Search results for: kernel modules

Authors: Amir H. Alavi, Kaveh Barri, Qianyun Zhang

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This study presents a new generation of lightweight and mechanically tunable structural composites with sensing and energy harvesting functionalities. This goal is achieved by integrating metamaterial and triboelectric energy harvesting concepts. Proof-of-concept polymeric beam prototypes are fabricated using 3D printing methods based on the proposed concept. Experiments and theoretical analyses are conducted to quantitatively investigate the mechanical and electrical properties of the designed multifunctional beams. The results show that these integrated structural elements can serve as nanogenerators and distributed sensing mediums without a need to incorporating any external sensing modules and electronics. The feasibility of design self-sensing and self-powering structural elements at multiscale for next generation infrastructure systems is further discussed.

Keywords: multifunctional structures, composites, metamaterial, triboelectric nanogenerator, sensors, structural health monitoring, energy harvesting

Procedia PDF Downloads 191

Authors: Djilani Kobibi Youcef Islam, Hadjeri Samir, Djehaf Mohamed Abdeldjalil

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The Flexible AC Transmission System (FACTS) technology is a new advanced solution that increases the reliability and provides more flexibility, controllability, and stability of a power system. The Unified Power Flow Controller (UPFC), as the most versatile FACTS device for regulating power flow, is able to control respectively transmission line real power, reactive power, and node voltage. The main purpose of this paper is to analyze the effect of the UPFC on the load flow, the power losses, and the voltage stability using NEPLAN software modules, Newton-Raphson load flow is used for the power flow analysis and the modal analysis is used for the study of the voltage stability. The simulation was carried out on the IEEE 14-bus test system.

Keywords: FACTS, load flow, modal analysis, UPFC, voltage stability

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Authors: Benchalak Muangmeesri

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Glazes ceramics are ceramic materials produced through controlled crystallization of a parent glass. The great variety of compositions and the possibility of developing special micro structures with specific technological properties have allowed glass ceramic materials to be used in a wide range of applications. At the same time, glazes ceramics need to improvement in the mechanical and chemical properties of glazed. The pick and place station is equipped with a three-axis module. test piece housings placed on the vacuum are detected module picks up a test piece insert from the slide and places it on the test piece housing. Overall, glazes ceramics are compared with automatically and manually of speed and position control. The handling modules of automatic transfer are a new generation of high speed and precision then these color results from absorption and thickness than manual is also included.

Keywords: glaze, PID control, pick and place, ceramic

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Authors: M. Tammaro, S. Manzo, J. Rimauro, A. Salluzzo, S. Schiavo

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The solar photovoltaic (PV) modules are considered to have a negligible environmental impact compared to the fossil energy. Therefore also the waste management and the corresponding potential environmental hazard needs to be considered. The case of the photovoltaic panel is unique because the time lag from the manufacturing to the decommissioning as waste usually takes 25-30 years. Then the environmental hazard associated with end life of PV panels has been largely related to their metal contents. The principal concern regards the presence of heavy metals as Cd in thin film (TF) modules or Pb and Cr in crystalline silicon (c-Si) panels. At the end of life of PV panels, these dangerous substances could be released in the environment, if special requirements for their disposal are not adopted. Nevertheless, in literature, only a few experimental study about metal emissions from silicon crystalline/thin film panels and the corresponding environmental effect are present. As part of a study funded by the Italian national consortium for the waste collection and recycling (COBAT), the present work was aimed to analyze experimentally the potential release into the environment of hazardous elements, particularly metals, from PV waste. In this paper, for the first time, eighteen releasable metals a large number of photovoltaic panels, by c-Si and TF, manufactured in the last 30 years, together with the environmental effects by a battery of ecotoxicological tests, were investigated. Leaching tests are conducted on the crushed samples of PV module. The test is conducted according to Italian and European Standard procedure for hazard assessment of the granular waste and of the sludge. The sample material is shaken for 24 hours in HDPE bottles with an overhead mixer Rotax 6.8 VELP at indoor temperature and using pure water (18 MΩ resistivity) as leaching solution. The liquid-to-solid ratio was 10 (L/S=10, i.e. 10 liters of water per kg of solid). The ecotoxicological tests were performed in the subsequent 24 hours. A battery of toxicity test with bacteria (Vibrio fisheri), algae (Pseudochirneriella subcapitata) and crustacea (Daphnia magna) was carried out on PV panel leachates obtained as previously described and immediately stored in dark and at 4°C until testing (in the next 24 hours). For understand the actual pollution load, a comparison with the current European and Italian benchmark limits was performed. The trend of leachable metal amount from panels in relation to manufacturing years was then highlighted in order to assess the environmental sustainability of PV technology over time. The experimental results were very heterogeneous and show that the photovoltaic panels could represent an environmental hazard. The experimental results showed that the amounts of some hazardous metals (Pb, Cr, Cd, Ni), for c-Si and TF, exceed the law limits and they are a clear indication of the potential environmental risk of photovoltaic panels "as a waste" without a proper management.

Keywords: photovoltaic panel, environment, ecotoxicity, metals emission

Procedia PDF Downloads 257

Authors: Almagul Assainova , Dariya Abykenova, Liudmila Goncharenko, Sergey Sybachin, Saule Rakhimova, Abay Aman

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The recognition of a handwritten Kazakh text is a relevant objective today for the digitization of materials. The study presents a model of a hybrid neural network for handwriting recognition, which includes a convolutional neural network and a multi-layer perceptron. Each network includes 1024 input neurons and 42 output neurons. The model is implemented in the program, written in the Python programming language using the EMNIST database, NumPy, Keras, and Tensorflow modules. The neural network training of such specific letters of the Kazakh alphabet as ә, ғ, қ, ң, ө, ұ, ү, h, і was conducted. The neural network model and the program created on its basis can be used in electronic document management systems to digitize the Kazakh text.

Keywords: handwriting recognition system, image recognition, Kazakh font, machine learning, neural networks

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

Procedia PDF Downloads 134

Authors: Francisco Bulnes

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Through the Fukaya conjecture and the wrapped Floer cohomology, the correspondences between paths in a loop space and states of a wrapping space of states in a Hamiltonian space (the ramification of field in this case is the connection to the operator that goes from TM to T*M) are demonstrated where these last states are corresponding to bosonic extensions of a spectrum of the space-time or direct image of the functor Spec, on space-time. This establishes a distinguished diffeomorphism defined by the mapping from the corresponding loops space to wrapping category of the Floer cohomology complex which furthermore relates in certain proportion D-branes (certain D-modules) with strings. This also gives to place to certain conjecture that establishes equivalences between moduli spaces that can be consigned in a moduli identity taking as space-time the Hitchin moduli space on G, whose dual can be expressed by a factor of a bosonic moduli spaces.

Keywords: Floer cohomology, Fukaya conjecture, Lagrangian submanifolds, quantum topological diffeomorphism

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Authors: Noura Al-Ajmi, Mohammed A. Almulla

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With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.

Keywords: headache diagnosis system, prescription recommender system, expert system, backward rule-based system

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Authors: C. Benjbara, A. Habbani

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Wireless networks are getting more and more used in every new technology or feature, especially those without infrastructure (Ad hoc mode) which provide a low cost alternative to the infrastructure mode wireless networks and a great flexibility for application domains such as environmental monitoring, smart cities, precision agriculture, and so on. These application domains present a common characteristic which is the need of coexistence and intercommunication between modules belonging to different types of ad hoc networks like wireless sensor networks, mesh networks, mobile ad hoc networks, vehicular ad hoc networks, etc. This vision to bring to life such heterogeneous networks will make humanity duties easier but its development path is full of challenges. One of these challenges is the communication complexity between its components due to the lack of common or compatible protocols standard. This article proposes a new patented routing protocol based on the OLSR standard in order to resolve the heterogeneous ad hoc networks communication issue. This new protocol is applied on a specific network architecture composed of MANET, VANET, and FANET.

Keywords: Ad hoc, heterogeneous, ID-Node, OLSR

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Authors: Nuran Bahar, Mehmet A. Akcayol

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In the aviation industry, many faults may occur frequently during the maintenance processes and assembly operations of complex structured aircrafts because of their high dependencies of components. These faults affect the quality of aircraft parts or developed modules adversely. Technical employee requires long time and high labor force while checking the correctness of each component. In addition, the person must be trained regularly because of the ever-growing and changing technology. Generally, the cost of this training is very high. Augmented Reality (AR) technology reduces the cost of training radically and improves the effectiveness of the training. In this study, the usage of AR technology in the aviation industry has been investigated and the effectiveness of AR with heads-up display glasses has been examined. An application has been developed for comparison of production process with AR and manual one.

Keywords: aerospace, assembly quality, augmented reality, heads-up display

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Authors: David Alejandro Torres-Lopez , Lilia Albores-Gallo, Ronald Soto-Calderon, Roberto Lagunes-Cordoba

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Autism Screening Questionnaire for Daycare Attendees (ASQ-DAT) is a screening instrument that assesses the risk of autism in children between 12 and 47 months, being the first free observational instrument created according to the criteria of the DSM-5 that can be applied by teachers in nurseries. The people in charge of answering the questionnaires are the daycare assistants. Its application presents a series of previous activities with which daycare assistants are familiar (dance, games, oral narration and breakfast), which are executed with the children and then answer a questionnaire with dichotomous questions "Yes/No" in approximately 3 minutes per child. The instrument was developed with the participation of nurseries according to the protocols of the creation of psychometric instruments of the Classical Test Theory having as a gold standard ADOS-2 Modules T and 1. The results of the investigation show that the use of ASQ-DAT combined with the application of M-CHAT / RF provides more information about the risk of ASD in young children, which allows improvements in the screening.

Keywords: diagnosis, screening, autism, daycare

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Authors: Sajid Hussain

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The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Serge B. Provost

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Numerous statistical inference and modeling methodologies are based on sample moments rather than the actual observations. A result justifying the validity of this approach is introduced. More specifically, it will be established that given the first n moments of a sample of size n, one can recover the original n sample points. This implies that a sample of size n and its first associated n moments contain precisely the same amount of information. However, it is efficient to make use of a limited number of initial moments as most of the relevant distributional information is included in them. Two types of density estimation techniques that rely on such moments will be discussed. The first one expresses a density estimate as the product of a suitable base density and a polynomial adjustment whose coefficients are determined by equating the moments of the density estimate to the sample moments. The second one assumes that the derivative of the logarithm of a density function can be represented as a rational function. This gives rise to a system of linear equations involving sample moments, the density estimate is then obtained by solving a differential equation. Unlike kernel density estimation, these methodologies are ideally suited to model ‘big data’ as they only require a limited number of moments, irrespective of the sample size. What is more, they produce simple closed form expressions that are amenable to algebraic manipulations. They also turn out to be more accurate as will be shown in several illustrative examples.

Keywords: density estimation, log-density, polynomial adjustments, sample moments

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Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Rolla Monib, Chris Ian Goodier, Alistair Gibb

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This paper aims to investigate decision support strategies in the EC sector to determine the most appropriate degree of modularization. This is achieved through three oil and gas (O&G) and two power plant case studies via semi-structured interviews (n=59 and n=27, respectively), analysis of project documents, and case study-specific semi-structured validation interviews (n=12 and n=8). New terminology to distinguish degrees of modularization is proposed, along with a decision-making support checklist and a diagrammatic decision-making support figure. Results indicate that the EC sub-sectors were substantially more satisfied with the application of component, structural, or traditional modularization compared with system modularization for some types of modules. Key drivers for decisions on the degree of modularization vary across module types. This paper can help the EC sector determine the most suitable degree of modularization via a decision-making support strategy.

Keywords: modularization, engineering construction, case study, decision support

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang

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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.

Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

Procedia PDF Downloads 349

Authors: Jongho Park, Jinwoong Choi, Sungnam Hong, Seung-Kyung Kye, Sun-Kyu Park

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Recently, to cope with the rapidly changing construction trend because of aging infrastructures, modular bridge technology has been studied actively. Modular bridge is easily constructed by assembling standardized precast structure members in the field. It will be possible to construct rapidly and reduce construction cost efficiently. However, the shape examination of the transverse connection of T-type girder newly developed between the segmented modules is not performed. Therefore, the investigation of the connection shape is needed. In this study, shape of the modular T-girder bridge transverse connection was analyzed by finite element model that was verified in study which was verification of model for transverse connection using Abaqus. Connection angle was chosen as the parameter. The result of analyses showed that optimal value of angle is 130 degree.

Keywords: modular bridge, optimal transverse shape, parameter, FEM

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Authors: Heikki Ruohomaa, Vesa Salminen

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Regions are facing huge competition to attract companies, businesses, inhabitants, students, etc. This way to improve living and business environment, which is rapidly changing due to digitalization. On the other hand, from the industry's point of view, the availability of a skilled labor force and an innovative environment are crucial factors. In this context, qualified staff has been seen to utilize the opportunities of digitalization and respond to the needs of future skills. World Manufacturing Forum has stated in the year 2019- report that in next five years, 40% of workers have to change their core competencies. Through digital transformation, new technologies like cloud, mobile, big data, 5G- infrastructure, platform- technology, data- analysis, and social networks with increasing intelligence and automation, enterprises can capitalize on new opportunities and optimize existing operations to achieve significant business improvement. Digitalization will be an important part of the everyday life of citizens and present in the working day of the average citizen and employee in the future. For that reason, the education system and education programs on all levels of education from diaper age to doctorate have been directed to fulfill this ecosystem strategy. Goal: The Fourth Industrial Revolution will bring unprecedented change to societies, education organizations and business environments. This article aims to identify how education, education content, the way education has proceeded, and overall whole the education business is changing. Most important is how we should respond to this inevitable co- evolution. Methodology: The study aims to verify how the learning process is boosted by new digital content, new learning software and tools, and customer-oriented learning environments. The change of education programs and individual education modules can be supported by applied research projects. You can use them in making proof- of- the concept of new technology, new ways to teach and train, and through the experiences gathered change education content, way to educate and finally education business as a whole. Major findings: Applied research projects can prove the concept- phases on real environment field labs to test technology opportunities and new tools for training purposes. Customer-oriented applied research projects are also excellent for students to make assignments and use new knowledge and content and teachers to test new tools and create new ways to educate. New content and problem-based learning are used in future education modules. This article introduces some case study experiences on customer-oriented digital transformation projects and how gathered knowledge on new digital content and a new way to educate has influenced education. The case study is related to experiences of research projects, customer-oriented field labs/learning environments and education programs of Häme University of Applied Sciences.

Keywords: education process, digitalization content, digital tools for education, learning environments, transdisciplinary co-operation

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Authors: David F. Nettleton, Christian Wasiak, Jonas Dorissen, David Gillen, Alexandr Tretyak, Elodie Bugnicourt, Alejandro Rosales

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In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: calibration, data modeling, industrial processes, machine learning

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Authors: Ursachi Andrei

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The Smart Grid Simulator is a computer software based on advanced algorithms which has as the main purpose to lower the energy bill in the most optimized price efficient way as possible for private households, companies or energy providers. It combines the energy provided by a number of solar modules and wind turbines with the consumption of one household or a cluster of nearby households and information regarding weather conditions and energy prices in order to predict the amount of energy that can be produced by renewable energy sources and the amount of energy that will be bought from the distributor for the following day. The user of the system will not only be able to minimize his expenditures on energy fractures, but also he will be informed about his hourly consumption, electricity prices fluctuation and money spent for energy bought as well as how much money he saved each day and since he installed the system. The paper outlines the algorithm that supports the Smart Grid Simulator idea and presents preliminary test results that support the discussion and implementation of the system.

Keywords: smart grid, sustainable energy, applied science, renewable energy sources

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Authors: Sameil Hanna Samweil Botros

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The variation of social networking websites inside higher training has garnered enormous hobby in recent years, with numerous researchers thinking about it as a possible shift from the conventional lecture room-based learning paradigm. However, this boom in research and carried out research, but the adaption of SNS-based modules has not proliferated inside universities. This paper commences its contribution with the aid of studying the numerous fashions and theories proposed in the literature and amalgamates together various effective aspects for the inclusion of social technology within e-gaining knowledge. A three-phased framework is similarly proposed, which informs the important concerns for the hit edition of SNS in improving the student's mastering experience. This suggestion outlines the theoretical foundations as a way to be analyzed in sensible implementation across worldwide university campuses.

Keywords: eLearning, institutionalization, teaching and learning, transformation vtuber, ray tracing, avatar agriculture, adaptive, e-learning, technology eLearning, higher education, social network sites, student learning

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Authors: Maibigue Nanglet, Arafat Ousman Béchir, Mahamat Hassan Béchir

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The economy of Central African countries is growing very fast, and the demand for energy is increasing every day. As a result, insufficient power generation is one of the major problems slowing down development. This paper explores the factors of degradation of the I-V curve of the PV Generator (GPV) in harsh environments, taking the case of two locals: Mongo and Abeche. Its objective is to quantify the voltage leaks due to the different GPV installation areas; after using the Newton-Raphson numerical method of the solar cell, a survey of several experimental measurement points was made. The results of the simulation in MATLAB/Simulink show a relative power loss factor of 11.8765% on the GPVs installed in Mongo and 8.5463% on those installed in Abeche; these results allow us to say that the supports on which the modules are installed have an average impact of 10.2114% on their efficiency.

Keywords: calculation, degradation, site, GPV, severe environment

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Authors: Anil Kumar Pandey

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Power plane noise is the most significant source of signal integrity (SI) issues in a high-speed digital design. In this paper, power integrity (PI) analysis of multiple power planes in a power delivery system of a 12-layer high-speed FPGA board is presented. All 10 power planes of HSD board are analyzed separately by using 3D Electromagnetic based PI solver, then the transient simulation is performed on combined PI data of all planes along with voltage regulator modules (VRMs) and 70 current drawing chips to get the board level power noise coupling on different high-speed signals. De-coupling capacitors are placed between power planes and ground to reduce power noise coupling with signals.

Keywords: power integrity, power-aware signal integrity analysis, electromagnetic simulation, channel simulation

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Authors: Xia Huo

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The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu and 34 children from Haojiang. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and both the alpha diversity index and specific metabolites in single-element models. The study found that the Bayesian kernel machine regression (BKMR) model showed a negative correlation between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the Chao 1 index, particularly beyond the 55th percentile. Furthermore, the EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our research suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the gut microbiota and its metabolites. These alterations may be associated with neurodevelopmental abnormalities in children.

Keywords: gut microbiota, 6PPDQ, PBDEs, PCBs, metal(loid)s, BKMR

Procedia PDF Downloads 49

Authors: M. Doumi, A. Miloudi, A. G. Aissaoui, K. Tahir, C. Belfedal, S. Tahir

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The studies on the photovoltaic system are extensively increasing because of a large, secure, essentially exhaustible and broadly available resource as a future energy supply. However, the output power induced in the photovoltaic modules is influenced by an intensity of solar cell radiation, temperature of the solar cells and so on. Therefore, to maximize the efficiency of the photovoltaic system, it is necessary to track the maximum power point of the PV array, for this Maximum Power Point Tracking (MPPT) technique is used. Some MPPT techniques are available in that perturbation and observation (P&O) and Fuzzy logic controller (FLC). The fuzzy control method has been compared with perturb and observe (P&O) method as one of the most widely conventional method used in this area. Both techniques have been analyzed and simulated. MPPT using fuzzy logic shows superior performance and more reliable control with respect to the P&O technique for this application.

Keywords: photovoltaic system, MPPT, perturb and observe, fuzzy logic

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Authors: Mahdiar Hosseighadiry, Farnaz Fotovatikhah, Razali Ismail, Mohsen Khaledian, Mehdi Saeidemanesh

Abstract:

In this paper, a new low-power high-performance full adder is presented based on a new design method. The proposed method relies on pass gate design and provides full-swing circuits with minimum number of transistors. The method has been applied on SUM, COUT and XOR-XNOR modules resulting on rail-to-rail intermediate and output signals with no feedback transistors. The presented full adder cell has been simulated in 45 and 32 nm CMOS technologies using HSPICE considering parasitic capacitance and compared to several well-known designs from literature. In addition, the proposed cell has been extensively evaluated with different output loads, supply voltages, temperatures, threshold voltages, and operating frequencies. Results show that it functions properly under all mentioned conditions and exhibits less PDP compared to other design styles.

Keywords: full adders, low-power, high-performance, VLSI design

Procedia PDF Downloads 382

Authors: Carla Teixeira, Diana Azevedo, Jonatas Bessa, Maria Guilam

Abstract:

The flipped classroom is a blended learning modality that combines face-to-face and virtual activities of self-learning, mediated by digital information and communication technologies, which reverses traditional teaching approaches and presents, as a presupposition, the previous study of contents by students. In the following face-to-face activities, the contents are discussed, producing active learning. This work aims to describe the systematization process of the use of flipped classrooms as a method to develop complementary national activities in PROFSAÚDE, a professional master's program in the area of public health, offered as a distance learning course, in the network, in Brazil. The complementary national activities were organized with the objective of strengthening and qualifying students´ learning process. The network gathers twenty-two public institutions of higher education in the country. Its national coordination conducted a survey to detect complementary educational needs, supposed to improve the formative process and align important content sums for the program nationally. The activities were organized both asynchronously, making study materials available in Google classrooms, and synchronously in a tele presential way, organized on virtual platforms to reach the largest number of students in the country. The asynchronous activities allowed each student to study at their own pace and the synchronous activities were intended for deepening and reflecting on the themes. The national team identified some professors' areas of expertise, who were contacted for the production of audiovisual content such as video classes and podcasts, guidance for supporting bibliographic materials and also to conduct synchronous activities together with the technical team. The contents posted in the virtual classroom were organized by modules and made available before the synchronous meeting; these modules, in turn, contain “pills of experience” that correspond to reports of teachers' experiences in relation to the different themes. In addition, activity was proposed, with questions aimed to expose doubts about the contents and a learning challenge, as a practical exercise. Synchronous activities are built with different invited teachers, based on the participants 'discussions, and are the forum where teachers can answer students' questions, providing feedback on the learning process. At the end of each complementary activity, an evaluation questionnaire is available. The responses analyses show that this institutional network experience, as pedagogical innovation, provides important tools to support teaching and research due to its potential in the participatory construction of learning, optimization of resources, the democratization of knowledge and sharing and strengthening of practical experiences on the network. One of its relevant aspects was the thematic diversity addressed through this method.

Keywords: active learning, flipped classroom, network education experience, pedagogic innovation

Procedia PDF Downloads 157