Search results for: computational brain

Authors: Emhimed Saffor

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

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

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Authors: Arya Prakash Padhi, Anupam Chakrabarti, Rajib Chowdhury

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Topology or layout optimization often gives better performing economic structures and is very helpful in the conceptual design phase. But traditionally it is being done in finite element-based optimization schemes which, although gives a good result, is very time-consuming especially in 3D structures. Among other alternatives machine learning, especially deep learning-based methods, have a very good potential in resolving this computational issue. Here convolutional neural network (3D-CNN) based variational auto encoder (VAE) is trained using a dataset generated from commercially available topology optimization code ABAQUS Tosca using solid isotropic material with penalization (SIMP) method for compliance minimization. The encoded data in latent space is then fed to a 3D generative adversarial network (3D-GAN) to generate the outcome in 64x64x64 size. Here the network consists of 3D volumetric CNN with rectified linear unit (ReLU) activation in between and sigmoid activation in the end. The proposed network is seen to provide almost optimal results with significantly reduced computational time, as there is no iteration involved.

Keywords: 3D generative adversarial network, deep learning, structural topology optimization, variational auto encoder

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Authors: Kamal Upadhyay, Zhou Hua, Yu Rui

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This paper aims to improve the streamline linked with the flow field and cavitation on the valve sleeve. We observed that local pressure fluctuation produces a low-pressure zone, central to the formation of vapor volume fraction within the valve chamber led to air-bubbles (or cavities). Thus, it allows simultaneously to a severe negative impact on the inner surface and lifespan of the valve sleeves. Cavitation reduction is a vitally important issue to pressure control valves. The optimization of the flow field is proposed in this paper to reduce the cavitation of valve sleeves. In this method, the inner wall of the valve sleeve is changed from a cylindrical surface to the conical surface, leading to the decline of the fluid flow velocity and the rise of the outlet pressure. Besides, the streamline is distributed inside the sleeve uniformly. Thus, the bubble generation is lessened. The fluid models are built and analysis of flow field distribution, pressure, vapor volume and velocity was carried out using computational fluid dynamics (CFD) and numerical technique. The results indicate that this structure can suppress the cavitation of valve sleeves effectively.

Keywords: streamline, cavitation, optimization, computational fluid dynamics

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Authors: Umut Yıldız, Berkin Kurtuluş, Yunus Emre Muslubaş

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Thrust vectoring, especially in military aviation, is a concept that sees much use to improve maneuverability in already agile aircraft. As this concept is fairly new and cost intensive to design and test, computational methods are useful in easing the preliminary design process. Computational Fluid Dynamics (CFD) can be utilized in many forms to simulate nozzle flow, and there exist various CFD studies in both 2D mechanical and 3D injection based thrust vectoring, and yet, 3D mechanical thrust vectoring analyses, at this point in time, are lacking variety. Additionally, the freely available test data is constrained to limited pitch angles and geometries. In this study, based on a test case provided by NASA, both steady and unsteady 3D CFD simulations are conducted to examine the aerodynamic performance of a mechanical thrust vectoring nozzle model and to validate the utilized numerical model. Steady analyses are performed to verify the flow characteristics of the nozzle at pitch angles of 0, 10 and 20 degrees, and the results are compared with experimental data. It is observed that the pressure data obtained on the inner surface of the nozzle at each specified pitch angle and under different flow conditions with pressure ratios of 1.5, 2 and 4, as well as at azimuthal angle of 0, 45, 90, 135, and 180 degrees exhibited a high level of agreement with the corresponding experimental results. To validate the CFD model, the insights from the steady analyses are utilized, followed by unsteady analyses covering a wide range of pitch angles from 0 to 20 degrees. Throughout the simulations, a mesh morphing method using a carefully calculated mathematical shape deformation model that simulates the vectored nozzle shape exactly at each point of its travel is employed to dynamically alter the divergent part of the nozzle over time within this pitch angle range. The mesh morphing based vectored nozzle shapes were compared with the drawings provided by NASA, ensuring a complete match was achieved. This computational approach allowed for the creation of a comprehensive database of results without the need to generate separate solution domains. The database contains results at every 0.01° increment of nozzle pitch angle. The unsteady analyses, generated using the morphing method, are found to be in excellent agreement with experimental data, further confirming the accuracy of the CFD model.

Keywords: thrust vectoring, computational fluid dynamics, 3d mesh morphing, mathematical shape deformation model

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Authors: Timothy Onyenobi

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Accidental fires and its dynamic as it relates to building compartmentation and the impact of the compartment morphology, is still an on-going area of study; especially with the use of computational fluid dynamics (CFD) modeling methods. With better knowledge on this subject come better solution recommendations by fire engineers. Interviews were carried out for this study where it was identified that the response perspectives to accidental fire were different with the fire engineer providing qualitative data which is based on “what is expected in real fires” and the fire fighters provided information on “what actually obtains in real fires”. This further led to a study and analysis of two real and comprehensively instrumented fire experiments: the Open Plan Office Project by National Institute of Standard and Technology (NIST) USA (to study time to flashover) and the TF2000 project by the Building Research Establishment (BRE) UK (to test for conformity with Building Regulation requirements). The findings from the analysis of the experiments revealed the relative yet critical weakness of fire prediction using a CFD model (usually used by fire engineers) as well as explained the differences in response perspectives of the fire engineers and firefighters from the interview analysis.

Keywords: CFD, compartment fire, experiment, fire fighters, fire engineers

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Authors: Sonia Besbes, Mahmoud El Hajem, Habib Ben Aissia, Jean Yves Champagne, Jacques Jay

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In this work, we present a computational study for the characterization of the flow in a rectangular bubble column. To simulate the dynamic characteristics of the flow, a three-dimensional transient numerical simulations based on a coupled discrete phase model (DPM) and Volume of Fluid (VOF) model are performed. Modeling of bubble column reactor is often carried out under the assumption of a flat liquid surface with a degassing boundary condition. However, the dynamic behavior of the top surface surmounting the liquid phase will to some extent influence the meandering oscillations of the bubble plume. Therefore it is important to capture the surface behavior, and the assumption of a flat surface may not be applicable. So, the modeling approach needs to account for a dynamic liquid surface induced by the rising bubble plume. The volume of fluid (VOF) model was applied for the liquid and top gas which both interacts with bubbles implemented with a discrete phase model. This model treats the bubbles as Lagrangian particles and the liquid and the top gas as Eulerian phases with a sharp interface. Two-way coupling between Eulerian phases and Lagrangian bubbles are accounted for in a single set continuous phase momentum equation for the mixture of the two Eulerian phases. The effect of gas flow rate on the dynamic and time-averaged flow properties was studied. The time averaged liquid velocity field predicted from simulations and from our previous PIV measurements shows that the liquid is entrained up flow in the wake of the bubbles and down flow near the walls. The simulated and measured vertical velocity profiles exhibit a reasonable agreement looking at the minimum velocity values near the walls and the maximum values at the column center.

Keywords: bubble column, computational fluid dynamics (CFD), coupled DPM and VOF model, hydrodynamics

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

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

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

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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Authors: Samuel Ahamefula

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Turbulent motion is a highly nonlinear and complex phenomenon, and its modelling is still very challenging. In this study, we developed a hybrid computational approach to accurately simulate fluid turbulence phenomenon. The focus is coupling and transitioning between Direct Numerical Simulation (DNS) and Large Eddy Simulating Wall Models (LES-WM) regions. In the framework, high-order fidelity fluid dynamical methods are utilized to simulate the unsteady compressible Navier-Stokes equations in the Eulerian format on the unstructured moving grids. The coupling and transitioning of DNS and LES-WM are conducted through the linearly staggered Dirichlet-Neumann coupling scheme. The high-fidelity framework is verified and validated based on namely, DNS ability for capture full range of turbulent scales, giving accurate results and LES-WM efficiency in simulating near-wall turbulent boundary layer by using wall models.

Keywords: computational methods, turbulence modelling, turbulence entropy, navier-stokes equations

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Authors: Sunghyoun Lee, Byeongkee Yu, Chanjung Lee, Yeongtaek Lim

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Oyster mushrooms are one of the ingredients that Koreans prefer. The oyster mushroom cultivation house has multiple layers in order to increase the mushroom production per unit area. However, the growing shelves in the house act as obstacles and hinder the circulation of the interior air, which leads to the difference of cultivation environment between the upper part and lower part of the growing shelves. Due to this difference of environments, growth distinction occurs according to the area of the growing shelves. It is known that minute air circulation around the mushroom cap facilitates the metabolism of mushrooms and improves its quality. This study has utilized the computational fluid dynamics (CFD) program, that is, FLUENT R16, in order to analyze the improvement of the internal environment uniformity of the oyster mushroom cultivation house. The analyzed factors are velocity distribution, temperature distribution, and humidity distribution. In order to maintain the internal environment uniformity of the oyster mushroom cultivation house, it appeared that installing circulation fan at the upper part of the working passage towards the ceiling is effective. When all the environmental control equipment – unit cooler, inlet fan, outlet fan, air circulation fan, and humidifier - operated simultaneously, the RMS figure on the growing shelves appeared as follows: velocity 28.23%, temperature 30.47%, humidity 7.88%. However, when only unit cooler and air circulation fan operated, the RMS figure on the growing shelves appeared as follows: velocity 22.28%, temperature 0.87%, humidity 0.82%. Therefore, in order to maintain the internal environment uniformity of the mushroom cultivation house, reducing the overall operating time of inlet fan, outlet fan, and humidifier is needed, and managing the internal environment with unit cooler and air circulation fan appropriately is essential.

Keywords: air circulation fan, computational fluid dynamics, multi-layered shelves cultivation, oyster mushroom cultivation house

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Authors: M. Nakisa, A. Maimun, Yasser M. Ahmed, F. Behrouzi, A. Tarmizi

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The practical application of the Computational Fluid Dynamics (CFD), for predicting the flow pattern around Multipurpose Amphibious Vehicle (MAV) hull has made much progress over the last decade. Today, several of the CFD tools play an important role in the land and water going vehicle hull form design. CFD has been used for analysis of MAV hull resistance, sea-keeping, maneuvering and investigating its variation when changing the hull form due to varying its parameters, which represents a very important task in the principal and final design stages. Resistance analysis based on CFD (Computational Fluid Dynamics) simulation has become a decisive factor in the development of new, economically efficient and environmentally friendly hull forms. Three-dimensional finite volume method (FVM) based on Reynolds Averaged Navier-Stokes equations (RANS) has been used to simulate incompressible flow around three types of MAV hull bow models in steady-state condition. Finally, the flow structure and streamlines, friction and pressure resistance and velocity contours of each type of hull bow will be compared and discussed.

Keywords: RANS simulation, multipurpose amphibious vehicle, viscous flow structure, mechatronic

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Authors: Ana Vieira, Fernando Lau, João Pedro Mortágua, Luís Cruz, Rui Santos

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The air transport impact on environment is more than ever a limitative obstacle to the aeronautical industry continuous growth. Over the last decades, considerable effort has been carried out in order to obtain quieter aircraft solutions, whether by changing the original design or investigating more silent maneuvers. The noise propagated by rotating surfaces is one of the most important sources of annoyance, being present in most aerial vehicles. Bearing this is mind, CEIIA developed a new computational chain for noise prediction with in-house software tools to obtain solutions in relatively short time without using excessive computer resources. This work is based on the new acoustic tool, which aims to predict the rotor noise generated during steady and maneuvering flight, making use of the flexibility of the C language and the advantages of GPU programming in terms of velocity. The acoustic tool is based in the Formulation 1A of Farassat, capable of predicting two important types of noise: the loading and thickness noise. The present work describes the most important features of the acoustic tool, presenting its most relevant results and framework analyses for helicopters and UAV quadrotors.

Keywords: rotor noise, acoustic tool, GPU Programming, UAV noise

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Authors: Iannick Gagnon, Alain April

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The performance of a metaheuristic on a given problem class depends on the class itself and the choice of parameters. Parameter tuning is the most time-consuming phase of the optimization process after the main calculations and it often nullifies the speed advantage of metaheuristics over traditional optimization algorithms. Several off-the-shelf parameter tuning algorithms are available, but when the objective function is expensive to evaluate, these can be prohibitively expensive to use. This paper presents a surrogate-like method for finding adequate parameters using fitness landscape analysis on simple benchmark functions and real-world objective functions. The result is a simple compound similarity metric based on the empirical correlation coefficient and a measure of convexity. It is then used to find the best benchmark functions to serve as surrogates. The near-optimal parameter set is then found using fractional factorial design. The real-world problem of NACA airfoil lift coefficient maximization is used as a preliminary proof of concept. The overall aim of this research is to reduce the computational overhead of metaheuristics parameterization.

Keywords: metaheuristics, stochastic optimization, particle swarm optimization, exploratory landscape analysis

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Authors: Myoung Je Song

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Ureteral stent insertion is being used as one of the clinical interventional treatments due to stenosis and/or obstruction in the ureter. For the development of the ureteral stents, we have to know the flow patterns with and without peristalsis in the ureter. The purpose of this study is to understand the flow characteristics and movement of the ureter for the ureter model according to the presence or absence of peristalsis and to use it as fundamental information to design the optimal ureteral stent. In this study, CFD (Computational Fluid Dynamics) and FSI (Fluid-Structure Interaction) approaches were applied and compared the flow characteristics in the ureter. The distribution of streamlines was different in the near ureteropelvic junction. As a result of analyzing the area change of the ureter, the area change was large at the frontal and posterior ends, and the frontal and posterior aspects of the area change were reversed. There was no significant difference in the flow rate at the ureter outlet, and the movement of the ureter was larger when peristalsis was considered. Finally, as an introductory stage for the development of ureteral stents, basic information about the ureters according to the presence or absence of peristalsis is acquired.

Keywords: computational fluid dynamics, fluid-structure interaction, peristalsis, urine flow

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Authors: Mansir Abubakar, Hazlina Hamdan, Norwati Mustapha, Teh Noranis Mohd Aris

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In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern.

Keywords: attribute correspondence, clustering, computational workload, k-medoids clustering, ontology matching

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Authors: Jaber H. Almutairi, Hosny Z. Abou-Ziyan, Issa F. Almesri, Mosab A. Alrahmani

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The present work investigates the behavior of fluid flow inside tunnel kilns using 3D-CFD (Computational Fluid Dynamics) simulations. The CFD simulations are carried out with the FLUENT software and validated against experimental results on fluid flow and heat transfer in tunnel kilns. A grid dependency study is conducted in the current work to improve the accuracy of the results. Three turbulence models k–ω, standard k–ε, and RNG k–ε are tested where k–ω model gives the best results in comparison with the experiment. The numerical results reveal an intriguing phenomenon where a long flow separation zone behind the setting is observed under different geometric and operation conditions. It was found that the uniformity of flow distribution can be substantially improved by rearranging the geometrical parameters of brick setting relative to kiln/setting. This improvement of flow distribution plays a critical role to enhance the quality and quantity of the production. It can be concluded that a better design and operation of tunnel kilns in terms of productivity and energy consumption can be obtained by taking into consideration the flow uniformity inside the tunnel kilns using CFD modelling.

Keywords: tunnel kilns, flow separation, flow uniformity, computational fluid dynamics

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Authors: S. Perera, T. R. Walsh, M. Solvang

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The structure-property relationships of calcium aluminosilicate (CAS) glass surfaces are of scientific and technological interest regarding dissolution phenomena. Molecular dynamics (MD) simulations can provide atomic-scale insights into the structure and properties of the CAS interfaces in vacuo as the first step to conducting computational dissolution studies on CAS surfaces. However, one limitation to date is that although the bulk properties of CAS glasses have been well studied by MD simulation, corresponding efforts on CAS surface properties are relatively few in number (both theoretical and experimental). Here, a systematic computational protocol to create CAS surfaces in vacuo is developed by evaluating the sensitivity of the resultant surface structure with respect to different factors. Factors such as the relative thickness of the surface layer, the relative thickness of the bulk region, the cooling rate, and the annealing schedule (time and temperature) are explored. Structural features such as ring size distribution, defect concentrations (five-coordinated aluminium (AlV), non-bridging oxygen (NBO), and tri-cluster oxygen (TBO)), and linkage distribution are identified as significant features in dissolution studies.

Keywords: MD simulation, CAS glasses, surface structure, structure-property, CAS interface

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

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

Keywords: communication, cooperation, development, interaction, neuroscience

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Authors: Sylvia Nkiru Ogbuoji

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

Keywords: consequences, corruption, education, funding

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Authors: Lev Bertovsky

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

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

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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

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

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Rutej R. Mehta, Michael A. Chappell

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Introduction: Arterial spin labelling (ASL) is an increasingly popular perfusion MRI technique, in which arterial blood water is magnetically labelled in the neck before flowing into the brain, providing a non-invasive measure of cerebral blood flow (CBF). The accuracy of ASL CBF measurements, however, is hampered by dispersion effects; the distortion of the ASL labelled bolus during its transit through the vasculature. In spite of this, the current recommended implementation of ASL – the white paper (Alsop et al., MRM, 73.1 (2015): 102-116) – does not account for dispersion, which leads to the introduction of errors in CBF. Given that the transport time from the labelling region to the tissue – the arterial transit time (ATT) – depends on the region of the brain and the condition of the patient, it is likely that these errors will also vary with the ATT. In this study, various dispersion models are assessed in comparison with the white paper (WP) formula for CBF quantification, enabling the errors introduced by the WP to be quantified. Additionally, this study examines the relationship between the errors associated with the WP and the ATT – and how this is influenced by dispersion. Methods: Data were simulated using the standard model for pseudo-continuous ASL, along with various dispersion models, and then quantified using the formula in the WP. The ATT was varied from 0.5s-1.3s, and the errors associated with noise artefacts were computed in order to define the concept of significant error. The instantaneous slope of the error was also computed as an indicator of the sensitivity of the error with fluctuations in ATT. Finally, a regression analysis was performed to obtain the mean error against ATT. Results: An error of 20.9% was found to be comparable to that introduced by typical measurement noise. The WP formula was shown to introduce errors exceeding 20.9% for ATTs beyond 1.25s even when dispersion effects were ignored. Using a Gaussian dispersion model, a mean error of 16% was introduced by using the WP, and a dispersion threshold of σ=0.6 was determined, beyond which the error was found to increase considerably with ATT. The mean error ranged from 44.5% to 73.5% when other physiologically plausible dispersion models were implemented, and the instantaneous slope varied from 35 to 75 as dispersion levels were varied. Conclusion: It has been shown that the WP quantification formula holds only within an ATT window of 0.5 to 1.25s, and that this window gets narrower as dispersion occurs. Provided that the dispersion levels fall below the threshold evaluated in this study, however, the WP can measure CBF with reasonable accuracy if dispersion is correctly modelled by the Gaussian model. However, substantial errors were observed with other common models for dispersion with dispersion levels similar to those that have been observed in literature.

Keywords: arterial spin labelling, dispersion, MRI, perfusion

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Authors: Mateen A Khan, Taj Mohammad, Md. Imtaiyaz Hassan

Abstract:

Alzheimer’s disease is characterized by the accumulation of the processing products of the amyloid beta peptide cleaved by amyloid precursor protein (APP). Iron increases the synthesis of amyloid beta peptides, which is why iron is present in Alzheimer's disease patients' amyloid plaques. Iron misregulation in the brain is linked to the overexpression of APP protein, which is directly related to amyloid-β aggregation in Alzheimer’s disease. The APP 5'-UTR region encodes a functional iron-responsive element (IRE) stem-loop that represents a potential target for modulating amyloid production. Targeted regulation of APP gene expression through the modulation of 5’-UTR sequence function represents a novel approach for the potential treatment of AD because altering APP translation can be used to improve both the protective brain iron balance and provide anti-amyloid efficacy. The molecular docking analysis of APP IRE RNA with eukaryotic translation initiation factors yields several models exhibiting substantial binding affinity. The finding revealed that the interaction involved a set of functionally active residues within the binding sites of eIF4F. Notably, APP IRE RNA and eIF4F interaction were stabilized by multiple hydrogen bonds with residues of APP IRE RNA and eIF4F. It was evident that APP IRE RNA exhibited a structural complementarity that tightly fit within binding pockets of eIF4F. The simulation studies further revealed the stability of the complexes formed between RNA and eIF4F, which is crucial for assessing the strength of these interactions and subsequent roles in the pathophysiology of Alzheimer’s disease. In addition, MD simulations would capture conformational changes in the IRE RNA and protein molecules during their interactions, illustrating the mechanism of interaction, conformational change, and unbinding events and how it may affect aggregation propensity and subsequent therapeutic implications. Our binding studies correlated well with the translation efficiency of APP mRNA. Overall, the outcome of this study suggests that the genomic modification and/or inhibiting the expression of amyloid protein by targeting APP IRE RNA can be a viable strategy to identify potential therapeutic targets for AD and subsequently be exploited for developing novel therapeutic approaches.

Keywords: Alzheimer's disease, Protein-RNA interaction analysis, molecular docking simulations, conformational dynamics, binding stability, binding kinetics, protein synthesis.

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Authors: Ruslan Safarov, Zhanat Shomanova, Yuri Nossenko, Zhandos Mussayev, Ayana Baltabek

Abstract:

Geochemical maps of distribution of polluting elements V, Cr, Mn, Co, Ni, Cu, Zn, Mo, Cd, Pb on the territory of the Pavlodar city (Kazakhstan), which is an industrial hub were designed. The samples of soil were taken from 100 locations. Elemental analysis has been performed using XRF. The obtained data was used for training of the computational model with gradient boosting algorithm. The optimal parameters of model as well as the loss function were selected. The computational model was used for prediction of polluting elements concentration for 1000 evenly distributed points. Based on predicted data geochemical maps were created. Additionally, the total pollution index Zc was calculated for every from 1000 point. The spatial distribution of the Zc index was visualized using GIS (QGIS). It was calculated that the maximum coverage area of the territory of the Pavlodar city belongs to the moderately hazardous category (89.7%). The visualization of the obtained data allowed us to conclude that the main source of contamination goes from the industrial zones where the strategic metallurgical and refining plants are placed.

Keywords: Pavlodar, geochemical map, gradient boosting, CatBoost, QGIS, spatial distribution, heavy metals

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Authors: Syed Asif Hassan, Tabrej Khan

Abstract:

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disorder, of the central nervous system (CNS). In the present scenario, the current therapies either do not halt the progression of the disease or have side effects which limit the usage of current Disease Modifying Therapies (DMTs) for a longer period of time. Therefore, keeping the current treatment failure schema, we are focusing on screening novel analogues of the available DMTs that specifically bind and inhibit the Sphingosine1-phosphate receptor1 (S1PR1) thereby hindering the lymphocyte propagation toward CNS. The novel drug-like analogs molecule will decrease the frequency of relapses (recurrence of the symptoms associated with MS) with higher efficacy and lower toxicity to human system. In this study, an integrated approach involving ligand-based virtual screening protocol (Ultrafast Shape Recognition with CREDO Atom Types (USRCAT)) to identify the non-toxic drug like analogs of the approved DMTs were employed. The potency of the drug-like analog molecules to cross the Blood Brain Barrier (BBB) was estimated. Besides, molecular docking and simulation using Auto Dock Vina 1.1.2 and GOLD 3.01 were performed using the X-ray crystal structure of Mtb LprG protein to calculate the affinity and specificity of the analogs with the given LprG protein. The docking results were further confirmed by DSX (DrugScore eXtented), a robust program to evaluate the binding energy of ligands bound to the ligand binding domain of the Mtb LprG lipoprotein. The ligand, which has a higher hypothetical affinity, also has greater negative value. Further, the non-specific ligands were screened out using the structural filter proposed by Baell and Holloway. Based on the USRCAT, Lipinski’s values, toxicity and BBB analysis, the drug-like analogs of fingolimod and BG-12 showed that RTL and CHEMBL1771640, respectively are non-toxic and permeable to BBB. The successful docking and DSX analysis showed that RTL and CHEMBL1771640 could bind to the binding pocket of S1PR1 receptor protein of human with greater affinity than as compared to their parent compound (Fingolimod). In this study, we also found that all the drug-like analogs of the standard MS drugs passed the Bell and Holloway filter.

Keywords: antagonist, binding affinity, chemotherapeutics, drug-like, multiple sclerosis, S1PR1 receptor protein

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Authors: Merve Colak, Gizem Donmez Yalcin

Abstract:

Glutamate has many neurological functions in the central nervous system and is found at high concentrations in the brain. Increased levels of glutamate in the neuronal space are toxic, causing neuron damage and death. This is called glutamate-induced excitotoxicity. Excitotoxicity is among the causes of many neurological diseases such as trauma, cerebral ischemia, epilepsy, Parkinson's Disease, Alzheimer's Disease. Since neuroblastoma cells are known to be excitotoxic, we propose that excitotoxicity can be studied in neuroblastoma cells. Excitotoxicity can be induced using kainic acid in neuroblastoma cells. Measuring the secretion of glutamate, excitotoxicity can be analyzed in neuroblastoma cells.

Keywords: glutamate, excitotoxicity, kainic acid, Sirt4

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Authors: Silvia D. Vaca, Benjamin J. Kuo, Joao Ricardo N. Vissoci, Catherine A. Staton, Linda W. Xu, Michael Muhumuza, Hussein Ssenyonjo, John Mukasa, Joel Kiryabwire, Henry E. Rice, Gerald A. Grant, Michael M. Haglund

Abstract:

Background: While delays to care exist in resource rich settings, greater delays are seen along the care continuum in low- and middle-income countries (LMICs) largely due to limited healthcare capacity to address the disproportional rates of traumatic brain injury (TBI) in Sub Saharan Africa (SSA). While many LMICs have government subsidized systems to offset surgical costs, the burden of securing funds by the patients for medications, supplies, and CT diagnostics poses a significant challenge to timely surgical interventions. In Kampala Uganda, the challenge of obtaining timely CT scans is twofold. First, due to a lack of a functional CT scanner at the tertiary hospital, patients need to arrange their own transportation to the nearby private facility for CT scans. Second, self-financing for the private CT scans ranges from $80 - $130, which is near the average monthly income in Kampala. These bottlenecks contribute significantly to the care continuum delays and are associated with poor TBI outcomes. Objective: The objectives of this study are to 1) describe the temporal delays through a modified three delays model that fits the context of neurosurgical interventions for TBI patients in Kampala and 2) investigate the association between delays and mortality. Methods: Prospective data were collected for 563 TBI patients presenting to a tertiary hospital in Kampala from 1 June – 30 November 2016. Four time intervals were constructed along five time points: injury, hospital arrival, neurosurgical evaluation, CT results, and definitive surgery. Time interval differences among mild, moderate and severe TBI and their association with mortality were analyzed. Results: The mortality rate of all TBI patients presenting to MNRH was 9.6%, which ranged from 4.7% for mild and moderate TBI patients receiving surgery to 81.8% for severe TBI patients who failed to receive surgery. The duration from injury to surgery varied considerably across TBI severity with the largest gap seen between mild TBI (174 hours) and severe TBI (69 hours) patients. Further analysis revealed care continuum differences for interval 3 (neurosurgical evaluation to CT result) and 4 (CT result to surgery) between severe TBI patients (7 hours for interval 3 and 24 hours for interval 4) and mild TBI patients (19 hours for interval 3, and 96 hours for interval 4). These post-arrival delays were associated with mortality for mild (p=0.05) and moderate TBI (p=0.03) patients. Conclusions: To our knowledge, this is the first analysis using a modified ‘three delays’ framework to analyze the care continuum of TBI patients in Uganda from injury to surgery. We found significant associations between delays and mortality for mild and moderate TBI patients. As it currently stands, poorer outcomes were observed for these mild and moderate TBI patients who were managed non-operatively or failed to receive surgery while surgical services were shunted to more severely ill patients. While well intentioned, high mortality rates were still observed for the severe TBI patients managed surgically. These results suggest the need for future research to optimize triage practices, understand delay contributors, and improve pre-hospital logistical referral systems.

Keywords: care continuum, global neurosurgery, Kampala Uganda, LMIC, Mulago, prospective registry, traumatic brain injury

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Authors: Victor Arokia Doss, Kuberapandian Dharaniyambigai, K. Julia Rose Mary

Abstract:

Advanced Glycation End (AGE) products are the end products due to the reaction between excess reducing sugar present in diabetes and free amino group in protein lipids and nucleic acids. Thus, non-enzymic glycation of molecules such as hemoglobin, collagen, and other structurally and functionally important proteins add to the pathogenic complications such as diabetic retinopathy, neuropathy, nephropathy, vascular changes, atherosclerosis, Alzheimer's disease, rheumatoid arthritis, and chronic heart failure. The most common non-cross linking AGE, carboxymethyl lysine (CML) is formed by the oxidative breakdown of fructosyllysine, which is a product of glucose and lysine. CML is formed in a wide variety of tissues and is an index to assess the extent of glycoxidative damage. Thus we have constructed a mathematical and computational model that predicts the effect of temperature differences in vivo, on the formation of CML, which is now being considered as an important intracellular milieu. This hybrid model that had been tested for its parameter fitting and its sensitivity with available experimental data paves the way for designing novel laboratory experiments that would throw more light on the pathological formation of AGE adducts and in the pathophysiology of diabetic complications.

Keywords: advanced glycation end-products, CML, mathematical model, computational model

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Authors: Theo Ndereyimana, Yann Dufresne, Micael Boulet, Stephane Moreau

Abstract:

The DEM (Discrete Element Method) is used a lot in the industry to simulate large-scale flows of particles; for instance, in a fluidized bed, it allows to predict of the trajectory of every particle. One of the main limits of the DEM is the computational time. The CGM (Coarse-Graining Method) has been developed to tackle this issue. The goal is to increase the size of the particle and, by this means, decrease the number of particles. The method leads to a reduction of the collision frequency due to the reduction of the number of particles. Multiple characteristics of the particle movement and the fluid flow - when there is a coupling between DEM and CFD (Computational Fluid Dynamics). The main characteristic that is impacted is the energy dissipation of the system, to regain the dissipation, an ADM (Additional Dissipative Mechanism) can be added to the model. The objective of this current work is to observe the influence of the choice of the ADM and the factor of coarse-graining on the numerical results. These results will be compared with experimental results of a fluidized bed and with a numerical model of the same fluidized bed without using the CGM. The numerical model is one of a 3D cylindrical fluidized bed with 9.6M Geldart B-type particles in a bubbling regime.

Keywords: additive dissipative mechanism, coarse-graining, discrete element method, fluidized bed

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