Search results for: automated cad drawing

Authors: Bo-Bin Xiaoa, Chia-Yi Liua

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In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

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Authors: Gintaras Mitrulevičius

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This text, which is based on the conference report, seeks to briefly examine the relationship between the concepts of social democracy and democratic socialism, drawing attention to the essential aspects of its development and, in particular, discussing the contradictions in the relationship between these concepts in the modern period. In the preparation of this text, such research methods as historical, historical-comparative methods were used, as well as methods of analyzing, synthesizing, and generalizing texts. The history of the use of terms in social democracy and democratic socialism shows that these terms were used alternately and almost synonymously. At the end of the 20th century, traditional social democracy was transformed into the so-called "new social democracy." Many of the new social democrats do not consider themselves democratic socialists and avoid the historically characteristic identification of social democracy with democratic socialism. It has become quite popular to believe that social democracy is a separate ideology from democratic socialism. Or that it has become a variant of the ideology of liberalism. This is a testimony to the crisis of ideological self-awareness of social democracy. Since the beginning of the 21st century, social democracy has also experienced a growing crisis of electoral support. This, among other things, led to her slight shift to the left. In this context, some social democrats are once again talking about democratic socialism. The rise of the ideas of democratic socialism in the United States was catalyzed by Bernie Sanders. But the proponents of democratic socialism in the United States have different concepts of democratic socialism. In modern Europe, democratic socialism is also spoken of by leftists of non-social democratic origin, whose understanding is different from that of democratic socialism inherent in classical social democracy. Some political scientists also single out the concepts in question. Analysis of the problem shows that there are currently several concepts of democratic socialism on the spectrum of the political left, both social-democratic and non-social-democratic.

Keywords: democratic socializm, socializm, social democracy, new social democracy, political ideologies

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Authors: Yen-Fen Tseng

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Studying migration regimes enables one to identify clusters of countries with policy features in common. A few researchers have pointed out the origin of hardship experienced by foreign workers in Taiwan, Japan, and South Korea, stems from their exclusionary migration regime. This paper aims to understand the causes and consequences of the East Asia migration regime, exploring the common exclusionary policies features of Taiwan, Japan, and South Korea, focusing on the foreign labor policy. It will then present explanations as to factors shaping migration regime; the perspective of factors within political system is adopted, as opposed to political economy and pluralist society approach. In the minds of political elites across East Asia, there exists a powerful belief in mono-ethnicity, namely, the benefits of mono-ethnicity and the social ill of “minority problems”. Guest workers policies of various alterations become the compromise between the want for foreign labor and the desire to maintain mono-ethnicity. The paper discusses the absence of immigrant settlement and formation of ethnic communities as a result of the reluctant hosts. Migrant workers in these societies commonly suffer from irregular working conditions as well as unprotected rights out of their denied legality. The case of Taiwan will be presented with greater details, drawing on data from both first-hand and secondary sources.

Keywords: migration regime, guest worker policies, East Asia, society

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Ting Li Liu

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‘Leftover woman’ or ‘3S’ woman is the term used to describe a well-educated, high income, independent woman who is single and never married around 30 years in Chinese society. With the naming of this demographic of ‘leftover women’, their family, dating culture, mate selection and marriage attract public concern. Massive media representations of ‘leftover women’ occur daily; the research aims to present several media representations of women’s anxiety about their singlehood and related marital issues around thirty. The research triangulates two areas of media representation of ‘leftover women’: films and audience reviews on ‘Douban Movie’ website. Drawing on traditional media studies, Fairclough’s critical discourse analysis combined with multimodal techniques is applied to the research to analyze the representations of ‘leftover women’ and their implications for marital culture in China, in conjunction with a feminist perspective. The conference paper will discuss two case studies: the film ‘The last woman standing’ and ‘I Do’. Paying attention to different aspects of ‘leftover women’, the research aims to re-examine the representations of ‘leftover women’ in selected scenes, such as their age anxiety, family, marriage, dating process, careers, etc. The paper also includes public beliefs about ‘leftover women’ from online audience reviews. In conclusion, the emergence of ‘leftover women’ is a reflection of Chinese tradition’s impact on people’s lives and new changes in Chinese families and their attitude to marriage.

Keywords: leftover women, marriage, family, media culture, China

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Authors: Nuraddeen Usman, Khiruddin Abdullah, Mohd Nawawi, Amin Khalil Ismail

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A magnetic survey is carried out in order to locate the remains of construction items, specifically concrete pillars. The conventional Euler deconvolution technique can perform the task but it requires the use of fixed structural index (SI) and the construction items are made of materials with different shapes which require different SI (unknown). A Euler deconvolution technique that estimate background, horizontal coordinate (xo and yo), depth and structural index (SI) simultaneously is prepared and used for this task. The synthetic model study carried indicated the new methodology can give a good estimate of location and does not depend on magnetic latitude. For field data, both the total magnetic field and gradiometer reading had been collected simultaneously. The computed vertical derivatives and gradiometer readings are compared and they have shown good correlation signifying the effectiveness of the method. The filtering is carried out using automated procedure, analytic signal and other traditional techniques. The clustered depth solutions coincided with the high amplitude/values of analytic signal and these are the possible target positions of the concrete pillars being sought. The targets under investigation are interpreted to be located at the depth between 2.8 to 9.4 meters. More follow up survey is recommended as this mark the preliminary stage of the work.

Keywords: concrete pillar, magnetic survey, geotechnical investigation, Euler Deconvolution

Procedia PDF Downloads 254

Authors: Madeleine Nicole G. Bernardo, Katrina T. Feliciano, Marcelo Nonato A. Nacionales III, Diane Frances M. Peralta, Denise Nicole V. Profeta

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This study aims to find out if heart rate variability and valence of emotion have an effect on perceived intensity of emotion. Psychology undergraduates (N = 60) from the University of the Philippines Diliman were shown 10 photographs from the Japanese Female Facial Expression (JAFFE) Database, along with a corresponding questionnaire with a Likert scale on perceived intensity of emotion. In this 3 x 2 mixed subjects factorial design, each group was either made to do a simple exercise prior to answering the questionnaire in order to increase the heart rate, listen to a heart rate of 120 bpm, or colour a drawing to keep the heart rate stable. After doing the activity, the participants then answered the questionnaire, providing a rating of the faces according to the participants’ perceived emotional intensity on the photographs. The photographs presented were either of positive or negative emotional valence. The results of the experiment showed that neither an induced fast heart rate or perceived fast heart rate had any significant effect on the participants’ perceived intensity of emotion. There was also no interaction effect of heart rate variability and valence of emotion. The insignificance of results was explained by the Philippines’ high context culture, accompanied by the prevalence of both intensely valenced positive and negative emotions in Philippine society. Insignificance in the effects were also attributed to the Cannon-Bard theory, Schachter-Singer theory and various methodological limitations.

Keywords: heart rate variability, perceived intensity of emotion, Philippines , valence of emotion

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Authors: Suleman Ibrahim

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Purpose: The research is seeking people's perceptions on cybercrime issues, rather than their knowledge of the facts. Unlike the Tripartite Cybercrime Framework (TCF), the binary models are ill-equipped to differentiate between cyber fraud (a socioeconomic crime) and cyber bullying or cyber stalking (psychosocial cybercrimes). Whilst the binary categories suggested that digital crimes are dichotomized: (i.e. cyber-enabled and cyber-dependent), the TCF, recently proposed, argued that cybercrimes can be conceptualized into three groups: socioeconomic, psychosocial and geopolitical. Concomitantly, as regards to the experience/perceptions of cybercrime, the TCF’s claim requires substantiation beyond its theoretical realm. Approach/Methodology: This scholar endeavor framed with the TCF, deploys a survey method to explore the experience of cybercrime across gender. Drawing from over 400 participants in the UK, this study aimed to contrast the differential perceptions/experiences of socioeconomic cybercrime (e.g. cyber fraud) and psychological cybercrime (e.g. cyber bullying and cyber stalking) across gender. Findings: The results revealed that cyber stalking was rated as least serious of the different digital crime categories. Further revealed that female participants judged all types of cybercrimes as more serious than male participants, with the exception of socioeconomic cybercrime – cyber fraud. This distinction helps to emphasize that gender cultures and nuances not only apply both online and offline, it emphasized the utilitarian value of the TCF. Originality: Unlike existing data, this study has contrasted the differential perceptions and experience of socioeconomic and psychosocial cybercrimes with more refined variables.

Keywords: gender variations, psychosocial cybercrime, socioeconomic cybercrime, tripartite cybercrime framework

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Authors: Fatima Ghassan Al-Abtah, Naser Al-Huniti, Elsadig Mahdi

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As the lightest constructional metal on earth, magnesium alloys offer excellent potential for weight reduction in the transportation industry, and it was observed that some magnesium alloys exhibit superior ductility and superplastic behavior at high temperatures. The main limitation of the superplastic forming (SPF) includes the low production rate since it needs a long forming time for each part. Through this study, an SPF process that starts with a mechanical pre-forming stage is developed to promote formability and reduce forming time. A two-dimensional finite element model is used to simulate the process. The forming process consists of two steps. At the pre-forming step (deep drawing), the sheet is drawn into the die to a preselected level, using a mechanical punch, and at the second step (SPF) a pressurized gas is applied at a controlled rate. It is shown that a significant reduction in forming time and improved final thickness uniformity can be achieved when the hybrid forming technique is used, where the process achieved a fully formed part at 400°C. Investigation for the impact of different forming process parameters achieved by comparing forming time and the distribution of final thickness that were obtained from the simulation analysis. Maximum thinning decreased from over 67% to less than 55% and forming time significantly decreased by more than 6 minutes, and the required gas pressure profile was predicted for optimum forming process parameters based on the 0.001/sec target constant strain rate within the sheet.

Keywords: magnesium, plasticity, superplastic forming, finite element analysis

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Authors: Oluwole Victor Falobi, Lawrence Olusola Ige

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The cardinal drawing force of this study is to examine the influence of audio-visual resources in teaching business subjects in selected secondary schools in IfakoIjaiye Local Government Area of Lagos State, Nigeria. A descriptive survey research design was employed for the study. By using a quantitative research approach and a sample size of 120 students were randomly selected from four public schools. Three research questions with one hypothesis guided the study. Data collected were analysed using frequency, the mean and standard deviation for the research questions, and Pearson Product Moment Correlation PPMC were used to analysed the inferential statistic. Findings from the study revealed that the Influence of audio-visual resources in teaching business subjects in selected secondary schools in IfakoIjaiye Local Government Area of Lagos State is low. It further revealed data the knowledge of teachers on the use of audio-visual resources is high in Ifako Local Government Area. It was recommended that government should create a timely monitoring system in other to check secondary school laboratories and classrooms to replace outdated facilities and also purchase needed facilities for effective teaching and learning to take place.

Keywords: audio-visual resources, business subjects, school, teaching

Procedia PDF Downloads 89

Authors: Hayder H. Kareem, Ayad K. Hussein, Aseel A. Alkatib

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Groundwater accumulated at geological formations constitutes a worldwide vital water resource component which can be used to supply agriculture, industry, and domestic uses. The subsurface environment is affected by human activities; consequently, planning and sustainable management of aquifers require serious attention, especially as the world is exposed to the problem of global warming. Establishing accurate and efficient groundwater models will provide confident results for the behavior of the aquifer's system. The new approach, 'Distributed Value Property Zones,' available in Visual MODFLOW, is used to reconstruct the subsurface zones of the Al-Najaf region aquifer, and then its effect is compared with those manual and automated (PEST) approaches. Results show that the model has become more accurate with the use of the new approach, as the calibration and results analyses revealed. The assessment of the Al-Najaf region groundwater aquifer has revealed a degree of insufficiency of the required pumping demand, which reflects dry areas in both of the aquifer's layers. In addition, with pumping, the Euphrates River loses water of 7458 m³/day to the aquifer, while without pumping, it gains 28837 m³/day from the rainfall's recharge. The distributed value property zones approach achieves a precise groundwater model to assess the state of the Al-Najaf region aquifer.

Keywords: Al-Najaf region, distributed value property zones approach, hydraulic conductivity, groundwater modelling using visual MODFLOW

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Authors: Clement Leroy, Guillaume Boitel

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This paper presents a comparative benchmarking review of a steady and transient three-dimensional (3D) flow computations in centrifugal pump using commercial (AnsysCFX) and open source (OpenFOAM) computational fluid dynamics (CFD) software. In centrifugal rotor-dynamic pump, the fluid enters in the impeller along to the rotating axis to be accelerated in order to increase the pressure, flowing radially outward into another stage, vaned diffuser or volute casing, from where it finally exits into a downstream pipe. Simulations are carried out at the best efficiency point (BEP) and part load, for single-phase flow with several turbulence models. The results are compared with overall performance report from experimental data. The use of CFD technology in industry is still limited by the high computational costs, and even more by the high cost of commercial CFD software and high-performance computing (HPC) licenses. The main objectives of the present study are to define OpenFOAM methodology for high-quality 3D steady and transient turbomachinery CFD simulation to conduct a thorough time-accurate performance analysis. On the other hand a detailed comparisons between computational methods, features on latest Ansys release 18 and OpenFOAM is investigated to assess the accuracy and industrial applications of those solvers. Finally an automated connected workflow (IoT) for turbine blade applications is presented.

Keywords: benchmarking, CFX, internet of things, openFOAM, time-accurate, turbomachinery

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Ayush Shrivastava, Arpit Chaudhary, Devang Kulshreshtha, Vibhav Prakash Singh, Rajeev Srivastava

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Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method.

Keywords: CAD, dispersed region growing algorithm (DRGA), image segmentation, mammography, pectoral muscle sliding window algorithm (PMSWA)

Procedia PDF Downloads 306

Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Karen Bell

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It is vital to integrate a working-class perspective into the just transition to an inclusive and sustainable society because of the particular expertise and interests that working-class people bring to the debates and actions. In class societies, those who are not well represented in the current structures of power can find it easier to see when the system is not working. They are also more likely to be impacted by the environmental crises because wealthier people can change their dwelling places, jobs and other aspects of their lives in the face of risks. Therefore, challenging the ‘post-material values thesis’, this paper argues that, if enabled to do so, working-class people are more likely to identify what needs to be addressed and changed in transition and can be more motivated to make the changes necessary than other social groups. However, they are often excluded from environmental decision-making and environmental social movements. The paper is based on a mixed methodology; drawing on secondary data, interview material, participant observation and documentary analysis. It is based on years of research and activism on environmental issues in working-class communities. The analysis and conclusion discusses the seven kinds of change required to address this problem: 1) organizational change - participatory practice (2) legislative change - make class an equalities and human rights issue (3) policy change - reduce inequality (4) social movement change - radicalize the environmental movement and support the environmental working-class (5) political change - create an eco-social state based on sharing (6) cultural change - integrate social and environmental justice, and (7) revolutionary change - dismantle capitalism.

Keywords: environmentalism, just transition, sustainability, working class

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Authors: Sang-Woo Kim, Young-Seon Lee

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An important concern in metal forming, even at elevated temperatures, is whether a desired deformation can be accomplished without any failure of the material. A detailed understanding of the critical condition for crack initiation provides not only the workability limit of a material but also a guide-line for process design. This paper describes the utilization of ductile fracture criteria in conjunction with the finite element method (FEM) for predicting the onset of fracture in warm metal working processes of magnesium alloy sheets. Critical damage values for various ductile fracture criteria were determined from uniaxial tensile tests and were expressed as the function of strain rate and temperature. In order to find the best criterion for failure prediction, Erichsen cupping tests under isothermal conditions and FE simulations combined with ductile fracture criteria were carried out. Based on the plastic deformation histories obtained from the FE analyses of the Erichsen cupping tests and the critical damage value curves, the initiation time and location of fracture were predicted under a bi-axial tensile condition. The results were compared with experimental results and the best criterion was recommended. In addition, the proposed methodology was used to predict the onset of fracture in non-isothermal deep drawing processes using an irregular shaped blank, and the results were verified experimentally.

Keywords: magnesium, AZ31 alloy, ductile fracture, FEM, sheet forming, Erichsen cupping test

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Yuliya Lipshits-Braziler

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For many young adults, career decision making is a difficult and complex process that may lead to indecision. Indecision is frequently associated with great psychological distress and low levels of well-being. One important resource for dealing with indecision is career decision self-efficacy (CDSE), which refers to people’s beliefs about their ability to successfully accomplish certain tasks involved in career choice. Drawing from Social Cognitive Theory, it has been hypothesized that CDSE correlates with (a) people’s likelihood to engage in or avoid career decision making tasks, (b) the amount of effort put into the decision making process, (c) the people’s persistence in decision making efforts when faced with difficulties, and (d) the eventual success in arriving at career decisions. Based on these assumptions, the present study examines the associations between the CDSE and 14 strategies for coping with career indecision among young adults. Using the structural equation modeling (SEM), the results showed that CDSE is positively associated with the use of productive coping strategies, such as information-seeking, problem-solving, positive thinking, and self-regulation. In addition, CDSE was negatively associated with nonproductive coping strategies, such as avoidance, isolation, ruminative thinking, and blaming others. Contrary to our expectations, CDSE was not significantly correlated with instrumental help-seeking, while it was negatively correlated with emotional help-seeking. The results of this study can be used to facilitate the development of interventions aiming to reinforce young adults’ career decision making self-efficacy, which may provide them with a basis for overcoming career indecision more effectively.

Keywords: career decision self-efficacy, career indecision, coping strategies, career counseling

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Authors: Yoftahe Nigussie

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This report presents a final year project pertaining to the design of Fire tube boiler for the purpose of producing saturated steam. The objective of the project is to produce saturated steam for different purpose with a capacity of 2000kg/h at 12bar design pressure by performing a design of a higher performance fire tube boiler that considered the requirements of cost minimization and parameters improvement. This is mostly done in selection of appropriate material for component parts, construction materials and production methods in different steps of analysis. In the analysis process, most of the design parameters are obtained by iterating with related formulas like selection of diameter of tubes with overall heat transfer coefficient optimization, and the other selections are also as like considered. The number of passes is two because of the size and area of the tubes and shell. As the analysis express by using heavy oil fuel no6 with a higher heating value of 44000kJ/kg and lower heating value of 41300kJ/kg and the amount of fuel consumed 140.37kg/hr. and produce 1610kw of heat with efficiency of 85.25%. The flow of the fluid is a cross flow because of its own advantage and the arrangement of the tube in-side the shell is welded with the tube sheet, and the tube sheet is attached with the shell and the end by using a gasket and weld. The design of the shell, using European Standard code section, is as like pressure vessel by considering the weight, including content and the supplementary accessories such as lifting lugs, openings, ends, man hole and supports with detail and assembly drawing.

Keywords: steam generation, external treatment, internal treatment, steam velocity

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Badar Khalid Hakim Alshabibi

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This study aims to examine the determinants of corporate board diligence in the listed firm in Sultanate of Oman, using four corporate board characteristics, the board size, board independence, board gender diversity, and nationality diversity. Design/methodology/approach: Using a sample comprised of all companies listed in the Muscat Securities Exchange over a ten-year period (2009–2019), the study applies Pooled OLS regression to examine the determinants of corporate board diligence. Findings: Drawing from the agency theory and institutional theory, the results reveal that the number of independent board members had statistical significance, suggesting that board independence can improve corporate board diligence, though board size and nationality diversity were found to have a negative association with corporate board diligence. There is no evidence, however, that board gender diversity improves corporate board diligence. Practical implications: The study provides insights for both the investors and regulatory authorities in developing economies. For the investors to be aware about the corporate board characteristics which enhance board monitoring, and for the regulatory authorities to consider revising the corporate governance codes which enhance the quality of governance practices. Originality/value: The study provides new evidence documenting the determinants of corporate board diligence in a developing country such as the Sultanate of Oman, which has a high potential for growth and attracting foreign investment, as stated in Oman vision 2040. In addition, this paper is the first to examine the association between corporate board diligence and corporate board diversity aspects.

Keywords: board diligence, board monitoring, board composition, board diversity, oman

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Authors: S. Mantzoukis, M. Gerasimou, P. Christodoulou, N. Varsamis, G. Kolliopoulou, N. Zotos

Abstract:

Purpose: Patients in Intensive Care Units (ICU) are exposed to a different spectrum of microorganisms relative to the hospital. Due to the fact that the majority of these patients are intubated, bronchial secretions should be examined. Material and Method: Bronchial secretions should be taken with care so as not to be mixed with sputum or saliva. The bronchial secretions are placed in a sterile container and then inoculated into blood, Mac Conkey No2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. After this period, if any number of microbial colonies are detected, gram staining is performed and then the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) followed by a sensitivity test in the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by a Kirby Bauer test. Results: In 2017 the Laboratory of Microbiology received 365 samples of bronchial secretions from the Intensive Care Unit. 237 were found positive. S. epidermidis was identified in 1 specimen, A. baumannii in 60, K. pneumoniae in 42, P. aeruginosa in 50, C. albicans in 40, P. mirabilis in 4, E. coli in 4, S. maltophilia in 6, S. marcescens in 6, S. aureus in 12, S. pneumoniae in 1, S. haemolyticus in 4, P. fluorescens in 1, E. aerogenes in 1, E. cloacae in 5. Conclusions: The majority of ICU patients appear to be a fertile ground for the development of infections. The nature of the findings suggests that a significant part of the bacteria found comes from the unit (nosocomial infection).

Keywords: bronchial secretions, cultures, infections, intensive care units

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Authors: Vinayak Bassi, Rajpreet Singh

Abstract:

Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

Procedia PDF Downloads 157

Authors: Prajwal Eachempati, Laurent Muzellec, Ashish Kumar Jha

Abstract:

We use Facebook as a case study to investigate the complex relationship between the firm’s public discourse (and actions) surrounding data privacy and the performance of a business model based on monetizing user’s data. We do so by looking at the evolution of public discourse over time (2004–2021) and relate topics to revenue and stock market evolution Drawing from archival sources like Zuckerberg We use LDA topic modelling algorithm to reveal 19 topics regrouped in 6 major themes. We first show how, by using persuasive and convincing language that promises better protection of consumer data usage, but also emphasizes greater user control over their own data, the privacy issue is being reframed as one of greater user control and responsibility. Second, we aim to understand and put a value on the extent to which privacy disclosures have a potential impact on the financial performance of social media firms. There we found significant relationship between the topics pertaining to privacy and social media/technology, sentiment score and stock market prices. Revenue is found to be impacted by topics pertaining to politics and new product and service innovations while number of active users is not impacted by the topics unless moderated by external control variables like Return on Assets and Brand Equity.

Keywords: public discourses, data protection, social media, privacy, topic modeling, business models, financial performance

Procedia PDF Downloads 88

Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

Abstract:

Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

Procedia PDF Downloads 241

Authors: Dongwan Kang, Joohyung Oh, Chaetae Im

Abstract:

Advancement of communication technologies and smart devices in the recent times is leading to changes into the integrated wired and wireless communication environments. Since early days, businesses had started introducing environments for mobile device application to their operations in order to improve productivity (efficiency) and the closed corporate environment gradually shifted to an open structure. Recently, individual user's interest in working environment using mobile devices has increased and a new corporate working environment under the concept of BYOD is drawing attention. BYOD (bring your own device) is a concept where individuals bring in and use their own devices in business activities. Through BYOD, businesses can anticipate improved productivity (efficiency) and also a reduction in the cost of purchasing devices. However, as a result of security threats caused by frequent loss and theft of personal devices and corporate data leaks due to low security, companies are reluctant about adopting BYOD system. In addition, without considerations to diverse devices and connection environments, there are limitations in detecting abnormal behaviors such as information leaks which use the existing network-based security equipment. This study suggests a method to detect abnormal behaviors according to individual behavioral patterns, rather than the existing signature-based malicious behavior detection and discusses applications of this method in BYOD environment.

Keywords: BYOD, security, anomaly behavior detection, security equipment, communication technologies

Procedia PDF Downloads 321

Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

Abstract:

Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

Procedia PDF Downloads 117