Search results for: Network Time Protocol

Authors: Marcelus U. Ajonina, Deodata B. Ngonga, Kenric B. Ware, Carine K. Nfor

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Background: Lack of knowledge of rational use of antimalarial drugs among dispensers is a serious problem, especially in areas of intense transmission, thus increasing the risk of resistance and adverse drug reactions. This study was aimed at assessing the knowledge of malaria as well as perception and dispensing practices of antimalarials among vendors in Buea community. Methods: A community-based cross-sectional survey of a random sample of 140 drug vendors living within the Buea community was conducted between March and June 2017. A questionnaire was designed to obtain information from drug vendors on the general knowledge of malaria as well as dispensing practices. Data were analyzed using SPSS Statistics 20.0 and were considered significant at p ≤ 0.05. Results: Knowledge of malaria symptoms, transmission, and prevention was reasonable among 55.8% (77) of the respondents. Only 33.6% (47) of the respondents could attribute the cause of malaria to protozoan of genus Plasmodium species. Of the 140 vendors, 115 (82.7%) prescribe antimalarial drugs. The knowledge of the national protocol was malaria case management among dispensers was 35.0%. Vendors in hospital/community pharmacies were 2.4 times (OR = 3.14, 95% CI: 4.14 - 8.74, p < 0.001) more knowledgeable about malaria treatment protocol than those of in drugstores. The prevalence of self-prescription of antimalarials was 39.3%. Self-prescription was significantly higher in drugstores than hospital/community pharmacies (p=0.004). In all, 56 (40.6%) of vendors showed good practices regarding antimalarial drug dispensing with the majority (51.7%) from community pharmacies (OR=2.27,95% CI: 1.13-4.56). Conclusion: Findings reveal moderate knowledge of malaria but poor prescription and dispensing practices of antimalarial drugs among vendors, thus indicating a need for routine monitoring and evaluation to prevent the emergence of resistant strains to current efficacious antimalarials.

Keywords: antimalarials, drug retail outlets, dispensing, drug resistance, prescription

Procedia PDF Downloads 141

Authors: Xiping Wang，Shuran Yao, Liqin Dai

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Many studies report the winter is getting warmer and the minimum air temperature is obviously rising as the important climate warming evidences. The exacerbated air temperature fluctuation tending to bring more severe weather variation is another important consequence of recent climate change which induced more disasters to crop growth in quite a certain regions. Hebei Province is an important winter wheat growing province in North of China that recently endures more winter freezing injury influencing the local winter wheat crop management. A winter wheat freezing injury assessment Bayesian Network framework was established for the objectives of estimating, assessing and predicting winter wheat freezing disasters in Hebei Province. In this framework, the freezing disasters was classified as three severity degrees (SI) among all the three types of freezing, i.e., freezing caused by severe cold in anytime in the winter, long extremely cold duration in the winter and freeze-after-thaw in early season after winter. The factors influencing winter wheat freezing SI include time of freezing occurrence, growth status of seedlings, soil moisture, winter wheat variety, the longitude of target region and, the most variable climate factors. The climate factors included in this framework are daily mean and range of air temperature, extreme minimum temperature and number of days during a severe cold weather process, the number of days with the temperature lower than the critical temperature values, accumulated negative temperature in a potential freezing event. The Bayesian Network model was evaluated using actual weather data and crop records at selected sites in Hebei Province using real data. With the multi-stage influences from the various factors, the forecast and assessment of the event-based target variables, freezing injury occurrence and its damage to winter wheat production, were shown better scoped by Bayesian Network model.

Keywords: bayesian networks, climatic change, freezing Injury, winter wheat

Procedia PDF Downloads 411

Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

Procedia PDF Downloads 513

Authors: M. K. Benhaoua, A. K. Singh, A. E. H. Benyamina

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In this paper, we propose heuristic for dynamic communications mapping that considers the placement of communications in order to optimize the overall performance. The mapping technique uses a newly proposed Algorithm to place communications between the tasks. The placement we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed mapping approach provides significant performance improvements when compared to those using static routing.

Keywords: Multi-Processor Systems-on-Chip (MPSoCs), Network-on-Chip (NoC), heterogeneous architectures, dynamic mapping heuristics

Procedia PDF Downloads 540

Authors: S. Perera, T. R. Walsh, M. Solvang

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The structure-property relationships of calcium aluminosilicate (CAS) glass surfaces are of scientific and technological interest regarding dissolution phenomena. Molecular dynamics (MD) simulations can provide atomic-scale insights into the structure and properties of the CAS interfaces in vacuo as the first step to conducting computational dissolution studies on CAS surfaces. However, one limitation to date is that although the bulk properties of CAS glasses have been well studied by MD simulation, corresponding efforts on CAS surface properties are relatively few in number (both theoretical and experimental). Here, a systematic computational protocol to create CAS surfaces in vacuo is developed by evaluating the sensitivity of the resultant surface structure with respect to different factors. Factors such as the relative thickness of the surface layer, the relative thickness of the bulk region, the cooling rate, and the annealing schedule (time and temperature) are explored. Structural features such as ring size distribution, defect concentrations (five-coordinated aluminium (AlV), non-bridging oxygen (NBO), and tri-cluster oxygen (TBO)), and linkage distribution are identified as significant features in dissolution studies.

Keywords: MD simulation, CAS glasses, surface structure, structure-property, CAS interface

Procedia PDF Downloads 103

Authors: Leila Keshavarz Afshar, Hedieh Sajedi

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Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

Procedia PDF Downloads 151

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

Procedia PDF Downloads 417

Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

Procedia PDF Downloads 264

Authors: Mohsen Maraoui

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In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

Procedia PDF Downloads 143

Authors: Elisabete Alberdi, Irantzu Álvarez, Laura Girón

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The objective of this work is to propose a cycle path or network of paths to the UPV/EHU Campus in Leioa. The proposal will be presented from the point of view of sustainability. In order to achieve this, the roads that are already built will be used, and the road or network will be proposed to be built with the least amount of money possible. To select the most suitable route for the bike lane, various sources of information have been used. Through this data, we analyse the transport infrastructure and the mobility around the UPV/EHU Campus in Leioa. This work aims to satisfy the mobility needs of users on the University Campus to contribute to the sustainability of the campus.

Keywords: cycle lane, sustainability, accessibility, transport, agenda 2030

Procedia PDF Downloads 231

Authors: Abdel-Aziz M. Mohamed, Nermeen Coutry

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Lead time is an important measure of supply chain performance. It impacts both customer satisfactions as well as the total cost of inventory. This paper presents the result of a study on the analysis of the customer order lead-time for a multinational company. In the study, the lead time was divided into three stages: order entry, order fulfillment, and order delivery. A sample of size 2,425 order lines from the company records were considered for this study. The sample data includes information regarding customer orders from the time of order entry until order delivery. Data regarding the lead time of each sage for different orders were also provided. Summary statistics on lead time data reveals that about 30% of the orders were delivered after the scheduled due date. The result of the multiple linear regression analysis technique revealed that component type, logistics parameter, order size and the customer type have significant impact on lead time. Data analysis on the stages of lead time indicates that stage 2 consumes over 50% of the lead time. Pareto analysis was made to study the reasons for the customer order delay in each of the 3 stages. Recommendation was given to resolve the problem.

Keywords: lead time reduction, customer satisfaction, service quality, statistical analysis

Procedia PDF Downloads 737

Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

Procedia PDF Downloads 206

Authors: Dinesh K. S., D. R. C. V. Jayadevan

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Autism is the most prevalent of a subset of the disorders organized under the umbrella of pervasive developmental disorders. After the publication of Andrew Wakefield's paper in lancet, many critiques deny this connection even without looking in to the matter. The British Medical Journal even put an editorial regarding this issue. BMJ 2010; 340:c1807. But ayurveda has ample of evidences to believe this connectivity. Dysbiosis, yeast growth of the gut, nutritional deficiencies, enzyme deficiencies, essential fatty acid deficiencies, Gastro esophageal reflux disease, indigestion, inflammatory bowel, chronic constipation & its cascade are few of them to note. The purpose of this paper is to present the observed changes in the behavioural symptoms of autistic babies after a gut management protocol which is a usual programme of our autism treatment plan especially after dysbiotic changes after antibiotic administration. Is there any correlation between changes (if significant) in gut symptoms and behavioral problems of autistic babies especially after a dysbiosis induced by antibiotics. Retrospective analysis of the case sheets of autistic patients admitted in Vaidyaratnam P.S.Varier Ayurveda College hospital, kottakkal,kerala, india from September 2010 are taken for the data processing. Autistic patients are used to come to this hospital as a part of their usual course of treatment. We investigated 40 cases diagnosed as autistic by clinical psychologists from different institutions who had dysbiosis induced by antibiotics. Significant change in gut symptoms before and after treatment p<0.05 in most of its components Significant change in behavioral symptoms before and after treatments p<0.05 in most of the components Correlation between gut symptoms change and behavioral symptoms changes after treatment is + 0.86. Conclusion : Selected Polyherbal Ayurveda treatment has significant role to play to make changes abnormal behaviors in autistic babies and has a positive correlation with changes in gut symptoms induced by dysbiosis of antibiotic intake.

Keywords: ayurveda, autism, dysbiosis, antibiotic

Procedia PDF Downloads 632

Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

Procedia PDF Downloads 149

Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

Procedia PDF Downloads 93

Authors: Asmau Mukhtar Ahmed, Olga Duran

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Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.

Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image

Procedia PDF Downloads 119

Authors: Alok Shiomurti Tripathi, Ajay Kumar Timiri, Papiya Mitra Mazumder, Anil Chandewar

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The present study evaluates the possible drug interaction between glimepiride (GLIM) and sildenafil citrate (SIL) in streptozotocin (STZ) induced in diabetic nephropathic (DN) animals and also postulates the possible mechanism of interaction by molecular modeling studies. Diabetic nephropathy was induced by single dose of STZ (60 mg/kg, ip) and confirms it by assessing the blood and urine biochemical parameters on 28th day of its induction. Selected DN animals were used for the drug interaction between GLIM (0.5mg/kg, p.o.) and SIL (2.5 mg/kg, p.o.) after 29th and 70th day of protocol. Drug interaction were assessed by evaluating the plasma drug concentration using HPLC-UV and also determine the change in the biochemical parameter in blood and urine. Mechanism of the interaction was postulated by molecular modeling study using Maestro module of Schrodinger software. DN was confirmed as there was significant alteration in the blood and urine biochemical parameter in STZ treated groups. The concentration of SIL increased significantly (p<0.001) in rat plasma when co administered with GLIM after 70th day of protocol. Molecular modelling study revealed few important interactions with rat serum albumin and CYP2C9.GLIM has strong hydrophobic interaction with binding site residues of rat serum albumin compared to SIL. Whereas, for CYP2C9, GLIM has strong hydrogen bond with polar contacts and hydrophobic interactions than SIL. Present study concludes that bioavailability of SIL increases when co-administered chronically with GLIM in the management of DN animals and mechanism has been supported by molecular modeling studies.

Keywords: diabetic nephropathy, glimepiride, sildenafil citrate, pharmacokinetics, homology modeling, schrodinger

Procedia PDF Downloads 382

Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

Procedia PDF Downloads 124

Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

Procedia PDF Downloads 386

Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

Procedia PDF Downloads 128

Authors: Souâad Boudebza, Omar Nouali, Faiçal Azouaou

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Social networks have recently gained a growing interest on the web. Traditional formalisms for representing social networks are static and suffer from the lack of semantics. In this paper, we will show how semantic web technologies can be used to model social data. The SemTemp ontology aligns and extends existing ontologies such as FOAF, SIOC, SKOS and OWL-Time to provide a temporal and semantically rich description of social data. We also present a modeling scenario to illustrate how our ontology can be used to model social networks.

Keywords: ontology, semantic web, social network, temporal modeling

Procedia PDF Downloads 393

Authors: Shagufta Tabassum, V. P. Pawar, jr., G. N. Shinde

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The study of dielectric relaxation properties of polar liquids in the binary mixture has been carried out at 10, 15, 20 and 25 ºC temperatures for 11 different concentrations using time domain reflectometry technique. The dielectric properties of a solute-solvent mixture of polar liquids in the frequency range of 10 MHz to 30 GHz gives the information regarding formation of monomers and multimers and also an interaction between the molecules of the liquid mixture under study. The dielectric parameters have been obtained by the least squares fit method using the Debye equation characterized by a single relaxation time without relaxation time distribution.

Keywords: excess properties, relaxation time, static dielectric constant, and time domain reflectometry technique

Procedia PDF Downloads 160

Authors: Ahmad Saleem

Abstract:

Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

Procedia PDF Downloads 101

Authors: Ewa Siedlecka

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Emotional stress is an important risk factor in the rate and severity of traffic accidents. Moreover, incorrect time perception is implicated in the increase of traffic violations, such as running red lights or collisions. While the role of emotional arousal on perceived time is well-established, the role of physiological arousal in time perception remains unexamined. Specific emotions can be, however, associated with distinct physiological responses. In the current research, two studies examined the role of physiological arousal in time perception. In the first experiment, 41 participants engaged in a cold pressor task and had their time perception measured throughout the experiment. In the second study, 138 participants engaged in either isometric or deep breathing exercises. These activities were designed to simulate the sympathetic and parasympathetic nervous systems, respectively. Participants completed a bisection task to measure time perception in both studies, as well as a physiological response via an Electrocardiography (ECG). Results found that activation of the parasympathetic nervous system is associated with greater time perception. These findings are discussed with reference to models of time perception, as well as implications for emotional driving and misperceptions of speed. It is important to consider the role of physiology in the misperception of time, as these factors can lead to increases in driving accidents.

Keywords: emotions, nervous system, physiology, time perception

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Authors: R. Venkatesan, A. Sabari

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Developments of social networking internet scenario are recommended for the requirements of scalable, authentic, secure group communication model like multicasting. Multicasting is an inter network service that offers efficient delivery of data from a source to multiple destinations. Even though multicast has been very successful at providing an efficient and best-effort data delivery service for huge groups, it verified complex process to expand other features to multicast in a scalable way. Separately, the requirement for secure electronic information had become gradually more apparent. Since multicast applications are deployed for mainstream purpose the need to secure multicast communications will become significant.

Keywords: multicasting, scalability, security, social network

Procedia PDF Downloads 294

Authors: Serap Durkut, A. Eser Elcin, Y. Murat Elcin

Abstract:

Stimuli-sensitive polymeric hydrogels have received extensive attention in the biomedical field due to their sensitivity to physical and chemical stimuli (temperature, pH, ionic strength, light, etc.). This study describes the rheological properties of a novel thermoresponsive poly(N-vinylcaprolactam)-g-collagen hydrogel. In the study, we first synthesized a facile and novel synthetic carboxyl group-terminated thermo-responsive poly(N-vinylcaprolactam)-COOH (PNVCL-COOH) via free radical polymerization. Further, this compound was effectively grafted with native collagen, by utilizing the covalent bond between the carboxylic acid groups at the end of the chains and amine groups of the collagen using cross-linking agent (EDC/NHS), forming PNVCL-g-Col. Newly-formed hybrid hydrogel displayed novel properties, such as increased mechanical strength and thermoresponsive characteristics. PNVCL-g-Col showed low critical solution temperature (LCST) at 38ºC, which is very close to the body temperature. Rheological studies determine structural–mechanical properties of the materials and serve as a valuable tool for characterizing. The rheological properties of hydrogels are described in terms of two dynamic mechanical properties: the elastic modulus G′ (also known as dynamic rigidity) representing the reversible stored energy of the system, and the viscous modulus G″, representing the irreversible energy loss. In order to characterize the PNVCL-g-Col, the rheological properties were measured in terms of the function of temperature and time during phase transition. Below the LCST, favorable interactions allowed the dissolution of the polymer in water via hydrogen bonding. At temperatures above the LCST, PNVCL molecules within PNVCL-g-Col aggregated due to dehydration, causing the hydrogel structure to become dense. When the temperature reached ~36ºC, both the G′ and G″ values crossed over. This indicates that PNVCL-g-Col underwent a sol-gel transition, forming an elastic network. Following temperature plateau at 38ºC, near human body temperature the sample displayed stable elastic network characteristics. The G′ and G″ values of the PNVCL-g-Col solutions sharply increased at 6-9 minute interval, due to rapid transformation into gel-like state and formation of elastic networks. Copolymerization with collagen leads to an increase in G′, as collagen structure contains a flexible polymer chain, which bestows its elastic properties. Elasticity of the proposed structure correlates with the number of intermolecular cross-links in the hydrogel network, increasing viscosity. However, at 8 minutes, G′ and G″ values sharply decreased for pure collagen solutions due to the decomposition of the elastic and viscose network. Complex viscosity is related to the mechanical performance and resistance opposing deformation of the hydrogel. Complex viscosity of PNVCL-g-Col hydrogel was drastically changed with temperature and the mechanical performance of PNVCL-g-Col hydrogel network increased, exhibiting lesser deformation. Rheological assessment of the novel thermo-responsive PNVCL-g-Col hydrogel, exhibited that the network has stronger mechanical properties due to both permanent stable covalent bonds and physical interactions, such as hydrogen- and hydrophobic bonds depending on temperature.

Keywords: poly(N-vinylcaprolactam)-g-collagen, thermoresponsive polymer, rheology, elastic modulus, stimuli-sensitive

Procedia PDF Downloads 245

Authors: Qiankun Zhong

Abstract:

In a global cultural context, film festival awards become authorities to define the aesthetic value of films. To study which genres and producing countries are valued by different film festivals and how those evaluations interact with each other, this research explored the interactions between the film festivals through their selection of movies and the factors that lead to the tendency of film festivals to nominate the same movies. To do this, the author employed a two-mode network on the movies that won the highest awards at five international film festivals with the highest attendance in the past ten years (the Venice Film Festival, the Cannes Film Festival, the Toronto International Film Festival, Sundance Film Festival, and the Berlin International Film Festival) and the film festivals that nominated those movies. The title, genre, producing country and language of 50 movies, and the range (regional, national or international) and organizing country or area of 129 film festivals were collected. These created networks connected by nominating the same films and awarding the same movies. The author then assessed the density and centrality of these networks to answer the question: What are the film festivals that tend to have more shared values with other festivals? Based on the Eigenvector centrality of the two-mode network, Palm Springs, Robert Festival, Toronto, Chicago, and San Sebastian are the festivals that tend to nominate commonly appreciated movies. In contrast, Black Movie Film Festival has the unique value of generally not sharing nominations with other film festivals. A homophily test was applied to access the clustering effects of film and film festivals. The result showed that movie genres (E-I index=0.55) and geographic location (E-I index=0.35) are possible indicators of film festival clustering. A blockmodel was also created to examine the structural roles of the film festivals and their meaning in real-world context. By analyzing the same blocks with film festival attributes, it was identified that film festivals either organized in the same area, with the same history, or with the same attitude on independent films would occupy the same structural roles in the network. Through the interpretation of the blocks, language was identified as an indicator that contributes to the role position of a film festival. Comparing the result of blockmodeling in the different periods, it is seen that international film festivals contrast with the Hollywood industry’s dominant value. The structural role dynamics provide evidence for a multi-value film festival network.

Keywords: film festivals, film studies, media industry studies, network analysis

Procedia PDF Downloads 321

Authors: Y. Zheng, M. Busi, A. F. Pedersen, M. A. Beltran, C. Gundlach

Abstract:

X-ray phase contrast imaging has shown to yield a better contrast compared to conventional attenuation X-ray imaging, especially for soft tissues in the medical imaging energy range. This can potentially lead to better diagnosis for patients. However, phase contrast imaging has mainly been performed using highly brilliant Synchrotron radiation, as it requires high coherence X-rays. Many research teams have demonstrated that it is also feasible using a laboratory source, bringing it one step closer to clinical use. Nevertheless, the requirement of fine gratings and high precision stepping motors when using a laboratory source prevents it from being widely used. Recently, a random phase object has been proposed as an analyzer. This method requires a much less robust experimental setup. However, previous studies were done using a particular X-ray source (liquid-metal jet micro-focus source) or high precision motors for stepping. We have been working on a much simpler setup with just small modification of a commercial bench-top micro-CT (computed tomography) scanner, by introducing a piece of sandpaper as the phase analyzer in front of the X-ray source. However, it needs a suitable algorithm for speckle tracking and 3D reconstructions. The precision and sensitivity of speckle tracking algorithm determine the resolution of the system, while the 3D reconstruction algorithm will affect the minimum number of projections required, thus limiting the temporal resolution. As phase contrast imaging methods usually require much longer exposure time than traditional absorption based X-ray imaging technologies, a dynamic phase contrast micro-CT with a high temporal resolution is particularly challenging. Different reconstruction methods, including neural network based techniques, will be evaluated in this project to increase the temporal resolution of the phase contrast micro-CT. A Monte Carlo ray tracing simulation (McXtrace) was used to generate a large dataset to train the neural network, in order to address the issue that neural networks require large amount of training data to get high-quality reconstructions.

Keywords: micro-ct, neural networks, reconstruction, speckle-based x-ray phase contrast

Procedia PDF Downloads 261

Authors: Jinan Fiaidhi, Sabah Mohammed

Abstract:

Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

Procedia PDF Downloads 115

Authors: M. Nasri Nasrabadi, A. A. Hassani

Abstract:

Karoon is one of the greatest and longest rivers of Iran, which because of the existence of numerous industrial, agricultural centers and drinking usage, has a strategic situation in the west and southwest parts of Iran, and the optimal monitoring of its water quality is an essential and indispensable national issue. Due to financial constraints, water quality monitoring network design is an efficient way to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. Considering the objectives of water usage, we evaluate existing water quality sampling stations of this river. There are several methods for assessment of existing monitoring stations such as Sanders method, multiple criteria decision making and dynamic programming approach (DPA) which DPA opted in this study. The results showed that due to the drinking water quality index out of 20 existing monitoring stations, nine stations should be retained on the river, that include of Gorgor-Band-Ghir of A zone, Dez-Band-Ghir of B zone, Teir, Pole Panjom and Zargan of C zone, Darkhoein, Hafar, Chobade, and Sabonsazi of D zone. In additional, stations of Dez river have the best conditions.

Keywords: DPA, karoon river, network monitoring, water quality, sampling site

Procedia PDF Downloads 380