Search results for: time series feature extraction

Authors: Fouad Berrabeh, Sabir Messalti

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The performances of the photovoltaic systems are very dependent on different conditions, such as solar irradiation, temperature, etc. Therefore, it is very important to provide detailed studies for different cases in order to provide continuously power, so the photovoltaic system must be properly sized. This paper presents the modelling and simulation of the photovoltaic cell using single diode model. I-V characteristics and P-V characteristics are presented and it verified at different conditions (irradiance effect, temperature effect, series resistance effect).

Keywords: photovoltaic cell, BP SX 150 BP solar photovoltaic module, irradiance effect, temperature effect, series resistance effect, I–V characteristics, P–V characteristics

Procedia PDF Downloads 486

Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

Procedia PDF Downloads 216

Authors: Maryam Mosleh, Mahmood Otadi

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The hybrid differential equations have a wide range of applications in science and engineering. In this paper, the homotopy analysis method (HAM) is applied to obtain the series solution of the hybrid differential equations. Using the homotopy analysis method, it is possible to find the exact solution or an approximate solution of the problem. Comparisons are made between improved predictor-corrector method, homotopy analysis method and the exact solution. Finally, we illustrate our approach by some numerical example.

Keywords: Fuzzy number, parametric form of a fuzzy number, fuzzy integrodifferential equation, homotopy analysis method

Procedia PDF Downloads 556

Authors: Srestha Chaturabul, Wanchalerm Srirachat, Thanaporn Wannachod, Prakorn Ramakul, Ura Pancharoen, Soorathep Kheawhom

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The separation of mercury(II) from petroleum-produced water from the Gulf of Thailand was carried out using a hollow fiber supported liquid membrane system (HFSLM). Optimum parameters for feed pretreatment were 0.2 M HCl, 4% (v/v) Aliquat 336 for extractant and 0.1 M thiourea for stripping solution. The best percentage obtained for extraction was 99.73% and for recovery 90.11%, respectively. The overall separation efficiency noted was 94.92% taking account of both extraction and recovery prospects. The model for this separation developed along a combined flux principle i.e. convection–diffusion–kinetic. The results showed excellent agreement with theoretical data at an average standard deviation of 1.5% and 1.8%, respectively.

Keywords: separation, mercury(ii), petroleum produced water, hollow fiber, liquid membrane

Procedia PDF Downloads 296

Authors: Nalini C. Iyer, Shraddha H., Anagha B. Varahamurthy, Dikshith C. S., Ishwar G. Kubasad, Vinayak I. Karalatti, Pavan B. Mulimani

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The current scenario of the pandemic due to COVID-19 has led to the awareness among the people to avoid unneces-sary contact in public places. There is a need to avoid contact with physical objects to stop the spreading of infection. The contactless feature has to be included in the systems in public places wherever possible. For example, attendance monitoring systems with fingerprint biometric can be replaced with a contactless feature. One more important protocol followed in the current situation is temperature monitoring and screening. The paper describes an attendance system with a contactless feature and temperature screening for the university. The system displays a QR code to scan, which redirects to the student login web page only if the location is valid (the location where the student scans the QR code should be the location of the display of the QR code). Once the student logs in, the temperature of the student is scanned by the contactless temperature sensor (mlx90614) with an error of 0.5°C. If the temperature falls in the range of the desired value (range of normal body temperature), then the attendance of the student is marked as present, stored in the database, and the door opens automatically. The attendance is marked as absent in the other case, alerted with the display of temperature, and the door remains closed. The door is automated with the help of a servomotor. To avoid the proxy, IR sensors are used to count the number of students in the classroom. The hardware system consisting of a contactless temperature sensor and IR sensor is implemented on the microcontroller, NodeMCU.

Keywords: NodeMCU, IR sensor, attendance monitoring, contactless, temperature

Procedia PDF Downloads 185

Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

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Authors: Patricia Jiménez, Rafael Corchuelo

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Nowadays the World Wide Web is the most popular source of information that relies on billions of on-line documents. Web mining is used to crawl through these documents, collect the information of interest and process it by applying data mining tools in order to use the gathered information in the best interest of a business, what enables companies to promote theirs. Unfortunately, it is not easy to extract the information a web site provides automatically when it lacks an API that allows to transform the user-friendly data provided in web documents into a structured format that is machine-readable. Rule-based information extractors are the tools intended to extract the information of interest automatically and offer it in a structured format that allow mining tools to process it. However, the performance of an information extractor strongly depends on the search heuristic employed since bad choices regarding how to learn a rule may easily result in loss of effectiveness and/or efficiency. Improving search heuristics regarding efficiency is of uttermost importance in the field of Web Information Extraction since typical datasets are very large. In this paper, we employ an information extractor based on a classical top-down algorithm that uses the so-called Information Gain heuristic introduced by Quinlan and Cameron-Jones. Unfortunately, the Information Gain relies on some well-known problems so we analyse an intuitive alternative, Termini, that is clearly more efficient; we also analyse other proposals in the literature and conclude that none of them outperforms the previous alternative.

Keywords: information extraction, search heuristics, semi-structured documents, web mining.

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Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Masood Roohi, Amir Taghavipour

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This paper describes a strategy to develop an energy management system (EMS) for a charge-sustaining power-split hybrid electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit from the advantages of both parallel and series architecture. However, it gets relatively more complicated to manage power flow between the battery and the engine optimally. The applied strategy in this paper is based on nonlinear model predictive control approach. First of all, an appropriate control-oriented model which was accurate enough and simple was derived. Towards utilization of this controller in real-time, the problem was solved off-line for a vast area of reference signals and initial conditions and stored the computed manipulated variables inside look-up tables. Look-up tables take a little amount of memory. Also, the computational load dramatically decreased, because to find required manipulated variables the controller just needed a simple interpolation between tables.

Keywords: hybrid electric vehicles, energy management system, nonlinear model predictive control, real-time

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Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Zexin Lei, Timothy Langrish

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Lutein is a type of carotene believed to be beneficial to the eyes. This study aims to explore the possibility of using a closed cycle spray drying system to produce lutein. The system contains a spray dryer, a condenser, a heater, and a pressure seal. Hexane, ethanol, and isopropanol will be used as organic solvents to compare the extraction effects. Several physical and chemical methods of cell disruption will be compared. By continuously sweeping the system with nitrogen, the oxygen content will be controlled below 2%, reducing the concentration of organic solvent below the explosion limit and preventing lutein from being oxidized. Lutein powder will be recovered in the collection device. The volatile organic solvent will be cooled in the condenser and deposited in the bottom until it is discharged from the bottom of the condenser.

Keywords: closed cycle spray drying system, Chlorella vulgaris, organic solvent, solvent recovery

Procedia PDF Downloads 134

Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Asad Abbas, Muhammad Fezan Afzal, Muhammad Latif Anjum, Muhammad Azmat

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The Internet of Things (IoT) is used to link up real objects that use the internet to interact. IoT applications allow handling and operating the equipment in accordance with environmental needs, such as transportation and healthcare. IoT devices are linked together via a number of agents that act as a middleman for communications. The operation of a heat sensor differs indoors and outside because agent applications work with environmental variables. In this article, we suggest using Software Product Line (SPL) to model IoT agents and applications' features on an XML-based basis. The contextual diversity within the same domain of application can be handled, and the reusability of features is increased by XML-based feature modelling. For the purpose of managing contextual variability, we have embraced XML for modelling IoT applications, agents, and internet-connected devices.

Keywords: IoT agents, IoT applications, software product line, feature model, XML

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Authors: S. A. Jumare, A. O. Tijani, M. F. Siraj, B. V. Babatunde

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This research investigated the comparative analysis of oil extracted from cotton and watermelon seeds using solvent extraction process. Normal ethyl-ether was used as solvent in the extraction process. The AOAC method of Analysis was employed in the determination of the physiochemical properties of the oil. The chemical properties of the oil determined include the saponification value, free fatty acid, iodine value, peroxide value and acid value. The physical properties of the oil determined include specific gravity, refractive index, colour, odour, taste and pH. The value obtained for cottonseed oil are saponification value (187mgKOH/g), free fatty acid (5.64mgKOH/g), iodine value (95.2g/100), peroxide value (9.33meq/kg), acid value (11.22mg/KOH/g), pH value (4.62), refractive index (1.46), and specific gravity (0.9) respectively, it has a bland odour, a reddish brown colour and a mild taste. The values obtained for watermelon seed oil are saponification value (83.3mgKOH/g), free fatty acid (6.58mg/KOH/g), iodine value (122.6g/100), peroxide value (5.3meq/kg), acid value (3.74mgKOH/g), pH value (6.3), refractive index (1.47), and specific gravity (0.9) respectively, it has a nutty flavour, a golden yellow colour and a mild taste. From the result obtained, it shows that cottonseed oil has high acid value which shows the stability of the oil and its stability to rancidity. Consequently, watermelon seed oil is order wise.

Keywords: extraction, solvent, cotton seeds, watermelon seeds

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Authors: Nadarajah I. Ramesh

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Much of the research on stochastic point process models for rainfall has focused on Poisson cluster models constructed from either the Neyman-Scott or Bartlett-Lewis processes. The doubly stochastic Poisson process provides a rich class of point process models, especially for fine-scale rainfall modelling. This paper provides an account of recent development on this topic and presents the results based on some of the fine-scale rainfall models constructed from this class of stochastic point processes. Amongst the literature on stochastic models for rainfall, greater emphasis has been placed on modelling rainfall data recorded at hourly or daily aggregation levels. Stochastic models for sub-hourly rainfall are equally important, as there is a need to reproduce rainfall time series at fine temporal resolutions in some hydrological applications. For example, the study of climate change impacts on hydrology and water management initiatives requires the availability of data at fine temporal resolutions. One approach to generating such rainfall data relies on the combination of an hourly stochastic rainfall simulator, together with a disaggregator making use of downscaling techniques. Recent work on this topic adopted a different approach by developing specialist stochastic point process models for fine-scale rainfall aimed at generating synthetic precipitation time series directly from the proposed stochastic model. One strand of this approach focused on developing a class of doubly stochastic Poisson process (DSPP) models for fine-scale rainfall to analyse data collected in the form of rainfall bucket tip time series. In this context, the arrival pattern of rain gauge bucket tip times N(t) is viewed as a DSPP whose rate of occurrence varies according to an unobserved finite state irreducible Markov process X(t). Since the likelihood function of this process can be obtained, by conditioning on the underlying Markov process X(t), the models were fitted with maximum likelihood methods. The proposed models were applied directly to the raw data collected by tipping-bucket rain gauges, thus avoiding the need to convert tip-times to rainfall depths prior to fitting the models. One advantage of this approach was that the use of maximum likelihood methods enables a more straightforward estimation of parameter uncertainty and comparison of sub-models of interest. Another strand of this approach employed the DSPP model for the arrivals of rain cells and attached a pulse or a cluster of pulses to each rain cell. Different mechanisms for the pattern of the pulse process were used to construct variants of this model. We present the results of these models when they were fitted to hourly and sub-hourly rainfall data. The results of our analysis suggest that the proposed class of stochastic models is capable of reproducing the fine-scale structure of the rainfall process, and hence provides a useful tool in hydrological modelling.

Keywords: fine-scale rainfall, maximum likelihood, point process, stochastic model

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Authors: Saeid Rabiei Majd, Motahareh Alvandi, Bahareh Asefi

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Extraction of maximum natural resources is one of the common policies of governments, especially petroleum resources that have high economic and strategic value. The incentive to access and maintain profitable oil markets for governments or international oil companies, causing neglects them to pay attention to environmental principles and sustainable development, which in turn drives up environmental and climate change. Significant damage to the environment can cause severe damage to citizens and indigenous people, such as the compulsory evacuation of their zone due to contamination of water and air resources, destruction of animals and plants. Hawizeh Marshes is a common aquatic and environmental ecosystem along the Iran-Iraq border that also has oil resources. This marsh has been very rich in animal, vegetative, and oil resources. Since 1990, the political motives, the strategic importance of oil extraction, and the disregard for the environmental rights of the Iraqi and Iranian governments in the region have caused 90% of the marshes and forced migration of indigenous people. In this paper, we examine the environmental degradation factors resulting from the adoption of policies and practices of governments in this region based on the principles of environmental rights and sustainable development. Revision of the implementation of the government’s policies and natural resource utilization systems can prevent the spread of climate change, which is a serious international challenge today.

Keywords: climate change, indigenous rights, petroleum operation, sustainable development principles, sovereignty on resources

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Authors: Fatima Zohra Zeggai, Benjamin Carbonnier, Aïcha Hachemaoui, Ahmed Yahiaoui, Samia Mahouche-Chergui, Zakaria Salmi

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A series of polyaniline (PANI)/modified Montmorillonite (MMT) Clay nanocomposite materials have been successfully prepared by In-Situ polymerization in the presence of modified MMT-Clay or Diazonium-MMT-Clay. The obtained nanocomposites were characterized and compared by various physicochemical techniques. The presence of physicochemical interaction, probably hydrogen bonding, between clay and polyaniline, which was confirmed by FTIR, UV-Vis Spectroscopy. The electrical conductivity of neat PANI and a series of the obtained nanocomposites were also studied by cyclic voltammograms.

Keywords: polyaniline, clay, nanocomposites, in-situ polymerization, polymers conductors, diazonium salt

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

Procedia PDF Downloads 241

Authors: S. M. Khairul Halim, Mojtaba Ghodsi, Morteza Mohammadzaheri

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Gait analysis is essential for diagnosing musculoskeletal and neurological conditions. However, current methods are often complex and expensive. This paper introduces a methodology for analysing gait parameters using a smart insole with a built-in accelerometer. The system measures stance time, swing time, step count, and cadence and wirelessly transmits data to a user-friendly IoT dashboard for centralized processing. This setup enables remote monitoring and advanced data analytics, making it a versatile tool for medical diagnostics and everyday usage. Integration with IoT enhances the portability and connectivity of the device, allowing for secure, encrypted data access over the Internet. This feature supports telemedicine and enables personalized treatment plans tailored to individual needs. Overall, the approach provides a cost-effective (almost 25 GBP), accurate, and user-friendly solution for gait analysis, facilitating remote tracking and customized therapy.

Keywords: gait analysis, IoT, smart insole, accelerometer sensor

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Authors: Fikru Fentaw Abera

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Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. Such hydrologic changes will affect almost every aspect of human well-being. The goal of this paper is to assess the impact of climate change on the hydrology of Upper Guder catchment located in northwest of Ethiopia. The GCM derived scenarios (HadCM3 A2a & B2a SRES emission scenarios) experiments were used for the climate projection. The statistical downscaling model (SDSM) was used to generate future possible local meteorological variables in the study area. The down-scaled data were then used as input to the soil and water assessment tool (SWAT) model to simulate the corresponding future stream flow regime in Upper Guder catchment of the Abay River Basin. A semi distributed hydrological model, SWAT was developed and Generalized Likelihood Uncertainty Estimation (GLUE) was utilized for uncertainty analysis. GLUE is linked with SWAT in the Calibration and Uncertainty Program known as SWAT-CUP. Three benchmark periods simulated for this study were 2020s, 2050s and 2080s. The time series generated by GCM of HadCM3 A2a and B2a and Statistical Downscaling Model (SDSM) indicate a significant increasing trend in maximum and minimum temperature values and a slight increasing trend in precipitation for both A2a and B2a emission scenarios in both Gedo and Tikur Inch stations for all three bench mark periods. The hydrologic impact analysis made with the downscaled temperature and precipitation time series as input to the hydrological model SWAT suggested for both A2a and B2a emission scenarios. The model output shows that there may be an annual increase in flow volume up to 35% for both emission scenarios in three benchmark periods in the future. All seasons show an increase in flow volume for both A2a and B2a emission scenarios for all time horizons. Potential evapotranspiration in the catchment also will increase annually on average 3-15% for the 2020s and 7-25% for the 2050s and 2080s for both A2a and B2a emissions scenarios.

Keywords: climate change, Guder sub-basin, GCM, SDSM, SWAT, SWAT-CUP, GLUE

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Authors: Sangkhao M., Butcher B. A.

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Diazepam (known as valium) is a medication for calming effect. Many reports on committed suicide cases shown that diazepam is frequently used for this purpose. This research aims to study effect of diazepam on the development of forensically important blowflies, Chrysomya megacephala (Diptera: Calliphoridae) using micro-computed tomography (micro CT). In this study, four rabbits were treated with three different lethal doses of diazepam and one control (LD₀, LD₅₀, LD₁₀₀ and LC). The rabbit’s livers were removed for rearing the blowflies. Pupae were sampled for two series (ages; S1: 24h and S2: 120h) of development. After preparing the specimens, all samples were performed Micro CT using Skyscan 1172. The results shown the effect of diazepam on internal organs and tissues such as brain, cavity of the body, gas bubble, meconium and especially fat body. In the control group, in series 1 (LCS1), fat body was equally dispersed in the head, thorax, and abdomen, development of internal organs were not completed, however, brain, thoracic muscle, wings, legs and rectum were able to observe at 24h after developing into the pupal stage. Development of each organ in the control group in the series two was completed. In the treatment groups, LD₀, LD₅₀, LD₁₀₀ (Series 1 and Series 2), tissues are different, such as gas bubble in LD₀S1, was observed due to rapidity morphological changes during the metamorphosis of blowfly’s pupa in this treatment. Meconium was observed in LD₅₀S2 group because excretion of metabolic waste was not completed. All of the samples in the treatment groups had differentiation of fat bodies because metabolic activities were not completed and these changes affected on functions of every internal system. Discovering of differentiated fat bodies are important results because fat bodies of insect functions as liver in human, therefore it is shown that toxin eliminates from blowfly’s body and homeostatic maintenance of the hemolymph proteins, lipid and carbohydrates in each treatment group are abnormal.

Keywords: forensic toxicology, forensic entomology, diptera, diazepam

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Authors: Alireza Pesarakloo, Masoumeh Najibzadeh

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We studied the development and morphology of different larval stages of Pelophylax bedriagae at two rearing temperatures (20 and 24°C). Eggs collected from a breeding site in south-western Iran. Diagnostic morphological characters are provided for Gosner (1960) larval stages 1-46. The larvae hatched about seven days after egg deposition. Principal diagnostic feature including the formation of the funnel-shaped oral disc became discernible about ten days after hatch at Gosner stage 21 and degenerated at Gosner stage 42. Larvae developed faster at higher temperatures. The largest body length of larval P. bedriagae measured about 54mm in 70 days after egg deposition. Based on our results, the longest metamorphosis time was observed on temperature (20°C) whilst the shortest metamorphosis time occurred on temperature (24°C). Compared with the majority of other Palearctic Anurans, it appears that embryonic and larval development is usually slow rapid in P. bedriagae.

Keywords: development, larval stages, Pelophylax bedriagae, temperatures

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Dewi Selvia Fardhyanti, Astrilia Damayanti

Abstract:

Coal tar is a liquid by-product of the process of coal gasification and carbonation, also in some industries such as steel, power plant, cement, and others. This liquid oil mixture contains various kinds of useful compounds such as aromatic compounds and phenolic compounds. These compounds are widely used as raw material for insecticides, dyes, medicines, perfumes, coloring matters, and many others. This research investigates thermodynamic modelling of liquid-liquid equilibria (LLE) in solvent extraction of o-Cresol from the coal tar. The equilibria are modeled by ternary components of Wohl, Van Laar, and Three-Suffix Margules models. The values of the parameters involved are obtained by curve-fitting to the experimental data. Based on the comparison between calculated and experimental data, it turns out that among the three models studied, the Three-Suffix Margules seems to be the best to predict the LLE of o-Cresol for those system.

Keywords: coal tar, o-Cresol, Wohl, Van Laar, three-suffix margules

Procedia PDF Downloads 275

Authors: Ashneel Ajay Singh, Naoki Suzuki, Kazumi Sakuramoto, Swastika Roshni, Paras Nath, Alok Kalla

Abstract:

Albacore tuna (Thunnus alalunga) is one of the commercially important species of tuna in the North Pacific region. Despite the long history of albacore fisheries in the Pacific, its ecological characteristics are not sufficiently understood. The effects of changing climate on numerous commercially and ecologically important fish species including albacore tuna have been documented over the past decades. The objective of this study was to explore and elucidate the relationship of environmental variables with the stock parameters of albacore tuna. The relationship of the North Pacific albacore tuna recruitment (R), spawning stock biomass (SSB) and recruits per spawning biomass (RPS) from 1970 to 2012 with the environmental factors of sea surface temperature (SST), Pacific decadal oscillation (PDO), El Niño southern oscillation (ENSO) and Pacific warm pool index (PWI) was construed. SST and PDO were used as independent variables with SSB to construct stock reproduction models for R and RPS as they showed most significant relationship with the dependent variables. ENSO and PWI were excluded due to collinearity effects with SST and PDO. Model selections were based on R2 values, Akaike Information Criterion (AIC) and significant parameter estimates at p<0.05. Models with single independent variables of SST, PDO, ENSO and PWI were also constructed to illuminate their individual effect on albacore R and RPS. From the results it can be said that SST and PDO resulted in the most significant models for reproducing North Pacific albacore tuna R and RPS time series. SST has the highest impact on albacore R and RPS when comparing models with single environmental variables. It is important for fishery managers and decision makers to incorporate the findings into their albacore tuna management plans for the North Pacific Oceanic region.

Keywords: Albacore tuna, El Niño southern oscillation, Pacific decadal oscillation, sea surface temperature

Procedia PDF Downloads 228

Authors: B. I. Zhumagaliyev, L. Sh. Balgabayeva, G. S. Nabiyeva, B. A. Tulegenova, P. Oralkhan, B. S. Kalenova, S. S. Akhmetov

Abstract:

The article describes an approach to build competence in research of Bachelor and Master, which is now an important feature of modern specialist in the field of engineering. Provides an example of methodical teaching methods with the research aspect, is including the formulation of the problem, the method of conducting experiments, analysis of the results. Implementation of methods allows the student to better consolidate their knowledge and skills at the same time to get research. Knowledge on the part of the media requires some training in the subject area and teaching methods.

Keywords: professional competence, model of it-specialties, teaching methods, educational technology, decision making

Procedia PDF Downloads 434

Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

Abstract:

Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

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Authors: Seok Hong Min, Tae Kwon Ha

Abstract:

With the increasing interest on Ti alloys, the extraction process of Ti from its typical ore, TiO₂, has long been and will be important issue. As an intermediate product for the production of pigment or titanium metal sponge, tetrachloride (TiCl₄) is produced by fluidized bed using high TiO₂ feedstock. The purity of TiCl₄ after chlorination is subjected to the quality of the titanium feedstock. Since the impurities in the TiCl₄ product are reported to final products, the purification process of the crude TiCl₄ is required. The purification process includes fractional distillation and chemical treatment, which depends on the nature of the impurities present and the required quality of the final product. In this study, thermodynamic analysis on the impurity effect in the chlorination process, which is the first step of extraction of Ti from TiO₂, has been conducted. All thermodynamic calculations were performed using the FactSage thermodynamical software.

Keywords: rutile, titanium, chlorination process, impurities, thermodynamic calculation, FactSage

Procedia PDF Downloads 307