Search results for: network formation

Authors: Aghbalou Nihad, Charki Abderafi, Saida Rahali, Reklaoui Kamal

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Maximize the benefit from the wind energy potential is the most interest of the wind power stakeholders. As a result, the wind tower size is radically increasing. Nevertheless, choosing an appropriate wind turbine for a selected site require an accurate estimate of vertical wind profile. It is also imperative from cost and maintenance strategy point of view. Then, installing tall towers or even more expensive devices such as LIDAR or SODAR raises the costs of a wind power project. Various models were developed coming within this framework. However, they suffer from complexity, generalization and lacks accuracy. In this work, we aim to investigate the ability of neural network trained using the Bayesian Regularization technique to estimate wind speed profile up to height of 100 m based on knowledge of wind speed lower heights. Results show that the proposed approach can achieve satisfactory predictions and proof the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights.

Keywords: bayesian regularization, neural network, wind shear, accuracy

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Authors: Cristiano Giordani, Marco Diociaiuti, Cecilia Bombelli, Laura Zanetti-Polzi, Marcello Belfiore, Raoul Fioravanti, Gianfranco Macchia

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We investigated induced functional effects by evaluating Ca2+-influx in liposomes and cell viability in HT22-DIFF neurons. Only solutions rich in unstructured Prefibrillar-Oligomers (PFOs) were able, in the presence of Monosialoganglioside-GM1 (GM1), to induce Ca2+-influx and were also neurotoxic, suggesting a correlation between the two phenomena. Thus, in the presence of GM1, we investigated the protein conformation and liposome modification due to the interaction. Circular Dichroism showed that GM1 fostered the formation of β-structures and Energy Filtered-Transmission Electron Microscopy that PFOs formed “amyloid-channels” as reported for Aβ. We speculate that electrostatic forces occurring between the positive PFOs and negative GM1 drive the initial binding, while the hydrophobic profile of the flexible PFO is responsible for the subsequent pore formation. Conversely, the rigid β-structured mature/fibers (MFs) and proto-fibers (PFs) were unable to induce membrane damage and Ca2+- influx.

Keywords: amyloid proteins, neurotoxicity, lipid-rafts, GM1

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Authors: Ahmed S. Eissa

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Whey represents about 85-95% of the milk volume and about 55% of milk nutrients. Whey proteins are of special importance in formulated foods due to their rich nutritional and functional benefits. Whey proteins form large polymers upon heating to a temperature greater than the denaturation temperature. Hydrophobic interactions play an important role in building whey protein polymers. In this study, dissociation of hydrophobic interactions of whey protein polymers was done by adding Sodium Dodecyl Sulphonate (SDS). At low SDS concentrations, protein polymers were dissociated to smaller chains, as revealed by dilution solution viscometry (DSV). Interestingly, at higher SDS concentrations, polymer molecules got larger in size. Intrinsic viscosity was increased to many folds when raising the SDS concentration from 0.5% to 2%. Complex molecular arrangement leads to the formation of larger macromolecules, due to micelle formation. The study opens a venue for manipulating and enhancing whey protein functional properties by manipulating the hydrophobic interactions.

Keywords: whey proteins, hydrophobic interactions, SDS

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Authors: E. Poupelloz, S. Gauffinet

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Ettringite Ca₆Al₂(SO₄)₃(OH)₁₂26H₂O is one of the major hydrates formed during cement hydration. Ettringite forms in Portland cement from the reaction between tricalcium aluminate Ca₃Al₂O₆ and calcium sulfate. Ettringite is also present in calcium sulfoaluminate cement in which it is the major hydrate, formed by the reaction between yeelimite Ca₄(AlO₂)₆SO₄ and calcium sulfate. About the formation of ettringite, numerous results are available in the literature even if some issues are still under discussion. However, almost all published work about ettringite was done on cementitious systems. Yet in cement, hydration reactions are very complex, the result of dissolution-precipitation processes and are submitted to various interactions. Understanding the formation process of a phase alone, here ettringite, is the first step to later understand the much more complex reactions happening in cement. This study is crucial for the comprehension of early cement hydration and physical behavior. Indeed formation of hydrates, in particular, ettringite, will have an influence on the rheological properties of the cement paste and on the need for admixtures. To make progress toward the understanding of existing phenomena, a specific study of nucleation and growth processes of ettringite was conducted. First ettringite nucleation was studied in ionic aqueous solutions, with controlled but different experimental conditions, as different supersaturation degrees (β), different pH or presence of exogenous ions. Through induction time measurements, interfacial ettringite crystals solution energies (γ) were determined. Growth of ettringite in supersaturated solutions was also studied through chain crystallization reactions. Specific BET surface area measurements and Scanning Electron Microscopy observations seemed to prove that growth process is favored over the nucleation process when ettringite crystals are initially present in a solution with a low supersaturation degree. The influence of stirring on ettringite formation was also investigated. Observation was made that intensity and nature of stirring have a high influence on the size of ettringite needles formed. Needle sizes vary from less than 10µm long depending on the stirring to almost 100µm long without any stirring. During all previously mentioned experiments, initially present ions are consumed to form ettringite in such a way that the supersaturation degree with regard to ettringite is decreasing over time. To avoid this phenomenon a device compensating the drop of ion concentrations by adding some more solutions, and therefore always have constant ionic concentrations, was used. This constant β recreates the conditions of the beginning of cement paste hydration, when the dissolution of solid reagents compensates the consumption of ions to form hydrates. This device allowed the determination of the ettringite precipitation rate as a function of the supersaturation degree β. Taking samples at different time during ettringite precipitation and doing BET measurements allowed the determination of the interfacial growth rate of ettringite in m²/s. This work will lead to a better understanding and control of ettringite formation alone and thus during cements hydration. This study will also ultimately define the impact of ettringite formation process on the rheology of cement pastes at early age, which is a crucial parameter from a practical point of view.

Keywords: cement hydration, ettringite, morphology of crystals, nucleation-growth process

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Authors: Mauricio A. Sarabia-Vallejos, Carmen M. Gonzalez-Henriquez, Adolfo Del Campo-Garcia, Aitzibier L. Cortajarena, Juan Rodriguez-Hernandez

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In this study, it was explored the formation and the morphological differences between wrinkled hydrogel patterns obtained via generation of surface instabilities. The slight variations in the polymerization conditions produce important changes in the material composition and pattern structuration. The compounds were synthesized using three main components, i.e. an amphiphilic monomer, hydroxyethyl methacrylate (HEMA), a hydrophobic monomer, trifluoroethyl methacrylate (TFMA), and a hydrophilic crosslinking agent, poly(ethylene glycol) diacrylate (PEGDA). The first part of this study was related to the formation of wrinkled surfaces using only HEMA and PEGDA and varying the amount of water added in the reaction. The second part of this study involves the gradual insertion of TFMA into the hydrophilic reaction mixture. Interestingly, the manipulation of the chemical composition of this hydrogel affects both surface morphology and physicochemical characteristics of the patterns, inducing transitions from one particular type of structure (wrinkles or ripples) to different ones (creases, folds, and crumples). Contact angle measurements show that the insertion of TFMA produces a slight decrease in surface wettability of the samples, remaining however highly hydrophilic (contact angle below 45°). More interestingly, by using confocal Raman spectroscopy, important information about the wrinkle formation mechanism is obtained. The procedure involving two consecutive thermal and photopolymerization steps lead to a “pseudo” two-layer system. Thus, upon photopolymerization, the surface is crosslinked to a higher extent than the bulk and water evaporation drives the formation of wrinkled surfaces. Finally, cellular, and bacterial proliferation studies were performed to the samples, showing that the amount of TFMA included in each sample slightly affects the proliferation of both (bacteria and cells), but in the case of bacteria, the morphology of the sample also plays an important role, importantly reducing the bacterial proliferation.

Keywords: antibiofouling properties, hydrophobic/hydrophilic balance, morphologic characterization, wrinkled hydrogel patterns

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Authors: Neil Erick Q. Madariaga, Noel B. Linsangan

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Body Mass Index (BMI) is one of the different ways to monitor the health of a person. It is based on the height and weight of the person. This study aims to compute for the BMI using an Android tablet by obtaining the height of the person by using a camera and measuring the weight of the person by using a weighing scale or load cell. The height of the person was estimated by applying background subtraction to the image captured and applying different processes such as getting the vanishing point and applying Artificial Neural Network. The weight was measured by using Wheatstone bridge load cell configuration and sending the value to the computer by using Gizduino microcontroller and Bluetooth technology after the amplification using AD620 instrumentation amplifier. The application will process the images and read the measured values and show the BMI of the person. The study met all the objectives needed and further studies will be needed to improve the design project.

Keywords: body mass index, artificial neural network, vanishing point, bluetooth, wheatstone bridge load cell

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Authors: Deepthinath Reghunathan, Satheesha Nayak, Sudarshan S., Prasad Alathady Maloor, Prakash Shetty

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Lumbar plexus is formed by the union of ventral rami of T12, L1, L2, L3 spinal nerves and the larger upper division of L4 lumbar spinal nerves. Variations in the normal anatomy of the lumbar and sacral plexus might be seen in some cases and are reported in the literature, but finding such an unusual case comprising of multiple variations which is normally not expected in a clinical setup, proves to be a vital piece of information for clinicians and medical practitioners. During the dissection of the abdomen and pelvis of an approximately 70 year old cadaver, we observed the following variations in the formation of the lumbar and sacral nerves. 1. The genitofemoral nerve bifurcated at a higher level; genital branch of genitofemoral nerve gave branches to the anterior abdominal wall muscles, 2. A communicating branch was given from the lateral cutaneous nerve of thigh to the medial cutaneous nerve of thigh, 3. A muscular branch was given from femoral nerve to psoas major, 4. There was absence of contribution of L4 spinal nerve in the formation of the lumbosacral trunk and 5. Lumbosacral trunk gave communicating branches to the femoral and obturator nerves. Most of the variations found were rare and finding all the above said variations in a single cadaver is even rare. Documentation of such rare cases with multiple variations in the formation of nerves from the lumbar plexus provides vital information on such occurrences. This information would in turn improve the knowledge of clinicians and surgeons dealing with this region. Emphasizing such knowledge of this region would prevent accidental damage to the structures with a variant anatomy.

Keywords: femoral nerve, genitofemoral nerve, lumbar plexus, lumbosacral trunk

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Salman Naseer

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One of the main challenges of operating a smart city (SC) is collecting the massive data generated from multiple data sources (DS) and to transmit them to the control units (CU) for further data processing and analysis. These ever-increasing data demands require not only more and more capacity of the transmission channels but also results in resource over-provision to meet the resilience requirements, thus the unavoidable waste because of the data fluctuations throughout the day. In addition, the high energy consumption (EC) and carbon discharges from these data transmissions posing serious issues to the environment we live in. Therefore, to overcome the issues of intensive EC and carbon emissions (CE) of massive data dissemination in Smart Cities, we propose an energy efficient and carbon reduction approach by utilizing the daily mobility of the existing vehicles as an alternative communications channel to accommodate the data dissemination in smart cities. To illustrate the effectiveness and efficiency of our approach, we take the Auckland City in New Zealand as an example, assuming massive data generated by various sources geographically scattered throughout the Auckland region to the control centres located in city centre. The numerical results show that our proposed approach can provide up to 5 times lower delay as transferring the large volume of data by utilizing the existing daily vehicles’ mobility than the conventional transmission network. Moreover, our proposed approach offers about 30% less EC and CE than that of conventional network transmission approach.

Keywords: smart city, delay tolerant network, infrastructure offloading, opportunistic network, vehicular mobility, energy consumption, carbon emission

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Melake Kuflom

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European power networks involve the use of multiple overhead transmission lines to construct a highly duplicated system that delivers reliable and stable electrical energy to the distribution level. The transmission line protection applied in the existing GB transmission network are normally independent unit differential and time stepped distance protection schemes, referred to as main-1 & main-2 respectively, with overcurrent protection as a backup. The increasing penetration of renewable energy sources, commonly referred as “weak sources,” into the power network resulted in the decline of fault level. Traditionally, the fault level of the GB transmission network has been strong; hence the fault current contribution is more than sufficient to ensure the correct operation of the protection schemes. However, numerous conventional coal and nuclear generators have been or about to shut down due to the societal requirement for CO2 emission reduction, and this has resulted in a reduction in the fault level on some transmission lines, and therefore an adaptive transmission line protection is required. Generally, greater utilization of renewable energy sources generated from wind or direct solar energy results in a reduction of CO2 carbon emission and can increase the system security and reliability but reduces the fault level, which has an adverse effect on protection. Consequently, the effectiveness of conventional protection schemes under low fault levels needs to be reviewed, particularly for future GB transmission network operating scenarios. The proposed paper will evaluate the transmission line challenges under high penetration of renewable energy sources andprovides alternative viable protection solutions based on the problem observed. The paper will consider the assessment ofrenewable energy sources (RES) based on a fully rated converter technology. The DIgSILENT Power Factory software tool will be used to model the network.

Keywords: fault level, protection schemes, relay settings, relay coordination, renewable energy sources

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Authors: P. Altay, G. Kartal, B. Kizilkaya, S. Kahraman, N. C. Gursoy

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Biopolishing is applied to remove the fuzz or pills on the fiber or fabric surface which will reduce its tendency to pill or fuzz after repetitive launderings. After biopolishing process, the fuzzes ripped by cellulase enzymes cannot be thoroughly removed from fabric surface, they remain on the fabric or fiber surface; accordingly disturb the user and lead to decrease in productivity of drying process. The main objective of this study is to develop a method for removing weakened fuzz fibers and surface pills from biofinished fabric surface before drying process. Fuzzes in the lattice structure of fabric were completely removed from the internal structure of the fabric by air blowing. The presence of fuzzes leads to problems with formation of pilling and faded appearance; the removal of fuzzes from the fabric results in reduced tendency to pill formation, cleaner, smoother and softer surface, improved handling properties of fabric with maintaining original color.

Keywords: biopolishing, fuzz fiber, weakened fiber, biofinished cotton fabric

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Authors: Muhammad Abdullah Arafat, Nahrin Nowrose

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Wireless charging of electric vehicles is becoming more and more attractive as large amount of power can now be transferred to a reasonable distance using magnetic resonance coupling method. However, proper tuning of the compensation network is required to achieve maximum power transmission. Due to the variation of coil distance from the nominal value as a result of change in tire condition, change in weight or uneven road condition, the tuning of the compensation network has become challenging. In this paper, a tuning method has been described to determine the optimum values of the compensation network in order to maximize the average output power. The simulation results show that 5.2 percent increase in average output power is obtained for 10 percent variation in coupling coefficient using the optimum values without the need of additional space and electro-mechanical components. The proposed method is applicable to both static and dynamic charging of electric vehicles.

Keywords: coupling coefficient, electric vehicles, magnetic resonance coupling, tuning capacitor, wireless power transfer

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

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Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Kaddi Mohammed, Benahmed Khelifa D. Benatiallah

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A sensor network consists of a large number of sensors deployed in areas to monitor and communicate with each other through a wireless medium. The collected routing data in the network consumes most of the energy of the sensor nodes. For this purpose, multiple routing approaches have been proposed to conserve energy resource at the sensors and to overcome the challenges of its limitation. In this work, we propose a new low energy consumption routing protocol for wireless sensor networks based on a meta-heuristic methods. Our protocol is to operate more fairly energy when routing captured data to the base station.

Keywords: WSN, routing, energy, heuristic

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Ranjita Ghoshmoulick, Aswathi Madhavan, Subhavna Juneja, Prasenjit Sen, Jaydeep Bhattacharya

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Type 2 diabetes mellitus (T2DM) is a very complex and multifactorial metabolic disease where the blood sugar level goes up. One of the major consequence of this elevated blood sugar is the formation of AGE (Advance Glycation Endproducts), from a series of chemical or biochemical reactions. AGE are detrimental because it leads to severe pathogenic complications. They are a group of structurally diverse chemical compounds formed from nonenzymatic reactions between the free amino groups (-NH2) of proteins and carbonyl groups (>C=O) of reducing sugars. The reaction is known as Maillard Reaction. It starts with the formation of reversible schiff’s base linkage which after sometime rearranges itself to form Amadori Product along with dicarbonyl compounds. Amadori products are very unstable hence rearrangement goes on until stable products are formed. During the course of the reaction a lot of chemically unknown intermediates and reactive byproducts are formed that can be termed as Early Glycation Products. And when the reaction completes, structurally stable chemical compounds are formed which is termed as Advanced Glycation Endproducts. Though all glycation products have not been characterized well, some fluorescence compounds e.g pentosidine, Malondialdehyde (MDA) or carboxymethyllysine (CML) etc as AGE and α-dicarbonyls or oxoaldehydes such as 3-deoxyglucosone (3-DG) etc as the intermediates have been identified. In this work Gold NanoParticle (GNP) was used as an optical indicator of glycation products. To achieve faster glycation kinetics and high AGE accumulation, fructose was used instead of glucose. Hemoglobin A0 (HbA0) was fructosylated by in-vitro method. AGE formation was measured fluorimetrically by recording emission at 450nm upon excitation at 350nm. Thereafter this fructosylated HbA0 was fractionated by column chromatography. Fractionation separated the proteinaceous substance from the AGEs. Presence of protein part in the fractions was confirmed by measuring the intrinsic protein fluorescence and Bradford reaction. GNPs were synthesized using the templates of chromatographically separated fractions of fructosylated HbA0. Each fractions gave rise to GNPs of varying color, indicating the presence of distinct set of glycation products differing structurally and chemically. Clear solution appeared due to settling down of particles in some vials. The reactive groups of the intermediates kept the GNP formation mechanism on and did not lead to a stable particle formation till Day 10. Whereas SPR of GNP showed monotonous colour for the fractions collected in case of non fructosylated HbA0. Our findings accentuate the use of GNPs as a simple colorimetric sensing platform for the identification of intermediates of glycation reaction which could be implicated in the prognosis of the associated health risk due to T2DM and others.

Keywords: advance glycation endproducts, glycation, gold nano particle, sensor

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Authors: Mardiana Ahamad Zabidi, Akmalluddin Md. Yunus

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Soursop (Anona muricata) is one of the underutilized tropical fruits containing nutrients, particularly dietary fibre and antioxidant properties that are beneficial to human health. This objective of this study is to investigate the feasibility of matured soursop pulp flour (SPF) to be substituted with high-protein wheat flour in bread. Bread formulation was substituted with different levels of SPF (0%, 5%, 10% and 15%). The effect on physicochemical properties and sensory attributes were evaluated. Higher substitution level of SPF resulted in significantly higher (p<0.05) fibre, protein and ash content, while fat and carbohydrate content reduced significantly (p<0.05). FESEM showed that the bread crumb surface of control and 5% SPF appeared to distribute evenly and coalesced by thin gluten film. However, higher SPF substitution level in bread formulation exhibited a deleterious effect by formation of discontinuous gluten network. For texture profile analysis, 5% SPF bread resulted in the lowest value of hardness. The score of sensory evaluation showed that 5% SPF bread received good acceptability and is comparable with control bread.

Keywords: soursop pulp flour, bread, physicochemical properties, sensory attributes, scanning electron microscopy (SEM)

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Authors: Yudhajit Datta

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Will is a phenomenon that has puzzled humanity for a long time. It is a belief that Will Power of an individual affects the success achieved by an individual in life. It is thought that a person endowed with great will power can overcome even the most crippling setbacks of life while a person with a weak will cannot make the most of life even the greatest assets. Behavioral aspects of the human experience such as will are rarely subjected to quantitative study owing to the numerous uncontrollable parameters involved. This work is an attempt to subject the phenomena of will to the test of an artificial neural network. The claim being tested is that will power of an individual largely determines success achieved in life. In the study, an attempt is made to incorporate the behavioral phenomenon of will into a computational model using data pertaining to the success of individuals obtained from an experiment. A neural network is to be trained using data based upon part of the model, and subsequently used to make predictions regarding will corresponding to data points of success. If the prediction is in agreement with the model values, the model is to be retained as a candidate. Ultimately, the best-fit model from among the many different candidates is to be selected, and used for studying the correlation between success and will.

Keywords: will power, will, success, apathy factor, random factor, characteristic function, life story

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Authors: Devadrita Dey Sarkar

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Oxidosqualene cyclase is a membrane bound enzyme in which helps in the formation of steroid scaffold in higher organisms. In a highly selective cyclization reaction oxidosqualene cyclase forms LANOSTEROL with seven chiral centres starting from the linear substrate 2,3-oxidosqualene. In humans OSC in cholesterol biosynthesis it represents a target for the discovery of novel anticholesteraemic drugs that could complement the widely used statins. The enzyme oxidosqualene: lanosterol cyclase (OSC) represents a novel target for the treatment of hypercholesterolemia. OSC catalyzes the cyclization of the linear 2,3-monoepoxysqualene to lanosterol, the initial four-ringed sterol intermediate in the cholesterol biosynthetic pathway. OSC also catalyzes the formation of 24(S), 25-epoxycholesterol, a ligand activator of the liver X receptor. Inhibition of OSC reduces cholesterol biosynthesis and selectively enhances 24(S),25-epoxycholesterol synthesis. Through this dual mechanism, OSC inhibition decreases plasma levels of low-density lipoprotein (LDL)-cholesterol and prevents cholesterol deposition within macrophages. The recent crystallization of OSC identifies the mechanism of action for this complex enzyme, setting the stage for the design of OSC inhibitors with improved pharmacological properties for cholesterol lowering and treatment of atherosclerosis. While studying and designing the inhibitor of oxidosqulene cyclase, I worked on the pdb id of 1w6k which was the most worked on pdb id and I used several methods, techniques and softwares to identify and validate the top most molecules which could be acting as an inhibitor for oxidosqualene cyclase. Thus, by partial blockage of this enzyme, both an inhibition of lanosterol and subsequently cholesterol formation as well as a concomitant effect on HMG-CoA reductase can be achieved. Both effects complement each other and lead to an effective control of cholesterol biosynthesis. It is therefore concluded that 2,3-oxidosqualene cyclase plays a crucial role in the regulation of intracellular cholesterol homeostasis. 2,3-Oxidosqualene cyclase inhibitors offer an attractive approach for novel lipid-lowering agents.

Keywords: anticholesteraemic, crystallization, statins, homeostasis

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Authors: Muhammad Rehan Usman, Muhammad Arslan Usman, Soo Young Shin

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The new era of digital communication has brought up many challenges that network operators need to overcome. The high demand of mobile data rates require improved networks, which is a challenge for the operators in terms of maintaining the quality of experience (QoE) for their consumers. In live video transmission, there is a sheer need for live surveillance of the videos in order to maintain the quality of the network. For this purpose objective algorithms are employed to monitor the quality of the videos that are transmitted over a network. In order to test these objective algorithms, subjective quality assessment of the streamed videos is required, as the human eye is the best source of perceptual assessment. In this paper we have conducted subjective evaluation of videos with varying spatial and temporal impairments. These videos were impaired with frame freezing distortions so that the impact of frame freezing on the quality of experience could be studied. We present subjective Mean Opinion Score (MOS) for these videos that can be used for fine tuning the objective algorithms for video quality assessment.

Keywords: frame freezing, mean opinion score, objective assessment, subjective evaluation

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Authors: Ayman M. Mansour, Bilal Hawashin, Hesham Alsalem

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Faster delivery of laboratory test results is one of the most noticeable signs of good laboratory service and is often used as a key performance indicator of laboratory performance. Despite the availability of technology, the delivery time of clinical laboratory test results continues to be a cause of customer dissatisfaction which makes patients feel frustrated and they became careless to get their laboratory test results. The Medical Clinical Laboratory test results are highly sensitive and could harm patients especially with the severe case if they deliver in wrong time. Such results affect the treatment done by physicians if arrived at correct time efforts should, therefore, be made to ensure faster delivery of lab test results by utilizing new trusted, Robust and fast system. In this paper, we proposed a distributed Multi-Agent System to enhance and faster the process of laboratory test results delivery using SMS. The developed system relies on SMS messages because of the wide availability of GSM network comparing to the other network. The software provides the capability of knowledge sharing between different units and different laboratory medical centers. The system was built using java programming. To implement the proposed system we had many possible techniques. One of these is to use the peer-to-peer (P2P) model, where all the peers are treated equally and the service is distributed among all the peers of the network. However, for the pure P2P model, it is difficult to maintain the coherence of the network, discover new peers and ensure security. Also, security is a quite important issue since each node is allowed to join the network without any control mechanism. We thus take the hybrid P2P model, a model between the Client/Server model and the pure P2P model using GSM technology through SMS messages. This model satisfies our need. A GUI has been developed to provide the laboratory staff with the simple and easy way to interact with the system. This system provides quick response rate and the decision is faster than the manual methods. This will save patients life.

Keywords: multi-agent system, delivery process, GSM technology, clinical laboratory results

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

Abstract:

Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Amr Amer

Abstract:

Cheeses are among those high-protein-containing foodstuffs in which enzymatic and microbial activities cause the formation of biogenic amines from amino acids decarboxylation. The amount of biogenic amines in cheese may act as a useful indicator of the hygienic quality of the product. In other words, their presence in cheese is related to its spoilage and safety. Formation of biogenic amines during Ras cheese (Egyptian hard cheese) ripening was investigated for 4 months. Three batches of Ras cheese were manufactured using Egyptian traditional method. From each batch, Samples were collected at 1, 7, 15, 30, 60, 90 and 120 days after cheese manufacture. The concentrations of biogenic amines (Tyramine, Histamine, Cadaverine and Tryptamine) were analyzed by high performance liquid chromatography (HPLC). There was a significant increased (P<0.05) in Tyramine levels from 4.34± 0.07 mg|100g in the first day of storage till reached 88.77± 0.14 mg|100g at a 120-day of storage. Also, Histamine and Cadaverine levels had the same increased pattern of Tyramine reaching 64.94± 0.10 and 28.28± 0.08 mg|100g in a 120- day of storage, respectively. While, there was a fluctuation in the concentration of Tryptamine level during ripening period as it decreased from 3.24± 0.06 to 2.66± 0.11 mg|100g at 60-day of storage then reached 5.38±0.08 mg|100g in a 120- day of storage. Biogenic amines can be formed in cheese during production and storage: many variables, as pH, salt concentration, bacterial activity as well as moisture, storage temperature and ripening time, play a relevant role in their formation. Comparing the obtained results with the recommended standard by Food and Drug Administration "FDA" (2001), High levels of biogenic amines in various Ras cheeses consumed in Egypt exceeded the permissible value (10 mg%) which seemed to pose a threat to public health. In this study, presence of high concentrations of biogenic amines (Tyramine, Histamine, cadaverine and Tryptamine) in Egyptian Ras cheeses reflects the bad hygienic conditions under which they produced and stored. Accordingly, the levels of biogenic amines in different cheeses should be come in accordance with the safe permissible limit recommended by FDA to ensure human safety.

Keywords: Ras cheese, biogenic amines, tyramine, histamine, cadaverine

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Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

Abstract:

The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

Abstract:

Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: A. Sri Janani, K. Immanuel Arokia James

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Cognitive radio ad hoc networks different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. Cognitive radio automatically detects available channels in wireless spectrum. This is a form of dynamic spectrum management. Cross-layer optimization is proposed, using this can allow far away secondary users can also involve into channel work. So it can increase the throughput and it will overcome the collision and time delay.

Keywords: cognitive radio, cross layer optimization, CR mesh network, heterogeneous spectrum, mesh topology, random routing optimization technique

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Authors: Heba Fouad, Tarek M. Madkour, Safwan A. Khedr

Abstract:

This paper investigates multiple factors that impact the formation of geopolymers and their compressive strength to be utilized in construction as an environmentally-friendly material. Bentonite and Kaolinite were thermally calcinated at 750 °C to obtain Metabentonite and Metakaolinite with higher reactivity. Both source materials were activated using a solution of sodium hydroxide (NaOH). Thereafter, samples were cured at different temperatures. The samples were analyzed chemically using a host of spectroscopic techniques. The bulk density and compressive strength of the produced Geopolymer pastes were studied. Findings indicate that the ratio of NaOH solution to source material affects the compressive strength, being optimal at 0.54. Moreover, controlled heat curing was proven effective to improve compressive strength. The existence of characteristic Fourier Transform Infrared Spectroscopy (FTIR) peaks at approximately 1020 cm-1 and 460 cm-1 which corresponds to the asymmetric stretching vibration of Si-O-T and bending vibration of Si-O-Si, hence, confirming the formation of the target geopolymer.

Keywords: calcination of metakaolinite, compressive strength, FTIR analysis, geopolymer, green cement

Procedia PDF Downloads 165