Search results for: passive optical networks (PONs)

Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: WenYing Claire Shih, Konstantinos Agrafiotis

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The circular economy has become one of the seven fundamental pillars of Taiwan’s economic development, as this is promulgated by the government. The model of the circular economy, with its fundamental premise of waste elimination, can transform the textile and clothing sectors from major pollutant industries to a much cleaner alternative for a better quality of all citizens’ lives. In a related vein, the notion of the creative economy and more specifically the fashion industry can prompt similar results in terms of jobs and wealth creation. The combining forces of the circular and creative economies and their beneficial output have resulted in the configuration of ethical urban networks which potentially may lead to sources of competitive advantage. All actors involved in the configuration of this urban ethical fashion network from public authorities to private enterprise can bring about positive changes in the urban setting. Preliminary results through action research show that this configuration is an attainable task in terms of circularity by reducing fabric waste produced from local textile mills and through innovative methods of design, production and retail around urban spaces where the network has managed to generate a stream of jobs and financial revenues for all participants. The municipal authorities as the facilitating platform have been of paramount importance in this public-private partnership. In the explorative pilot study conducted about a network of production, consumption in terms of circularity of fashion products, we have experienced a positive disposition. As the network will be fully functional by attracting more participant firms from the textile and clothing sectors, it can be beneficial to Taiwan’s soft power in the region and simultaneously elevate citizens’ awareness on circular methods of fashion production, consumption and disposal which can also lead to the betterment of urban lifestyle and may open export horizons for the firms.

Keywords: the circular economy, the creative economy, ethical urban networks, action research

Procedia PDF Downloads 129

Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Abdulrahman S. Alqahtani

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The revolution of computing and networks could revolutionise terrorism in the same way that it has brought about changes in other aspects of life. The modern technological era has faced countries with a new set of security challenges. There are many states and potential adversaries who have the potential and capacity in cyberspace, which makes them able to carry out cyber-attacks in the future. Some of them are currently conducting surveillance, gathering and analysis of technical information, and mapping of networks and nodes and infrastructure of opponents, which may be exploited in future conflicts. This poster presents the results of the quantitative study (survey) to test the validity of the proposed theoretical framework for the cyber terrorist threats. This theoretical framework will help to in-depth understand these new digital terrorist threats. It may also be a practical guide for managers and technicians in critical infrastructure, to understand and assess the threats they face. It might also be the foundation for building a national strategy to counter cyberterrorism. In the beginning, it provides basic information about the data. To purify the data, reliability and exploratory factor analysis, as well as confirmatory factor analysis (CFA) were performed. Then, Structural Equation Modelling (SEM) was utilised to test the final model of the theory and to assess the overall goodness-of-fit between the proposed model and the collected data set.

Keywords: cyberterrorism, critical infrastructure, , national security, theoretical framework, terrorism

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Authors: Rajeev Kumar, Angad Singh Kushwaha, Amritanshu Pandey, S. K. Srivastava

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Photonic crystals (PCs) have attracted much attention due to its electromagnetic properties and potential applications. In PCs, there is certain range of wavelength where electromagnetic waves are not allowed to pass are called photonic band gap (PBG). A localized defect mode will appear within PBG, due to change in the interference behavior of light, when we create a defect in the periodic structure. We can also create different types of defect structures by inserting or removing a layer from the periodic layered structure in two and three-dimensional PCs. We can design microcavity, waveguide, and perfect mirror by creating a point defect, line defect, and palanar defect in two and three- dimensional PC structure. One-dimensional and two-dimensional PCs with defects were reported theoretically and experimentally by Smith et al.. in conventional photonic band gap structure. In the present paper, we have presented the defect mode tunability in tilted non-graded photonic crystal (NGPC) and linearly graded photonic crystal (LGPC) using lead sulphide (PbS) and titanium dioxide (TiO2) in the infrared region. A birefringent defect layer is created in NGPC and LGPC using potassium titany phosphate (KTP). With the help of transfer matrix method, the transmission properties of proposed structure is investigated for transverse electric (TE) and transverse magnetic (TM) polarization. NGPC and LGPC without defect layer is also investigated. We have found that a photonic band gap (PBG) arises in the infrared region. An additional defect layer of KTP is created in NGPC and LGPC structure. We have seen that an additional transmission mode appers in PBG region. It is due to the addition of defect layer. We have also seen the effect, linear gradation in thickness, angle of incidence, tilt angle, and thickness of defect layer, on PBG and additional transmission mode. We have observed that the additional transmission mode and PBG can be tuned by changing the above parameters. The proposed structure may be used as channeled filter, optical switches, monochromator, and broadband optical reflector.

Keywords: defect modes, graded photonic crystal, photonic crystal, tilt angle

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Authors: Shahriyar Keshtgar, Mahmoud Reza Heyhat, Sasan Bagheri, Ebrahim Gholami, Seyed Naser Raiisosadat

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Various tectonic interpretations have been presented by different researchers to explain the geological evolution of eastern Iran, but there are still many ambiguities and many disagreements about the geodynamic nature of the Paleogene mountain range of eastern Iran. The purpose of this research is to clarify and discuss the tectonic position of the foreland and hinterland regions of eastern Iran from the tectonic perspective of sedimentary basins. In the tectonic model of oceanic subduction crust under the Afghan block, the hinterland is located to the east and on the Afghan block, and the foreland is located on the passive margin of the Sistan open ocean in the west. After the collision of the two microcontinents, the foreland basin must be located somewhere on the passive margin of the Lut block. This basin can deposit thick Paleocene to Oligocene sediments on the Cretaceous and older sediments. Thrust faults here will move towards the west. If we accept the subduction model of the Sistan Ocean under the Lut Block, the hinterland is located to the west towards the Lut Block, and the foreland basin is located towards the Sistan Ocean in the east. After the collision of the two microcontinents, the foreland basin with Paleogene sediments should expand on the Sefidaba basin. Thrust faults here will move towards the east. If we consider the two-sided subduction model of the ocean crust under both Lut and Afghan continental blocks, the tectonic position of the foreland and hinterland basins will not change and will be similar to the one-sided subduction models. After the collision of two microcontinents, the foreland basin should develop in the central part of the eastern Iranian orogen. In the oroclinic buckling model, the foreland basin will continue not only in the east and west but continuously in the north as well. In this model, since there is practically no collision, the foreland basin is not developed, and the remnants of the Sistan Ocean ophiolites and their deep turbidite sediments appear in the axial part of the mountain range, where the Neh and Khash complexes are located. The structural data from this research in the northern border of the Sistan belt and the Lut block indicate the convergence of the tectonic vergence directions towards the interior of the Sistan belt (in the Ahangaran area towards the southwest, in the north of Birjand towards the south-southeast, in the Sechengi area to the southeast). According to this research, not only the general movement of thrust sheets do not follow the linear orogeny models, but the expected active foreland basins have not been formed in the mentioned places in eastern Iran. Therefore, these results do not follow previous tectonic models for eastern Iran (i.e., rifting of eastern Iran continental crust and subsequent linear collision of the Lut and Afghan blocks), but it seems that was caused by buckling model in the Late Eocene-Oligocene.

Keywords: foreland, hinterland, tectonic vergence, orocline buckling, eastern Iran

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Authors: N. A. Hamizi, M. R. Johan, Y. H. Hor, A. N. Sabri, Y. Y. A. Yong

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In this research, Mn-doped CdSe QDs is synthesized by using paraffin liquid as the reacting solvent and oleic acid as the ligands for Cd in order to produce Mn-doped CdSe QDs in zinc-blende crystal structure. Characterization studies for synthesized Mn-doped CdSe QDs are carried out using UV-visible and photoluminescence spectroscopy. The absorption wavelengths in UV-vis test and emission wavelengths in PL test were increase with the increases in the ripening temperature and time respectively.

Keywords: semiconductor, chemical synthesis, optical properties, ripening

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

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Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

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This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

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Authors: Hadi Adibi-Asl, Alireza Doosthosseini, Amir Taghavipour

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This paper studies the design of an adaptive control strategy to tune an active steering system for better drivability and maneuverability. In the first step, adaptive control strategy is applied to estimate the uncertain parameters on-line (e.g. cornering stiffness), then the estimated parameters are fed into the pole placement controller to generate corrective feedback gain to improve the steering system dynamic’s characteristics. The simulations are evaluated for three types of road conditions (dry, wet, and icy), and the performance of the adaptive pole placement control (APPC) are compared with pole placement control (PPC) and a passive system. The results show that the APPC strategy significantly improves the yaw rate and side slip angle of a bicycle plant model.

Keywords: adaptive control, active steering, pole placement, vehicle dynamics

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Elnaz Kanani-Kuchesfehani, Andrea Schiffauerova

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Due to the novelty of the nanotechnology science, its highly knowledge intensive content, and its invaluable application in almost all technological fields, the close interaction between university and industry is essential. A possible gap between academic strengths to generate good nanotechnology ideas and industrial capacity to receive them can thus have far-reaching consequences. In order to be able to enhance the collaboration between the two parties, a better understanding of knowledge transfer within the university-industry relationship is needed. The objective of this research is to investigate the research collaboration between academia and industry in Canadian nanotechnology and to propose the best cooperative strategy to maximize the quality of the produced knowledge. First, a network of all Canadian academic and industrial nanotechnology inventors is constructed using the patent data from the USPTO (United States Patent and Trademark Office), and it is analyzed with social network analysis software. The actual level of university-industry collaboration in Canadian nanotechnology is determined and the significance of each group of actors in the network (academic vs. industrial inventors) is assessed. Second, a novel methodology is proposed, in which the network of nanotechnology inventors is assessed from a game theoretic perspective. It involves studying a cooperative game with n players each having at most n-1 decisions to choose from. The equilibrium leads to a strategy for all the players to choose their co-worker in the next period in order to maximize the correlated payoff of the game. The payoffs of the game represent the quality of the produced knowledge based on the citations of the patents. The best suggestion for the next collaborative relationship is provided for each actor from a game theoretic point of view in order to maximize the quality of the produced knowledge. One of the major contributions of this work is the novel approach which combines game theory and social network analysis for the case of large networks. This approach can serve as a powerful tool in the analysis of the strategic interactions of the network actors within the innovation systems and other large scale networks.

Keywords: cooperative strategy, game theory, industry-university collaboration, knowledge production, social network analysis

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Authors: M. Kowalski, M. Kastek, M. Szustakowski

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Multispectral screening systems are becoming more popular because of their very interesting properties and applications. One of the most significant applications of multispectral screening systems is prevention of terrorist attacks. There are many kinds of threats and many methods of detection. Visual detection of objects hidden under clothing of a person is one of the most challenging problems of threats detection. There are various solutions of the problem; however, the most effective utilize multispectral surveillance imagers. The development of imaging devices and exploration of new spectral bands is a chance to introduce new equipment for assuring public safety. We investigate the possibility of long lasting detection of potentially dangerous objects covered with various types of clothing. In the article we present the results of comparative studies of passive imaging in three spectrums – visible, infrared and terahertz

Keywords: terahertz, infrared, object detection, screening camera, image processing

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Tingkai Zhao, Jingtian Hu, Xiarong Peng, Wenbo Yang, Tiehu Li

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Homogeneous amorphous carbon nanotube (ACNT) networks have been synthesized using floating catalyst chemical vapor deposition method on a three-dimensional (3D) graphene aerogel (GA)/BaFe₁₂O₁₉ nanorod (BNR) composite which prepared by a self-propagating combustion process. The as-synthesized ACNT/GA/BNR composite which has 3D network structures could be directly used as a good absorber in the electromagnetic wave absorbent materials. The experimental results indicated that the maximum absorbing peak of ACNT/GA/BNR composite with a thickness of 2 mm was -18.35 dB at 10.64 GHz in the frequency range of 2-18 GHz. The bandwidth of the reflectivity below -10 dB is 3.32 GHz. The 3D graphene aerogel structures which composed of dense interlined tubes and amorphous structure of ACNTs bearing quantities of dihedral angles could consume the incident waves through multiple reflection and scattering inside the 3D web structures. The interlinked ACNTs have both the virtues of amorphous CNTs (multiple reflections inside the wall) and crystalline CNTs (high conductivity), consuming the electromagnetic wave as resistance heat. ACNT/GA/BNR composite has a good electromagnetic wave absorbing performance.

Keywords: amorphous carbon nanotubes, graphene aerogel, barium ferrite nanorod, electromagnetic wave absorption

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Authors: Vijay Marisetty, Poonam Singh

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Research on the effect of director's network connections on investor welfare is inconclusive. Some studies suggest that directors' connections are beneficial, in terms of, improving earnings information, firms valuation for new investors. On the other hand, adverse effects of directorial networks are also reported, in terms of higher earnings management, options back dating fraud, reduction in firm performance, lower board monitoring. From regulatory perspective, the role of directorial networks on corporate welfare is crucial. Cognizant of the possible ill effects associated with directorial networks, large investors, for better representation on the boards, are building their own database of prospective directors who are highly qualified, however, sourced from outside the highly connected directorial labor market. For instance, following Dodd-Frank Reform Act, California Public Employees' Retirement Systems (CalPERs) has initiated a database for registering aspiring and highly qualified directors to nominate them for board seats (proxy access). Our paper stems from this background and tries to explore the chances of outside directors getting directorships who lack established network connections. The paper is able to identify such aspiring directors' information by accessing a unique Indian data sourced from an online portal that aims to match the supply of registered aspirants with the growing demand for outside directors in India. The online portal's tie-up with stock exchanges ensures firms to access the new pool of directors. Such direct access to the background details of aspiring directors over a period of 10 years, allows us to examine the chances of aspiring directors without corporate network, to enter directorial network. Using this resume data of 16105 aspiring corporate directors in India, who have no prior board experience in the directorial labor market, the paper analyses the entry dynamics in corporate directors' labor market. The database also allows us to investigate the value of corporate network by comparing non-network new entrants with incumbent networked directors. The study develops measures of network centrality and network degree based on merit, i.e. network of individuals belonging to elite educational institutions, like Indian Institute of Management (IIM) or Indian Institute of Technology (IIT) and based on job or company, i.e. network of individuals serving in the same company. The paper then measures the impact of these networks on the appointment of first time directors and subsequent appointment of directors. The paper reports the following main results: 1. The likelihood of becoming a corporate director, without corporate network strength, is only 1 out 100 aspirants. This is inspite of comparable educational background and similar duration of corporate experience; 2. Aspiring non-network directors' elite educational ties help them to secure directorships. However, for post-board appointments, their newly acquired corporate network strength overtakes as their main determinant for subsequent board appointments and compensation. The results thus highlight the limitations in increasing board diversity.

Keywords: aspiring corporate directors, board diversity, director labor market, director networks

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Authors: Eileen Y. Wong, Katherine Jin, Anat Talmon

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Background: Help seeking behaviors are highly contingent on socio-cultural factors such as ethnicity. Both Latino and Asian Americans underutilize mental health services compared to their White American counterparts. This difference may be related to the composite of one’s social support system, which includes family cohesion and social networks. Previous studies have found that Latino families are characterized by higher levels of family cohesion and social support, and Asian American families with greater family cohesion exhibit lower levels of help seeking behaviors. While both are broadly considered collectivist communities, within-culture variability is also significant. Therefore, this study aims to investigate the relationship between help seeking behaviors in the two cultures with levels of family cohesion and strength of social network. We also consider such relationships in light of previous traumatic events and diagnoses, particularly post-traumatic stress disorder (PTSD), to understand whether clinically diagnosed individuals differ in their strength of network and help seeking behaviors. Method: An adult sample (N = 2,990) from the National Latino and Asian American Study (NLAAS) provided data on participants’ social network, family cohesion, likelihood of seeking professional help, and DSM-IV diagnoses. T-tests compared Latino American (n = 1,576) and Asian American respondents (n = 1,414) in strength of social network, level of family cohesion, and likelihood of seeking professional help. Linear regression models were used to identify the probability of help-seeking behavior based on ethnicity, PTSD diagnosis, and strength of social network. Results: Help-seeking behavior was significantly associated with family cohesion and strength of social network. It was found that higher frequency of expressing one’s feelings with family significantly predicted lower levels of help-seeking behaviors (β = [-.072], p = .017), while higher frequency of spending free time with family significantly predicted higher levels of help-seeking behaviors (β = [.129], p = .002) in the Asian American sample. Subjective importance of family relations compared to that of one’s peers also significantly predict higher levels of help-seeking behaviors (β = [.095], p = .011) in the Asian American sample. Frequency of sharing one’s problems with relatives significantly predicted higher levels of help-seeking behaviors (β = [.113], p < .01) in the Latino American sample. A PTSD diagnosis did not have any significant moderating effect. Conclusion: Considering the underutilization of mental health services in Latino and Asian American minority groups, it is crucial to understand ways in which help seeking behavior can be encouraged. Our findings suggest that different dimensions within family cohesion and social networks have differential impacts on help-seeking behavior. Given the multifaceted nature of family cohesion and cultural relevance, the implications of our findings for theory and practice will be discussed.

Keywords: family cohesion, social networks, Asian American, Latino American, help-seeking behavior

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Authors: Viera Zatkalíková, Lenka Markovičová, Aneta Tor-Swiatek

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The effect of molybdate addition to chloride environment on resistance of AISI 316Ti stainless steel to pitting corrosion was studied. Potentiodynamic polarisation tests were performed in 1 M and 0.1 M chloride acidified solutions with various additions of sodium molybdate at room temperature. The presented results compare the effect of molybdate anions on quality of passive film (expressed by the pitting potential) in both chloride solutions. The pitting potential increases with the increase inhibitor concentration. The inhibitive effect of molybdate ions is stronger in chloride solution of lower aggressiveness (0.1M).

Keywords: AISI 316Ti steel, molybdate inhibitor, pitting corrosion, pitting potential, potentiodynamic polarisation

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Authors: Semere Araya Asefa

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I described twisted bilayer crescent metasurfaces that link optical properties between two layers and enhance circular dichroism. The interactions between the metasurface layers cause circular dichroism. And we evaluated the parameters that affect the chiroptical response of the crescent

Keywords: chiroptical response, chiral metasurface, circular dichroism, chiral sensing

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: C. Senabre, S. Valero, M. Lopez, E. Velasco, M. Sanchez

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This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology.

Keywords: short-term load forecasting, mixed effects models, neural networks, mixed effects models

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Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN

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Authors: Robson C. Santos, David D. Oliveira, Matheus M. Reis, Gerson G. Cunha, Marcos A. C. Moreira

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The project demonstrates the construction of a solar power generation, integrated inverter equipment to a "Grid-Tie" by converting direct current generated by solar panels, into alternating current, the same parameters of frequency and voltage concessionaire distribution network. The energy generated is quantified by smart metering module that transmits the information in specified periods of time to a microcontroller via GSM modem. The modem provides the measured data on the internet, using networks and cellular antennas. The monitoring, fault detection and maintenance are performed by a supervisory station. Employed board types, best inverter selection and studies about control equipment and devices have been described. The article covers and explores the global trend of implementing smart distribution electrical energy networks and the incentive to use solar renewable energy. There is the possibility of the excess energy produced by the system be purchased by the local power utility. This project was implemented in residences in the rural community of the municipality of Cabo Frio/RJ. Data could be seen through daily measurements during the month of November 2013.

Keywords: rural residence, supervisory, smart grid, solar energy

Procedia PDF Downloads 579

Authors: Kritiyaporn Kunsook

Abstract:

Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

Procedia PDF Downloads 363

Authors: Won Jun Jo, Man Young Kim

Abstract:

To meet stringent diesel particulate matter regulations, DPF system is essential after treatment technology providing exceptional reliability and filtration performance. At low load driving conditions, the passive type of DPF system is ineffective for regeneration method due to the inadequate of engine exhaust heat in removing accumulated soot from the filter. Therefore, DPF cleaning device is necessary to remove the soot particles. In this work, the numerical analysis on the active regeneration of DPF in DPF cleaning device is performed to find the optimum operating conditions. In order to find the DPF regeneration characteristics during active regeneration, 5 different initial soot loading condition are investigated. As the initial soot mass increases, the maximum temperature of DPF and regeneration rate also increase.

Keywords: active regeneration, DPF cleaning device, pressure drop, Diesel Particulate Filter, particulate matters, computational fluid dynamics

Procedia PDF Downloads 289

Authors: Heeba A. Gurku

Abstract:

Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

Procedia PDF Downloads 78

Authors: Daniel Y. Abebe, Jaehyouk Choi

Abstract:

The design philosophy of building structure has been changing over time. The reason behind this is an increase in human interest regarding the improvements in building materials and technology that will affect how we live, the aim to speed up construction period, and the environmental effect which includes earthquakes and other natural disasters. One technique which takes into account the above case is using a prefabricable structural system, in which each and every structural element is designed and prefabricated and assembled on a site so that the construction speed is increased and the environmental impact is also enhanced. This system has immense advantages such as reduced construction cost, reusability, recyclability, faster construction period and less enviromental effect. In this study, some of the developed and evaluated structural elements of building structures are presented.

Keywords: eccentrically braced frame, natural disaster, prefabricable structural system, removable link, SGSS

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Authors: Mahbobeh Bozhmehrani, Afshin Farah Bakhsh

Abstract:

Nanoparticles with PbS core of 12 nm and shell of approximately 3 nm were synthesized at PbS:ZnS ratios of 1.01:0.1 using Merca Ptopropionic Acid as stabilizing agent. PbS/ZnS nanoparticles present a dramatically increase of Photoluminescence intensity, confirming the confinement of the PbS core by increasing the Quantum Yield from 0.63 to 0.92 by the addition of the ZnS shell. In this case, the synthesis by microwave method allows obtaining nanoparticles with enhanced optical characteristics than those of nanoparticles synthesized by colloidal method.

Keywords: Pbs/Zns, quantum dots, colloidal method, microwave

Procedia PDF Downloads 279

Authors: Serkan Sayin, Songul F. Varol

Abstract:

High attention on the organic light-emitting diodes has been paid since their efficient properties in the flat panel displays, and solid-state lighting was realized. Because of their high efficient electroluminescence, brightness and providing eminent in the emission range, organic light emitting diodes have been preferred a material compared with the other materials consisting of the liquid crystal. Calixarenes obtained from the reaction of p-tert-butyl phenol and formaldehyde in a suitable base have been potentially used in various research area such as catalysis, enzyme immobilization, and applications, ion carrier, sensors, nanoscience, etc. In addition, their tremendous frameworks, as well as their easily functionalization, make them an effective candidate in the applied chemistry. Herein, a calix[4]arene derivative has been synthesized, and its structure has been fully characterized using Fourier Transform Infrared Spectrophotometer (FTIR), proton nuclear magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR), liquid chromatography-mass spectrometry (LC-MS), and elemental analysis techniques. The calixarene derivative has been employed as an emitting layer in the fabrication of the organic light-emitting diodes. The optical and electrochemical features of calixarane-contained organic light-emitting diodes (Clx-OLED) have been also performed. The results showed that Clx-OLED exhibited blue emission and high external quantum efficacy. As a conclusion obtained results attributed that the synthesized calixarane derivative is a promising chromophore with efficient fluorescent quantum yield that provides it an attractive candidate for fabricating effective materials for fluorescent probes and labeling studies. This study was financially supported by the Scientific and Technological Research Council of Turkey (TUBITAK Grant no. 117Z402).

Keywords: calixarene, OLED, supramolecular chemistry, synthesis

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Authors: Jihad S. Daba, J. P. Dubois, Yvette Antar

Abstract:

With the exponential growth of cellular users, a new generation of cellular networks is needed to enhance the required peak data rates. The co-channel interference between neighboring base stations inhibits peak data rate increase. To overcome this interference, multi-cell cooperation known as coordinated multipoint transmission is proposed. Such a solution makes use of multiple-input-multiple-output (MIMO) systems under two different structures: Micro- and macro-diversity. In this paper, we study the capacity and bit error rate in cellular networks using MIMO technology. We analyse both micro- and macro-diversity schemes and develop a hybrid model that switches between macro- and micro-diversity in the case of hard handoff based on a cut-off range of signal-to-noise ratio values. We conclude that our hybrid switched micro-macro MIMO system outperforms classical MIMO systems at the cost of increased hardware and software complexity.

Keywords: cooperative multipoint transmission, ergodic capacity, hard handoff, macro-diversity, micro-diversity, multiple-input-multiple output systems, orthogonal frequency division multiplexing

Procedia PDF Downloads 304