Search results for: electrical network frequency stability

Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

Procedia PDF Downloads 337

Authors: Ayse Ozbil, Gorsev Argin, Demet Yesiltepe

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Data for this cross-sectional study were drawn from questionnaires conducted in 10 elementary schools (1000 students, ages 12-14) located in Istanbul, Turkey. School environments (1600 meter buffers around the school) were evaluated through GIS-based land-use data (parcel level land use density) and street-level topography. Street networks within the same buffers were evaluated by using angular segment analysis (Integration and Choice) implemented in Depthmap as well as two segment-based connectivity measures, namely Metric and Directional Reach implemented in GIS. Segment Angular Integration measures how accessible each space from all the others within the radius using the least angle measure of distance. Segment Angular Choice which measures how many times a space is selected on journeys between all pairs of origins and destinations. Metric Reach captures the density of streets and street connections accessible from each individual road segment. Directional Reach measures the extent to which the entire street network is accessible with few direction changes. In addition, socio-economic characteristics (annual income, car ownership, education-level) of parents, obtained from parental questionnaires, were also included in the analysis. It is shown that surrounding street network configuration is strongly associated with both walk-mode shares and average walking distances to/from schools when controlling for parental socio-demographic attributes as well as land-use compositions and topographic features in school environments. More specifically, findings suggest that the scale at which urban form has an impact on pedestrian travel is considerably larger than a few blocks around the school.

Keywords: Istanbul, street network layout, urban form, walking to/from school

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Authors: N. Deepika, P. A. L. Narayana

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Linear stability analysis of double diffusive convection in a horizontal porous layer saturated with fluid is examined by considering the effects of viscous dissipation, concentration based internal heat source and vertical throughflow. The basic steady state solution for Governing equations is computed. Linear stability analysis has been implemented numerically by using Runge-kutta method. Critical thermal Rayleigh number Rac is obtained for various values of solutal Rayleigh number Sa, vertical Peclet number Pe, Gebhart number Ge, Lewis number Le and measure of concentration based internal heat source $\gamma$. It is observed that Ge has destabilizing effect for upward throughflow and stabilizing effect for downward throughflow. For sufficient value of Pe, $\gamma$ has considerable destabilizing effect for upward throughflow, insignificant destabilizing effect for downward throughflow.

Keywords: porous medium, concentration based internal heat source, vertical throughflow, viscous dissipation

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Authors: Azeem Ur Rehman, Asma Tayyaba

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This article deals with the promotional effects of CeO2 on PtPd/CeO2-OMC electro catalysts. The synthesized catalysts are characterized using different physico chemical techniques and evaluated in a formic acid oxidation fuel cell. N2 adsorption/desorption analysis shows that CeO2 modification increases the surface area of OMC from 1005 m2/g to 1119 m2/g. SEM, XRD and TEM analysis reveal that the presence of CeO2 enhances the active metal(s) dispersion on the CeO2-OMC surface. The average particle size of the dispersed metal decreases with the increase of Pt/Pd ratio on CeO2-OMC support. Cyclic voltametry measurement of Pd/CeO2-OMC gives 12 % higher anodic current activity with 83 mV negative shift of the peak potential as compared to unmodified Pd/OMC. In bimetallic catalysts, the addition of Pt improves the activity and stability of the catalysts significantly. Among the bimetallic samples, Pd3Pt1/CeO2-OMC displays superior current density (74.6 mA/cm2), which is 28.3 times higher than that of Pt/CeO2-OMC. It also shows higher stability in extended period of runs with least indication of CO poisoning effects.

Keywords: CeO2, ordered mesoporous carbon (OMC), electro catalyst, formic acid fuel cell

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Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Afshin Karimzadeh Fard

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Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the points specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN, CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process.

Keywords: artificial neural networks, milling process, rotational speed, temperature

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Authors: Mahmoud Samkan

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In this paper, the design and assessment of new coordination between Power System Stabilizers (PSSs) and Static Var Compensator (SVC) in a multimachine power system via statistical method are proposed. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is handled as an optimization problem. The Bacterial Swarming Optimization (BSO), which synergistically couples the Bacterial Foraging (BF) with the Particle Swarm Optimization (PSO), is employed to seek for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is enhanced. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO (BSOPSS) and optimized SVC controller based on BSO (BSOSVC). Moreover, a statistical T test is executed to validate the robustness of coordinated controller versus uncoordinated one.

Keywords: SVC, PSSs, multimachine power system, coordinated design, bacteria swarm optimization, statistical assessment

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Authors: Boris Blostotsky, Elia Efraim

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Testing of columns in sway mode is performed in order to determine the maximal allowable load limited by plastic deformations or their end connections and a critical load limited by columns stability. Motivation to determine accurate value of critical force is caused by its using as follow: - critical load is maximal allowable load for given column configuration and can be used as criterion of perfection; - it is used in calculation prescribed by standards for design of structural elements under combined action of compression and bending; - it is used for verification of theoretical analysis of stability at various end conditions of columns. In the present work a new non-destructive method for determination of columns critical buckling load in sway mode is proposed. The method allows performing measurements during the tests under loads that exceeds the columns critical load without losing its stability. The possibility of such loading is achieved by structure of the loading system. The system is performed as frame with rigid girder, one of the columns is the tested column and the other is additional two-hinged strut. Loading of the frame is carried out by the flexible traction element attached to the girder. The load applied on the tested column can achieve a values that exceed the critical load by choice of parameters of the traction element and the additional strut. The system lateral stiffness and the column critical load are obtained by the dynamic method. The experiment planning and the comparison between the experimental and theoretical values were performed based on the developed dependency of lateral stiffness of the system on vertical load, taking into account a semi-rigid connections of the column's ends. The agreement between the obtained results was established. The method can be used for testing of real full-size columns in industrial conditions.

Keywords: buckling, columns, dynamic method, semi-rigid connections, sway mode

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Authors: Gebregziabher Brhane Berhe, Bing Joe Hwange, Wei-Nien Su

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For a high-voltage cell, severe capacity fading is usually observed when the commercially carbonate-based electrolyte is employed due to the oxidative decomposition of solvents. To mitigate this capacity fading, an advanced electrolyte of fluoroethylene carbonate, ethyl methyl carbonate (EMC), and 1,1,2,2-Tetrafluoroetyle-2,2,3,3-tetrafluoropropyl ether (TTE) (in vol. ratio of 3:2:5) is dissolved with oxidative stability. A high-voltage lithium-ion battery was designed by coupling sulfured carbon anode from polyacrylonitrile (S-C(PAN)) and LiN0.5Mn1.5 O4 (LNMO) cathode. The discharged capacity of the cell made with modified electrolyte reaches 688 mAhg-1S a rate of 2 C, while only 19 mAhg-1S for the control electrolyte. The adopted electrolyte can effectively stabilize the sulfurized carbon anode and LNMO cathode surfaces, as the X-ray photoelectron spectroscopy (XPS) results confirmed. The developed robust high-voltage lithium-ion battery enjoys wider oxidative stability, high rate capability, and good cyclic performance, which can be attributed to the partially fluorinated electrolyte formulations with balanced viscosity and conductivity.

Keywords: high voltage, LNMO, fluorinated electrolyte, lithium-ion batteries

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Khaled Bahgat

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In the present work, the characterization of 4-Amino-3-phenyl-1H-1,2,4-triazole-5(4H)-thione (APTT) molecule was carried out by quantum chemical method and vibrational spectral techniques. The FT-IR (4000–400 cm_1) and FT-Raman (4000–100 cm_1) spectra of APTT were recorded in solid phase. The UV–Vis absorption spectrum of the APTT was recorded in the range of 200–400 nm. The molecular geometry, harmonic vibrational frequencies and bonding features of APTT in the ground state have been calculated by HF and DFT methods using 6-311++G(d,p) basis set. The complete vibrational frequency assignments were made by normal coordinate analysis (NCA) following the scaled quantum mechanical force field methodology (SQMF). The molecular stability and bond strength were investigated by applying the natural bond orbital analysis (NBO) and natural localized molecular orbital (NLMO) analysis. The electronic properties, such as excitation energies, absorption wavelength, HOMO and LUMO energies were performed by time depended DFT (TD-DFT) approach. The 1H and 13C nuclear magnetic resonance chemical shift of the molecule were calculated using the gauge-including atomic orbital (GIAO) method and compared with experimental results. Finally, the calculation results were analyzed to simulate infrared, FT-Raman and UV spectra of the title compound which shows better agreement with observed spectra.

Keywords: 4-amino-3-phenyl-1H-1, 2, 4-triazole-5(4H)-thione, vibrational assignments, normal coordinate analysis, quantum mechanical calculations

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Authors: Saeid Bahiraei, Hassan Daneshmandi , Ali Asghar Norasteh

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The present study aims at the effects of 8 weeks of selected corrective exercise training in stable and unstable levels on the postural control people with ID. Problems and limitations of movement in individuals with intellectual disability (ID) are highly common, which particularly may cause the loss of basic performance and limitation of the person's independence in doing their daily activities. In the present study, thirty-four young adult intellectual disabilities were selected randomly and divided into three groups. In order to measure the balance variable indicators, BESTest was used. The intervention group did the selected performance exercise in 8 weeks (3 times of 45 to 50 minutes a week). Meanwhile, the control group did not experience any kind of exercise. Statistical analysis was performed in SPSS on a significant level (p<0/05). The results showed the compromise between time and the group in all the BESTest tests is significant (P=0/001). The results of the research test compared to the studied groups with time measurements showed that there is a significant difference in the unstable group in Biomechanical constraints (P<0/05). And also, a significant difference exists in the stable and unstable level instability limits/Vertically, Postural responses, and Anticipatory postural adjustment variables (except for the follow-up and pre-test levels), Stability in Gait and Sensory Orientation in the pre-test, post-test, and follow up- pre-test stage of the test (P<0/05). In the comparison between the times of measurement with the groups under study, the results showed that Biomechanical Constraints, Anticipatory Postural adjustment and Postural responses at the pre-test-follow upstage, there was a significant difference between unstable-stable and unstable-control groups (P<0/05), it was also significant between all groups in Stability Limits/Vertically, Sensory Orientation, Stability in Gait and Overall stability index variables (P<0/05). The findings showed that the practice group at an unstable level has move improvement compared to the practice group at a stable level. In conclusion, this study presents evidence that shows selected performative practices can be recognized as a comprehensive and effective mediator in the betterment and improvement of the balance in intellectually disabled people and also affect the performative and moving activities.

Keywords: intellectual disability, BSETest, rehabilitation, postural control

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

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In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the Spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are Class balancing, Data shuffling, and Standardization were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the Sequential model and Relu activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: neural network, pineapple, soluble solid content, spectroscopy

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Authors: Ahmad Mansoor Stanikzai, Yoshitaka Kajita

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This research is a very essential towards transportation planning of Kabul New City. In this research, the travel demand of Kabul metropolitan area (Existing and Kabul New City) are evaluated for three different target years (2015, current, 2025, mid-term, 2040, long-term). The outcome of this study indicates that, though currently the vehicle volume is less the capacity of existing road networks, Kabul city is suffering from daily traffic congestions. This is mainly due to lack of transportation management, the absence of proper policies, improper public transportation system and violation of traffic rules and regulations by inhabitants. On the other hand, the observed result indicates that the current vehicle to capacity ratio (VCR) which is the most used index to judge traffic status in the city is around 0.79. This indicates the inappropriate traffic condition of the city. Moreover, by the growth of population in mid-term (2025) and long-term (2040) and in the case of no development in the road network and transportation system, the VCR value will dramatically increase to 1.40 (2025) and 2.5 (2040). This can be a critical situation for an urban area from an urban transportation perspective. Thus, by introducing high-capacity public transportation system and the development of road network in Kabul New City and integrating these links with the existing city road network, significant improvements were observed in the value of VCR.

Keywords: Afghanistan, Kabul new city, planning, policy, urban transportation

Procedia PDF Downloads 318

Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

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Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: Othmane Essahili, Mohamed Ilsouk, Carine Duhayon, Omar Moudam

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Due to their excellent and promising properties, a great deal of attention has recently been devoted to luminescent materials, particularly those utilizing rare earth elements. These materials play an essential role in low-cost energy conversion technology applications, such as luminescent solar concentrators (LSCs). They also have potential applications in Agri-PV systems and smart building windows. Luminescent materials based on europium (III) complexes are known for their high luminescence efficiency, long fluorescence lifetimes, and sharp emission bands. However, they present certain drawbacks related to their limited absorption capacity due to the forbidden 4f-4f electronic transitions. To address these drawbacks, using β-diketonate ligands as sensitizers appears as a promising solution to enhance luminescence intensity through the antenna effect, where the ligand's excited energy is transferred to the europium ions. In this study, we synthesized β-diketonate-based europium complexes with phenanthroline derivatives, modified with various methyl groups, to examine their effects on the complexes' stability in poly(methyl methacrylate) (PMMA) films. Our findings reveal that these complexes exhibit remarkable red emission and high photoluminescence quantum yield. Stability tests under different conditions for 1200 hours showed that complexes with a higher number of methyl substitutions offer improved photoluminescent stability and resistance to degradation, particularly in outdoor settings. This research underscores the potential of chemically tuned phenanthroline ligands in developing stable, efficient luminescent materials for future optoelectronic devices, including efficient and durable LSCs.

Keywords: luminescent materials, photochemistry, luminescent solar concentrators, β-diketonate-based europium complexes

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Authors: Ali R. Ghadrdan-Mashhadi, Hosein Hamidi-Nejat, Parisa Alizadehnia

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Cryptosporidia and Giardia are protozoan parasites that have worldwide distribution and infect a variety of animals. Although, the infection to these parasites rarely caused to illness in horses, but some veterinarian recorded the clinical signs (such as diarrhea and malabsorbtion) especially in foals. In present study, the frequency of Cryptosporidiosis and Giardiasis in horses in Ahvaz investigated. The feces samples were taken from 100 horses that keep in seven horse breeding clubs, during spring and summer. The ages of horses were from 1 month to 27 years old. Fecal samples were stained by modified Ziehl-Neelsen and Tri-chrome methods. Results were analyzed with Chi-square Test and Fisher’s exact test. The results showed that the rate of infection to Cryptosporidium and Giardia were 18% and 40%, respectively. There weren't significant differences between infection to Cryptosporidium and Giardia with sex, age and fecal constancy. Although, the rate of infection to Cryptosporidium in studied horses is very similar to other studies but it seems, the rate of infection to Giardia is high in compare to other studies were done in the other countries.

Keywords: Ahvaz, cryptosporidium, giardia, horse

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Adeniyi Onasanya, Maher Elshakankiri

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In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

Keywords: IoT, smart health care system, business analytics, (wireless) sensor network, cancer care services, cloud services

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Authors: F. Z. Mahi, H. Marinchio, C. Palermo, L. Varani

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We propose an analytical approach for the admittance response calculation of the high mobility InGaAs channel transistors. The development of the small-signal admittance takes into account the longitudinal and transverse electric fields through a pseudo two-dimensional approximation of the Poisson equation. The total currents and the potentials matrix relation between the gate and the drain terminals determine the frequency-dependent small-signal admittance response. The analytical results show that the admittance spectrum exhibits a series of resonant peaks corresponding to the excitation of plasma waves. The appearance of the resonance is discussed and analyzed as functions of the channel length and the temperature. The model can be used, on one hand, to control the appearance of plasma resonances, and on the other hand, can give significant information about the admittance phase frequency dependence.

Keywords: small-signal admittance, Poisson equation, currents and potentials matrix, the drain and the gate terminals, analytical model

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Authors: L. Nouri Taiba, Y. Bouhamidi, F. Kaouah, Z. Bendjama, M. Trari

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In the present study, an ecofriendly biocomposite namely calcium alginate immobilized Ammi Visnaga (Khella) extraction waste (SWAV/CA) was prepared by electrostatic extrusion method and used on the cadmium biosorption from aqueous phase with and without the assistance of ultrasound in batch conditions. The influence of low frequency ultrasound (37 and 80 KHz) on the cadmium biosorption kinetics was studied. The obtained results show that the ultrasonic irradiation significantly enhances and improves the efficiency of the cadmium removal. The Pseudo first order, Pseudo-second-order, Intraparticle diffusion, and Elovich models were evaluated using the non-linear curve fitting analysis method. Modeling of kinetic results shows that biosorption process is best described by the pseudo-second order and Elovich, in both the absence and presence of ultrasound.

Keywords: biocomposite, biosorption, cadmium, non-linear analysis, ultrasound

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Gholam Hossein Javanmard, Roghayeh Mohammadi Garegozlo

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The present study was a randomized three-group double-blind clinical trial with repeated measures designs which aimed to determine the pure effect and combined effect of mindfulness based-relaxation (MBR) technique and Transcranial Electrical Simulation (tCES) on psychological distress decreasing of patients with type-2 diabetes. The sample of the study consisted of 30 patients with type-2 diabetes who were selected from the Diabetes Association of Bonab city in Iran. The participants were matched and then randomly assigned to the three groups of 10 subjects (MBR, CES, MBR+CES). The subjects received interventions related to their group in 10 individual sessions. Pre-test, post-test, and one-month follow-up were conducted using DASS-42. Analysis of variance with repeated measures showed a significant change in psychological distress. Multivariate covariance analysis and the paired interpersonal comparative test of Ben Foruni indicated that both interventions of MBR and CES have a similar effect on psychological distress decreasing in the post-test and follow-up phase. But, the combined therapy of MBR+CES was more efficient, and it had a more stable effect. However, all three interventions, especially combined intervention of MBR+CES, as efficient and stable treatment, are suggested for improving the psychological status of diabetic patients.

Keywords: mindfulness based-relaxation, transcranial electrical simulation, type 2 diabetes, psychological distress

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Authors: Avinash Nayak, Debopam Das

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The current work implements the variational principle to find the optimum initial perturbation that provides maximum growth in an impulsively blocked channel flow. The conventional method for studying temporal stability has always been through modal analysis. In most of the transient flows, this modal analysis is still followed with the quasi-steady assumption, i.e. change in base flow is much slower compared to perturbation growth rate. There are other studies where transient analysis on time dependent flows is done by formulating the growth of perturbation as an initial value problem. But the perturbation growth is sensitive to the initial condition. This study intends to find the initial perturbation that provides the maximum growth at a later time. Here, the expression of base flow for blocked channel is derived and the formulation is based on the two dimensional perturbation with stream function representing the perturbation quantity. Hence, the governing equation becomes the Orr-Sommerfeld equation. In the current context, the cost functional is defined as the ratio of disturbance energy at a terminal time 'T' to the initial energy, i.e. G(T) = ||q(T)||2/||q(0)||2 where q is the perturbation and ||.|| defines the norm chosen. The above cost functional needs to be maximized against the initial perturbation distribution. It is achieved with the constraint that perturbation follows the basic governing equation, i.e. Orr-Sommerfeld equation. The corresponding adjoint equation is derived and is solved along with the basic governing equation in an iterative manner to provide the initial spatial shape of the perturbation that provides the maximum growth G (T). The growth rate is plotted against time showing the development of perturbation which achieves an asymptotic shape. The effects of various parameters, e.g. Reynolds number, are studied in the process. Thus, the study emphasizes on the usage of optimal perturbation and its growth to understand the stability characteristics of time dependent flows. The assumption of quasi-steady analysis can be verified against these results for the transient flows like impulsive blocked channel flow.

Keywords: blocked channel flow, calculus of variation, hydrodynamic stability, optimal perturbation

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Authors: A. Goma Amira, U. E. Mahrous

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Measuring the effect of perinatal lead exposure on learning ability of offspring is considered as a sensitive and selective index for providing an early marker for central nervous system damage produced by this toxic metal. A total of 35 Sprague-Dawley adult rats were used to investigate the effect of lead acetate toxicity on behavioral patterns of adult female rats and learning ability of offspring. Rats were allotted into 4 groups, group one received 1g/l lead acetate (n=10), group two received 1.5g/l lead acetate (n=10), group three received 2g/l lead acetate in drinking water (n=10), and control group did not receive lead acetate (n=5) from 8th day of pregnancy till weaning of pups. The obtained results revealed a dose-dependent increase in the feeding time, drinking frequency, licking frequency, scratching frequency, licking litters, nest building, and retrieving frequencies, while standing time increased significantly in rats treated with 1.5g/l lead acetate than other treated groups and control. On the contrary, lying time decreased gradually in a dose-dependent manner. Moreover, movement activities were higher in rats treated with 1g/l lead acetate than other treated groups and control. Furthermore, time spent in closed arms was significantly lower in rats given 2g/l lead acetate than other treated groups, while they spent significantly much time spent in open arms than other treated groups which could be attributed to occurrence of adaptation. Furthermore, number of entries in open arms was-dose dependent. However, the ratio between open/closed arms revealed a significant decrease in rats treated with 2g/l lead acetate than the control group.

Keywords: lead toxicity, rats, learning ability, behavior

Procedia PDF Downloads 367

Authors: Xupin Zhang, Maria Caterina Bramati, Enrest Fokoue

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Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling.

Keywords: dynamic bayesian networks, indicator estimation, job placement, social networks

Procedia PDF Downloads 355

Authors: J. Samuel, S. Al-Enezi, A. Al-Banna

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High-density polyethylene reinforced with carbon nanofibers (HDPE/CNF) have been prepared via melt processing using dialkylimidazolium tetrafluoroborate (ionic liquid) as a dispersion agent. The prepared samples were characterized by thermogravimetric (TGA) and differential scanning calorimetric (DSC) analyses. The samples blended with imidazolium ionic liquid exhibit higher thermal stability. DSC analysis showed clear miscibility of ionic liquid in the HDPE matrix and showed single endothermic peak. The melt rheological analysis of HDPE/CNF composites was performed using an oscillatory rheometer. The influence of CNF and ionic liquid concentration (ranging from 0, 0.5, and 1 wt%) on the viscoelastic parameters was investigated at 200 °C with an angular frequency range of 0.1 to 100 rad/s. The rheological analysis shows the shear-thinning behavior for the composites. An improvement in the viscoelastic properties was observed as the nanofiber concentration increases. The progress in the modulus values was attributed to the structural rigidity imparted by the high aspect ratio CNF. The modulus values and complex viscosity of the composites increased significantly at low frequencies. Composites blended with ionic liquid exhibit slightly lower values of complex viscosity and modulus over the corresponding HDPE/CNF compositions. Therefore, reduction in melt viscosity is an additional benefit for polymer composite processing as a result of wetting effect by polymer-ionic liquid combinations.

Keywords: high-density polyethylene, carbon nanofibers, ionic liquid, complex viscosity

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Authors: Raghad A. Altowairgi

Abstract:

With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

Abstract:

Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

Procedia PDF Downloads 259

Authors: Gurkirandeep Kaur, Rana Pratap Yadav

Abstract:

This experimental study is aimed to examine the radiation characteristics of different plasma structures of a surface wave-driven plasma antenna by an automated measuring system. In this study, a 30 cm long plasma column of argon gas with a diameter of 3 cm is excited by surface wave discharge mechanism operating at 13.56 MHz with RF power level up to 100 Watts and gas pressure between 0.01 to 0.05 mb. The study reveals that a single structured plasma monopole can be modified into an array of plasma antenna elements by forming multiple striations or plasma blobs inside the discharge tube by altering the values of plasma properties such as working pressure, operating frequency, input RF power, discharge tube dimensions, i.e., length, radius, and thickness. It is also reported that plasma length, electron density, and conductivity are functions of operating plasma parameters and controlled by changing working pressure and input power. To investigate the antenna radiation efficiency for the far-field region, an automation-based radiation measuring system has been fabricated and presented in detail. This developed automated system involves a combined setup of controller, dc servo motors, vector network analyzer, and computing device to evaluate the radiation intensity, directivity, gain and efficiency of plasma antenna. In this system, the controller is connected to multiple motors for moving aluminum shafts in both elevation and azimuthal plane whereas radiation from plasma monopole antenna is measured by a Vector Network Analyser (VNA) which is further wired up with the computing device to display radiations in polar plot forms. Here, the radiation characteristics of both continuous and array plasma monopole antenna have been studied for various working plasma parameters. The experimental results clearly indicate that the plasma antenna is as efficient as a metallic antenna. The radiation from plasma monopole antenna is significantly influenced by plasma properties which provides a wider range in radiation pattern where desired radiation parameters like beam-width, the direction of radiation, radiation intensity, antenna efficiency, etc. can be achieved in a single monopole. Due to its wide range of selectivity in radiation pattern; this can meet the demands of wider bandwidth to get high data speed in communication systems. Moreover, this developed system provides an efficient and cost-effective solution for measuring the radiation pattern in far-field zone for any kind of antenna system.

Keywords: antenna radiation characteristics, dynamically reconfigurable, plasma antenna, plasma column, plasma striations, surface wave

Procedia PDF Downloads 107