Search results for: least square support vector machine

Authors: Michael Ngongeh Mombe

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This Paper seeks to investigate an assertion made by some colleagues that there is no need paying a human translator to translate their literary texts, that there are softwares such as Reverso that can be used to do the translation. The main objective of this study is to examine the veracity of this assertion using Reverso to translate a literary text without any post-editing by a human translator. The work is based on two theories: Skopos and Communicative theories of translation. The work is a documentary research where data were collected from published documents in libraries, on the internet and from the translation produced by Reverso. We made a comparative text analyses of both source and target texts in a bid to highlight the weaknesses and strengths of the software. Findings of this work revealed that those who advocate the use of only Machine translation do so in ignorance of the translation mistakes usually made by the software. From the review of all the 268 segments of translation, we found out that the translation produced by Reverso is fraught with errors. We therefore recommend the use of human translators to either do the translation of their literary texts or revise the translation produced by machine to conform to the skopos of the work. This paper is based on Reverso translation. Similar works in the near future will be based on the other translation softwares to determine their weaknesses and strengths.

Keywords: machine translation, human translator, Reverso, literary text

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Saci Abdelhakim Ferkous, Khedoudja Soudani, Smail Haddadi

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This paper presents the results of a laboratory experimental study that explores the skid resistance of asphalt concrete mixtures made of local low-performance aggregates by partially replacing sand with olive mill waste (OMW). OMW was mixed with aggregates using a dry process by replacing sand with contents of 5%, 7%, 10% and 15%. The mechanical performances of the mixtures were evaluated using the Marshall and Duriez tests. A modified accelerated polishing machine was used as polishing equipment, and a British pendulum tester (BPT) was used to test the skid resistance of the samples. Finally, texture parameter analysis was performed using scanning electron microscopy (SEM) and Mountains Map software to assess the effect of OMW on the friction coefficient evolution. Using a distinct road wheel for a modified version of an accelerated polishing machine, which is normally used to determine the polished stone value of aggregates, the results showed that the addition of OMW up to 10% conferred a better skid resistance in comparison to normal asphalt concrete. The presence of olive mill waste in the mixture until 15% guarantees a gain of 22%-29% in skid resistance after polishing compared with the reference mix. Indeed, from texture parameter analysis, it was observed that there was differential wear of the lightweight aggregates (OMW) compared to the other aggregates during the polishing process, which created a new surface microtexture that had new peaks and led to a good level of friction compared to the mixtures without OMW. In general, it was found that OMW is a promising modifier for asphalt mixtures with both engineering and economic merits.

Keywords: skid resistance, olive mill waste, polishing resistance, accelerated polishing machine, local materials, sustainable development.

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Authors: Jonathan Turner, Julie Doyle, Laura O’Philbin, Dympna O’Sullivan

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People living with dementia should be at the centre of decision-making regarding goals for daily living. These goals include basic activities (dressing, hygiene, and mobility), advanced activities (finances, transportation, and shopping), and meaningful activities that promote well-being (pastimes and intellectual pursuits). However, there is limited involvement of people living with dementia in the design of technology to support their goals. A project is described that is co-designing intelligent computer-based support for, and with, people affected by dementia and their carers. The technology will support self-management, empower participation in shared decision-making with carers and help people living with dementia remain healthy and independent in their homes for longer. It includes information from the patient’s care plan, which documents medications, contacts, and the patient's wishes on end-of-life care. Importantly for this work, the plan can outline activities that should be maintained or worked towards, such as exercise or social contact. The authors discuss how to integrate care goal information from such a care plan with data collected from passive sensors in the patient’s home in order to deliver individualized planning and interventions for persons with dementia. A number of scientific challenges are addressed: First, to co-design with dementia patients and their carers computerized support for shared decision-making about their care while allowing the patient to share the care plan. Second, to develop a new and open monitoring framework with which to configure sensor technologies to collect data about whether goals and actions specified for a person in their care plan are being achieved. This is developed top-down by associating care quality types and metrics elicited from the co-design activities with types of data that can be collected within the home, from passive and active sensors, and from the patient’s feedback collected through a simple co-designed interface. These activities and data will be mapped to appropriate sensors and technological infrastructure with which to collect the data. Third, the application of machine learning models to analyze data collected via the sensing devices in order to investigate whether and to what extent activities outlined via the care plan are being achieved. The models will capture longitudinal data to track disease progression over time; as the disease progresses and captured data show that activities outlined in the care plan are not being achieved, the care plan may recommend alternative activities. Disease progression may also require care changes, and a data-driven approach can capture changes in a condition more quickly and allow care plans to evolve and be updated.

Keywords: care goals, decision-making, dementia, self-care, sensors

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Authors: Snehalata Tembhurne

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Introduction: A healthy menstrual cycle is a sign of women’s sound health.Various variables may influence the length and regularity of menstrual cycle.Studies have revealed that menstrual cycle abnormalities may be associated with psychological stress,lack of physical exercise, alteration in body composition,endocrine disturbances,higher estrogen levels as seen in obese females.Hence there is an urgent need to find out the relationship between variations in body mass composition(BMI & body fat%) with menstrual abnormalities like primary dysmenorrhoea. Aim: To find out the relationship between body mass composition and primary dysmenorrhea. Objectives: 1.To check whether there is any association between body mass index and primary dysmenorrhoea.2.To check whether there is any association between body fat percentage and primary dysmenorrhoea. NULL HYPOTHESES-There is no relationship between body mass composition and primary dysmenorrhea. Hypothesis: There exists a relationship between body mass composition and primary dysmenorrhea. Materials and Methods: The study was conducted over a period of 6 months with 90 samples selected on random basis. The procedure was explained to the participant and a written consent was taken thereafter. The participant was made to stand on the BODY COMPOSITION SCANNING MONITOR, which scanned the physical profile of the participant (height, weight, BMI, body fat percentage and visceral fat).Thereafter, the candidate was asked about her menstrual irregularities and was asked to grade her level of dysmenorrhoea (if present) using the Verbal Dimensional Dysmenorrhea Scale. Results: Chi square test of association was used to find out the association between body mass composition(body mass index,body fat percentage) and primary dysmenorrhea.The chi-square value for association between body mass index and primary dysmenorrhea was 38.63 p<0.001 which was statistically significant.The chi-square value for the association of body fat % & primary dysmenorrhea was 30.09,p<0.001which was statistically significant. Conclusion: Study shows that there exists a significant relationship between body mass composition and primary dysmenorrhea and as the value of Body mass index and body fat percentages goes on increasing in females, the severity of primary dysmenorrhea also increases.

Keywords: body mass index, body composition screening monitor, primary dysmenorrhea, verbal dimensional dysmenorrhea scale

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Authors: Sriraman Kothuri, Komal Teja Mattupalli

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In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

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Authors: B. G. Abiona

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This study compared profitability analysis of integrated and non-integrated fish farming in Ogun State, Nigeria. Primary data were collected using interview guide. Random sampling techniques was used to select 133 non-integrated fish farmers (NIFF) and 216 integrated fish farmers (IFF) (n = 349) from the study area. Data were analyzed using Chi-square, T-test and Pearson Product moment correlation. Results showed that 92.5% of NIFF was male compared to IFF (90.7%). Also, 96.8% of IFF and 79.7% of NIFF were married. The mean ages of sampled farmers were 44 years (NIFF) and 46 years (IFF) while the mean fish farming experiences were 4 years (NIFF) and 5 years (IFF). Also, the average net profit per year of integrated fish farmers was ₦162,550 compared to NIFF (₦61,638). The chi-square analyses showed that knowledge of fish farming had significant relationship with respondents sex (χ2 = 9.44, df = 2, p < 0.05), age (r = 0.20, p< 0.05) and farming experience (r = p = 0.05). Significant differences exist between integrated and non-integrated fish farming, considering their knowledge of fish farming (t = 21.5, χ = 43.01, p < 0.05). The study concluded that IFF are more profitable compared to NIFF. It was recommended that private investors and NGOs should sponsor short training and courses which will enhance efficiency of fish farming to boost productivity among fish farmers.

Keywords: profitability analysis, farms, integration

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Authors: T. D. Tran, H. Schlegel, R. Neugebauer

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Ball screws are an important component in machine tools. In mechatronic systems and machine tools, a ball screw has to work usually at a high speed. Otherwise the axial compliance of the ball screw, in combination with the inertia of the slide, the motor, the coupling and the screw, will cause an oscillation resonance, which limits the systems bandwidth and consequently influences performance of the motion controller. In this paper, the modal analysis method by measuring and analysing the vibrating parameters of the ball screw system to determine the dynamic characteristic of existing structures is used. On the one hand, the results of this study were obtained by the theoretical analysis and the modal testing of a ball screw system test station with the help of an impact hammer, respectively using excitation by motor. The experimental study showed oscillating forms of the ball screw for each frequency and obtained eigenfrequencies of the ball screw system. On the other hand, in this research a simulation with the help of the numerical modal analysis in order to analyse the oscillation and to find the eigenfrequencies of the ball screw system is used. Furthermore, the model order reduction by modal reduction and also according to Guyan is carried out. On the basis of these results a secure and also rapid commissioning of the control loops with regard to operating in their optimal function is targeted.

Keywords: modal analysis, ball screw, controller system, machine tools

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Authors: Seyed Hassan Moosa-kazemi, Hassan Vatandoost

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Malaria, Dirofilaria immitis (dog heart worm), and D. repens (dirofilariasis), which are transmitted by mosquitoes, have been reported in Iran. The Iranian mosquito fauna includes seven genera, 65 species, and three subspecies. Aedes albopictus has been reported since. West Nile, Sindbis, Dengue, Japanese encephalitis viruses, and the nematode Setaria (setariasis) has been reported in the country but there are no information about their vectors in Iran. Iran is malaria elimination phase. Insecticides residual spraying (IRS), distributed of insecticides long lasting treated nets (ITNs), fogging, release of larvivours fishes and Bacillus thuringiensis, chemical larviciding, as well as case finding and manipulation and modification of breeding places carried out thought the IVM program in the country. Prolonged exposure to insecticides over several generations of the vectors, develop resistance, a capacity to survive contact with insecticides. However, use of insecticides in agriculture has often been implicated as contributing to resistance in mosquito’s vectors. Resistance of mosquitoes to some insecticides has been documented just within a few years after the insecticides were introduced. Some enzymes such as monooxygenases, esterases and glutathione S-transferases have been considered as a reason for resistance to pyrethroid insecticides. In conclusion, regarding to documented resistance and tolerance of mosquitoes vectors to some insecticides, resistance management is suggested by using new insecticide with novel mode of action.

Keywords: control, Iran, resistance, vector

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Authors: J. Kaabi, Y. Harrath

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This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm.

Keywords: flow shop scheduling, genetic algorithm, maintenance, priority rules

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Authors: Armel Asongu Nkembi, Ahmad Fawad

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The aim of this paper is to determine in a concise way the transfer function that characterizes a DC electrical motor with a helix. In practice it can be obtained by applying a particular input to the system and then, based on the observation of its output, determine an approximation to the transfer function of the system. In our case, we use a step input and find the transfer function parameters that give the simulated first-order time response. The simulation of the system is done using MATLAB/Simulink. In order to determine the parameters, we assume a first order system and use the Broida approximation to determine the parameters and then its Mean Square Error (MSE). Furthermore, we design a PID controller for the control process first in the continuous time domain and tune it using the Ziegler-Nichols open loop process. We then digitize the controller to obtain a digital controller since most systems are implemented using computers, which are digital in nature.

Keywords: transfer function, step input, MATLAB, Simulink, DC electrical motor, PID controller, open-loop process, mean square process, digital controller, Ziegler-Nichols

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Authors: Jung–Min Yang

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In this paper, fault recovery for parallel interconnected asynchronous sequential machines is studied. An adversarial input can infiltrate into one of two submachines comprising parallel composition of the considered asynchronous sequential machine, causing an unauthorized state transition. The control objective is to elucidate the condition for the existence of a corrective controller that makes the closed-loop system immune against any occurrence of adversarial inputs. In particular, an efficient existence condition is presented that does not need the complete modeling of the interconnected asynchronous sequential machine.

Keywords: asynchronous sequential machines, parallel composi-tion, corrective control, fault tolerance

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

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Authors: Ismail El Gayar, Ossama Ismail, Yousri El Gamal

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This paper presented a technique to solve one of the transportation problems that faces us in real life which is the Bus Scheduling Problem. Most of the countries using buses in schools, companies and traveling offices as an example to transfer multiple passengers from many places to specific place and vice versa. This transferring process can cost time and money, so we build a decision support system that can solve this problem. In this paper, a genetic algorithm with the shortest path technique is used to generate a competitive solution to other well-known techniques. It also presents a comparison between our solution and other solutions for this problem.

Keywords: bus scheduling problem, decision support system, genetic algorithm, shortest path

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Bernd Engel, Sara Salman Hassan Al-Maeeni

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A substantial amount of natural resources are nowadays consumed at a growing rate, as humans all over the world used materials obtained from the Earth. Machinery manufacturing industry is one of the major resource consumers on a global scale. Even though the incessant finding out of the new material, metals, and resources, it is urgent for the industry to develop methods to use the Earth's resources intelligently and more sustainable than before. Re-engineering of machine tools regarding design and failure analysis is an approach whereby out-of-date machines are upgraded and returned to useful life. To ensure the reliable future performance of the used machine components, it is essential to investigate the machine component failure through the material, design, and surface examinations. This paper presents an experimental approach aimed at inspecting the shaft of the rotary draw bending machine as a case to study. The testing methodology, which is based on the principle of the three-point bending test, allows assessing the shaft elastic behavior under loading. Furthermore, the shaft elastic characteristics include the maximum linear deflection, and maximum bending stress was determined by using an analytical approach and finite element (FE) analysis approach. In the end, the results were compared with the ones obtained by the experimental approach. In conclusion, it is seen that the measured bending deflection and bending stress were well close to the permissible design value. Therefore, the shaft can work in the second life cycle. However, based on previous surface tests conducted, the shaft needs surface treatments include re-carburizing and refining processes to ensure the reliable surface performance.

Keywords: deflection, FE analysis, shaft, stress, three-point bending

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Authors: Csaba I. Hencz, István Á. Harmati

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Nowadays the significance of road transport is gradually increasing. All transport companies are working in the same external environment where the speed of transport is defined by traffic rules. The main objective is to accelerate the speed of service and it is only dependent on the individual abilities of the managing members. These operational control units make decisions quickly (in a typically experiential and/or intuitive way). For this reason, support for these decisions is an important task. Our goal is to create a decision support model based on fuzzy signatures that can assist the work of operational management automatically. If the model sets parameters properly, the management of transport could be more economical and efficient.

Keywords: freight transport, decision support, information handling, fuzzy methods

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Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

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Authors: A. Bouamrane, M. T. Bouziane, K. Boutebba, Y. Djebbar

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This paper aims to develop a decision support tool to provide solutions to the problems of sewer networks management/maintenance in order to assist the manager to sort sections upon priority of intervention by taking account of the technical, economic, social and environmental standards as well as the managers’ strategy. This solution uses the Analytic Network Process (ANP) developed by Thomas Saaty, coupled with a set of tools for modelling and collecting integrated data from a geographic information system (GIS). It provides to the decision maker a tool adapted to the reality on the ground and effective in usage compared to the means and objectives of the manager.

Keywords: multi-criteria decision support, maintenance, Geographic Information System, modelling

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Authors: Acumsiri Tantikarnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020. Using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for nineteen selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (Labor Force Survey: LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: income inequality, nighttime light, population density, Thailand, urbanization

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Authors: Shilpa Avarebeel

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Background: India is one of the seven identified countries in South-East Asia region, regularly reporting dengue infection and may soon transform into a major niche for dengue epidemics. Objective: To study the clinical profile of dengue in our setting with special reference to respiratory complication. Study design: Descriptive and exploratory study, for one year in 2014. All patients confirmed as dengue infection were followed and their clinical profile, along with outcome was determined. Study proforma was designed based on the objective of the study and it was pretested and used after modification. Data was analyzed using statistical software SPSS-Version 16. Data were expressed as mean ±S .D for parametric variables and actual frequencies or percentage for non-parametric data. Comparison between groups was done using students’ t-test for independent groups, Chie square test, one-way ANOVA test, Karl Pearson’s correlation test. Statistical significance is taken at P < 0.05. Results: Study included 134 dengue positive cases. 81% had dengue fever, 18% had dengue hemorrhagic fever, and one had dengue shock syndrome. Most of the cases reported were during the month of June. Maximum number of cases was in the age group of 26-35 years. Average duration of hospital stay was less than seven days. Fever and myalgia was present in all the 134 patients, 16 had bleeding manifestation. 38 had respiratory symptoms, 24 had breathlessness, and 14 had breathlessness and dry cough. On clinical examination of patients with respiratory symptoms, all twenty-eight had hypoxia features, twenty-four had signs of pleural effusion, and four had ARDS features. Chest x-ray confirmed the same. Among the patients with respiratory symptoms, the mean platelet count was 26,537 c/cmm. There was no statistical significant difference in the platelet count in those with ARDS and other dengue complications. Average four units of platelets were transfused to all those who had ARDS in view of bleeding tendency. Mechanical ventilator support was provided for ARDS patients. Those with pleural effusion and pulmonary oedema were given NIV (non-invasive ventilation) support along with supportive care. However, steroids were given to patients with ARDS and 10 patients with signs of respiratory distress. 100%. Mortality was seen in patients with ARDS. Conclusion: Dengue has to be checked for those presenting with fever and breathlessness. Supportive treatments remain the cornerstone of treatment. Platelet transfusion has to be given only by clinical judgment. Steroids have no role except in early ARDS, which is controversial. Early NIV support helps in speedy recovery of dengue patients with respiratory distress.

Keywords: adult respiratory distress syndrome, dengue fever, non-invasive ventilation, pulmonary complication

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Authors: Sonto Maputle

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Background: Support during labour increase women's chances of having positive childbirth experiences as well as childbirth outcomes. The purpose of this study was to determine the support provided by midwives to women during labour at the public hospital in Limpopo Province. The study was conducted at the Tertiary hospital in Limpopo Province. Methods: A qualitative, participant observation approach was used. Population consisted of all women that were admitted to deliver their babies and the midwives who provided midwifery care in the obstetric unit of one tertiary public hospital in Limpopo Province. Non-probability, purposive and convenience sampling were used to sample 24 women and 12 midwives. Data were collected through participant observations which included unstructured conversations with the use of observational guide, field notes of events and conversations that occurred when women interact with midwives were recorded verbatim and a Visual Analog Scale to complement the observations. Data was analysed qualitatively but were presented in the tables and bar graphs. Results: Five themes emerged as support provided by midwives during labour, namely; communication between women and midwives, informational support, emotional support activities, interpretation of the experienced labour pain and supportive care activities during labour. Conclusion: The communication was occurring when the midwife was rendering midwifery care and very limited for empowering. The information sharing focused on the assistive actions rather than on the activities that would promote mothers’ participation. The emotional support activities indicated lack of respect and disregard cultural preferences and this contributed to inability to exercise choices in decision-making. The study recommended the implementation of Batho Pele principles in order to provide woman-centred care during labour.

Keywords: communication between women and midwives, labour pains, informational and emotional support, physical comforting measures

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Authors: Hong Zhang

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The main purpose of this study is to show that machine translation (MT) post-editing (PE) training can help our Chinese students learn Spanish as a second language. Our hypothesis is that they might make better use of it by learning PE skills specific for foreign language learning. We have developed PE training materials based on the data collected in a previous study. Training material included the special error types of the output of MT and the error types that our Chinese students studying Spanish could not detect in the experiment last year. This year we performed a pilot study in order to evaluate the PE training materials effectiveness and to what extent PE training helps Chinese students who study the Spanish language. We used screen recording to record these moments and made note of every action done by the students. Participants were speakers of Chinese with intermediate knowledge of Spanish. They were divided into two groups: Group A performed PE training and Group B did not. We prepared a Chinese text for both groups, and participants translated it by themselves (human translation), and then used Google Translate to translate the text and asked them to post-edit the raw MT output. Comparing the results of PE test, Group A could identify and correct the errors faster than Group B students, Group A did especially better in omission, word order, part of speech, terminology, mistranslation, official names, and formal register. From the results of this study, we can see that PE training can help Chinese students learn Spanish as a second language. In the future, we could focus on the students’ struggles during their Spanish studies and complete the PE training materials to teach Chinese students learning Spanish with machine translation.

Keywords: machine translation, post-editing, post-editing training, Chinese, Spanish, foreign language learning

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Authors: Santosh Kumar, M. Muralidhar

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The term ‘ergonomics’ is derived from two Greek words: ‘ergon’, meaning work and ‘nomoi’, meaning natural laws. Ergonomics is the study of how working conditions, machines and equipment can be arranged in order that people can work with them more efficiently. In this research communication an attempt has been made to study the effect of hand-arm vibrational exposure on the workers of a gear manufacturing plant by comparison of potential Carpal Tunnel Syndrome (CTS) symptoms and effect of different exposure levels of vibration on occurrence of CTS in actual industrial environment. Chi square test and correlation analysis have been considered for statistical analysis. From Chi square test, it has been found that the potential CTS symptoms occurrence is significantly dependent on the level of vibrational exposure. Data analysis indicates that 40.51% workers having potential CTS symptoms are exposed to vibration. Correlation analysis reveals that potential CTS symptoms are significantly correlated with exposure to level of vibration from handheld tools and to repetitive wrist movements.

Keywords: CTS symptoms, hand-arm vibration, ergonomics, physical tests

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Authors: Homa Ghave, Parmis Shahmaleki

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This paper presents a new multi-criteria stochastic mathematical model for a single machine scheduling with outsourcing allowed. There are multiple jobs processing in batch. For each batch, all of job or a quantity of it can be outsourced. The jobs have stochastic processing time and lead time and deterministic due dates arrive randomly. Because of the stochastic inherent of processing time and lead time, we use the chance constrained programming for modeling the problem. First, the problem is formulated in form of stochastic programming and then prepared in a form of deterministic mixed integer linear programming. The objectives are considered in the model to minimize the maximum tardiness and outsourcing cost simultaneously. Several procedures have been developed to deal with the multi-criteria problem. In this paper, we utilize the concept of satisfaction functions to increases the manager’s preference. The proposed approach is tested on instances where the random variables are normally distributed.

Keywords: single machine scheduling, multi-criteria mathematical model, outsourcing strategy, uncertain lead times and processing times, chance constrained programming, satisfaction function

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Authors: Ashlyn Smith, Chloe Zampelli, Vincent Manna, Vernon Murray

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According to Polaris (a UN affiliate), there are currently 44 million human trafficking victims globally. Using a sample of 137 labor trafficking victims in Poland, we found that all were Ukrainian citizens. We categorized victims according to the “Victim Intervention Marketing” (Murray) social marketing framework. The largest victim type consisted of “Willing Assimilators” (57%). This means they entered their particular trafficking situations without coercion and were left at will. Such victims are typically driven by financial desperation. Twenty percent (20%) of Willing Assimilators were men, and 80% were women. Victims who were not Willing Assimilators were forced as either “Enlightened Apostates” (37%) or “Tricked and Trapped” (7%). All of the forced victims were women. Crosstabs with Chi-square test (Pearson Chi-Square test significance = .002) results indicated that the male victims were all between 30 and 38 years old, while female victim ages ranged from 24 to 47. Accordingly, labor trafficking victim interventions in Poland should be age-sensitive and focus on three areas: 1) economic development for the Willing Assimilators, 2) training to identify fraudulent job postings, etc. for the Tricked and Trapped segment, and 3) training to equip potential victims to distrust certain close “loved ones” for the Enlightened Apostates.

Keywords: Poland, labor trafficking, social marketing, victim intervention marketing

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Authors: Adrienne Kline, Jaydip Desai

Abstract:

Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

Procedia PDF Downloads 502

Authors: C. Bay, A. Mahr, H. Groneberg, F. Döpper

Abstract:

Additive Manufacturing processes are becoming increasingly established in the industry for the economic production of complex prototypes and functional components. Laser beam melting (LBM), the most frequently used Additive Manufacturing technology for metal parts, has been gaining in industrial importance for several years. The LBM process chain – from material storage to machine set-up and component post-processing – requires many manual operations. These steps often depend on the manufactured component and are therefore not standardized. These operations are often not performed in a standardized manner, but depend on the experience of the machine operator, e.g., levelling of the build plate and adjusting the first powder layer in the LBM machine. This lack of standardization limits the reproducibility of the component quality. When processing metal powders with inhalable and alveolar particle fractions, the machine operator is at high risk due to the high reactivity and the toxic (e.g., carcinogenic) effect of the various metal powders. Faulty execution of the operation or unintentional omission of safety-relevant steps can impair the health of the machine operator. In this paper, all the steps of the LBM process chain are first analysed in terms of their influence on the two aforementioned challenges: reproducibility and work safety. Standardization to avoid errors increases the reproducibility of component quality as well as the adherence to and correct execution of safety-relevant operations. The corresponding lean method 5S will therefore be applied, in order to develop approaches in the form of recommended actions that standardize the work processes. These approaches will then be evaluated in terms of ease of implementation and their potential for improving reproducibility and work safety. The analysis and evaluation showed that sorting tools and spare parts as well as standardizing the workflow are likely to increase reproducibility. Organizing the operational steps and production environment decreases the hazards of material handling and consequently improves work safety.

Keywords: additive manufacturing, lean production, reproducibility, work safety

Procedia PDF Downloads 184

Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

Abstract:

Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

Procedia PDF Downloads 94

Authors: Soheila Sadeghi

Abstract:

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

Procedia PDF Downloads 49