Search results for: post-editing machine translation output

Authors: Young-Su Ryu, Kyung-Won Park, Jae-Hoon Song, Ki-Won Kwon

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In this paper, an embedded frequency modulation (FM) transmission control software (SW) for a low power battery system is designed and implemented. The simultaneous translation systems for various languages are needed as so many international conferences and festivals are held in world wide. Especially in portable transmitting and receiving systems, the ability of long operation life is used for a measure of value. This paper proposes an embedded FM transmission control SW for low power battery system and shows the results of the SW implemented on a portable FM transmission system.

Keywords: FM transmission, simultaneous translation system, portable transmitting and receiving systems, low power embedded control SW

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Muhaned Zaidi, Ian Grout, Abu Khari bin A’ain

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In this paper, a two-stage op-amp design is considered using both Miller and negative Miller compensation techniques. The first op-amp design uses Miller compensation around the second amplification stage, whilst the second op-amp design uses negative Miller compensation around the first stage and Miller compensation around the second amplification stage. The aims of this work were to compare the gain and phase margins obtained using the different compensation techniques and identify the ability to choose either compensation technique based on a particular set of design requirements. The two op-amp designs created are based on the same two-stage rail-to-rail output CMOS op-amp architecture where the first stage of the op-amp consists of differential input and cascode circuits, and the second stage is a class AB amplifier. The op-amps have been designed using a 0.35mm CMOS fabrication process.

Keywords: op-amp, rail-to-rail output, Miller compensation, Negative Miller capacitance

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Authors: Hanaa Sameen Ameen Bajilan

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Human rights should be protected and promoted without regard to race, ethnicity, religion, political philosophy, or sexual orientation, following our firm convictions. Thus, any infringement of these rights or disdain for; any use of violence against women undermines the principles and human values of equality and endangers the entire society, including its potential to live in peace and to make growth and development. This paper represents the condition of the new Iraqi women regarding issues such as the gender wage gap, education, health, and violence against women. The study aims to determine the impact of traditions and customs on the legal position of Iraqi women. First, it seeks to assess the effects of culture as a historical agency on the legal status of Iraqi women. Second, the influence of cultural developments in the later part of the twentieth century on Iraqi women's legal standing, and third, the importance of cultural variety as a progressive cultural component in women's legal position. Finally, the study highlights the representation of women in Iraq: Gender wage Gap, Women's liberation between culture and law, and the role of women within Iraqi society based on an Iraqi novel named (Orange Light) in Arabic: برتقالو ضو. in her book, the Iraqi writer Nadia Al-Abru succeeds in portraying the post-war society's devotion to the sexual, emotional and mental marginalization of women in terms of the value of attendance. Since the study of Iraqi women's literature in Arabic-English translation is a new avenue of research that contributes to all three areas, this investigation aims to establish critical lines of engagement between contemporary Iraqi women's literature in English translation and feminist translation conceptual frameworks, and this is accomplished by first focusing on why analyzing Iraqi women writers' novels in Arabic-English translation is a timeline of inquiry that contributes to existing and emerging knowledge fields concerning Iraqi women writers' contemporary critical contexts and scholarship on Arab women's literature in Arabic-English translation.

Keywords: women in İraq, equality, violence, gender wage gap, Nadia Al-Abru, (orange light), women's liberation, İraqi women's literature,

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Authors: Catarina Belchior, Catarina Martins, Sara Mendes, Ana Rita S. Valente, Elsa Marta Soares

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Introduction: The negative feelings and attitudes that a person who stutters can develop are extremely relevant when considering assessment and intervention in Speech and Language Therapy. This relates to the fact that the person who stutters can experience feelings such as shame, fear and negative beliefs when communicating. Considering the complexity and importance of integrating diverse aspects in stuttering intervention, it is central to identify those emotions as early as possible. Therefore, this research aimed to achieve the translation, adaptation to European Portuguese and to analyze the content validation of the Preschooler Awareness Stuttering Survey (Abbiati, Guitar & Hutchins, 2015), an instrument that allows the assessment of the impact of stuttering on preschool children who stutter considering feelings and attitudes. Methodology: Cross-sectional descriptive qualitative research. The following methodological procedures were followed: translation, back-translation, panel of experts and pilot study. This abstract describes the results of the first three phases of this process. The translation was accomplished by two Speech Language Therapists (SLT). Both professionals have more than five years of experience and are users of English language. One of them has a broad experience in the field of stuttering. Back-translation was conducted by two bilingual individuals without experience in health or any knowledge about the instrument. The panel of experts was composed by 3 different SLT, experts in the field of stuttering. Results and Discussion: In the translation and back-translation process it was possible to verify differences in semantic and idiomatic equivalences of several concepts and expressions, as well as the need to include new information to enhance the understanding of the application of the instrument. The meeting between the two translators and the researchers allowed the achievement of a consensus version that was used in back-translation. Considering adaptation and content validation, the main change made by the experts was the conceptual equivalence of the questions and answers of the instrument's sheets. Considering that in the translated consensus version the questions began with various nouns such as 'is' or 'the cow' and that the answers did not contain the adverb 'much' as in the original instrument, the panel agreed that it would be more appropriate if the questions all started with 'how' and that all the answers should present the adverb 'much'. This decision was made to ensure that the translate instrument would be similar to the original and so that the results obtained could be comparable between the original and the translated instrument. There was also elaborated one semantic equivalence between concepts. The panel of experts found that all other items and specificities of the instrument were adequate, concluding the adequacy of the instrument considering its objectives and its intended target population. Conclusion: This research aspires to diversify the existing validated resources in this scope, adding a new instrument that allows the assessment of preschool children who stutter. Consequently, it is hoped that this instrument will provide a real and reliable assessment that can lead to an appropriate therapeutic intervention according to the characteristics and needs of each child.

Keywords: stuttering, assessment, feelings and attitudes, speech language therapy

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Shaza Melies, Saadeya Salem, Seham Kareh

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Cartoon films are multisemiotic as various modes integrate in the production of meaning. This study aims to examine the cultural and linguistic specific references in the Egyptian dubbed cartoon film Mulan. The study examines the translation strategies implemented in the Egyptian dubbed version of Mulan to meet the cultural preferences of the audience. The study reached the following findings: Using the traditional translation strategies does not deliver the intended meaning of the source text and causes loss in the intended humor. As a result, the findings showed that in the dubbed version, translators tend to omit, change, or add information to the target text to be accepted by the audience. The contrastive analysis of the Mulan (English and dubbed versions) proves the connotations that the dubbing has taken to be accepted by the target audience. Cartoon films are multisemiotic as various modes integrate in the production of meaning. This study aims to examine the cultural and linguistic specific references in the Egyptian dubbed cartoon film Mulan. The study examines the translation strategies implemented in the Egyptian dubbed version of Mulan to meet the cultural preferences of the audience. The study reached the following findings: Using the traditional translation strategies does not deliver the intended meaning of the source text and causes loss in the intended humor. As a result, the findings showed that in the dubbed version, translators tend to omit, change, or add information to the target text to be accepted by the audience. The contrastive analysis of the Mulan (English and dubbed versions) proves the connotations that the dubbing has taken to be accepted by the target audience.

Keywords: domestication, dubbing, Mulan, translation theories

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Aliasghar Arab

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Autonomous robots interacting with humans, as safety-critical nonlinear control systems, are complex closed-loop cyber-physical dynamical machines. Keeping these intelligent yet complicated systems safe and smooth during their operations is challenging. The aim of the safe predictive output feedback linearization control synthesis is to design a novel controller for smooth trajectory following while unsafe situations must be avoided. The controller design should obtain a linearized output for smoothness and invariance to a safety subset. Inspired by finite-horizon nonlinear model predictive control, the problem is formulated as constrained nonlinear dynamic programming. The safety constraints can be defined as control barrier functions. Avoiding unsafe maneuvers and performing smooth motions increases the predictability of the robot’s movement for humans when robots and people are working together. Our results demonstrate the proposed output linearization method obeys the safety constraints and, compared to existing safety-guaranteed methods, is smoother and performs better.

Keywords: robotics, collaborative robots, safety, autonomous robots

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Vincenzo Capalbo, Gianpaolo Ghiani, Emanuele Manni

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Global e-commerce sales have reached unprecedented levels in the past few years. As this trend is only predicted to go up as we continue into the ’20s, new challenges will be faced by companies when planning and controlling e-commerce logistics. In this paper, we survey the related literature on Optimization and Machine Learning as well as on combined methodologies. We also identify the distinctive features of next-generation planning algorithms - namely scalability, model-and-run features and learning capabilities - that will be fundamental to cope with the scale and complexity of logistics in the next decade.

Keywords: e-commerce, hardware acceleration, logistics, machine learning, mixed integer programming, optimization

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: Aldan A. Sapargaliyev, Yerbol A. Sapargaliyev

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Since the invention, the electric machine (EM) can be defined as oEM – one-vector electric machine, as it works due to one-vector inductive coupling with use of one-vector electromagnet. The disadvantages of oEM are large size and limited efficiency at low and medium power applications. This paper describes multi-vector electric machine (mEM) based on multi-vector inductive coupling, which is characterized by the increased surface area of ​​the inductive coupling per EM volume, with a reduced share of inefficient and energy-consuming part of the winding, in comparison with oEM’s. Particularly, it is considered, calculated and compared the performance of three different electrical motors and their power at the same volumes and rotor frequencies. It is also presented the result of calculation of correlation between power density and volume for oEM and mEM. The method of multi-vector inductive coupling enables mEM to possess 1.5-4.0 greater density of power per volume and significantly higher efficiency, in comparison with today’s oEM, especially in low and medium power applications. mEM has distinct advantages, when used in transport vehicles such as electric cars and aircrafts.

Keywords: electric machine, electric motor, electromagnet, efficiency of electric motor

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Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

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Authors: Ming Qian, Davis Qian

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This study compares the different organization structures of Chinese and English writing discourses in the engineering and scientific fields, and recommends approaches for translators to convert the organization structures properly. Based on existing intercultural communication literature, English authors tend to deductively give their main points at the beginning, following with detailed explanations or arguments afterwards while the Chinese authors tend to place their main points inductively towards the end. In this study, this hypothesis has been verified by the authors’ Chinese-to-English translation experiences in the fields of science and engineering (e.g. journal papers, conference papers and monographs). The basic methodology used is the comparison of writings by Chinese authors with writings of the same or similar topic written by English authors in terms of organization structures. Translators should be aware of this nuance, so that instead of limiting themselves to translating the contents of an article in its original structure, they can convert the structures to fill the cross-culture gap. This approach can be controversial because if a translator changes the structure organization of a paragraph (e.g. from a 'because-therefore' inductive structure by a Chinese author to a deductive structure in English), this change of sentence order could be questioned by the original authors. For this reason, translators need to properly inform the original authors on the intercultural differences of English and Chinese writing (e.g. inductive structure versus deductive structure), and work with the original authors to maintain accuracy while converting from one structure used in a source language to another structure in the target language. The authors have incorporated these methodologies into their translation practices and work closely with the authors on the inter-cultural organization structure mapping. Translating discourse organization structure should become a standard practice in the translation process.

Keywords: discourse structure, information structure, intercultural communication, translation practice

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Yeong-cheng Wang, Jin-Yinn Wang, Ming-Shan Lin, Jian-Li Dong

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To universalize the adoption of sustainable energy, the R.O.C. government encourages public buildings to introduce the PV power station on the building roof, whereas most old buildings did not include the considerations of photovoltaic (PV) power facilities in the design phase. Several factors affect the PV electricity output, the temperature is the key one, different PV technologies have different temperature coefficients. Other factors like PV panel azimuth, panel inclination from the horizontal plane, and row to row distance of PV arrays, mix up at the beginning of system design. The goal of this work is to maximize the annual energy output of a roof mount PV system. Tables to simplify the design work are developed; the results can be used for engineering project quote directly.

Keywords: optimal inclination, array azimuth, annual output

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Authors: A. Şahinoğlu, R. Gürbüz, A. Güllü, M. Karhan

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This study in production machines, it is aimed that machine will automatically perceive cutting data and alter cutting parameters. The two most important parameters have to be checked in machine control unit are progress feed rate and speeds. These parameters are aimed to be controlled by sounds of machine. Optimum sound’s features introduced to computer. During process, real time data is received and converted by Matlab software. Data is converted into numerical values. According to them progress and speeds decreases/increases at a certain rate and thus optimum sound is acquired. Cutting process is made in respect of optimum cutting parameters. During chip remove progress, features of cutting tools, kind of cut material, cutting parameters and used machine; affects on various parameters. Instead of required parameters need to be measured such as temperature, vibration, and tool wear that emerged during cutting process; detailed analysis of the sound emerged during cutting process will provide detection of various data that included in the cutting process by the much more easy and economic way. The relation between cutting parameters and sound is being identified.

Keywords: cutting process, sound processing, intelligent late, sound analysis

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

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Authors: M. Kaouane, A. Boukhelifa, A. Cheriti

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In this paper, a new Buck-Boost DC-DC converter is designed and simulated for photovoltaic energy system. The presented Buck-Boost converter has a double switch. Moreover, its output voltage is regulated to a constant value whatever its input is. In the presented work, the Buck-Boost transfers the produced energy from the photovoltaic generator to an R-L load. The converter is controlled by the pulse width modulation technique in a way to have a suitable output voltage, in the other hand, to carry the generator’s power, and put it close to the maximum possible power that can be generated by introducing the right duty cycle of the pulse width modulation signals that control the switches of the converter; each component and each parameter of the proposed circuit is well calculated using the equations that describe each operating mode of the converter. The proposed configuration of Buck-Boost converter has been simulated in Matlab/Simulink environment; the simulation results show that it is a good choice to take in order to maintain the output voltage constant while ensuring a good energy transfer.

Keywords: Buck-Boost converter, switch, photovoltaic, PWM, power, energy transfer

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Authors: Saber Noie

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This research follows some objectives to consider the mystic terms as one of the main issues in translation of poems. Firstly, it is an attempt to find out what strategies have been used to find equivalents for source text mystic. Second, it is hoped that this study of the translations of the mystic terms in Attar’s poems will further address and explore the problems in translating mystic texts, proposed by other Persian poets and suggest instructional points from Davis work for translation education. In order to deal with such a breadth of work, a new conceptual tool was developed, as explained by Van Doorslaer (2007). This study shows that according to Van Doorslaer’s map, the mystic terms can be transferred to the target language (TL) with their exact content of the source language (SL) if the translator has a good choice for any term.

Keywords: metaphor, mystic, mysticism, source language (SL), target language (TL)

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Authors: Masoud Soltani Hosseini, Fereshteh Rahmani, Mohammad Tajik Mansouri, Ali Zolghadr

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Climate change is accompanied with ambient temperature increase and water accessibility limitation. The main objective of this paper is to investigate the effects of climate change on thermal power plants including gas turbines, steam and combined cycle power plants in Iran. For this purpose, the ambient temperature increase and water accessibility will be analyzed and their effects on power output and efficiency of thermal power plants will be determined. According to the results, the ambient temperature has high effect on steam power plants with indirect cooling system (Heller). The efficiency of this type of power plants decreases by 0.55 percent per 1oC ambient temperature increase. This amount is 0.52 and 0.2 percent for once-through and wet cooling systems, respectively. The decrease in power output covers a range of 0.2% to 0.65% for steam power plant with wet cooling system and gas turbines per 1oC air temperature increase. Based on the thermal power plants distribution in Iran and different scenarios of climate change, the total amount of power output decrease falls between 413 and 1661 MW due to ambient temperature increase. Another limitation incurred by climate change is water accessibility. In optimistic scenario, the power output of steam plants decreases by 1450 MW in dry and hot climate areas throughout next decades. The remaining scenarios indicate that the amount of decrease in power output would be by 4152 MW in highlands and cold climate. Therefore, it is necessary to consider appropriate solutions to overcome these limitations. Considering all the climate change effects together, the actual power output falls in range of 2465 and 7294 MW and efficiency loss covers the range of 0.12 to .56 % in different scenarios.

Keywords: climate, change, thermal, power plants

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Authors: Sahil Kumar, Surinder Pal, Rahul Kapoor

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This paper is focused on to define the basic difference between hot and cold chamber high pressure die casting process which is not fully defined in a research before paper which we have studied. The pressure die casting is basically defined into two types (1) Hot chamber Die Casting (2) Cold chamber Die Casting. Cold chamber die casting is used for casting alloys that require high pressure and have a high melting temperature, such as brass, aluminum, magnesium, copper based alloys and other high melting point nonferrous alloys. Hot chamber die casting is suitable for casting zinc, tin, lead, and low melting point alloys. In hot chamber die casting machine, the molten metal is an integral pan of the machine. It mainly consists of hot chamber and gooseneck type metal container made of cast iron. This machine is mainly used for low melting alloys and alloys of metals like zinc, lead etc. Metals and alloys having a high melting point and those which are having an affinity for iron cannot be cast by this machine, which could otherwise attack the shot sleeve and damage the machine.

Keywords: hot chamber die casting, cold chamber die casting, metals and alloys, casting technology

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Yessica Nataliani, Miin-Shen Yang

Abstract:

Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices.

Keywords: group technology, cell formation, spectral clustering, grouping efficiency

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Authors: A. Hassannia, S. Ramezani

Abstract:

The technology of superconductivity has developed in many power system devices such as transmission cable, transformer, current limiter, motor and generator. Superconducting wires can carry high density current without loss, which is the capability that is used to design the compact, lightweight and more efficient electrical machines. Superconducting motors have found applications in marine and air propulsion systems as well as superconducting generators are considered in low power hydraulic and wind generators. This paper presents a rim-driven superconducting synchronous generator for hydraulic power plant. The rim-driven concept improves the performance of hydro turbine. Furthermore, high magnetic field that is produced by superconducting windings allows replacing the rotor core. As a consequent, the volume and weight of the machine is decreased significantly. In this paper, a 1 MW coreless rim-driven superconducting synchronous generator is designed. Main performance characteristics of the proposed machine are then evaluated using finite elements method and compared to an ordinary similar size synchronous generator.

Keywords: coreless machine, electrical machine design, hydraulic generator, rim-driven machine, superconducting generator

Procedia PDF Downloads 149