Search results for: stream classification

Authors: Shang-Yu Chiang, Yu-Shan Tsai, Shih-Hsien Sung, Chung-Ming Lo

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Mitral regurgitation (MR) is a heart disorder which the mitral valve does not close properly when the heart pumps out blood. MR is the most common form of valvular heart disease in the adult population. The diagnostic echocardiographic finding of MR is straightforward due to the well-known clinical evidence. In the determination of MR severity, quantification of sonographic findings would be useful for clinical decision making. Clinically, the vena contracta is a standard for MR evaluation. Vena contracta is the point in a blood stream where the diameter of the stream is the least, and the velocity is the maximum. The quantification of vena contracta, i.e. the vena contracta width (VCW) at mitral valve, can be a numeric measurement for severity assessment. However, manually delineating the VCW may not accurate enough. The result highly depends on the operator experience. Therefore, this study proposed an automatic method to quantify VCW to evaluate MR severity. Based on color Doppler ultrasound, VCW can be observed from the blood flows to the probe as the appearance of red or yellow area. The corresponding brightness represents the value of the flow rate. In the experiment, colors were firstly transformed into HSV (hue, saturation and value) to be closely align with the way human vision perceives red and yellow. Using ellipse to fit the high flow rate area in left atrium, the angle between the mitral valve and the ultrasound probe was calculated to get the vertical shortest diameter as the VCW. Taking the manual measurement as the standard, the method achieved only 0.02 (0.38 vs. 0.36) to 0.03 (0.42 vs. 0.45) cm differences. The result showed that the proposed automatic VCW extraction can be efficient and accurate for clinical use. The process also has the potential to reduce intra- or inter-observer variability at measuring subtle distances.

Keywords: mitral regurgitation, vena contracta, color doppler, image processing

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: S. Chuenchooklin, S. Taweepong, U. Pangnakorn

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This research was conducted in the Mae Sot Watershed whereas located in the Moei River Basin at the Upper Salween River Basin in Tak Province, Thailand. The Mae Sot Municipality is the largest urbanized in Tak Province and situated in the midstream of the Mae Sot Watershed. It usually faces flash flood problem after heavy rain due to poor flood management has been reported since economic rapidly bloom up in recently years. Its catchment can be classified as ungauged basin with lack of rainfall data and no any stream gaging station was reported. It was attached by most severely flood event in 2013 as the worst studied case for those all communities in this municipality. Moreover, other problems are also faced in this watershed such shortage water supply for domestic consumption and agriculture utilizations including deterioration of water quality and landslide as well. The research aimed to increase capability building and strengthening the participation of those local community leaders and related agencies to conduct better water management in urban area was started by mean of the data collection and illustration of appropriated application of some short period rainfall forecasting model as the aim for better flood relief plan and management through the hydrologic model system and river analysis system programs. The authors intended to apply the global rainfall data via the integrated data viewer (IDV) program from the Unidata with the aim for rainfall forecasting in short period of 7 - 10 days in advance during rainy season instead of real time record. The IDV product can be present in advance period of rainfall with time step of 3 - 6 hours was introduced to the communities. The result can be used to input to either the hydrologic modeling system model (HEC-HMS) or the soil water assessment tool model (SWAT) for synthesizing flood hydrographs and use for flood forecasting as well. The authors applied the river analysis system model (HEC-RAS) to present flood flow behaviors in the reach of the Mae Sot stream via the downtown of the Mae Sot City as flood extents as water surface level at every cross-sectional profiles of the stream. Both models of HMS and RAS were tested in 2013 with observed rainfall and inflow-outflow data from the Mae Sot Dam. The result of HMS showed fit to the observed data at dam and applied at upstream boundary discharge to RAS in order to simulate flood extents and tested in the field, and the result found satisfied. The result of IDV’s rainfall forecast data was compared to observed data and found fair. However, it is an appropriate tool to use in the ungauged catchment to use with flood hydrograph and river analysis models for future efficient flood relief plan and management.

Keywords: global rainfall, flood forecast, hydrologic modeling system, river analysis system

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Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

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In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Suraj Gururaj, Sumantha Udupa U.

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Browsing the Web has emerged as the de facto activity performed on the Internet. Although browsing gets tracked, the manageability aspect of Web browsing history is very poor. In this paper, we have a workable solution implemented by using machine learning and natural language processing techniques for efficient manageability of user’s browsing history. The significance of adding such a capability to a Web browser is that it ensures efficient and quick information retrieval from browsing history, which currently is very challenging. Our solution guarantees that any important websites visited in the past can be easily accessible because of the intelligent and automatic classification. In a nutshell, our solution-based paper provides an implementation as a browser extension by intelligently classifying the browsing history into most relevant category automatically without any user’s intervention. This guarantees no information is lost and increases productivity by saving time spent revisiting websites that were of much importance.

Keywords: adhoc retrieval, Chrome extension, supervised learning, tile, Web personalization

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Authors: Irfan Mohiuddin, Ahmad Al Mogren

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In recent years, cloud computing has earned substantial attention in the Oil and Gas Industry and provides services in all the phases of the industry lifecycle. Oil and gas supply infrastructure, in particular, is more vulnerable to accidental, natural and intentional threats because of its widespread distribution. Numerous surveys have been conducted on cloud security and privacy. However, to the best of our knowledge, hardly any survey is carried out that reviews cyber security in all phases with a cloud computing perspective. Moreover, a distinctive classification is performed for all the cloud-based cyber security measures based on the cloud component in use. The classification approach will enable researchers to identify the required technique used to enhance the security in specific cloud components. Also, the limitation of each component will allow the researchers to design optimal algorithms. Lastly, future directions are given to point out the imminent challenges that can pave the way for researchers to further enhance the resilience to cyber security threats in the oil and gas industry.

Keywords: cyber security, cloud computing, safety and security, oil and gas industry, security threats, oil and gas pipelines

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Authors: Andra-Iulia Ursa

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James Joyce employs several unconventional stylistic features in this landmark novel meant to experiment with language. The episode known as “Sirens” is entirely conceived around music and linguistic structures subordinated to sound. However, the aspiration to the condition of music is reflected throughout this entire literary work, as musical effects are echoed systematically. The numerous melodies scattered across the narrative play an important role in enhancing the thoughts and feelings that pass through the minds of the characters. Often the lyrics are distorted or interweaved with other words, preoccupations or memories, intensifying the stylistic effect. The Victorian song “Love’s old sweet song” is one of the most commonly referred to and meaningful musical allusions in Ulysses, becoming a leitmotif of infidelity. The lyrics of the song “M’appari”, from the opera “Martha”, are compared to an event from Molly and Bloom’s romantic history. Moreover, repeated phrases using words from “The bloom is on the rye” or “The croppy boy” serve as glances into the minds of the characters. Therefore, the central purpose of this study is to shed light on the way musical allusions flit through the episodes from the point of view of the stream of consciousness technique and to compare and analyse how these constructions are rendered into Romanian. Mircea Ivănescu, the single Romanian translator who succeeded in carrying out the translation of the entire ‘stylistic odyssey’, received both praises and disapprovals from the critics. This paper is not meant to call forth eventual flaws of the Romanian translation, but rather to elaborate the complexity of the task. Following an attentive examination and analysis of the two texts, from the point of view of form and meaning of the references to various songs, the conclusions of this study will be able to point out the intricacies of the process of translation.

Keywords: Joyce, melodious constructions, stream of consciousness, style, translation

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Authors: P. M. Kundu, L. R. Singo, J. O. Odiyo

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Luvuvhu River catchment in South Africa experiences floods resulting from heavy rainfall of intensities exceeding 15 mm per hour associated with the Inter-tropical Convergence Zone (ITCZ). The generation of runoff is triggered by the rainfall intensity and soil moisture status. In this study, remote sensing and GIS techniques were used to analyze the hydrologic response to land cover changes. Runoff was calculated as a product of the net precipitation and a curve number coefficient. It was then routed using the Muskingum-Cunge method using a diffusive wave transfer model that enabled the calculation of response functions between start and end point. Flood frequency analysis was determined using theoretical probability distributions. Spatial data on land cover was obtained from multi-temporal Landsat images while data on rainfall, soil type, runoff and stream discharges was obtained by direct measurements in the field and from the Department of Water. A digital elevation model was generated from contour maps available at http://www.ngi.gov.za. The results showed that land cover changes had impacted negatively to the hydrology of the catchment. Peak discharges in the whole catchment were noted to have increased by at least 17% over the period while flood volumes were noted to have increased by at least 11% over the same period. The flood time to peak indicated a decreasing trend, in the range of 0.5 to 1 hour within the years. The synergism between remotely sensed digital data and GIS for land surface analysis and modeling was realized, and it was therefore concluded that hydrologic modeling has potential for determining the influence of changes in land cover on the hydrologic response of the catchment.

Keywords: catchment, digital elevation model, hydrological model, routing, runoff

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Xiaoping Su, Gabriel G. Malouf

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Introduction: Breast cancer is a heterogeneous disease that can be classified in 4 subgroups using transcriptional profiling. The role of lncRNA expression in human breast cancer biology, prognosis, and molecular classification remains unknown. Methods and results: Using an integrative comprehensive analysis of lncRNA, mRNA and DNA methylation in 900 breast cancer patients from The Cancer Genome Atlas (TCGA) project, we unraveled the molecular portraits of 1,700 expressed lncRNA. Some of those lncRNAs (i.e, HOTAIR) are previously reported and others are novel (i.e, HOTAIRM1, MAPT-AS1). The lncRNA classification correlated well with the PAM50 classification for basal-like, Her-2 enriched and luminal B subgroups, in contrast to the luminal A subgroup which behaved differently. Importantly, estrogen receptor (ESR1) expression was associated with distinct lncRNA networks in lncRNA clusters III and IV. Gene set enrichment analysis for cis- and trans-acting lncRNA showed enrichment for breast cancer signatures driven by breast cancer master regulators. Almost two third of those lncRNA were marked by enhancer chromatin modifications (i.e., H3K27ac), suggesting that lncRNA expression may result in increased activity of neighboring genes. Differential analysis of gene expression profiling data showed that lncRNA HOTAIRM1 was significantly down-regulated in basal-like subtype, and DNA methylation profiling data showed that lncRNA HOTAIRM1 was highly methylated in basal-like subtype. Thus, our integrative analysis of gene expression and DNA methylation strongly suggested that lncRNA HOTAIRM1 should be a tumor suppressor in basal-like subtype. Conclusion and significance: Our study depicts the first lncRNA molecular portrait of breast cancer and shows that lncRNA HOTAIRM1 might be a novel tumor suppressor.

Keywords: lncRNA profiling, breast cancer, HOTAIRM1, tumor suppressor

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Authors: Dimiter M. Dimitrov, Abdullah Sadaawi

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The National Assessment for Schools (NAFS) in Saudi Arabia consists of standardized tests in Mathematics, Reading, and Science for school grade levels 3, 6, and 9. One main goal is to classify students into four categories of NAFS performance (minimal, basic, proficient, and advanced) by schools and the entire national sample. The NAFS scoring and equating is performed on a bounded scale (D-scale: ranging from 0 to 1) in the framework of the recently developed “D-scoring method of measurement.” The specificity of the NAFS measurement framework and data complexity presented both challenges and opportunities to (a) the estimation of score reliability for schools, (b) setting cut-scores for the classification of students into categories of performance, and (c) generating plausible values for distributions of student performance on the D-scale. The estimation of score reliability at the school level was performed in the framework of generalizability theory (GT), with students “nested” within schools and test items “nested” within test forms. The GT design was executed via a multilevel modeling syntax code in R. Cut-scores (on the D-scale) for the classification of students into performance categories was derived via a recently developed method of standard setting, referred to as “Response Vector for Mastery” (RVM) method. For each school, the classification of students into categories of NAFS performance was based on distributions of plausible values for the students’ scores on NAFS tests by grade level (3, 6, and 9) and subject (Mathematics, Reading, and Science). Plausible values (on the D-scale) for each individual student were generated via random selection from a statistical logit-normal distribution with parameters derived from the student’s D-score and its conditional standard error, SE(D). All procedures related to D-scoring, equating, generating plausible values, and classification of students into performance levels were executed via a computer program in R developed for the purpose of NAFS data analysis.

Keywords: large-scale assessment, reliability, generalizability theory, plausible values

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Authors: Aguasanta Miguel Sarmiento, José Miguel Dávila, María Luisa de la Torre

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Acid mine drainage (AMD) is an acidic leachate with high levels of metals and sulphates in solution, which seriously affects the durability and strength of metallic materials used in the construction of structural and mechanical components. This paper presents the results of the evolution over time of the reduction in tensile strength and defects in AISI 304 stainless steel in contact with acid mine drainage. For this purpose, a total of 30 bars with a diameter of 8 mm and a length of 14 cm were placed transversely in the course of a stream contaminated by AMD from the sulphide mines of the Iberian Pyritic Belt (SW Spain). This stream has average pH values of 2.6, a potential of 660 mV and average concentrations of 12 g/L of sulphates, 1.2 g/L of Fe, 191 mg/L of Zn, etc. Every two months of exposure, 6 stainless steel bars were extracted from the acid stream. They were subjected to surface roughness analysis carried out with the help of Mitutoyo Surftest SJ-210 surface roughness tester. The analysis was carried out at three different points on 5 specimens from each series. The average reading of each parameter is calculated in order to ensure the accuracy of the measurements and the surface coverage. Arithmetic mean roughness value (Ra), mean roughness depth (Rz) and root mean square roughness (Rq) were measured. Five specimens from each series were statically tensile tested using universal equipment (Servosis ME 403 of 200kN). The specimens were clamped at their ends with two grips for cylindrical sections and the tensile force was applied at a constant speed of 0.5 kN/s, according to the requirements of standard UNE-EN ISO 6892-1: 2020. To determine the modulus of elasticity, limits close to 15% and 55% of the maximum load were used, depending on the course of each test. Field Emission Scanning Electron Microscopy (FESEM) was used to observe corrosion products and defects generated by exposure to AMD. Energy dispersive X-ray spectrometry (EDS) was used to analyse the chemical composition of the corrosion products formed. For this purpose, small pieces were cut from the resulting specimens, cleaned and embedded in epoxy resin. The results show that after only 5 months of exposure of AISI 304 stainless steel to the mining environment, the surface roughness increases significantly, with average depths almost 6 times greater than the initial one. Cracks are observed on the surface of the material, which increase in size with the time of exposure. A large number of grains with a composition of more than 57% Pb and 16% Sn can be observed inside these cracks. Tensile tests show a reduction in the resistance of this material after only two months of exposure. The results show the serious problems that would result from the use of this material for the use of mechanical components in a sulphide mining environment, not only because of the significant reduction in the lifetime of such components, but also because of the implications for human safety.

Keywords: acid mine drainage, corrosion, mechanical properties, stainless steel

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: I. Benhsine, M. Hellou, F. Lominé, Y. Roques

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During the manufacture of fertilizer, it is necessary to add water for granulation purposes. The water content is then removed or reduced using rotary dryers. They are commonly used to dry wet granular materials and they are usually fitted with lifting flights. The transport of granular materials occurs when particles cascade from the lifting flights and fall into the air stream. Each cascade consists of a lifting and a falling cycle. Lifting flights are thus of great importance for the transport of granular materials along the dryer. They also enhance the contact between solid particles and the air stream. Optimization of the drying process needs an understanding of the behavior of granular materials inside a rotary dryer. Different approaches exist to study the movement of granular materials inside the dryer. Most common of them are based on empirical formulations or on study the movement of the bulk material. In the present work, we are interested in the behavior of each particle in the cross section of the dryer using Discrete Element Method (DEM) to understand. In this paper, we focus on studying the hold-up, the cascade patterns, the falling time and the falling length of the particles leaving the flights. We will be using two segment flights. Three different profiles are used: a straight flight (180° between both segments), an angled flight (with an angle of 150°), and a right-angled flight (90°). The profile of the flight affects significantly the movement of the particles in the dryer. Changing the flight angle changes the flight capacity which leads to different discharging profile of the flight, thus affecting the hold-up in the flight. When the angle of the flight is reduced, the range of the discharge angle increases leading to a more uniformed cascade pattern in time. The falling length and the falling time of the particles also increase up to a maximum value then they start decreasing. Moreover, the results show an increase in the falling length and the falling time up to 70% and 50%, respectively, when using a right-angled flight instead of a straight one.

Keywords: discrete element method, granular materials, lifting flight, rotary dryer

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Authors: Alma Gómez-Loredo, José González-Valdez, Jorge Benavides, Marco Rito-Palomares

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Fucoxanthin is a carotenoid that exerts multiple beneficial effects on human health, including antioxidant, anti-cancer, antidiabetic and anti-obesity activity; making the development of a whole process for its production and recovery an important contribution. In this work, the lab-scale production and purification of fucoxanthin in Isocrhysis galbana have been studied. In batch cultures, low light intensities (13.5 μmol/m2s) and bubble agitation were the best conditions for production of the carotenoid with product yields of up to 0.143 mg/g. After fucoxanthin ethanolic extraction from biomass and hexane partition, further recovery and purification of the carotenoid has been accomplished by means of alcohol – salt Aqueous Two-Phase System (ATPS) extraction followed by an ultrafiltration (UF) step. An ATPS comprised of ethanol and potassium phosphate (Volume Ratio (VR) =3; Tie-line Length (TLL) 60% w/w) presented a fucoxanthin recovery yield of 76.24 ± 1.60% among the studied systems and was able to remove 64.89 ± 2.64% of the carotenoid and chlorophyll pollutants. For UF, the addition of ethanol to the original recovered ethanolic ATPS stream to a final relation of 74.15% (w/w) resulted in a reduction of approximately 16% of the protein contents, increasing product purity with a recovery yield of about 63% of the compound in the permeate stream. Considering the production, extraction and primary recovery (ATPS and UF) steps, around a 45% global fucoxanthin recovery should be expected. Although other purification technologies, such as Centrifugal Partition Chromatography are able to obtain fucoxanthin recoveries of up to 83%, the process developed in the present work does not require large volumes of solvents or expensive equipment. Moreover, it has a potential for scale up to commercial scale and represents a cost-effective strategy when compared to traditional separation techniques like chromatography.

Keywords: aqueous two-phase systems, fucoxanthin, Isochrysis galbana, microalgae, ultrafiltration

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Authors: Alina Muradyan, Lina Babayan, Arsen Nanyan, Gohar Galstyan, Vigen Khachatryan

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We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of ∼> 65000 color templates for comparison with observed objects. The method aims for extracting the information content of object colors in a statistically correct way, and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach based on the same probability density functions. For the redshift estimation, we employ an advanced version of the Minimum Error Variance estimator which determines the redshift error from the redshift dependent probability density function itself. The method was originally developed for the Calar Alto Deep Imaging Survey (CADIS), but is now used in a wide variety of survey projects. We checked its performance by spectroscopy of CADIS objects, where the method provides high reliability (6 errors among 151 objects with R < 24), especially for the quasar selection, and redshifts accurate within σz ≈ 0.03 for galaxies and σz ≈ 0.1 for quasars. For an optimization of future survey efforts, a few model surveys are compared, which are designed to use the same total amount of telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. If photon noise were the only error source, broad-band surveys and medium-band surveys should perform equally well, as long as they provide the same spectral coverage. In practice, medium-band surveys show superior performance due to their higher tolerance for calibration errors and cosmic variance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, which are most critical for surveys with few, broad and deeply exposed filters, but less severe for surveys with many, narrow and less deep filters.

Keywords: VO, ArVO, DFBS, FITS, image processing, data analysis

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Authors: Simin Eydivand, Ali Ghanadieslami, Reza Amiri

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Natural gas liquids (NGLs) are light hydrocarbons that are dissolved in associated or non‐associated natural gas in a hydrocarbon reservoir and are produced within a gas stream. There are different ways to calculate the price of NGL. In this study, a spreadsheet calculation method is used for calculation of NGL price with an attractive economy of IRR 25%. For a typical NGL Plant with 3,200,000 t/y capacity of investment and operation of 90% capacity to have IRR 25%, the price of NGL is calculated 277 $/t.

Keywords: natural gas liquid, NGL, LPG, price, NGL fractionation, NF, investment, IRR, NPV

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Authors: Yu-Hong Lin

Abstract:

Background: An adverse drug reaction (ADR) reported is an injury which caused by taking medicines. Sometimes the severity of ADR reported may be minor, but sometimes it could be a life-threatening situation. In order to provide healthcare professionals as a better reference in clinical practice, we do data collection and analysis from our hospital. Methods: This was a retrospective study of ADRs reported performed from 2014 to 2015 in our hospital in Taiwan. We collected assessment items of ADRs reported, which contain gender and age, occurring sources, Anatomical Therapeutic Chemical (ATC) classification of suspected drugs, types of adverse reactions, Naranjo score calculating by Naranjo Adverse Drug Reaction Probability Scale and so on. Results: The investigation included two hundred and seven ADRs reported. Most of ADRs reported were occurring in outpatient department (92%). The average age of ADRs reported was 65.3 years. Less than 65 years of age were in the majority in this study (54%). Majority of all ADRs reported were males (51%). According to ATC classification system, the major classification of suspected drugs was cardiovascular system (19%) and antiinfectives for systemic use (18%) respectively. Among the adverse reactions, Dermatologic Effects (35%) were the major type of ADRs. Also, the major Naranjo scores of all ADRs reported ranged from 1 to 4 points (91%), which represents a possible correlation between ADRs reported and suspected drugs. Conclusions: Definitely, ADRs reported is still an extremely important information for healthcare professionals. For that reason, we put all information of ADRs reported into our hospital's computer system, and it will improve the safety of medication use. By hospital's computer system, it can remind prescribers to think of information about patient's ADRs reported. No drugs are administered without risk. Therefore, all healthcare professionals should have a responsibility to their patients, who themselves are becoming more aware of problems associated with drug therapy.

Keywords: adverse drug reaction, Taiwan, healthcare professionals, safe use of medicines

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Authors: Levent Dumenci, Laura A. Siminoff

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Health literacy has been shown to predict a variety of health outcomes. Reliable identification of persons with limited cancer health literacy (LCHL) has been proved questionable with existing instruments using an arbitrary cut point along a continuum. The CHLT-6, however, uses a latent mixture modeling approach to identify persons with LCHL. The purpose of this study was to estimate two-week and six-month stability of identifying persons with LCHL using the CHLT-6 with a discrete latent variable approach as the underlying measurement structure. Using a test-retest design, the CHLT-6 was administered to cancer patients with two-week (N=98) and six-month (N=51) intervals. The two-week and six-month latent test-retest agreements were 89% and 88%, respectively. The chance-corrected latent agreements estimated from Dumenci’s latent kappa were 0.62 (95% CI: 0.41 – 0.82) and .47 (95% CI: 0.14 – 0.80) for the two-week and six-month intervals, respectively. High levels of latent test-retest agreement between limited and adequate categories of cancer health literacy construct, coupled with moderate to good levels of change-corrected latent agreements indicated that the CHLT-6 classification of limited versus adequate cancer health literacy is relatively stable over time. In conclusion, the measurement structure underlying the instrument allows for estimating classification errors circumventing limitations due to arbitrary approaches adopted by all other instruments. The CHLT-6 can be used to identify persons with LCHL in oncology clinics and intervention studies to accurately estimate treatment effectiveness.

Keywords: limited cancer health literacy, the CHLT-6, discrete latent variable modeling, latent agreement

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Shaojun Zheng, Lei Xu, Yunzi Wang

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The main purpose of the research is to use the cultural ecology to analyze the renewal of Shenzhen urban village in the process of China's urbanization and to evaluate and guide the renewal, which will combine the society value and economic efficiency and activate urban villages. The urban village has a long history. There are also many old buildings, various residents, and a strong connection with the surrounding environment. Cultural ecology, which uses the knowledge of ecology to study culture, provides us a cultural perspective in the renewal. We take Hubei village in Shenzhen as our example. By using cultural ecology, we find a new way dealing with the relationship between culture and other factors. It helps us to give the buildings and space the culture meanings from different scales. It enables us to find a unique development pattern of urban village. After analyzing several famous cultural blocks cases, we find it is possible to connect the unique culture of urban village with the renovation of its buildings, community, and commerce. We propose the following strategies with specific target: 1. Building renovation: We repair and rebuild the origin buildings as little as possible, and retain the original urban space tissue as much as possible to keep the original sense of place and the cultural atmosphere. 2. Community upgrade: We reshape the village stream, fix the original function, add event which will activate people to complete the existing cultural circle 3. District commerce: We implant food and drink district, boutique commercial, and creative industries, to make full use of the historical atmosphere of the site to enhance the culture feelings For the renewal of a seemingly chaotic mixed urban village, it is important to break out from the conventional practices of building shopping malls or residential towers. Without creating those building landmarks, cultural ecology activates the urban village by exploiting its unique culture, which makes the old and new combine and becomes a new stream of energy, forming the new cultural, commercial and stylish landmark of the city.

Keywords: cultural ecology, urban village, renewal, combination

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Mubashir Hussain, Mu Lv, Xiaohan Dong, Zhiyang Li, Bin Liu, Nongyue He

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Detection and classification of microbes have a vast range of applications in biomedical engineering especially in detection, characterization, and quantification of bacterial contaminants. For identification of pathogens, different techniques are emerging in the field of biomedical engineering. Latest technology uses light scattering, capable of identifying different pathogens without any need for biochemical processing. Bacterial Pathogens Identification System (BPIS) which uses a laser beam, passes through the sample and light scatters off. An assembly of photodetectors surrounded by the sample at different angles to detect the scattering of light. The algorithm of the system consists of two parts: (a) Library files, and (b) Comparator. Library files contain data of known species of bacterial microbes in the form of binned plots, while comparator compares data of unknown sample with library files. Using collected data of unknown bacterial species, highest voltage values stored in the form of peaks and arranged in 3D histograms to find the frequency of occurrence. Resulting data compared with library files of known bacterial species. If sample data matching with any library file of known bacterial species, sample identified as a matched microbe. An experiment performed to identify three different bacteria particles: Enterococcus faecalis, Pseudomonas aeruginosa, and Escherichia coli. By applying algorithm using library files of given samples, results were compromising. This system is potentially applicable to several biomedical areas, especially those related to cell morphology.

Keywords: microbial identification, laser scattering, peak identification, binned plots classification

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Authors: Mohamad H. Atyeh, Ahmad Khaldi

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The main objective of this research is to measure the relationship between the Kuwait stock Exchange (KSE) index and its two marketing sectors after the new market classification. The findings of this research are important for Public economic policy makers as they need to know if the new system (new classification) is efficient and to what level, to monitor the markets and intervene with appropriate measures. The data used are the daily index of the whole Kuwaiti market and the daily closing price, number of deals and volume of shares traded of two marketing sectors (consumer goods and consumer services) for the period from the 13th of May 2012 till the 12th of December 2016. The results indicate a positive direct impact of the closing price, volume and deals indexes of the consumer goods and the consumer services companies on the overall KSE index, volume and deals of the Kuwaiti stock market (KSE).

Keywords: correlation, market capitalization, Kuwait Stock Exchange (KSE), marketing sectors, stock performance

Procedia PDF Downloads 324