Search results for: Kernel logs

Authors: Soualem Mami Zoubida, Brixi Nassima, Beghdad Choukri, Belarbi Meriem

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Cereals are the most important source of dietary fiber in the Nordic diet. The fiber in cereals is located mainly in the outer layers of the kernel; particularly in the bran. Improved diet can help unlock the door to good health. Whole grains are an important source of nutrients that are in short supply in our diet, including digestible carbohydrates, dietary fiber, trace minerals, and other compounds of interest in disease prevention, including phytoestrogens and antioxidants (1). The objective of this study is to know the composition of whole grain cereals (rye, millet, white, and red sorghum) which a majority pushes in the south of Algeria. This shows that the millet has a high rate of the sugar estimated at 67.6%. The high proportion of proteins has been found in the two varieties of sorghum and rye. The millet presents the great percentage in lipids compared with the others cereals. And at the last, a red sorghum has the highest rate of fiber(2). These nutrients, as well as other components of whole grain cereals, have, in terms of health, an increased effect if they are consumed together.

Keywords: chemical composition, miller, Secale cereal, Sorghum bicolor

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Rafiu O. Sanni, Abayomi O. Olajuyigbe, Nelson R. Osungbemiro, Rotimi F. Olaniyan

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The Owena River lies within the drainage basins of the Oni, Siluko, and Ogbesse rivers. The river’s immediate surroundings are covered by dense forests, interspersed by plantations of cocoa, oil palm, kolanut, bananas, and other crops. The objectives were to identify the aquatic plants community, comprising the algae and aquatic macrophytes, observe their population dynamics in relation to the two seasons and identify their economic importance, especially to the neighbouring community. The study sites were determined using a stratified sampling method. Three strata were marked out for sampling namely strata I (upstream)–5 stations, strata II (reservoir) –2 stations, and strata III (outflow) 2 stations. These nine stations were tagged st1, st2, st3…st9. The aquatic macrophytes were collected using standard methods and identified at the University of Ibadan herbarium while the algal samples were collected using standard methods for microalgae. The periphytonic species were scraped from surfaces of rocks (perilithic), sucked with large syringe from mud (epipellic), scraped from suspended logs, washed from roots of aquatic angiosperms (epiphytic), as well as shaken from other particles such as suspended plant parts. Some were collected physically by scooping floating thallus of non-microscopic multicellular forms. The specimens were taken to the laboratory and observed under a microscope with mounted digital camera for photomicrography. Identification was done using Prescott.

Keywords: aquatic plants, aquatic macrophytes, algae, Owena river

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Authors: YU Kaijun, Liang Dong, Zhang Yarong, Jin Zhenzhou, Yang Zhaobao

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For the impact monitoring of distributed structures, the traditional positioning methods are based on the time difference, which includes the four-point arc positioning method and the triangulation positioning method. But in the actual operation, these two methods have errors. In this paper, the Multi-Agent Blackboard Coordination Principle is used to combine the two methods. Fusion steps: (1) The four-point arc locating agent calculates the initial point and records it to the Blackboard Module.(2) The triangulation agent gets its initial parameters by accessing the initial point.(3) The triangulation agent constantly accesses the blackboard module to update its initial parameters, and it also logs its calculated point into the blackboard.(4) When the subsequent calculation point and the initial calculation point are within the allowable error, the whole coordination fusion process is finished. This paper presents a Multi-Agent collaboration method whose agent framework is JADE. The JADE platform consists of several agent containers, with the agent running in each container. Because of the perfect management and debugging tools of the JADE, it is very convenient to deal with complex data in a large structure. Finally, based on the data in Jade, the results show that the impact location method based on Multi-Agent coordination fusion can reduce the error of the two methods.

Keywords: impact monitoring, structural health monitoring(SHM), multi-agent system(MAS), black-board coordination, JADE

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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

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Authors: Yawen Wang, Hongchang Chen, Shaomei Li, Chao Gao, Jiangpeng Zhang

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As the main challenge for target tracking is accounting for target scale change and real-time, we combine Mean-Shift and PCA-SIFT algorithm together to solve the problem. We introduce similarity comparison method to determine how the target scale changes, and taking different strategies according to different situation. For target scale getting larger will cause location error, we employ backward tracking to reduce the error. Mean-Shift algorithm has poor performance when tracking scale-changing target due to the fixed bandwidth of its kernel function. In order to overcome this problem, we introduce PCA-SIFT matching. Through key point matching between target and template, that adjusting the scale of tracking window adaptively can be achieved. Because this algorithm is sensitive to wrong match, we introduce RANSAC to reduce mismatch as far as possible. Furthermore target relocating will trigger when number of match is too small. In addition we take comprehensive consideration about target deformation and error accumulation to put forward a new template update method. Experiments on five image sequences and comparison with 6 kinds of other algorithm demonstrate favorable performance of the proposed tracking algorithm.

Keywords: target tracking, PCA-SIFT, mean-shift, scale-adaptive

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Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

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This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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We describe an innovative method for highly specific detection of miRNAs using a specially modified method of poly(A) adaptor RT-qPCR. We use uniquely designed specific extension sequence, which plays important role in providing an opportunity to affect high specificity of miRNA detection. This method involves two steps of reactions as like previously reported and which are poly(A) tailing and reverse-transcription followed by real-time PCR. Firstly, miRNAs are extended by a poly(A) tailing reaction and then converted into cDNA. Here, we remarkably reduced the reaction time by the application of short length of poly(T) adaptor. Next, cDNA is hybridized to the 3’-end of a specific extension sequence which contains miRNA sequence and results in producing a novel PCR template. Thereafter, the SYBR Green-based RT-qPCR progresses with a universal poly(T) adaptor forward primer and a universal reverse primer. The target miRNA, miR-106b in human brain total RNA, could be detected quantitatively in the range of seven orders of magnitude, which demonstrate that the assay displays a dynamic range of at least 7 logs. In addition, the better specificity of this novel extension-based assay against well known poly(A) tailing method for miRNA detection was confirmed by melt curve analysis of real-time PCR product, clear gel electrophoresis and sequence chromatogram images of amplified DNAs.

Keywords: microRNA(miRNA), specific extension sequence, RT-qPCR, poly(A) tailing assay, reverse transcription

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Authors: Ching-Ting Hsu, Wei-Hua Ho, Yi-Chun Chang

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Video object tracking is one of the popular research topics in computer graphics area. The trajectory can be applied in security, traffic control, even the sports training. The trajectory for sports training can be utilized to analyze the athlete’s performance without traditional sensors. There are many relevant works which utilize mean shift algorithm with background subtraction. This kind of the schemes should select a kernel function which may affect the accuracy and performance. In this paper, we consider the motion information in the pre-coded bitstream. The proposed algorithm extracts the trajectory by composing the motion vectors from the pre-coded bitstream. We gather the motion vectors from the overlap area of the object and calculate mean of the overlapped motion vectors. We implement and simulate our proposed algorithm in H.264 video codec. The performance is better than relevant works and keeps the accuracy of the object trajectory. The experimental results show that the proposed trajectory extraction can extract trajectory form the pre-coded bitstream in high accuracy and achieve higher performance other relevant works.

Keywords: H.264, video bitstream, video object tracking, sports training

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Authors: Xiongxiong You, Zhanwen Niu

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Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method.

Keywords: adaptive selection, expensive optimization, rotor system, surrogates assisted evolutionary algorithms

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Authors: Save Kumwenda, Khumbo Kalulu, Kondwani Chidziwisano, Limbani Kalumbi, Vincent Doyle, Bagrey Ngwira

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Introduction: The Malawi Government in partnership with Non-Governmental Organizations adopted Community Led Total Sanitation (CLTS) in 2008 as an approach in sanitation and hygiene promotion with an aim of declaring Malawi Open Defeacation Free (ODF) by 2015. While there is a significant body of research into CLTS available in public domain, there is little research done on challenges faced in implementing CLTS in Malawi. Methods: A cross-sectional qualitative study was carried out in three districts of Ntcheu, Balaka, and Phalombe. Data was collected using Focus Group Discussions (FGDs) and Key informant interviews (KII) and analysed manually. Results: In total, 96 people took part in FGDs and 9 people in KII. It was shown that choice of leaders after triggering was commonly done by chiefs, facilitators, and VHC without following CLTS principles as opposed to identifying individuals who showed leadership skills. Despite capacity building initiatives involving District Coordinating Teams, lack of resources to undertake follow-ups contributed to failure to sustain ODF in the community. It was also found that while most respondents appreciating the need for no subsidies, the elderly and those with disabilities felt the need for external support because do not have money for buying strong logs, slabs for durable toilet floor and also to hire people to build latrines for them. Conclusion: Effective implementation of CLTS requires comprehensive consideration of various issues that may affect its success.

Keywords: open defecation, community-led, sanitation, faecal matter, hygiene, Malawi

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Yang Yu, Jian Wang, Jiqiang Liu, Lei Han, Xudong He, Shaohua Lv

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As the increasing network attacks become more and more complex, network situation assessment based on log analysis cannot meet the requirements to ensure network security because of the low quality of logs and alerts. This paper addresses the lack of consideration of security attributes of hosts and attacks in the network. Identity and effectiveness of Distributed Denial of Service (DDoS) are hard to be proved in risk assessment based on alerts and flow matching. This paper proposes a multi-dimension threat situation assessment method based on network security attributes. First, the paper offers an improved Common Vulnerability Scoring System (CVSS) calculation, which includes confident risk, integrity risk, availability risk and a weighted risk. Second, the paper introduces deterioration rate of properties collected by sensors in hosts and network, which aimed at assessing the time and level of DDoS attacks. Third, the paper introduces distribution of asset value in security attributes considering features of attacks and network, which aimed at assessing and show the whole situation. Experiments demonstrate that the approach reflects effectiveness and level of DDoS attacks, and the result can show the primary threat in network and security requirement of network. Through comparison and analysis, the method reflects more in security requirement and security risk situation than traditional methods based on alert and flow analyzing.

Keywords: DDoS evaluation, improved CVSS, network security attribute, threat situation assessment

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Authors: M. N. Yahaya, K. A. Salako, U. Z. Magawata

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Vertical electrical sounding (VES) method was used to investigate the groundwater potential at the southern part of Gulumbe district, Kebbi State, north-western part of Nigeria. The study was carried out with the aim of determining the subsurface layer’s parameters (resistivity and thickness) and uses the same to characterize the groundwater potential of the study area. The Schlumberger configuration was used for data acquisition. A total number of thirty-three (33) sounding points (VES) were surveyed over six profiles. The software IPI2WIN was used to obtain n-layered geo-electric sections. The geo-electric section drawn from the results of the interpretation revealed that three subsurface layers could be delineated, which comprise of top soil, sand, sandstone, coarse sand, limestone, and gravelly sand. The results of the resistivity sounding were correlated with the lithological logs of nearby boreholes that expose cross-section geologic units around the study area. We found out that the area is dominated by three subsurface layers. The coarse sand layers constituted the aquifer zones in the majority of sounding stations. Thus, this present study concluded that the depth of any borehole in the study area should be located between the depth of 18.5 to 39 m. The study further classified the VES points penetrated based on their conductivity content as highly suitable, suitable, moderately suitably, and poor zones for groundwater exploration. Hence, from this research, we recommended that boreholes can be sited in high conductivity zones across VES 2, 11, 13, 16, 20, 21, 27, and 33, respectively.

Keywords: vertical electrical sounding, resistivity, geo-electric, resistivity, aquifer and groundwater

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Authors: Seung Moon Woo, Youn Suk Chung, Sang Yong Nam

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In the present time, we need advanced water treatment technology for separation of virus and bacteria in effluent which occur epidemic and waterborne diseases. Water purification system is mainly divided into two categorizations like reverse osmosis (RO) and ultrafiltration (UF). Membrane used in these systems requires higher durability because of operating in harsh condition. Of these, the membrane using in UF system has many advantages like higher efficiency and lower energy consume for water treatment compared with RO system. In many kinds of membrane, hollow fiber type membrane is possible to make easily and to get optimized property by control of various spinning conditions such as temperature of coagulation bath, concentration of polymer, addition of additive, air gap and internal coagulation. In this study, polysulfone hollow fiber membrane was successfully prepared by phase inversion method for separation of virus and bacteria. When we prepare the hollow fiber membrane, we controlled various factors such as the polymer concentration, air gap and internal coagulation to investigate effect to membrane property. Morphology of surface and cross section of membrane were measured by field emission scanning electron microscope (FE-SEM). Water flux of membrane was measured using test modules. Mean pore diameter of membrane was calculated using rejection of polystyrene (PS) latex beads for separation of virus and bacteria. Flux and mean flow pore diameter of prepared membrane show 1.5 LPM, 0.03 μm at 1.0 kgf/cm2. The bacteria and virus removal performance of prepared UF membranes were over 6 logs.

Keywords: hollow fiber membrane, drinking water, ultrafiltration, bacteria

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Authors: Sajid Hussain

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The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Serge B. Provost

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Numerous statistical inference and modeling methodologies are based on sample moments rather than the actual observations. A result justifying the validity of this approach is introduced. More specifically, it will be established that given the first n moments of a sample of size n, one can recover the original n sample points. This implies that a sample of size n and its first associated n moments contain precisely the same amount of information. However, it is efficient to make use of a limited number of initial moments as most of the relevant distributional information is included in them. Two types of density estimation techniques that rely on such moments will be discussed. The first one expresses a density estimate as the product of a suitable base density and a polynomial adjustment whose coefficients are determined by equating the moments of the density estimate to the sample moments. The second one assumes that the derivative of the logarithm of a density function can be represented as a rational function. This gives rise to a system of linear equations involving sample moments, the density estimate is then obtained by solving a differential equation. Unlike kernel density estimation, these methodologies are ideally suited to model ‘big data’ as they only require a limited number of moments, irrespective of the sample size. What is more, they produce simple closed form expressions that are amenable to algebraic manipulations. They also turn out to be more accurate as will be shown in several illustrative examples.

Keywords: density estimation, log-density, polynomial adjustments, sample moments

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Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Cissy Li

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Mentorship is a commonly used strategy for career development that has obvious benefits for students in undergraduate pre-service teacher training programs. In contrast to teaching practicum, which generally involves pedagogical supervision and performance evaluation by teachers, mentorship is more focused on sharing experiences, supporting challenges, and nurturing skills in order to promote personal and professional growth. To empower pre-service teachers and prepare them for potential challenges in the context of local English language teaching (ELT), an alumni mentoring program was established in the framework of communities of practice (CoP), with the mentors being in-service graduates working in local schools and mentees being students on the teacher-training programme in a Hong Kong university. By triangulating audio transcripts of mentoring sessions delivered by three top mentors with data from questionnaire responses and mentor logs, this paper examines the mentoring styles of the three best mentors from the metadiscursive perspective. It was found that, in a community of practice, mentors who may seem to enjoy a relative more dominant position, in fact, had to strategically and pragmatically employ metadiscursive resources to manage relationships with the mentees and organize talks in the mentoring process. Other attributing factors for a successful mentoring session include mentor personality and prior mentorship experiences, nature of the activities in the session, and group dynamics. This paper concludes that it is the combination of all the factors that constitute a particular mentoring style. The findings have implications for mentoring programs in teacher preparation.

Keywords: mentoring, teacher education, mentoring style, metadiscourse

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Authors: David F. Nettleton, Christian Wasiak, Jonas Dorissen, David Gillen, Alexandr Tretyak, Elodie Bugnicourt, Alejandro Rosales

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In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: calibration, data modeling, industrial processes, machine learning

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Authors: Abdalla Abdelnabi, Yousf Abushalah

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The G Formation is a major gas producing reservoir in the SM Field, eastern, Libya. It is called G limestone because it consists of shallow marine limestone. Well data and 3D-Seismic in conjunction with the results of a previous study were used to delineate the hydrocarbon reservoir of Middle Eocene G-Formation of SM Field area. The data include three-dimensional seismic data acquired in 2009. It covers approximately an area of 75 mi² and with more than 9 wells penetrating the reservoir. Seismic data are used to identify any stratigraphic and structural and features such as channels and faults and which may play a significant role in hydrocarbon traps. The well data are used to calculation petrophysical analysis of S field. The average porosity of the Middle Eocene G Formation is very good with porosity reaching 24% especially around well W 6. Average water saturation was calculated for each well from porosity and resistivity logs using Archie’s formula. The average water saturation for the whole well is 25%. Structural mapping of top and bottom of Middle Eocene G formation revealed the highest area in the SM field is at 4800 ft subsea around wells W4, W5, W6, and W7 and the deepest point is at 4950 ft subsea. Correlation between wells using well data and structural maps created from seismic data revealed that net thickness of G Formation range from 0 ft in the north part of the field to 235 ft in southwest and south part of the field. The gas water contact is found at 4860 ft using the resistivity log. The net isopach map using both the trapezoidal and pyramid rules are used to calculate the total bulk volume. The original gas in place and the recoverable gas were calculated volumetrically to be 890 Billion Standard Cubic Feet (BSCF) and 630 (BSCF) respectively.

Keywords: 3D seismic data, well logging, petrel, kingdom suite

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Authors: Xia Huo

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The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu and 34 children from Haojiang. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and both the alpha diversity index and specific metabolites in single-element models. The study found that the Bayesian kernel machine regression (BKMR) model showed a negative correlation between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the Chao 1 index, particularly beyond the 55th percentile. Furthermore, the EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our research suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the gut microbiota and its metabolites. These alterations may be associated with neurodevelopmental abnormalities in children.

Keywords: gut microbiota, 6PPDQ, PBDEs, PCBs, metal(loid)s, BKMR

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Authors: Mehrnaz Alibeikloo, Hadi Khabbaz, Behzad Fatahi

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The spectral random field method is one of the widely used methods to obtain more reliable and accurate results in geotechnical problems involving material variability. Karhunen-Loeve (K-L) expansion method was applied to perform random field discretization of cross-correlated creep parameters. Karhunen-Loeve expansion method is based on eigenfunctions and eigenvalues of covariance function adopting Kernel integral solution. In this paper, the accuracy of Karhunen-Loeve expansion was investigated to predict long-term settlement of soft soils adopting elastic visco-plastic creep model. For this purpose, a parametric study was carried to evaluate the effect of K-L expansion terms and spatial correlation length on the reliability of results. The results indicate that small values of spatial correlation length require more K-L expansion terms. Moreover, by increasing spatial correlation length, the coefficient of variation (COV) of creep settlement increases, confirming more conservative and safer prediction.

Keywords: Karhunen-Loeve expansion, long-term settlement, reliability analysis, spatial correlation length

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Authors: A. Bahrani

Abstract:

In order to study the effect of restricted irrigation systems and different potassium fertilizer on water use efficiency and yield of rapeseed (Brassica napus L.), an experiment was conducted in an arid area in Khuzestan, Iran in 2013. The main plots consisted of three irrigation methods: FI (full irrigation), alternate furrow irrigation (AFI) and fixed furrow irrigation (FFI). Each subplot received three rates of K fertiliser application: 0, 150 or 300 kg ha-1. The results showed that the plots receiving the full irrigation resulted in significantly higher grain yields, 1000-kernel weight and grain number per pod than both alternate treatments. However, the highest WUE were obtained in alternate furrow irrigation and 300 kg K ha-1 and the lowest one was found in the FI treatment and 0 kg K ha-1. Potassium application increased RWC in alternate furrow irrigation and fixed furrow irrigation than FI treatment. Maximum oil content was observed in those treatments where full irrigation was applied while minimum oil content was produced in FFI irrigated treatments. Potassium fertilizer also increased grain oil by 15 % than control. Deficit irrigation reduced oleic acid and erucic acid. However, oleic acid and linoleic acid increased with increasing of potassium.

Keywords: erucic acid, irrigation methods, linoleic acid, oil percent, oleic acid

Procedia PDF Downloads 257

Authors: Filip Buruiana, Alexander Hall, Reimar Hofmann, Thomas Hofmann, Silviu Ganceanu, Alexandru Tudorica

Abstract:

In-memory column-stores make interactive analysis feasible for many big data scenarios. PowerDrill is a system used internally at Google for exploration in logs data. Even though it is a highly parallelized column-store and uses in memory caching, interactive response times cannot be achieved for all datasets (note that it is common to analyze data with 50 billion records in PowerDrill). In this paper, we investigate two orthogonal approaches to optimize performance at the expense of an acceptable loss of accuracy. Both approaches can be implemented as outer wrappers around existing database engines and so they should be easily applicable to other systems. For the first optimization we show that memory is the limiting factor in executing queries at speed and therefore explore possibilities to improve memory efficiency. We adapt some of the theory behind data sketches to reduce the size of particularly expensive fields in our largest tables by a factor of 4.5 when compared to a standard compression algorithm. This saves 37% of the overall memory in PowerDrill and introduces a 0.4% relative error in the 90th percentile for results of queries with the expensive fields. We additionally evaluate the effects of using sampling on accuracy and propose a simple heuristic for annotating individual result-values as accurate (or not). Based on measurements of user behavior in our real production system, we show that these estimates are essential for interpreting intermediate results before final results are available. For a large set of queries this effectively brings down the 95th latency percentile from 30 to 4 seconds.

Keywords: big data, in-memory column-store, high-performance SQL queries, approximate SQL queries

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Authors: Gabriel Efomeh Omolaiye, Olatunji Seminu, Jimoh Ajadi, Yusuf Ayoola Jimoh

Abstract:

The Nigerian part of the Chad Basin till date has been one of the few critically studied basins, with few published scholarly works, compared to other basins such as Niger Delta, Dahomey, etc. This work was undertaken by the integration of 3D seismic interpretations and the well data analysis of eight wells fairly distributed in block A, Baga/Lake sub-basin in Borno basin with the aim of determining the thickness of Chad, Kerri-Kerri, Fika, and Gongila Formations in the sub-basin. Da-1 well (type-well) used in this study was subdivided into stratigraphic units based on the regional stratigraphic subdivision of the Chad basin and was later correlated with other wells using similarity of observed log responses. The combined density and sonic logs were used to generate synthetic seismograms for seismic to well ties. Five horizons were mapped, representing the tops of the formations on the 3D seismic data covering the block; average velocity function with maximum error/residual of 0.48% was adopted in the time to depth conversion of all the generated maps. There is a general thickening of sediments from the west to the east, and the estimated thicknesses of the various formations in the Baga/Lake sub-basin are Chad Formation (400-750 m), Kerri-Kerri Formation (300-1200 m), Fika Formation (300-2200 m) and Gongila Formation (100-1300 m). The thickness of the Bima Formation could not be established because the deepest well (Da-1) terminates within the formation. This is a modification to the previous and widely referenced studies of over forty decades that based the estimation of formation thickness within the study area on the observed outcrops at different locations and the use of few well data.

Keywords: Baga/Lake sub-basin, Chad basin, formation thickness, seismic, velocity

Procedia PDF Downloads 151

Authors: Shalini Priyadarshini

Abstract:

The notion of performance measurement has held great appeal for the industry and research communities alike. This idea is also true for the construction sector, and some propose that performance measurement and productivity analysis are two separate management functions, where productivity is a subset of performance, the latter requiring comprehensive analysis of comparable factors. Labor productivity is considered one of the best indicators of production efficiency. The construction industry continues to account for a disproportionate share of injuries and illnesses despite adopting several technological and organizational interventions that promote worker safety. Specialty trades contractors typically complete a large fraction of work on any construction project, but insufficient body of work exists that address subcontractor safety and productivity issues. Literature review has revealed the possibility of a relation between productivity, safety and other factors and their links to project, organizational, task and industry performance. This research posits that there is an association between productivity and performance at project as well as organizational levels in the construction industry. Moreover, prior exploration of the importance of safety within the performance-productivity framework has been anecdotal at best. Using structured questionnaire survey and organization- and project level data, this study, which is a combination of cross-sectional and longitudinal research designs, addresses the identified research gap and models the relationship between productivity, safety, and performance with a focus on specialty trades in the construction sector. Statistical analysis is used to establish a correlation between the variables of interest. This research identifies the need for developing and maintaining productivity and safety logs for smaller businesses. Future studies can design and develop research to establish causal relationships between these variables.

Keywords: construction, safety, productivity, performance, specialty trades

Procedia PDF Downloads 249

Authors: Dalal Sadeqi

Abstract:

Groundwater is an important component of Kuwait’s water resources. Although limited in quantity and often poor in quality, the significance of this natural source of water cannot be overemphasized. Recharge of groundwater in Kuwait occurs during periodical storm events, especially in open desert areas. Runoff water dissolves accumulated surficial meteoric salts and subsequently leaches them into the groundwater following a period of evaporative enrichment at or near the soil surface. Geochemical processes governing groundwater recharge vary in time and space. Stable isotope (18O and 2H) and geochemical signatures are commonly used to gain some insight into recharge processes and groundwater salinization mechanisms, particularly in arid and semiarid regions. This article addresses the mechanism used in identifying and characterizing the main water shed areas in Kuwait using stable isotopes in an attempt to determine favorable groundwater recharge sites in the country. Stable isotopes of both rainwater and groundwater were targeted in different hydrogeological settings. Additionally, data and information obtained from subsurface logs in the study area were collected and analyzed to develop a better understanding of the lateral and vertical extent of the groundwater aquifers. Geographic Information System (GIS) and RockWorks 3D modelling software were used to map out the hydrogeomorphology of the study area and the subsurface lithology of the investigated aquifers. The collected data and information, including major ion chemistry, isotopes, subsurface characteristics, and hydrogeomorphology, were integrated in a GIS platform to identify and map out suitable natural recharge areas as part of an integrated water resources management scheme that addresses the challenges of the sustainability of the groundwater reserves in the country.

Keywords: scarcity, integrated, recharge, isotope

Procedia PDF Downloads 86