear systems of equations, computational efficiency, iterate

Procedia PDF Downloads 640

Authors: Said Algarni

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The purpose of this study was to investigate selected numerical methods that demonstrate good performance in solving PDEs. We adapted alternative method that involve rational polynomials. Padé time stepping (PTS) method, which is highly stable for the purposes of the present application and is associated with lower computational costs, was applied. Furthermore, PTS was modified for our study which focused on diffusion equations. Numerical runs were conducted to obtain the optimal local error control threshold.

Keywords: Padé time stepping, finite difference, reaction diffusion equation, PDEs

Procedia PDF Downloads 300

Authors: Masato Saeki

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Presented herein is a novel algorithms for calculating the damping performance of particle dampers. The particle damper is a passive vibration control technique and has many practical applications due to simple design. It consists of granular materials constrained to move between two ends in the cavity of a primary vibrating system. The damping effect results from the exchange of momentum during the impact of granular materials against the wall of the cavity. This damping has the advantage of being independent of the environment. Therefore, particle damping can be applied in extreme temperature environments, where most conventional dampers would fail. It was shown experimentally in many papers that the efficiency of the particle dampers is high in the case of resonant vibration. In order to use the particle dampers effectively, it is necessary to solve the equations of motion for each particle, considering the granularity. The discrete element method (DEM) has been found to be effective for revealing the dynamics of particle damping. In this method, individual particles are assumed as rigid body and interparticle collisions are modeled by mechanical elements as springs and dashpots. However, the computational cost is significant since the equation of motion for each particle must be solved at each time step. In order to improve the computational efficiency of the DEM, the new algorithms are needed. In this study, new algorithms are proposed for implementing the high performance DEM. On the assumption that behaviors of the granular particles in the each divided area of the damper container are the same, the contact force of the primary system with all particles can be considered to be equal to the product of the divided number of the damper area and the contact force of the primary system with granular materials per divided area. This convenience makes it possible to considerably reduce the calculation time. The validity of this calculation method was investigated and the calculated results were compared with the experimental ones. This paper also presents the results of experimental studies of the performance of particle dampers. It is shown that the particle radius affect the noise level. It is also shown that the particle size and the particle material influence the damper performance.

Keywords: particle damping, discrete element method (DEM), granular materials, numerical analysis, equivalent noise level

Procedia PDF Downloads 456

Authors: Chryssa Thermolia, Ekaterini S. Bei, Stelios Sotiriadis, Kostas Stravoskoufos, Euripides G. M. Petrakis

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Moving into a new era of healthcare, new tools and devices are developed to extend and improve health services, such as remote patient monitoring and risk prevention. In this concept, Internet of Things (IoT) and Cloud Computing present great advantages by providing remote and efficient services, as well as cooperation between patients, clinicians, researchers and other health professionals. This paper focuses on patients suffering from bipolar disorder, a brain disorder that belongs to a group of conditions called effective disorders, which is characterized by great mood swings.We exploit the advantages of Semantic Web and Cloud Technologies to develop a patient monitoring system to support clinicians. Based on intelligently filtering of evidence-knowledge and individual-specific information we aim to provide treatment notifications and recommended function tests at appropriate times or concluding into alerts for serious mood changes and patient’s non-response to treatment. We propose an architecture, as the back-end part of a cloud platform for IoT, intertwining intelligence devices with patients’ daily routine and clinicians’ support.

Keywords: bipolar disorder, intelligent systems patient monitoring, semantic web technologies, healthcare

Procedia PDF Downloads 511

Authors: Hargunjeet Shergill, Palwinder Singh

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The present paper analyzes the lack of teachers' awareness and knowledge regarding the Attention Deficit Hyperactivity Disorder in the college students. Attention deficit hyperactivity disorder causes individuals to consistently display extreme inattention, impulsivity and in many cases hyperactivity as a result of the physiological differences of the brain. Teachers have a formative influence on their students and can play a key role in identifying and supporting students with Attention Deficit/Hyperactivity Disorder (ADHD). Despite the pervasiveness and salience of this disorder, educators at college continue to labor under a number of misconceptions about the nature of ADHD. In order to fulfill this important role, it is imperative for teachers to have explicit knowledge about this disorder. ADHD in college students remains the most under-recognized and undertreated mental health condition. The overall aim of this study is to investigate teachers’ knowledge and misconceptions of ADHD with a particular focus on recognition, assessment and management of ADHD in adult college students. It designed to assess the college teachers' knowledge, opinions, and experience related to the diagnosis of attention-deficit/hyperactivity disorder (ADHD) and by maintaining open lines of communication with the students and understanding some key elements that can affect students’ overall growth and ability. The discussion focuses on the value of the role of teachers and their relationship with each college student dealing with ADHD.

Keywords: attention deficit hyperactivity disorder, development of ADHD, diagnostic criteria, role of teachers

Procedia PDF Downloads 218

Authors: Terry Teo, Sun Woh Lye, Yufei Li, Zainuddin Zakaria

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Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that when using the channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available BCI 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities.

Keywords: assessment, neurophysiology, monitoring, EEG

Procedia PDF Downloads 77

Authors: Weili Jiang, Simon Cadavid Lopera, Klaus Drechsler

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Fuel cell vehicle has become the most competitive solution for the transportation sector in the hydrogen economy. Type IV pressure vessel is currently the most popular and widely developed technology for the on-board storage, based on their high reliability and relatively low cost. Due to the stringent requirement on mechanical performance, the pressure vessel is subject to great amount of composite material, a major cost driver for the hydrogen tanks. Evidently, the optimization of composite layup design shows great potential in reducing the overall material usage, yet requires comprehensive understanding on underlying mechanisms as well as the influence of different design parameters on mechanical performance. Given the type of materials and manufacturing processes by which the type IV pressure vessels are manufactured, the design and optimization are a nuanced subject. The manifold of stacking sequence and fiber orientation variation possibilities have an out-standing effect on vessel strength due to the anisotropic property of carbon fiber composites, which make the design space high dimensional. Each variation of design parameters requires computational resources. Using finite element analysis to evaluate different designs is the most common method, however, the model-ing, setup and simulation process can be very time consuming and result in high computational cost. For this reason, it is necessary to build a reliable automation scheme to set up and analyze the di-verse composite layups. In this research, the simulation process of different tank designs regarding various parameters is conducted and automatized in a commercial finite element analysis framework Abaqus. Worth mentioning, the modeling of the composite overwrap is automatically generated using an Abaqus-Python scripting interface. The prediction of the winding angle of each layer and corresponding thickness variation on dome region is the most crucial step of the modeling, which is calculated and implemented using analytical methods. Subsequently, these different composites layups are simulated as axisymmetric models to facilitate the computational complexity and reduce the calculation time. Finally, the results are evaluated and compared regarding the ultimate tank strength. By automatically modeling, evaluating and comparing various composites layups, this system is applicable for the optimization of the tanks structures. As mentioned above, the mechanical property of the pressure vessel is highly dependent on composites layup, which requires big amount of simulations. Consequently, to automatize the simulation process gains a rapid way to compare the various designs and provide an indication of the optimum one. Moreover, this automation process can also be operated for creating a data bank of layups and corresponding mechanical properties with few preliminary configuration steps for the further case analysis. Subsequently, using e.g. machine learning to gather the optimum by the data pool directly without the simulation process.

Keywords: type IV pressure vessels, carbon composites, finite element analy-sis, automation of simulation process

Procedia PDF Downloads 137

Authors: Mehdi Nakisa, Adi Maimun, Yasser M. Ahmed, Fatemeh Behrouzi

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Squat is the reduction in under-keel clearance between a vessel at-rest and underway due to the increased flow of water past the moving body. The forward motion of the ship induces a relative velocity between the ship and the surrounding water that causes a water level depression in which the ship sinks. The problem of ship squat is one among the crucial factors affecting the navigation of ships in restricted waters. This article investigates the LNG ship squat, its effects on flow streamlines around the ship hull and ship behavior and motion using computational fluid dynamics which is applied by Ansys-Fluent.

Keywords: ship squat, CFD, confined, mechanic

Procedia PDF Downloads 620

Authors: Erdogan Kaygan, Alvin Gatto

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An investigation of adaptable winglets for enhancing morphing aircraft performance is described in this paper. The concepts investigated consist of various winglet configurations fundamentally centered on a baseline swept wing. The impetus for the work was to identify and optimize winglets to enhance the aerodynamic efficiency of a morphing aircraft. All computations were performed with Athena Vortex Lattice modelling with varying degrees of twist and cant angle considered. The results from this work indicate that if adaptable winglets were employed on aircraft’s improvements in aircraft performance could be achieved.

Keywords: aircraft, drag, twist, winglet

Procedia PDF Downloads 587

Authors: Zina Ghiloufi, Tahar Khir

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A transient three-dimensional computational fluid dynamics (CFD) model is developed to determine the velocity and temperature distribution in different positions cold room during pre-cooling of dates. The turbulence model used is the k-ω Shear Stress Transport (SST) with the standard wall function, the air. The numerical results obtained show that cooling rate is not uniform inside the room; the product at the medium of room has a slower cooling rate. This cooling heterogeneity has a large effect on the energy consumption during cold storage.

Keywords: CFD, cold room, cooling rate, dDates, numerical simulation, k-ω (SST)

Procedia PDF Downloads 236