Search results for: RBF neural network modelling

Authors: Jang kyun Cho, Jeong-dong Lee

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The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Shahul Hamid Khan, S. Santhosh, Abhinav Kumar Sharma

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Environmental performance along with social performance is becoming vital factors for industries to achieve global standards. With a good environmental policy global industries are differentiating them from their competitors. This paper concentrates on multi stage, multi product and multi period manufacturing network. Bi-objective mathematical models for total cost and total emission for the entire forward supply chain are considered. Here five different problems are considered by varying the number of suppliers, manufacturers, and environmental levels, for illustrating the taken mathematical model. GA, and Random search are used for finding the optimal solution. The input parameters of the optimal solution are used to find the tradeoff between the initial investment by the industry and the long term benefit of the environment.

Keywords: closed loop supply chain, genetic algorithm, random search, green supply chain

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Authors: África Vicario, Fernando J. Álvarez, Felipe Parralejo, Fernando Aranda

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The irrigation systems of traditional agriculture, such as gravity-fed irrigation, produce a great waste of water because, generally, there is no control over the amount of water supplied in relation to the water needed. The AIRON Project tries to solve this problem by implementing an IoT-based system to sensor the irrigation plots so that the state of the crops and the amount of water used for irrigation can be known remotely. The IoT system consists of a sensor network that measures the humidity of the soil, the weather conditions (temperature, relative humidity, wind and solar radiation) and the irrigation water flow. The communication between this network and a central gateway is conducted by means of long-range wireless communication that depends on the characteristics of the irrigation plot. The main objective of the AIRON project is to deploy an IoT sensor network in two different plots of the irrigation community of Aranjuez in the Spanish region of Madrid. The first plot is 2 km away from the central gateway, so LoRa has been used as the base communication technology. The problem with this plot is the absence of mains electric power, so devices with energy-saving modes have had to be used to maximize the external batteries' use time. An ESP32 SOC board with a LoRa module is employed in this case to gather data from the sensor network and send them to a gateway consisting of a Raspberry Pi with a LoRa hat. The second plot is located 18 km away from the gateway, a range that hampers the use of LoRa technology. In order to establish reliable communication in this case, the long-term evolution (LTE) standard is used, which makes it possible to reach much greater distances by using the cellular network. As mains electric power is available in this plot, a Raspberry Pi has been used instead of the ESP32 board to collect sensor data. All data received from the two plots are stored on a proprietary server located at the irrigation management company's headquarters. The analysis of these data by means of machine learning algorithms that are currently under development should allow a short-term prediction of the irrigation water demand that would significantly reduce the waste of this increasingly valuable natural resource. The major finding of this work is the real possibility of deploying a remote sensing system for irrigated plots by using Commercial-Off-The-Shelf (COTS) devices, easily scalable and adaptable to design requirements such as the distance to the control center or the availability of mains electrical power at the site.

Keywords: internet of things, irrigation water control, LoRa, LTE, smart farming

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Authors: Mohammad Reza Taherkhani‎

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

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Authors: Aimen Saleh

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The Silurian Lower Acacus sandstone is one of the main reservoirs in North West Libya. Our aim in this study is to grasp a robust understanding of the hydrocarbon potential and distribution in the area. To date, the depositional environment of the Lower Acacus reservoir still open to discussion and contradiction. Henceforth, building three dimensional (3D) property modelling is one way to support the analysis and description of the reservoir, its properties and characterizations, so this will be of great value in this project. The 3D model integrates different data set, these incorporates well logs data, petrophysical reservoir properties and seismic data as well. The finalized depositional environment model of the Lower Acacus concludes that the area is located in a deltaic transitional depositional setting, which ranges from a wave dominated delta into tide dominated delta type. This interpretation carried out through a series of steps of model generation, core description and Formation Microresistivity Image tool (FMI) interpretation. After the analysis of the core data, the Lower Acacus layers shows a strong effect of tidal energy. Whereas these traces found imprinted in different types of sedimentary structures, for examples; presence of some crossbedding, such as herringbones structures, wavy and flaser cross beddings. In spite of recognition of some minor marine transgression events in the area, on the contrary, the coarsening upward cycles of sand and shale layers in the Lower Acacus demonstrate presence of a major regressive phase of the sea level. However, consequently, we produced a final package of this model in a complemented set of facies distribution, porosity and oil presence. And also it shows the record of the petroleum system, and the procedure of Hydrocarbon migration and accumulation. Finally, this model suggests that the area can be outlined into three main segments of hydrocarbon potential, which can be a textbook guide for future exploration and production strategies in the area.

Keywords: Acacus, Ghadames , Libya, Silurian

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Authors: Alaa Allakany, Koji Okamura

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Multicast technology is an efficient and scalable technology for data distribution in order to optimize network resources. However, in the IP network, the responsibility for management of multicast groups is distributed among network routers, which causes some limitations such as delays in processing group events, high bandwidth consumption and redundant tree calculation. Software Defined Networking (SDN) represented by OpenFlow presented as a solution for many problems, in SDN the control plane and data plane are separated by shifting the control and management to a remote centralized controller, and the routers are used as a forwarder only. In this paper we will proposed fast switching mechanism for solving the problem of link failure in multicast tree based on Tabu Search heuristic algorithm and modifying the functions of OpenFlow switch to fasts switch to the pack up sub tree rather than sending to the controller. In this work we will implement multicasting OpenFlow controller, this centralized controller is a core part in our multicasting approach, which is responsible for 1- constructing the multicast tree, 2- handling the multicast group events and multicast state maintenance. And finally modifying OpenFlow switch functions for fasts switch to pack up paths. Forwarders, forward the multicast packet based on multicast routing entries which were generated by the centralized controller. Tabu search will be used as heuristic algorithm for construction near optimum multicast tree and maintain multicast tree to still near optimum in case of join or leave any members from multicast group (group events).

Keywords: multicast tree, software define networks, tabu search, OpenFlow

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Authors: Aliasghar Baziar, Hassan Masoumi, Alireza Ale Saadi

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The control of the response of electric motors plays a significant role in the damping of transient responses. In this regard, this paper presents a static VAR compensator (SVC) based on a fuzzy logic which is applied to an industrial power network consisting of three phase synchronous, asynchronous and DC motor loads. The speed and acceleration variations of a specific machine are the inputs of the proposed fuzzy logic controller (FLC). In order to verify the effectiveness and proficiency of the proposed Fuzzy Logic based SVC (FLSVC), several non-linear time-domain digital simulation tests are performed. The proposed fuzzy model can properly control the response of electric motors. The results show that the FLSVC is successful to improve the voltage profile significantly over a wide range of operating conditions and disturbances thus improving the overall dynamic performance of the network.

Keywords: fuzzy logic controller, VAR compensator, single cage asynchronous motor, DC motor

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Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

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Authors: Ahmed Abdelghany, Bernard Uguen, Christophe Moy, Dominique Lemur

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The Internet of Things (IoT) is developed to ensure monitoring and connectivity within different applications. Thus, it is critical to study the channel propagation characteristics in Low Power Wide Area Network (LPWAN), especially Long Range Wide Area Network (LoRaWAN). In this paper, an in-depth investigation of the reciprocity between the uplink and downlink Channel State Information (CSI) is done by performing an outdoor measurement campaign in the area of Campus Beaulieu in Rennes. At each different location, the CSI reciprocity is quantified using the Pearson Correlation Coefficient (PCC) which shows a very high linear correlation between the uplink and downlink CSI. This reciprocity feature could be utilized for the physical layer security between the node and the gateway. On the other hand, most of the CSI shapes from different locations are highly uncorrelated from each other. Hence, it can be anticipated that this could achieve significant localization gain by utilizing the frequency hopping in the LoRa systems by getting access to a wider band.

Keywords: IoT, LPWAN, LoRa, effective signal power, onsite measurement

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

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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

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Authors: Muhammad Aliyu

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Significant improvement in Information and Communication Technology (ICT) and the enforced global competition are revolutionizing the way knowledge is managed and the way organizations compete. The emergence of new organizations calls for a new way to fraternize knowledge, which is known as 'knowledge fraternization.' In this modern economy, it is the knowledge if properly managed that can harness the organization's competitive advantage. This competitive advantage is realized through the full utilization of information and data coupled with the harnessing of people’s skills and ideas as well as their commitment and motivations, which can be accomplished through socializing the knowledge management processes. A fraternize network for knowledge management is a web-based system designed using PHP that is Dreamweaver web development tool, with the help of CS4 Adobe Dreamweaver as the PHP code Editor that supports the use of Cascadian Style Sheet (CSS), MySQL with Xamp, Php My Admin (Version 3.4.7) localhost server via TCP/IP for containing the databases of the system to support this in a distributed way, spreading the workload over the whole organization. This paper reviews the technologies and the technology tools to be used in the development of social networks in an organization.

Keywords: Information and Communication Technology (ICT), knowledge, fraternization, social network

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Authors: Krishna Pal Singh, Shailendra Kumar Gupta, Olaf Wolkenhauer

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Background: Our study aimed to identify common genes and potential targets across the four diseases, which include rheumatoid arthritis, melanoma metastasis, ulcerative colitis, and Crohn’s disease. We used a network and systems biology approach to identify the hub gene, which can act as a potential target for all four disease conditions. The regulatory network was extracted from the PPI using the MCODE module present in Cytoscape. Our objective was to investigate the significance of hub genes in these diseases using gene ontology and KEGG pathway enrichment analysis. Methods: Our methodology involved collecting disease gene-related information from DisGeNET databases and performing protein-protein interaction (PPI) network and core genes screening. We then conducted gene ontology and KEGG pathway enrichment analysis. Results: We found that IL6 plays a critical role in all disease conditions and in different pathways that can be associated with the development of all four diseases. Conclusions: The theoretical importance of our research is that we employed various systems and structural biology techniques to identify a crucial protein that could serve as a promising target for treating multiple diseases. Our data collection and analysis procedures involved rigorous scrutiny, ensuring high-quality results. Our conclusion is that IL6 plays a significant role in all four diseases, and it can act as a potential target for treating them. Our findings may have important implications for the development of novel therapeutic interventions for these diseases.

Keywords: melanoma metastasis, rheumatoid arthritis, inflammatory bowel diseases, integrated bioinformatics analysis

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Authors: Yi-De Liu

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The aim of this paper is to discuss how the construct of networking and social capital can be used to understand the effect events can have on the cultural sector. Based on case study, this research sought the views of those working in the cultural sector on Liverpool’s year as the European Capital of Culture (ECOC). Methodologically, this study involves literature review to prompt theoretical sensitivity, the collection of primary data via online survey (n= 42) and follow-up telephone interviews (n= 8) to explore the emerging findings in more detail. The findings point to a number of ways in which the ECOC constitutes a boost for networking and its effects on city’s cultural sector, including organisational learning, aspiration and leadership. The contributions of this study are two-fold: (1) Evaluating the long-term effects on network formation in the cultural sector following major event; (2) conceptualising the impact assessment of organisational social capital for future ECOC or similar events.

Keywords: network, social capital, cultural impact, european capital of culture

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Authors: Mustafa Mutahari, Nao Sugiki, Kojiro Matsuo

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The four decades of war, lack of job opportunities, poverty, lack of services, and natural disasters in different provinces of Afghanistan have contributed to a rapid increase in the population of Kabul, the capital city of Afghanistan. Population census has not been conducted since 1979, the first and last population census in Afghanistan. However, according to population estimations by Afghan authorities, the population of Kabul has been estimated at more than 4 million people, whereas the city was designed for two million people. Although the major transport mode of Kabul residents is public transport, responsible authorities within the country failed to supply the required means of transportation systems for the city. Besides, informal resettlement, lack of intersection control devices, presence of illegal vendors on streets, presence of illegal and unstandardized on-street parking and bus stops, driver`s unprofessional behavior, weak traffic law enforcement, and blocked roads and sidewalks have contributed to the extreme traffic congestion of Kabul. In 2018, the government of Afghanistan approved the Kabul city Urban Design Framework (KUDF), a vision towards the future of Kabul, which provides strategies and design guidance at different scales to direct urban development. Considering traffic congestion of the city and its budget limitations, the KUDF proposes a BRT route network with seven lines to reduce the traffic congestion, and it is said to facilitate more than 50% of Kabul population to benefit from this service. Based on the KUDF, it is planned to increase the BRT mode share from 0% to 17% and later to 30% in medium and long-term planning scenarios, respectively. Therefore, a detailed research study is needed to evaluate the proposed system before the implementation stage starts. The integrated land-use transport model is an effective tool to evaluate the Kabul BRT because of its future assessment capabilities that take into account the interaction between land use and transportation. This research aims to analyze and evaluate the proposed BRT route network with the application of an integrated land-use and transportation model. The research estimates the population distribution and travel behavior of Kabul within small boundary scales. The actual road network and land-use detailed data of the city are used to perform the analysis. The BRT corridors are evaluated not only considering its impacts on the spatial interactions in the city`s transportation system but also on the spatial developments. Therefore, the BRT are evaluated with the scenarios of improving the Kabul transportation system based on the distribution of land-use or spatial developments, planned development typology and population distribution of the city. The impacts of the new improved transport system on the BRT network are analyzed and the BRT network is evaluated accordingly. In addition, the research also focuses on the spatial accessibility of BRT stops, corridors, and BRT line beneficiaries, and each BRT stop and corridor are evaluated in terms of both access and geographic coverage, as well.

Keywords: accessibility, BRT, integrated land-use and transport model, travel behavior, spatial development

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Authors: Bashar Mahmoud, Lee Mortimer, Michael Fairweather

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New methods have recently been introduced to improve the thermal property values of molten nitrate salts (a binary mixture of NaNO3:KNO3in 60:40 wt. %), by doping them with minute concentration of nanoparticles in the range of 0.5 to 1.5 wt. % to form the so-called: Nano-heat-transfer-fluid, apt for thermal energy transfer and storage applications. The present study aims to assess the stability of these nanofluids using the advanced computational modelling technique, Lagrangian particle tracking. A multi-phase solid-liquid model is used, where the motion of embedded nanoparticles in the suspended fluid is treated by an Euler-Lagrange hybrid scheme with fixed time stepping. This technique enables measurements of various multi-scale forces whose characteristic (length and timescales) are quite different. Two systems are considered, both consisting of 50 nm Al2O3 ceramic nanoparticles suspended in fluids of different density ratios. This includes both water (5 to 95 °C) and molten nitrate salt (220 to 500 °C) at various volume fractions ranging between 1% to 5%. Dynamic properties of both phases are coupled to the ambient temperature of the fluid suspension. The three-dimensional computational region consists of a 1μm cube and particles are homogeneously distributed across the domain. Periodic boundary conditions are enforced. The particle equations of motion are integrated using the fourth order Runge-Kutta algorithm with a very small time-step, Δts, set at 10-11 s. The implemented technique demonstrates the key dynamics of aggregated nanoparticles and this involves: Brownian motion, soft-sphere particle-particle collisions, and Derjaguin, Landau, Vervey, and Overbeek (DLVO) forces. These mechanisms are responsible for the predictive model of aggregation of nano-suspensions. An energy transport-based method of predicting the thermal conductivity of the nanofluids is also used to determine thermal properties of the suspension. The simulation results confirms the effectiveness of the technique. The values are in excellent agreement with the theoretical and experimental data obtained from similar studies. The predictions indicates the role of Brownian motion and DLVO force (represented by both the repulsive electric double layer and an attractive Van der Waals) and its influence in the level of nanoparticles agglomeration. As to the nano-aggregates formed that was found to play a key role in governing the thermal behavior of nanofluids at various particle concentration. The presentation will include a quantitative assessment of these forces and mechanisms, which would lead to conclusions about nanofluids, heat transfer performance and thermal characteristics and its potential application in solar thermal energy plants.

Keywords: thermal energy storage, molten salt, nano-fluids, multi-scale computational modelling

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Authors: Hossein Afzali

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Increasingly, decision analytic models are used to inform decisions about whether or not to publicly fund new health technologies. It is well noted that the accuracy of model predictions is strongly influenced by the appropriateness of model structuring. However, there is relatively inadequate methodological guidance surrounding this issue in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC) and The National Institute for Health and Care Excellence (NICE) in the UK. This presentation aims to discuss issues around model structuring within decision making with a focus on (1) the need for a transparent and evidence-based model structuring process to inform the most appropriate set of structural aspects as the base case analysis; (2) the need to characterise structural uncertainty (If there exist alternative plausible structural assumptions (or judgements), there is a need to appropriately characterise the related structural uncertainty). The presentation will provide an opportunity to share ideas and experiences on how the guidelines developed by national funding bodies address the above issues and identify areas for further improvements. First, a review and analysis of the literature and guidelines developed by PBAC and NICE will be provided. Then, it will be discussed how the issues around model structuring (including structural uncertainty) are not handled and justified in a systematic way within the decision-making process, its potential impact on the quality of public funding decisions, and how it should be presented in submissions to national funding bodies. This presentation represents a contribution to the good modelling practice within the decision-making process. Although the presentation focuses on the PBAC and NICE guidelines, the discussion can be applied more widely to many other national funding bodies that use economic evaluation to inform funding decisions but do not transparently address model structuring issues e.g. the Medical Services Advisory Committee (MSAC) in Australia or the Canadian Agency for Drugs and Technologies in Health.

Keywords: decision-making process, economic evaluation, good modelling practice, structural uncertainty

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: artificial neural networks, ANN, discrete wavelet transform, DWT, gray-level co-occurrence matrix, GLCM, k-nearest neighbor, KNN, region of interest, ROI

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Authors: Bandreddy Anand Babu, Srinivasa Rao Mandadi, C. Pradeep Reddy, N. Ramesh Babu

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In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming.

Keywords: ARIMA, ANFIS, fuzzy surmising tool stash, weather forecasting, MATLAB

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Ghobad Gorji, Hasan Golabi

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The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is directly generated into the lower band of the UWB spectrum, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying (DCSK), were studied before, and their performance was evaluated. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: UWB, DCC, IEEE 802.15.4a, COOK, DCSK

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Authors: Zouari Ezzeddine, Ghraieb Ikram

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From the results of the academic literature which evokes the limitations of previous studies, this article shows the reasons for multidimensionality Prediction of financial bubbles. A new framework for modeling study predicting financial bubbles by linking a set of variable presented on several dimensions dictating its multidimensional character. It takes into account the preferences of financial actors. A multicriteria anticipation of the appearance of bubbles in international financial markets helps to fight against a possible crisis.

Keywords: classical measures, predictions, financial bubbles, multidimensional, artificial neural networks

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity

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Authors: Leon Mishnaevsky Jr., Gaoming Dai

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3D numerical simulations of fatigue damage of multiscale fiber reinforced polymer composites with secondary nanoclay reinforcement are carried out. Macro-micro FE models of the multiscale composites are generated automatically using Python based software. The effect of the nanoclay reinforcement (localized in the fiber/matrix interface (fiber sizing) and distributed throughout the matrix) on the crack path, damage mechanisms and fatigue behavior is investigated in numerical experiments.

Keywords: computational mechanics, fatigue, nanocomposites, composites

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Harshadkumar Rajgor, Jeff Butterworth

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Background: Barretts oesophagus increases the risk of developing oesophageal adenocarcinoma. Over the last 40 years, there has been a 6 fold increase in the incidence of oesophageal adenocarcinoma in the western world and the incidence rates are increasing at a greater rate than cancers of the colon, breast and lung. Endoscopic mucosal resection (EMR) is a relatively new technique being used by 2 centres in the greater midlands cancer network. EMR can be used for curative or staging purposes, for high-grade dysplasia’s and T1 tumours of the oesophagus. EMR is also suitable for those who are deemed high risk for oesophagectomy. EMR has a recurrence rate of 21% according to the Wiesbaden data. Method: A retrospective study of prospectively collected data was carried out involving 24 patients who had EMR for curative or staging purposes. Complications of residual or recurrent disease following EMR that required further treatment were investigated. Results: In 54% of cases residual or recurrent disease was suspected. 96% of patients were given clear and concise information regarding their diagnosis of high-grade dysplasia or T1 tumours. All 24 patients consulted the same specialist healthcare team. Conclusion: EMR is a safe and effective treatment for patients who have high-grade dysplasia and T1NO tumours. In 54% of cases residual or recurrent disease was suspected. Initially, only single resections were undertaken. Multiple resections are now being carried out to reduce the risk of recurrence. Complications from EMR remain low in this series and consisted of a single episode of post procedural bleeding.

Keywords: endoscopic mucosal resection, oesophageal dysplasia, T1 tumours, cancer network

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Authors: Rita Sassine, Yassine Hassani, Mohamad Al Omari, Stéphanie Guibert

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Building Information Modelling (BIM) is the process of creating, managing, and centralizing information during the building lifecycle. BIM can be used all over a construction project, from the initiation phase to the planning and execution phases to the maintenance and lifecycle management phase. For existing buildings, BIM can be used for specific applications such as lifecycle management. However, most of the existing buildings don’t have a BIM model. Creating a compatible BIM for existing buildings is very challenging. It requires special equipment for data capturing and efforts to convert these data into a BIM model. The main difficulties for such projects are to define the data needed, the level of development (LOD), and the methodology to be adopted. In addition to managing information for an existing building, studying the impact of the built environment is a challenging topic. So, integrating the existing terrain that surrounds buildings into the digital model is essential to be able to make several simulations as flood simulation, energy simulation, etc. Making a replication of the physical model and updating its information in real-time to make its Digital Twin (DT) is very important. The Digital Terrain Model (DTM) represents the ground surface of the terrain by a set of discrete points with unique height values over 2D points based on reference surface (e.g., mean sea level, geoid, and ellipsoid). In addition, information related to the type of pavement materials, types of vegetation and heights and damaged surfaces can be integrated. Our aim in this study is to define the methodology to be used in order to provide a 3D BIM model for the site and the existing building based on the case study of “Ecole Spéciale des Travaux Publiques (ESTP Paris)” school of engineering campus. The property is located on a hilly site of 5 hectares and is composed of more than 20 buildings with a total area of 32 000 square meters and a height between 50 and 68 meters. In this work, the campus precise levelling grid according to the NGF-IGN69 altimetric system and the grid control points are computed according to (Réseau Gédésique Français) RGF93 – Lambert 93 french system with different methods: (i) Land topographic surveying methods using robotic total station, (ii) GNSS (Global Network Satellite sytem) levelling grid with NRTK (Network Real Time Kinematic) mode, (iii) Point clouds generated by laser scanning. These technologies allow the computation of multiple building parameters such as boundary limits, the number of floors, the floors georeferencing, the georeferencing of the 4 base corners of each building, etc. Once the entry data are identified, the digital model of each building is done. The DTM is also modeled. The process of altimetric determination is complex and requires efforts in order to collect and analyze multiple data formats. Since many technologies can be used to produce digital models, different file formats such as DraWinG (DWG), LASer (LAS), Comma-separated values (CSV), Industry Foundation Classes (IFC) and ReViT (RVT) will be generated. Checking the interoperability between BIM models is very important. In this work, all models are linked together and shared on 3DEXPERIENCE collaborative platform.

Keywords: building information modeling, digital terrain model, existing buildings, interoperability

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Authors: Shreyas Srinivas Rangan, Jurgis Porins

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The internet traffic has increased exponentially due to the high demand for data rates by the users, and the constantly increasing metro networks and access networks are focused on improving the maximum transmit distance of the long-reach optical networks. One of the common methods to improve the maximum transmit distance of the long-reach optical networks at the component level is to use broadband optical amplifiers. The Erbium Doped Fiber Amplifier (EDFA) provides high amplification with low noise figure but due to the characteristics of EDFA, its operation is limited to C-band and L-band. In contrast, the Raman amplifier exhibits a wide amplification spectrum, and negative noise figure values can be achieved. To obtain such results, high powered pumping sources are required. Operating Raman amplifiers with such high-powered optical sources may cause fire hazards and it may damage the optical system. In this paper, we implement a hybrid optical amplifier configuration. EDFA and Raman amplifiers are used in this hybrid setup to combine the advantages of both EDFA and Raman amplifiers to improve the reach of the system. Using this setup, we analyze the maximum transmit distance of the network by obtaining a correlation diagram between the length of the single-mode fiber (SMF) and the Bit Error Rate (BER). This hybrid amplifier configuration is implemented in a Wavelength Division Multiplexing (WDM) system with a BER of 10⁻⁹ by using NRZ modulation format, and the gain uniformity noise ratio (signal-to-noise ratio (SNR)), the efficiency of the pumping source, and the optical signal gain efficiency of the amplifier are studied experimentally in a mathematical modelling environment. Numerical simulations were implemented in RSoft OptSim simulation software based on the nonlinear Schrödinger equation using the Split-Step method, the Fourier transform, and the Monte Carlo method for estimating BER.

Keywords: Raman amplifier, erbium doped fibre amplifier, bit error rate, hybrid optical amplifiers

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Authors: Chenlu Zhang, Wanni Zhang, Florian Schaule

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Indoor environment in office buildings directly affects comfort, productivity, health, and well-being of building occupants. Wireless environmental sensor networks have been deployed in many modern offices to monitor and control the indoor environments. However, indoor environmental variables are not strong enough predictors of comfort and productivity levels of every occupant due to personal differences, both physiologically and psychologically. This study proposes human-centric sensor networks that integrate wireless environmental sensors, body area network sensors and participatory sensing technologies to collect data from both environment and human and support building operations. The sensor networks have been tested in one small-size and one medium-size office rooms with 22 participants for five months. Indoor environmental data (e.g., air temperature and relative humidity), physiological data (e.g., skin temperature and Galvani skin response), and physiological responses (e.g., comfort and self-reported productivity levels) were obtained from each participant and his/her workplace. The data results show that: (1) participants have different physiological and physiological responses in the same environmental conditions; (2) physiological variables are more effective predictors of comfort and productivity levels than environmental variables. These results indicate that the human-centric sensor networks can support human-centric building control and improve comfort and productivity in offices.

Keywords: body area network, comfort and productivity, human-centric sensors, internet of things, participatory sensing

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Authors: Majid Aldalbahi

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In 2009, over 3.4 billion people in the world resided in urban areas as a result of rapid urban growth. This figure is estimated to increase to 6.5 billion by 2050. This urban growth phenomenon has raised challenges for many countries in both the developing and developed worlds. Urban growth is a complicated process involving the spatiotemporal changes of all socio-economic and physical components at different scales. The socio-economic components of urban growth are related to urban population growth and economic growth, while physical components of urban growth and economic growth are related to spatial expansion, land cover change and land use change which are the focus of this research. The interactions between these components are complex and no-linear. Several factors and forces cause these complex interactions including transportation and communication, internal and international migrations, public policies, high natural growth rates of urban populations and public policies. Urban growth has positive and negative consequences. The positive effects relates to planned and orderly urban growth, while negative effects relate to unplanned and scattered growth, which is called sprawl. Although urban growth is considered as necessary for sustainable urbanization, uncontrolled and rapid growth cause various problems including consumption of precious rural land resources at urban fringe, landscape alteration, traffic congestion, infrastructure pressure, and neighborhood conflicts. Traditional urban planning approaches in fast growing cities cannot accommodate the negative consequences of rapid urban growth. Microsimulation programme, and modelling techniques are effective means to provide new urban development, management and planning methods and approaches. This paper aims to use these techniques to understand and analyse the complex interactions for the case study of Riyadh city, a fast growing city in Saudi Arabia.

Keywords: policy implications, urban planning, traffic congestion, urban growth, Suadi Arabia, Riyadh

Procedia PDF Downloads 473