Search results for: distributed sensor networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5723

Search results for: distributed sensor networks

1283 Biological Control of Woolly Apple Aphid, Eriosoma Lanigerum (Hausmann) in the Nursery Production of Spruce

Authors: Snezana Rajkovic, Miroslava Markovic, Ljubinko Rakonjac, Aleksandar Lucic, Radoslav Rajkovic

Abstract:

Woolly apple aphid, Eriosoma lanigerum (Hausmann) is a widely distributed pest of apple trees, especially where its parasites have been killed by insecticides. It can also be found on pear, hawthorn, mountain ash, and elm trees. Relatively small to medium-sized aphids, characterized by a reddish-brown body, a blood-red stain when crushed and a fluffy, flocculent wax covering. Specialized dermal glands produce the characteristic fluffy or powdery wax, which gives E. lanigerum its characteristic 'woolly' appearance. Also, woolly apple aphid is a problemm in nursery production of spure.The experiments were carried out in the nursery “Nevade” in Gornji Milanovac, "Srbijasume" on the spruce seedlings, aged 2 years. In this study, organic insecticide King Bo, aqueous solution (a. i. oxymatrine 0.2% + psoralen 0.4%), manufacturer Beijing Kingbo Biotech Co. Ltd., Beijing, China. extracted from plants and used as pesticides in nursery production were investigated. King Bo, bioinsecticide is manufactured from refined natural herbal extract several wild medicinal plants, such as Sophora flavescens Ait, Veratrum nigrum L, A. Carmichael, etc. Oxymatrine 2.4 SL is a stomach poison that has antifeeding and repellent action. This substance stimulates development and growth in a host plant and also controls the appearance of downy mildew.The trials were set according to instructions of methods-monitoring of changes in the number of larvae and adults compared to before treatment. The treatment plan was made according to fully randomized block design. The experiment was conducted in four repetitions. The basic plot had the area of 25 m2. Phytotoxicity was estimated by PP methods 1/135 (2), the intensity of infection according to Towsend-Heuberger, the efficiency by Abbott, the analysis of variance with Ducan test and PP/181 (2).

Keywords: bioinsecticide, efficacy, nurssery production, woolly apple aphid

Procedia PDF Downloads 544
1282 Speech Identification Test for Individuals with High-Frequency Sloping Hearing Loss in Telugu

Authors: S. B. Rathna Kumar, Sandya K. Varudhini, Aparna Ravichandran

Abstract:

Telugu is a south central Dravidian language spoken in Andhra Pradesh, a southern state of India. The available speech identification tests in Telugu have been developed to determine the communication problems of individuals having a flat frequency hearing loss. These conventional speech audiometric tests would provide redundant information when used on individuals with high-frequency sloping hearing loss because of better hearing sensitivity in the low- and mid-frequency regions. Hence, conventional speech identification tests do not indicate the true nature of the communication problem of individuals with high-frequency sloping hearing loss. It is highly possible that a person with a high-frequency sloping hearing loss may get maximum scores if conventional speech identification tests are used. Hence, there is a need to develop speech identification test materials that are specifically designed to assess the speech identification performance of individuals with high-frequency sloping hearing loss. The present study aimed to develop speech identification test for individuals with high-frequency sloping hearing loss in Telugu. Individuals with high-frequency sloping hearing loss have difficulty in perception of voiceless consonants whose spectral energy is above 1000 Hz. Hence, the word lists constructed with phonemes having mid- and high-frequency spectral energy will estimate speech identification performance better for such individuals. The phonemes /k/, /g/, /c/, /ṭ/ /t/, /p/, /s/, /ś/, /ṣ/ and /h/are preferred for the construction of words as these phonemes have spectral energy distributed in the frequencies above 1000 KHz predominantly. The present study developed two word lists in Telugu (each word list contained 25 words) for evaluating speech identification performance of individuals with high-frequency sloping hearing loss. The performance of individuals with high-frequency sloping hearing loss was evaluated using both conventional and high-frequency word lists under recorded voice condition. The results revealed that the developed word lists were found to be more sensitive in identifying the true nature of the communication problem of individuals with high-frequency sloping hearing loss.

Keywords: speech identification test, high-frequency sloping hearing loss, recorded voice condition, Telugu

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1281 Computer Network Applications, Practical Implementations and Structural Control System Representations

Authors: El Miloudi Djelloul

Abstract:

The computer network play an important position for practical implementations of the differently system. To implement a system into network above all is needed to know all the configurations, which is responsible to be a part of the system, and to give adequate information and solution in realtime. So if want to implement this system for example in the school or relevant institutions, the first step is to analyze the types of model which is needed to be configured and another important step is to organize the works in the context of devices, as a part of the general system. Often before configuration, as important point is descriptions and documentations from all the works into the respective process, and then to organize in the aspect of problem-solving. The computer network as critic infrastructure is very specific so the paper present the effectiveness solutions in the structured aspect viewed from one side, and another side is, than the paper reflect the positive aspect in the context of modeling and block schema presentations as an better alternative to solve the specific problem because of continually distortions of the system from the line of devices, programs and signals or packed collisions, which are in movement from one computer node to another nodes.

Keywords: local area networks, LANs, block schema presentations, computer network system, computer node, critical infrastructure packed collisions, structural control system representations, computer network, implementations, modeling structural representations, companies, computers, context, control systems, internet, software

Procedia PDF Downloads 358
1280 Optimizing Pavement Construction Procedures in the Southern Desert of Libya

Authors: Khlifa El Atrash, Gabriel Assaf

Abstract:

Libya uses a volumetric analysis in designing asphalt mixtures, which can also be upgraded in hot, arid weather. However, in order to be effective, it should include many important aspects which are materials, environment, and method of construction. However, the quality of some roads was below a satisfactory level. This paper examines the factors that contribute to low quality of road performance in Libya. To evaluate these factors, a questionnaire survey and a laboratory comparative study were performed for a few mixes under-represented of temperature and traffic load. In laboratory, rutting test conducted on two different asphalt mixture, these mixes included, an asphalt concrete mix using local aggregate and asphalt binder B(60/70) at the optimum Marshall asphalt content, another mixes designed using Superpave design procedure with the same materials and performance asphalt binder grade PG (70-10). In the survey, the questionnaire was distributed to 55 engineers and specialists in this field. The interview was conducted to a few others, and the factors that were leading to poor performance of asphalt roads were listed as; 1) Owner Experience and technical staff 2) Asphalt characteristics 3) Updating and development of Asphalt Mix Design methods 4) Lack of data collection by authorization Agency 5) Construction and compaction process 6) Mentoring and controlling mixing procedure. Considering and improving these factors will play an important role to improve the pavement performances, longer service life, and lower maintenance costs. This research summarized some recommendations for making asphalt mixtures used in hot, dry areas. Such asphalt mixtures should use asphalt binder which is less affected by pavement temperature change and traffic load. The properties of the mixture, such as durability, deformation, air voids, and performance, largely depend on the type of materials, environment, and mixing method. These properties, in turn, affect the pavement performance.

Keywords: volumetric analysis, pavement performances, hot climate, traffic load, pavement temperature, asphalt mixture, environment, design and construction

Procedia PDF Downloads 265
1279 Reconstructability Analysis for Landslide Prediction

Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

Procedia PDF Downloads 61
1278 Research Action Fields at the Nexus of Digital Transformation and Supply Chain Management: Findings from Practitioner Focus Group Workshops

Authors: Brandtner Patrick, Staberhofer Franz

Abstract:

Logistics and Supply Chain Management are of crucial importance for organisational success. In the era of Digitalization, several implications and improvement potentials for these domains arise, which at the same time could lead to decreased competitiveness and could endanger long-term company success if ignored or neglected. However, empirical research on the issue of Digitalization and benefits purported to it by practitioners is scarce and mainly focused on single technologies or separate, isolated Supply Chain blocks as e.g. distribution logistics or procurement only. The current paper applies a holistic focus group approach to elaborate practitioner use cases at the nexus of the concepts of Supply Chain Management (SCM) and Digitalization. In the course of three focus group workshops with over 45 participants from more than 20 organisations, a comprehensive set of benefit entitlements and areas for improvement in terms of applying digitalization to SCM is developed. The main results of the paper indicate the relevance of Digitalization being realized in practice. In the form of seventeen concrete research action fields, the benefit entitlements are aggregated and transformed into potential starting points for future research projects in this area. The main contribution of this paper is an empirically grounded basis for future research projects and an overview of actual research action fields from practitioners’ point of view.

Keywords: digital supply chain, digital transformation, supply chain management, value networks

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1277 Statistical Time-Series and Neural Architecture of Malaria Patients Records in Lagos, Nigeria

Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

Abstract:

Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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1276 Real-Time Pedestrian Detection Method Based on Improved YOLOv3

Authors: Jingting Luo, Yong Wang, Ying Wang

Abstract:

Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

Procedia PDF Downloads 139
1275 SIM (Subscriber Identity Module) Banking

Authors: Okanta Andrew, Richmond Kweku Frempong

Abstract:

As mobile networks are upgraded with technologies like WAP, GPRS and UMTS to deliver next-generation multimedia services, so are the banks and other financial institutions also getting ready to unleash the financial products on the mobile platform to meet growing demand for mobile based application services. Hence, the onset of Unstructured Supplementary Services (USSD) Banking which would make banking services available at anywhere, anytime through a string of interactive SMS sessions between a mobile device and an application server of a service provider. The aim of this studies was to find out whether the public will accept the sim banking service when it is implemented. Our target group includes: Working class. E. g. Businessmen/women, office workers, fishermen, market women, teachers etc. Nonworking class. E. g. Students (Tertiary, Senior High School), housewives. etc. The survey was in the form of a questionnaire and a verbal interview (video) which was to investigate their idea about the current banking system and the yet to be introduced sim banking concept. Meanwhile, some challenges accompanied the progression of data gathering because some populace showed reluctance in freeing their information. One other suggestion was that government should put measures against foremost challenges obstructing sim banking in Ghana counter to computers hackers. Government and individual have a key role to undertake to give suitable support to facelift the sim banking industry in the country. It was also suggested that Government put strong regulations on the use of sim banking products and services to streamline all the activities and also create awareness of the need for sim banking and emphasize its relevance in the aspect of national GDP.

Keywords: banking, mobile banking, SIM banking, mobile banking in Ghana

Procedia PDF Downloads 482
1274 A Four-Step Ortho-Rectification Procedure for Geo-Referencing Video Streams from a Low-Cost UAV

Authors: B. O. Olawale, C. R. Chatwin, R. C. D. Young, P. M. Birch, F. O. Faithpraise, A. O. Olukiran

Abstract:

Ortho-rectification is the process of geometrically correcting an aerial image such that the scale is uniform. The ortho-image formed from the process is corrected for lens distortion, topographic relief, and camera tilt. This can be used to measure true distances, because it is an accurate representation of the Earth’s surface. Ortho-rectification and geo-referencing are essential to pin point the exact location of targets in video imagery acquired at the UAV platform. This can only be achieved by comparing such video imagery with an existing digital map. However, it is only when the image is ortho-rectified with the same co-ordinate system as an existing map that such a comparison is possible. The video image sequences from the UAV platform must be geo-registered, that is, each video frame must carry the necessary camera information before performing the ortho-rectification process. Each rectified image frame can then be mosaicked together to form a seamless image map covering the selected area. This can then be used for comparison with an existing map for geo-referencing. In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) Decompilation of video stream into individual frames; (2) Finding of interior camera orientation parameters; (3) Finding the relative exterior orientation parameters for each video frames with respect to each other; (4) Finding the absolute exterior orientation parameters, using self-calibration adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a 2-D planimetric mapping, which can be compared with a well referenced existing digital map for the purpose of georeferencing and aerial surveillance. A test field located in Abuja, Nigeria was used for testing our method. Fifteen minutes video and telemetry data were collected using the UAV and the data collected were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images are more reliable than those from original perspective photographs when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 meters.

Keywords: geo-referencing, ortho-rectification, video frame, self-calibration

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1273 Ubiquitous Learning Environments in Higher Education: A Scoping Literature Review

Authors: Mari A. Virtanen, Elina Haavisto, Eeva Liikanen, Maria Kääriäinen

Abstract:

Ubiquitous learning and the use of ubiquitous learning environments herald a new era in higher education. Ubiquitous environments fuse together authentic learning situations and digital learning spaces where students can seamlessly immerse themselves into the learning process. Definitions of ubiquitous learning are wide and vary in the previous literature and learning environments are not systemically described. The aim of this scoping review was to identify the criteria and the use of ubiquitous learning environments in higher education contexts. The objective was to provide a clear scope and a wide view for this research area. The original studies were collected from nine electronic databases. Seven publications in total were defined as eligible and included in the final review. An inductive content analysis was used for the data analysis. The reviewed publications described the use of ubiquitous learning environments (ULE) in higher education. Components, contents and outcomes varied between studies, but there were also many similarities. In these studies, the concept of ubiquitousness was defined as context-awareness, embeddedness, content-personalization, location-based, interactivity and flexibility and these were supported by using smart devices, wireless networks and sensing technologies. Contents varied between studies and were customized to specific uses. Measured outcomes in these studies were focused on multiple aspects as learning effectiveness, cost-effectiveness, satisfaction, and usefulness. This study provides a clear scope for ULE used in higher education. It also raises the need for transparent development and publication processes, and for practical implications of ubiquitous learning environments.

Keywords: higher education, learning environment, scoping review, ubiquitous learning, u-learning

Procedia PDF Downloads 258
1272 Development of Power System Stability by Reactive Power Planning in Wind Power Plant With Doubley Fed Induction Generators Generator

Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Oriol Gomis Bellmunt, Vinicius Albernaz Lacerda Freitas

Abstract:

The use of distributed and renewable sources in power systems has grown significantly, recently. One the most popular sources are wind farms which have grown massively. However, ¬wind farms are connected to the grid, this can cause problems such as reduced voltage stability, frequency fluctuations and reduced dynamic stability. Variable speed generators (asynchronous) are used due to the uncontrollability of wind speed specially Doubley Fed Induction Generators (DFIG). The most important disadvantage of DFIGs is its sensitivity to voltage drop. In the case of faults, a large volume of reactive power is induced therefore, use of FACTS devices such as SVC and STATCOM are suitable for improving system output performance. They increase the capacity of lines and also passes network fault conditions. In this paper, in addition to modeling the reactive power control system in a DFIG with converter, FACTS devices have been used in a DFIG wind turbine to improve the stability of the power system containing two synchronous sources. In the following paper, recent optimal control systems have been designed to minimize fluctuations caused by system disturbances, for FACTS devices employed. For this purpose, a suitable method for the selection of nine parameters for MPSH-phase-post-phase compensators of reactive power compensators is proposed. The design algorithm is formulated ¬¬as an optimization problem searching for optimal parameters in the controller. Simulation results show that the proposed controller Improves the stability of the network and the fluctuations are at desired speed.

Keywords: renewable energy sources, optimization wind power plant, stability, reactive power compensator, double-feed induction generator, optimal control, genetic algorithm

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1271 Diagnosis of the Heart Rhythm Disorders by Using Hybrid Classifiers

Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

Abstract:

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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1270 Health Trajectory Clustering Using Deep Belief Networks

Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

Abstract:

We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

Procedia PDF Downloads 366
1269 Household Water Source Substitution and Demand for Water Connections

Authors: Elizabeth Spink

Abstract:

The United Nations' Sustainable Development Goal 6 sets a target for safe and affordable drinking water for all. Developing country governments aiming to achieve this goal often face significant challenges when trying to service last mile customers, particularly those in peri-urban and rural areas. Expansion of water networks often requires high connection fees from households, and demand for connections may be low if there are cheaper substitute sources of water available. This research studies the effect of the availability of substitute sources of water on demand for individual water connections in Livingstone, Zambia, using an event study analysis of metering campaigns. Metering campaigns reduce the share of a household's neighbors that can provide free water to the household if their water connection becomes disconnected due to nonpayment. The results show that household payments in newly metered regions increase by 10 percentage points in the months following metering events, with a decrease in disconnections of 6 percentage points for low-income households. To isolate the effect of changes in a household's substitution possibilities, a similar analysis is conducted among households that neighbor the metered region. These results show mixed evidence of the impact of substitutes on payment behavior and disconnections. The results suggest that metering may be effective in increasing household demand for individual water connections primarily through a lower monthly cost burden for newly metered households.

Keywords: piped-water access, water demand, water utilities, water sharing

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1268 The Classification Accuracy of Finance Data through Holder Functions

Authors: Yeliz Karaca, Carlo Cattani

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This study focuses on the local Holder exponent as a measure of the function regularity for time series related to finance data. In this study, the attributes of the finance dataset belonging to 13 countries (India, China, Japan, Sweden, France, Germany, Italy, Australia, Mexico, United Kingdom, Argentina, Brazil, USA) located in 5 different continents (Asia, Europe, Australia, North America and South America) have been examined.These countries are the ones mostly affected by the attributes with regard to financial development, covering a period from 2012 to 2017. Our study is concerned with the most important attributes that have impact on the development of finance for the countries identified. Our method is comprised of the following stages: (a) among the multi fractal methods and Brownian motion Holder regularity functions (polynomial, exponential), significant and self-similar attributes have been identified (b) The significant and self-similar attributes have been applied to the Artificial Neuronal Network (ANN) algorithms (Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP)) (c) the outcomes of classification accuracy have been compared concerning the attributes that have impact on the attributes which affect the countries’ financial development. This study has enabled to reveal, through the application of ANN algorithms, how the most significant attributes are identified within the relevant dataset via the Holder functions (polynomial and exponential function).

Keywords: artificial neural networks, finance data, Holder regularity, multifractals

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1267 Neural Network Models for Actual Cost and Actual Duration Estimation in Construction Projects: Findings from Greece

Authors: Panagiotis Karadimos, Leonidas Anthopoulos

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Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

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1266 Modelling Biological Treatment of Dye Wastewater in SBR Systems Inoculated with Bacteria by Artificial Neural Network

Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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1265 Geomatic Techniques to Filter Vegetation from Point Clouds

Authors: M. Amparo Núñez-Andrés, Felipe Buill, Albert Prades

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More and more frequently, geomatics techniques such as terrestrial laser scanning or digital photogrammetry, either terrestrial or from drones, are being used to obtain digital terrain models (DTM) used for the monitoring of geological phenomena that cause natural disasters, such as landslides, rockfalls, debris-flow. One of the main multitemporal analyses developed from these models is the quantification of volume changes in the slopes and hillsides, either caused by erosion, fall, or land movement in the source area or sedimentation in the deposition zone. To carry out this task, it is necessary to filter the point clouds of all those elements that do not belong to the slopes. Among these elements, vegetation stands out as it is the one we find with the greatest presence and its constant change, both seasonal and daily, as it is affected by factors such as wind. One of the best-known indexes to detect vegetation on the image is the NVDI (Normalized Difference Vegetation Index), which is obtained from the combination of the infrared and red channels. Therefore it is necessary to have a multispectral camera. These cameras are generally of lower resolution than conventional RGB cameras, while their cost is much higher. Therefore we have to look for alternative indices based on RGB. In this communication, we present the results obtained in Georisk project (PID2019‐103974RB‐I00/MCIN/AEI/10.13039/501100011033) by using the GLI (Green Leaf Index) and ExG (Excessive Greenness), as well as the change to the Hue-Saturation-Value (HSV) color space being the H coordinate the one that gives us the most information for vegetation filtering. These filters are applied both to the images, creating binary masks to be used when applying the SfM algorithms, and to the point cloud obtained directly by the photogrammetric process without any previous filter or the one obtained by TLS (Terrestrial Laser Scanning). In this last case, we have also tried to work with a Riegl VZ400i sensor that allows the reception, as in the aerial LiDAR, of several returns of the signal. Information to be used for the classification on the point cloud. After applying all the techniques in different locations, the results show that the color-based filters allow correct filtering in those areas where the presence of shadows is not excessive and there is a contrast between the color of the slope lithology and the vegetation. As we have advanced in the case of using the HSV color space, it is the H coordinate that responds best for this filtering. Finally, the use of the various returns of the TLS signal allows filtering with some limitations.

Keywords: RGB index, TLS, photogrammetry, multispectral camera, point cloud

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1264 Supply Chain of Energy Resources and Its Alternatives Due to the Arab Spring: The Case of Egyptian Natural Gas Flow to Jordan

Authors: Moh’d Anwer Al-Shboul

Abstract:

The year 2011 was a challenging year for Jordanian economy, which felt a variety of effects from the Arab Spring which took place in neighboring countries. Since February, 5th 2012, the Arab Gas Supply Pipeline, which carries natural gas from Egypt through the Sinai Peninsula and to Jordan and Israel, has been attacked more than 39 times. Jordan imported about 80 percent of its necessity of natural gas (about 250 million cubic feet of natural gas per day) from Egypt to generate particularly electricity, with the reminder of being produced locally. Jordan has utilized multiple alternatives to address the interruption of available natural gas supply from Egypt. The Jordanian distributed power plants now rely on the use of heavy fuel oil and diesel for electricity generation, in this case, it costs Jordan about four times than natural gas. The substitution of Egyptian natural gas supplies by fuel oil and diesel, coupled with the 32 percent rise in global fuel prices, has increased Jordan’s energy import bill by over 50 percent in 2011, reaching more than 16 percent of the 2011 GDP. The increase in the cost of electricity generation pushed the Jordanian economy to borrow from multiple internal and external resource channels, thus increasing the public debt. The Jordanian government’s short-term solution to the reduced natural gas supply from Egypt was alternatively purchasing the necessary quantities from some Gulf countries such as Qatar and/or Saudi Arabia, which can be imported with two possible methods. The first method is to rent a ship equipped with a liquefied natural gas (LNG) terminal, which is currently operating. The second method requires equipping the Aqaba port with an LNG terminal, which also currently is operating. In the long-term, a viable solution to depending on importing expensive and often unreliable natural gas supplies from surrounding countries is to depend more heavily on renewable supply energy, including solar, wind, and water energy.

Keywords: energy supply resources, Arab spring, liquefied natural gas, pipeline, Jordan

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1263 Brain Networks and Mathematical Learning Processes of Children

Authors: Felicitas Pielsticker, Christoph Pielsticker, Ingo Witzke

Abstract:

Neurological findings provide foundational results for many different disciplines. In this article we want to discuss these with a special focus on mathematics education. The intention is to make neuroscience research useful for the description of cognitive mathematical learning processes. A key issue of mathematics education is that students often behave as if their mathematical knowledge is constructed in isolated compartments with respect to the specific context of the original learning situation; supporting students to link these compartments to form a coherent mathematical society of mind is a fundamental task not only for mathematics teachers. This aspect goes hand in hand with the question if there is such a thing as abstract general mathematical knowledge detached from concrete reality. Educational Neuroscience may give answers to the question why students develop their mathematical knowledge in isolated subjective domains of experience and if it is generally possible to think in abstract terms. To address these questions, we will provide examples from different fields of mathematics education e.g. students’ development and understanding of the general concept of variables or the mathematical notion of universal proofs. We want to discuss these aspects in the reflection of functional studies which elucidate the role of specific brain regions in mathematical learning processes. In doing this the paper addresses concept formation processes of students in the mathematics classroom and how to support them adequately considering the results of (educational) neuroscience.

Keywords: brain regions, concept formation processes in mathematics education, proofs, teaching-learning processes

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1262 Effect of Rehabilitative Nursing Program on Pain Intensity and Functional Status among Patients with Discectomy

Authors: Amal Shehata

Abstract:

Low back pain related to disc prolapse is localized in the lumbar area and it may be radiated to the lower extremities, starting from neurons near or around the spinal canal. Most of the population may be affected with disc prolapse within their lifetime and leads to lost productivity, disability and loss of function. The study purpose was to examine the effect of rehabilitative nursing program on pain intensity and functional status among patients with discectomy. Design: Aquasi experimental design was utilized. Setting: The study was carried out at neurosurgery department and out patient's clinic of Menoufia University and Teaching hospitals at Menoufia governorate, Egypt. Instrument of the study: Five Instruments were used for data collection: Structured interviewing questionnaire, Functional assessment instrument, Observational check list, Numeric rating Scale and Oswestry low back pain disability questionnaire. Results: There was an improvement in mean total knowledge score about disease process, discectomy and rehabilitation program in study group (25.32%) than control group (7.32%). There was highly statistically significant improvement in lumbar flexibility among study group (80%) than control group (30%) after rehabilitation program than before. Also there was a decrease in pain score in study group (58% no pain) than control group (28% no pain) after rehabilitation program. There was an improvement in total disability score of study group (zero %) regarding effect of pain on the activity of daily living after rehabilitation program than control group (16%). Conclusion: Application of rehabilitative nursing program for patient with discectomy had proven a positive effect in relation to knowledge score, pain reduction, activity of daily living and functional abilities. Recommendation: A continuous rehabilitative nursing program should be carried out for all patients immediately after discectomy surgery on regular basis. Also A colored illustrated booklet about rehabilitation program should be available and distributed for all patients before surgery.

Keywords: discectomy, rehabilitative nursing program, pain intensity, functional status

Procedia PDF Downloads 139
1261 Resource Orchestration Based on Two-Sides Scheduling in Computing Network Control Sytems

Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

Abstract:

Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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1260 Human Immunodeficiency Virus (HIV) Test Predictive Modeling and Identify Determinants of HIV Testing for People with Age above Fourteen Years in Ethiopia Using Data Mining Techniques: EDHS 2011

Authors: S. Abera, T. Gidey, W. Terefe

Abstract:

Introduction: Testing for HIV is the key entry point to HIV prevention, treatment, and care and support services. Hence, predictive data mining techniques can greatly benefit to analyze and discover new patterns from huge datasets like that of EDHS 2011 data. Objectives: The objective of this study is to build a predictive modeling for HIV testing and identify determinants of HIV testing for adults with age above fourteen years using data mining techniques. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes among adult Ethiopians. Decision tree, Naïve-Bayes, logistic regression and artificial neural networks of data mining techniques were used to build the predictive models. Results: The target dataset contained 30,625 study participants; of which 16, 515 (53.9%) were women. Nearly two-fifth; 17,719 (58%), have never been tested for HIV while the rest 12,906 (42%) had been tested. Ethiopians with higher wealth index, higher educational level, belonging 20 to 29 years old, having no stigmatizing attitude towards HIV positive person, urban residents, having HIV related knowledge, information about family planning on mass media and knowing a place where to get testing for HIV showed an increased patterns with respect to HIV testing. Conclusion and Recommendation: Public health interventions should consider the identified determinants to promote people to get testing for HIV.

Keywords: data mining, HIV, testing, ethiopia

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1259 A Sectional Control Method to Decrease the Accumulated Survey Error of Tunnel Installation Control Network

Authors: Yinggang Guo, Zongchun Li

Abstract:

In order to decrease the accumulated survey error of tunnel installation control network of particle accelerator, a sectional control method is proposed. Firstly, the accumulation rule of positional error with the length of the control network is obtained by simulation calculation according to the shape of the tunnel installation-control-network. Then, the RMS of horizontal positional precision of tunnel backbone control network is taken as the threshold. When the accumulated error is bigger than the threshold, the tunnel installation control network should be divided into subsections reasonably. On each segment, the middle survey station is taken as the datum for independent adjustment calculation. Finally, by taking the backbone control points as faint datums, the weighted partial parameters adjustment is performed with the adjustment results of each segment and the coordinates of backbone control points. The subsections are jointed and unified into the global coordinate system in the adjustment process. An installation control network of the linac with a length of 1.6 km is simulated. The RMS of positional deviation of the proposed method is 2.583 mm, and the RMS of the difference of positional deviation between adjacent points reaches 0.035 mm. Experimental results show that the proposed sectional control method can not only effectively decrease the accumulated survey error but also guarantee the relative positional precision of the installation control network. So it can be applied in the data processing of tunnel installation control networks, especially for large particle accelerators.

Keywords: alignment, tunnel installation control network, accumulated survey error, sectional control method, datum

Procedia PDF Downloads 188
1258 The Second Generation of Tyrosine Kinase Inhibitor Afatinib Controls Inflammation by Regulating NLRP3 Inflammasome Activation

Authors: Shujun Xie, Shirong Zhang, Shenglin Ma

Abstract:

Background: Chronic inflammation might lead to many malignancies, and inadequate resolution could play a crucial role in tumor invasion, progression, and metastases. A randomised, double-blind, placebo-controlled trial shows that IL-1β inhibition with canakinumab could reduce incident lung cancer and lung cancer mortality in patients with atherosclerosis. The process and secretion of proinflammatory cytokine IL-1β are controlled by the inflammasome. Here we showed the correlation of the innate immune system and afatinib, a tyrosine kinase inhibitor targeting epidermal growth factor receptor (EGFR) in non-small cell lung cancer. Methods: Murine Bone marrow derived macrophages (BMDMs), peritoneal macrophages (PMs) and THP-1 were used to check the effect of afatinib on the activation of NLRP3 inflammasome. The assembly of NLRP3 inflammasome was check by co-immunoprecipitation of NLRP3 and apoptosis-associated speck-like protein containing CARD (ASC), disuccinimidyl suberate (DSS)-cross link of ASC. Lipopolysaccharide (LPS)-induced sepsis and Alum-induced peritonitis were conducted to confirm that afatinib could inhibit the activation of NLRP3 in vivo. Peripheral blood mononuclear cells (PBMCs) from non-small cell lung cancer (NSCLC) patients before or after taking afatinib were used to check that afatinib inhibits inflammation in NSCLC therapy. Results: Our data showed that afatinib could inhibit the secretion of IL-1β in a dose-dependent manner in macrophage. Moreover, afatinib could inhibit the maturation of IL-1β and caspase-1 without affecting the precursors of IL-1β and caspase-1. Next, we found that afatinib could block the assembly of NLRP3 inflammasome and the ASC speck by blocking the interaction of the sensor protein NLRP3 and the adaptor protein ASC. We also found that afatinib was able to alleviate the LPS-induced sepsis in vivo. Conclusion: Our study found that afatinib could inhibit the activation of NLRP3 inflammasome in macrophage, providing new evidence that afatinib could target the innate immune system to control chronic inflammation. These investigations will provide significant experimental evidence in afatinib as therapeutic drug for non-small cell lung cancer or other tumors and NLRP3-related diseases and will explore new targets for afatinib.

Keywords: inflammasome, afatinib, inflammation, tyrosine kinase inhibitor

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1257 Comparison of the Effect of Nano Calcium Carbonate and CaCO₃ on Egg Production, Egg Traits and Calcium Retention in Laying Japanese Quail

Authors: Farhad Ahmadi, Hamed Kimiaee, Fariba Rahimi

Abstract:

This research study focuses on the effect of different levels and sources of calcium on egg production, egg traits, and calcium retention in laying Japanese quail. The study aims to determine the impact of nano calcium carbonate (NCC) and calcium carbonate (CC) on these factors. The research was conducted using a total of 280 laying quail with an average age of 8 weeks. The quails were randomly distributed in a completely randomized design (CRD) with 7 treatments, 4 replications, and 10 quails in each pen. The study lasted for 90 days. The experimental diets included a control group (T1) with a basal diet consisting of 3.17% CaCO₃, and other groups supplemented with different levels (0.5%, 0.1%, and 0.15%) of either calcium carbonate (CC) or nano calcium carbonate (NCC). The quails had free access to water and feed throughout the study period. Findings: The results of the study showed that NCC at the levels of 0.1% and 0.15% (T6 and T7) improved eggshell thickness, shell thickness, and shell breaking strength compared to the control group. Although not statistically significant, there was an increasing trend in quail egg production and calcium retention in the calcareous shell of the egg in birds that consumed the experimental diets containing different levels of NCC compared to the control and other treatment groups. Quail egg production was recorded monthly for each treatment group. At the end of the study, a total of 40 eggs (10 eggs/replicate) from each treatment group were randomly selected for analysis. Parameters such as eggshell thickness, shell thickness, shell breaking strength, and calcium retention were measured. Statistical analysis was performed to compare the results between the different treatment groups. In conclusion, this study suggests that NCC at the levels of 0.1% and 0.15% can improve the quantity and quality of eggs and calcium retention in laying Japanese quail. These findings highlight the potential benefits of using NCC as a calcium source in quail diets. Further research could be conducted to explore the mechanisms behind these improvements and optimize the dosage of NCC for maximum effect.

Keywords: egg, calcium, nanoparticles, physiology

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1256 Assessment of Spatial and Temporal Variations of Some Biological Water Quality Parameters in Mat River, Albania

Authors: Etleva Hamzaraj, Eva Kica, Anila Paparisto, Pranvera Lazo

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Worldwide demographic developments of recent decades have been associated with negative environmental consequences. For this reason, there is a growing interest in assessing the state of natural ecosystems or assessing human impact on them. In this respect, this study aims to evaluate the change in water quality of the Mat River for a period of about ten years to highlight human impact. In one year, period of study, several biological and environmental parameters are determined to evaluate river water quality, and the data collected are compared with those of a similar study in 2007. Samples are collected every month in five stations evenly distributed along the river. Total coliform bacteria, the number of heterotrophic bacteria in water, and benthic macroinvertebrates are used as biological parameters of water quality. The most probable number index is used for evaluation of total coliform bacteria in water, while the number of heterotrophic bacteria is determined by counting colonies on plates with Plate Count Agar, cultivated with 0.1 ml sample after series dilutions. Benthic macroinvertebrates are analyzed by the number of individuals per taxa, the value of biotic index, EPT Richness Index value and tolerance value. Environmental parameters like pH, temperature, and electrical conductivity are measured onsite. As expected, the bacterial load was higher near urban areas, and the pollution increased with the course of the river. The maximum concentration of fecal coliforms was 1100 MPN/100 ml in summer and near the most urbanized area of the river. The data collected during this study show that after about ten years, there is a change in water quality of Mat River. According to a similar study carried out in 2007, the water of Mat River was of ‘excellent’ quality. But, according to this study, the water was classified as of ‘excellent’ quality only in one sampling site, near river source, while in all other stations was of ‘good’ quality. This result is based on biological and environmental parameters measured. The human impact on the quality of water of Mat River is more than evident.

Keywords: water quality, coliform bacteria, MPN index, benthic macroinvertebrates, biotic index

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1255 Rural Water Management Strategies and Irrigation Techniques for Sustainability. Nigeria Case Study; Kwara State

Authors: Faith Eweluegim Enahoro-Ofagbe

Abstract:

Water is essential for sustaining life. As a limited resource, effective water management is vital. Water scarcity has become more common due to the effects of climate change, land degradation, deforestation, and population growth, especially in rural communities, which are more susceptible to water-related issues such as water shortage, water-borne disease, et c., due to the unsuccessful implementation of water policies and projects in Nigeria. Since rural communities generate the majority of agricultural products, they significantly impact on water management for sustainability. The development of methods to advance this goal for residential and agricultural usage in the present and the future is a challenge for rural residents. This study evaluated rural water supply systems and irrigation management techniques to conserve water in Kwara State, North-Central Nigeria. Suggesting some measures to conserve water resources for sustainability, off-season farming, and socioeconomic security that will remedy water degradation, unemployment which is one of the causes of insecurity in the country, by considering the use of fabricated or locally made irrigation equipment, which are affordable by rural farmers, among other recommendations. Questionnaires were distributed to respondents in the study area for quantitative evaluation of irrigation methods practices. For physicochemical investigation, samples were also gathered from their available water sources. According to the study's findings, 30 percent of farmers adopted intelligent irrigation management techniques to conserve water resources, saving 45% of the water previously used for irrigation. 70 % of farmers practice seasonal farming. Irrigation water is drawn from river channels, streams, and unlined and unprotected wells. 60% of these rural residents rely on private boreholes for their water needs, while 40% rely on government-supplied rural water. Therefore, the government must develop additional water projects, raise awareness, and offer irrigation techniques that are simple to adapt for water management, increasing socio-economic productivity, security, and water sustainability.

Keywords: water resource management, sustainability, irrigation, rural water management, irrigation management technique

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1254 Detection of Atrial Fibrillation Using Wearables via Attentional Two-Stream Heterogeneous Networks

Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

Procedia PDF Downloads 116