Search results for: process data

Authors: Efri Mardawati, Ronny Purwadi, Made Tri Ari Penia Kresnowati, Tjandra Setiadi

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EFB are mainly composed of cellulose (≈ 43%), hemicellulose (≈ 23%) and lignin (≈20%). The palm oil empty fruit bunches (EFB) is the lignosellulosic waste from crude palm oil industries mainly compose of (≈ 43%), hemicellulose (≈ 23%) and lignin (≈20%). Xylan, a polymer made of pentose sugar xylose and the most abundant component of hemicellulose in plant cell wall. Further xylose can be used as a raw material for production of a wide variety of chemicals such as xylitol, which is extensively used in food, pharmaceutical and thin coating applications. Currently, xylose is mostly produced from xylan via chemical hydrolysis processes. However, these processes are normally conducted at a high temperature and pressure, which is costly, and the required downstream processes are relatively complex. As an alternative method, enzymatic hydrolysis of xylan to xylose offers an environmentally friendly biotechnological process, which is performed at ambient temperature and pressure with high specificity and at low cost. This process is catalysed by xylanolytic enzymes that can be produced by some fungal species such as Aspergillus niger, Penicillium crysogenum, Tricoderma reseei, etc. Fungal that will be used to produce crude xylanase enzyme in this study is T. Viride ITB CC L.67. It is the purposes of this research to study the influence of pretreatment of EFB for the enzymatic hydrolysis process, optimation of temperature and pH of the hydrolysis process, the influence of substrate and enzyme concentration to the enzymatic hydrolysis process, the dynamics of hydrolysis process and followingly to study the kinetics of this process. Xylose as the product of enzymatic hydrolysis process analyzed by HPLC. The results show that the thermal pretreatment of EFB enhance the enzymatic hydrolysis process. The enzymatic hydrolysis can be well approached by the Michaelis Menten kinetic model, and kinetic parameters are obtained from experimental data.

Keywords: oil palm empty fruit bunches (EFB), xylose, enzymatic hydrolysis, kinetic modelling

Procedia PDF Downloads 368

Authors: Faisal A. Aburub

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Organizations today need to invest in software in order to run their businesses, and to the organizations’ objectives, the software should be in line with the business process. This research presents an approach for linking process models and system models. Particularly, the new approach aims to synthesize sequence diagram based on role activity diagram (RAD) model. The approach includes four steps namely: Create business process model using RAD, identify computerized activities, identify entities in sequence diagram and identify messages in sequence diagram. The new approach has been validated using the process of student registration in University of Petra as a case study. Further research is required to validate the new approach using different domains.

Keywords: business process modelling, system models, role activity diagrams, sequence diagrams

Procedia PDF Downloads 357

Authors: Suppara Charoenpoom

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This study focuses on 1) a good-quality management procedures of buildings and environment in schools 2) designing the management procedures and 3) creating an operation manual for the procedures. This study is the combination of qualitative and quantitative research method. Populations in the research were 83 deans and directors of primary and secondary schools from the 10th educational district in Samut Songkram. Sample group was selected from the voluntary deans and directors. There were 14 participants in sample group. Research tools in this study were divided into 2 categories. The first one was data-collecting tools, which were in-depth interview and questionnaires. The second one was the designing tools to help creating management procedures: quality business, quality work procedure and key quality indicator of each activity in schools. All data were analyzed by mean and standard deviation. The result from this study has found out 1 effective process of building and environment management for basic education schools which is called Quality Business Process (QBP) and 7 Quality Work Procedures (QWP). In terms of academic feasibility checkup by experts, the research had shown that new design of building and environment management was approved unanimously. It means that new process of building and environment management in schools works very well and can be adapted. After examining the possibility of management process being used in schools by calculating the mean value among sample group (14 school deans and directors), the mean value was between 0.64-1.00. It means that the new design of building and environment management can be operated effectively in schools. For the satisfaction part, deans and school directors gave the satisfaction score in the highest level (Mean = 4.7372, S.D. = 0.4385).

Keywords: buildings, environment, procedures, quality management

Procedia PDF Downloads 210

Authors: Tobias Beyer, Christoph Friedrich

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Modern simulation methodology using finite element models is nowadays a recognized tool for product design/optimization. Likewise, manufacturing process design is increasingly becoming the focus of simulation methodology in order to enable sustainable results based on reduced real-life tests here as well. In this article, two process simulations -radial riveting and roller burnishing- used for mechanical joining of components are explained. In the first step, the required boundary conditions are developed and implemented in the respective simulation models. This is followed by process space validation. With the help of the validated models, the interdependencies of the input parameters are investigated and evaluated by means of sensitivity analyses. Limit case investigations are carried out and evaluated with the aid of the process simulations. Likewise, a comparison of the two joining methods to each other becomes possible.

Keywords: FEM, model-based process development, process simulation, radial riveting, roller burnishing, sensitivity analysis

Procedia PDF Downloads 86

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a simple user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud computing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 331

Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

Procedia PDF Downloads 226

Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

Procedia PDF Downloads 82

Authors: Se-Han Lee, Kwang-Woo Go, Gwang-Hyun Ahn, Hee-Sung Park, Cheol-Kyu Han, Jun-Bo Shim, Geun-Chul Kang, Hyun-Jung Lee

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In recent years, with the advent of smart cars and the expansion of the market, the announcement of 'Adventures in Automotive Networks and Control Units' at the DEFCON21 conference in 2013 revealed that cars are not safe from hacking. As a result, the HEAVENS model considering not only the functional safety of the vehicle but also the security has been suggested. However, the HEAVENS model only presents a simple process, and there are no detailed procedures and activities for each process, making it difficult to apply it to the actual vehicle security vulnerability check. In this paper, we propose an automated attack database that systematically summarizes attack vectors, attack types, and vulnerable vehicle models to prepare for various car hacking attacks, and data flow diagrams that can detect various vulnerabilities and suggest a way to materialize the HEAVENS model.

Keywords: automotive security, HEAVENS, car hacking, security model, information security

Procedia PDF Downloads 329

Authors: Nils Mueller, Gregor Herz, Erik Reichelt, Matthias Jahn

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In the context of climate change, the reduction of greenhouse gas emissions in all economic sectors is considered to be an important factor in order to meet the demands of a sustainable energy system. The steel industry as one of the large industrial CO₂ emitters is currently highly dependent on fossil resources. In order to reduce coke consumption and thereby CO₂ emissions while still being able to further utilize existing blast furnaces, the possibility of including a direct reduction process (DRP) into a fully integrated steel mill was investigated. Therefore, a blast furnace model, derived from literature data and implemented in Aspen Plus, was used to analyze the impact of DRI in the blast furnace process. Furthermore, a state-of-the-art DRP was modeled to investigate the possibility of substituting the reducing agent natural gas with hydrogen. A sensitivity analysis was carried out in order to find the boundary percentage of hydrogen as a reducing agent without penalty to the DRI quality. Lastly, the two modeled process steps were combined to form a route of producing pig iron. By varying boundary conditions of the DRP while recording the CO₂ emissions of the two process steps, the overall potential for the reduction of CO₂ emissions was estimated. Within the simulated range, a maximum reduction of CO₂ emissions of 23.5% relative to typical emissions of a blast furnace could be determined.

Keywords: blast furnace, CO₂ mitigation, DRI, hydrogen

Procedia PDF Downloads 262

Authors: H. Abdolvand, M. Riazat, H. Sohrabi, G. Faraji

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An experimental investigation into the Tube Backward Extrusion (TBE) process on AZ31 magnesium alloy is studied. Microstructures and grain size distribution of the specimens before and after TBE process are investigated by optical microscopy. Tensile and Vickers microhardness tests along extrusion direction were performed at room temperature. It is found that the average grain size is refined remarkably from the initial 33 µm down to 3.5 µm after TBE process. Also, the microhardness increased significantly to 58 HV after the process from an initial value of 36 HV.

Keywords: tube backward extrusion, AZ31, grain size distribution, grain refinement

Procedia PDF Downloads 477

Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

Procedia PDF Downloads 420

Authors: Tomasz Grzes, Maciej Kopczynski, Jaroslaw Stepaniuk

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The rough sets theory developed by Prof. Z. Pawlak is one of the tools that can be used in the intelligent systems for data analysis and processing. Banking, medicine, image recognition and security are among the possible fields of utilization. In all these fields, the amount of the collected data is increasing quickly, but with the increase of the data, the computation speed becomes the critical factor. Data reduction is one of the solutions to this problem. Removing the redundancy in the rough sets can be achieved with the reduct. A lot of algorithms of generating the reduct were developed, but most of them are only software implementations, therefore have many limitations. Microprocessor uses the fixed word length, consumes a lot of time for either fetching as well as processing of the instruction and data; consequently, the software based implementations are relatively slow. Hardware systems don’t have these limitations and can process the data faster than a software. Reduct is the subset of the decision attributes that provides the discernibility of the objects. For the given decision table there can be more than one reduct. Core is the set of all indispensable condition attributes. None of its elements can be removed without affecting the classification power of all condition attributes. Moreover, every reduct consists of all the attributes from the core. In this paper, the hardware implementation of the two-stage greedy algorithm to find the one reduct is presented. The decision table is used as an input. Output of the algorithm is the superreduct which is the reduct with some additional removable attributes. First stage of the algorithm is calculating the core using the discernibility matrix. Second stage is generating the superreduct by enriching the core with the most common attributes, i.e., attributes that are more frequent in the decision table. Described above algorithm has two disadvantages: i) generating the superreduct instead of reduct, ii) additional first stage may be unnecessary if the core is empty. But for the systems focused on the fast computation of the reduct the first disadvantage is not the key problem. The core calculation can be achieved with a combinational logic block, and thus add respectively little time to the whole process. Algorithm presented in this paper was implemented in Field Programmable Gate Array (FPGA) as a digital device consisting of blocks that process the data in a single step. Calculating the core is done by the comparators connected to the block called 'singleton detector', which detects if the input word contains only single 'one'. Calculating the number of occurrences of the attribute is performed in the combinational block made up of the cascade of the adders. The superreduct generation process is iterative and thus needs the sequential circuit for controlling the calculations. For the research purpose, the algorithm was also implemented in C language and run on a PC. The times of execution of the reduct calculation in a hardware and software were considered. Results show increase in the speed of data processing.

Keywords: data reduction, digital systems design, field programmable gate array (FPGA), reduct, rough set

Procedia PDF Downloads 192

Authors: Marcelino Caetano Noronha, Vosco Pereira, Jose Soares Pinto, Ferdinando Da C. Saores

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One particularly popular social media platform is Youtube. It’s a video-sharing platform where users can submit videos, and other users can like, dislike or comment on the videos. In this study, we conduct a binary classification task on YouTube’s video comments and review from the users regarding the accession process of Timor Leste to become the eleventh member of the Association of South East Asian Nations (ASEAN). We scrape the data directly from the public YouTube video and apply several pre-processing and weighting techniques. Before conducting the classification, we categorized the data into two classes, namely positive and negative. In the classification part, we apply Support Vector Machine (SVM) algorithm. By comparing with Naïve Bayes Algorithm, the experiment showed SVM achieved 84.1% of Accuracy, 94.5% of Precision, and Recall 73.8% simultaneously.

Keywords: classification, YouTube, sentiment analysis, support sector machine

Procedia PDF Downloads 82

Authors: Shyam Parhi

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Airports GIS program collects Airports data, validate and verify it, and stores it in specific database. Airports GIS allows authorized users to submit changes to airport data. The verified data is used to develop several engineering applications. One of these applications is electronic Airport Layout Plan (eALP) whose primary aim is to move from paper to digital form of ALP. The first phase of development of eALP was completed recently and it was tested for a few pilot program airports across different regions. We conducted gap analysis and noticed that a lot of development work is needed to fine tune at least six mandatory sheets of eALP. It is important to note that significant amount of programming is needed to move from out-of-box ArcGIS to a much customized ArcGIS which will be discussed. The ArcGIS viewer capability to display essential features like runway or taxiway or the perpendicular distance between them will be discussed. An enterprise level workflow which incorporates coordination process among different lines of business will be highlighted.

Keywords: geospatial data, geology, geographic information systems, aviation

Procedia PDF Downloads 393

Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

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Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

Procedia PDF Downloads 114

Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

Procedia PDF Downloads 157

Authors: Massimiliano Condotta, Giovanni Borga, Chiara Scanagatta

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Urban systems are places where the various actors involved interact and enter in conflict, in particular with reference to topics such as traffic congestion and security. But topics of discussion, and often clash because of their strong complexity, are air and noise pollution. For air pollution, the complexity stems from the fact that atmospheric pollution is due to many factors, but above all, the observation and measurement of the amount of pollution of a transparent, mobile and ethereal element like air is very difficult. Often the perceived condition of the inhabitants does not coincide with the real conditions, because it is conditioned - sometimes in positive ways other in negative ways - from many other factors such as the presence, or absence, of natural elements such as trees or rivers. These problems are seen with noise pollution as well, which is also less considered as an issue even if it’s problematic just as much as air quality. Starting from these opposite positions, it is difficult to identify and implement valid, and at the same time shared, mitigation solutions for the problem of urban pollution (air and noise pollution). The LOOPER (Learning Loops in the Public Realm) project –described in this paper – wants to build and test a methodology and a platform for participatory co-design, planning, and construction process inside a learning loop process. Novelties in this approach are various; the most relevant are three. The first is that citizens participation starts since from the research of problems and air quality analysis through a participatory data collection, and that continues in all process steps (design and construction). The second is that the methodology is characterized by a learning loop process. It means that after the first cycle of (1) problems identification, (2) planning and definition of design solution and (3) construction and implementation of mitigation measures, the effectiveness of implemented solutions is measured and verified through a new participatory data collection campaign. In this way, it is possible to understand if the policies and design solution had a positive impact on the territory. As a result of the learning process produced by the first loop, it will be possible to improve the design of the mitigation measures and start the second loop with new and more effective measures. The third relevant aspect is that the citizens' participation is carried out via Urban Living Labs that involve all stakeholder of the city (citizens, public administrators, associations of all urban stakeholders,…) and that the Urban Living Labs last for all the cycling of the design, planning and construction process. The paper will describe in detail the LOOPER methodology and the technical solution adopted for the participatory data collection and design and construction phases.

Keywords: air quality, co-design, learning loops, noise pollution, urban living labs

Procedia PDF Downloads 340

Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

Procedia PDF Downloads 451

Authors: Veli Durmus, Mert Uydaci

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Every patient who needs to get a medical treatment should share health-related personal data with healthcare providers. Therefore, personal health data plays an important role to make health decisions and identify health threats during every encounter between a patient and caregivers. In other words, health data can be defined as privacy and sensitive information which is protected by various health laws and regulations. In many cases, the data are an outcome of the confidential relationship between patients and their healthcare providers. Globally, almost all nations have own laws, regulations or rules in order to protect personal data. There is a variety of instruments that allow authorities to use the health data or to set the barriers data sharing across international borders. For instance, Directive 95/46/EC of the European Union (EU) (also known as EU Data Protection Directive) establishes harmonized rules in European borders. In addition, the General Data Protection Regulation (GDPR) will set further common principles in 2018. Because of close policy relationship with EU, this study provides not only information on regulations, directives but also how they play a role during the legislative process in Turkey. Even if the decision is controversial, the Board has recently stated that private or public healthcare institutions are responsible for the patient call system, for doctors to call people waiting outside a consultation room, to prevent unlawful processing of personal data and unlawful access to personal data during the treatment. In Turkey, vast majority private and public health organizations provide a service that ensures personal data (i.e. patient’s name and ID number) to call the patient. According to the Board’s decision, hospital or other healthcare institutions are obliged to take all necessary administrative precautions and provide technical support to protect patient privacy. However, this application does not effectively and efficiently performing in most health services. For this reason, it is important to draw a legal framework of personal health data by stating what is the main purpose of this regulation and how to deal with complicated issues on personal health data in Turkey. The research is descriptive on data protection law for health care setting in Turkey. Primary as well as secondary data has been used for the study. The primary data includes the information collected under current national and international regulations or law. Secondary data include publications, books, journals, empirical legal studies. Consequently, privacy and data protection regimes in health law show there are some obligations, principles and procedures which shall be binding upon natural or legal persons who process health-related personal data. A comparative approach presents there are significant differences in some EU member states due to different legal competencies, policies, and cultural factors. This selected study provides theoretical and practitioner implications by highlighting the need to illustrate the relationship between privacy and confidentiality in Personal Data Protection in Health Law. Furthermore, this paper would help to define the legal framework for the health law case studies on data protection and privacy.

Keywords: data protection, personal data, privacy, healthcare, health law

Procedia PDF Downloads 193

Authors: Sze Ming Yau

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Social worker-client interacting experience is scarcely studied in Hong Kong. Through this qualitative study, the experiences of Hong Kong social work practitioners in relating with clients provide new insights on social worker training and development. Thematic analysis is applied to examine the data collected by in-depth interviews with six local social work practitioners. The results show all practitioners have experienced both positive and challenging situations during the relating process. Their reactions either facilitate or hinder the process. Most of the practitioners’ strong reactions can be accounted for by using the concept of countertransference reactions during the interview session with clients. Moreover, they also have rarely reviewed the implications of those reactions after the session. In addition to countertransference, the self-expectation of practitioners also influences the relating process. Their self-expectations of being capable to help lead to anxiety. Though countertransference and anxiety of practitioners significantly influence the relating process, the practitioners do not adequately address personal issues and anxiety. Enhancing case conceptualization ability is their major coping strategy. The study has implications, including enhancement of social work training, workplace support, practitioner’s self-reflection, and integration of theory and practice.

Keywords: coping, countertransference, reactions, relating process, social workers

Procedia PDF Downloads 244

Authors: Balamurugan Baladhandapani, Ganesh Renganathan, V. R. Sanal Kumar

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In this paper a comprehensive review on various factory layouts has been carried out for designing a lucrative process layout for medium scale industries. Industry data base reveals that the end product rejection rate is on the order of 10% amounting large profit loss. In order to avoid these rejection rates and to increase the quality product production an intermediate non-destructive testing facility (INDTF) has been recommended for increasing the overall profit. We observed through detailed case studies that while introducing INDTF to medium scale industries the expensive production process can be avoided to the defective products well before its final shape. Additionally, the defective products identified during the intermediate stage can be effectively utilized for other applications or recycling; thereby the overall wastage of the raw materials can be reduced and profit can be increased. We concluded that the prudent design of a factory layout through critical path method facilitating with INDTF will warrant profitable outcome.

Keywords: intermediate non-destructive testing, medium scale industries, process layout design

Procedia PDF Downloads 481

Authors: Ali Abd El-Aty, Ahmed Tauseef, Ahmad Farooq

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This research aims at analyzing and optimizing the hydroforming process parameters to achieve a sound bulged tube without failure. Theoretical constitutive model is formulated to develop a working diagram including process window, which represents the optimize region to carry out the hydroforming process and predict the type of tube failure during the process accurately. The model is applied into different bulging ratios for low carbon steel (C1010). From this study, it is concluded that the tubes with bulging ratios up to 50% and 70% are successfully formed without defects. The tubes with bulging ratio of 90% are successfully formed by hydroforming with optimized the loading path (axial feed versus internal pressure) within the process window. The working diagram is modified due to different types of formation of wrinkling during the hydroforming process. The formation of wrinkles with increasing axial feed can be useful in terms of the achievement of higher bulging ratio and/or less thinning and this type of wrinkles can be overcome through the internal pressure in the later stage of the hydroforming process. On the other hand, the formation of wrinkles may be harmful, if it cannot be reversed.

Keywords: finite element, hydroforming, process window, wrinkling

Procedia PDF Downloads 261

Authors: S. J. Yune, K. H. Park

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The purpose of this study was to examine the validity of the research process for enhancing the humanities competence of the medical students. The research process was developed to be operated as a core subject course of 3 semesters. Among them, the research process for enhancing humanities capacity consisted of humanities and societies (6 teams) and education-psychology (2teams). The subjects of this study were 88-second grade students and 22 professors who participated in the research process. Among them, 13 professors participated in the study of humanities and 37 students. In the validity test, the professors were more likely to have more validity in the research process than the students in all areas of logic (p = .001), influence (p = .037), process (p = .001). The validity of the professor was higher than that of the students. The professors highly evaluated the students' learning outcomes and showed the most frequency to the prize group. As a result of analyzing the agreement between the students and the professors through the Kappa coefficient, the agreement degree of communication and cooperation competence was moderate to .430. Problem-solving ability was .340, which showed a fair degree of agreement. However, other factors showed only a slight degree of agreement of less than .20.

Keywords: research process, medical school, humanities competence, validity verification

Procedia PDF Downloads 164

Authors: Agustinus Rudatyo Himamunanto, Anastasia Rita Widiarti

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Transliteration using Javanese manuscripts is one of methods to preserve and legate the wealth of literature in the past for the present generation in Indonesia. The transliteration manual process commonly requires philologists and takes a relatively long time. The automatic transliteration process is expected to shorten the time so as to help the works of philologists. The preprocessing and segmentation stage firstly done is used to manage the document images, thus obtaining image script units that will compile input document images free from noise and have the similarity in properties in the thickness, size, and slope. The next stage of characteristic extraction is used to find unique characteristics that will distinguish each Javanese script image. One of characteristics that is used in this research is the number of black pixels in each image units. Each image of Java scripts contained in the data training will undergo the same process similar to the input characters. The system testing was performed with the data of the book Hamong Tani. The book Hamong Tani was selected due to its content, age and number of pages. Those were considered sufficient as a model experimental input. Based on the results of random page automatic transliteration process testing, it was determined that the maximum percentage correctness obtained was 81.53%. The percentage of success was obtained in 32x32 pixel input image size with the 5x5 image window. With regard to the results, it can be concluded that the automatic transliteration model offered is relatively good.

Keywords: Javanese script, character recognition, statistical, automatic transliteration

Procedia PDF Downloads 321

Authors: Manirath Wongsim

Abstract:

Information Quality (IQ) has become a critical, strategic issue in Accounting Information Systems (AIS) adoption. In order to implement AIS adoption successfully, it is important to consider the quality of information use throughout the adoption process, which seriously impacts the effectiveness of AIS adoption practice and the optimization of AIS adoption decisions. There is a growing need for research to provide insights into issues and solutions related to IQ in AIS adoption. The need for an integrated approach to improve IQ in AIS adoption, as well as the unique characteristics of accounting data, demands an AIS adoption specific IQ framework. This research aims to explore ways of managing information quality and AIS adoption to investigate the relationship between the IQ issues and AIS adoption process. This study has led to the development of a framework for understanding IQ management in AIS adoption. This research was done on 44 respondents as ten organisations from manufacturing firms in Thailand. The findings of the research’s empirical evidence suggest that IQ dimensions in AIS adoption to provide assistance in all process of decision making. This research provides empirical evidence that information quality of AIS adoption affect decision making and suggests that these variables should be considered in adopting AIS in order to improve the effectiveness of AIS.

Keywords: information quality, information quality dimensions, accounting information systems, accounting information system adoption

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Authors: Wuttigrai Ngamsirijit

Abstract:

Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.    

Keywords: decision making, human capital analytics, talent management, talent value chain

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Authors: Klara Smith-Etxeberria

Abstract:

main goal of this study was to analyze the predictive ability of some variables associated with the parental divorce process alongside attachment history with parents on both, mother-child and father-child relationship quality. Our sample consisted of 173 undergraduate and vocational school students from the Autonomous Community of the Basque Country. All of them belonged to a divorced family. Results showed that adequate maternal strategies during the divorce process (e.g.: stable, continuous and positive role as a mother) was the variable with greater predictive ability on mother-child relationships quality. In addition, secure attachment history with mother also predicted positive mother-child relationships. On the other hand, father-child relationship quality was predicted by adequate paternal strategies during the divorce process, such as his stable, continuous and positive role as a father, along with not badmouthing the mother and promoting good mother-child relationships. Furthermore, paternal negative emotional state due to divorce was positively associated with father-child relationships quality, and both, history of attachment with mother and with father predicted father-child relationships quality. In conclusion, our data indicate that both, paternal and maternal strategies for children´s adequate adjustment during the divorce process influence on mother-child and father-child relationships quality. However, these results suggest that paternal strategies during the divorce process have a greater predictive ability on father-child relationships quality, whereas maternal positive strategies during divorce determine positive mother-child relationships among young adults.

Keywords: father-child relationships quality, mother-child relationships quality, parental divorce process, young adulthood

Procedia PDF Downloads 239

Authors: Timothy Wale Olaosebikan

Abstract:

The most active resource in any organization is the manpower. Every other resource remains inactive unless there is competent manpower to handle them. Manpower training is needed to enhance productivity and overall performance of the organizations. This is due to the recognition of the important role of manpower training in attainment of organizational goals. Corporate Compliance conjures visions of an incomprehensible matrix of laws and regulations that defy logic and control by even the most seasoned manpower training professionals. Similarly, corporate compliance can be viewed as one of the most significant problems faced in manpower training process for any organization, therefore, commands relevant attention and comprehension. Consequently, this study investigated the nexus between manpower training and corporate compliance. Collection of data for the study was effected through the use of questionnaire with a sample size of 265 drawn by stratified random sampling. The data were analyzed using descriptive and inferential statistics. The findings of the study show that about 75% of the respondents agree that there is a strong relationship between manpower training and corporate compliance, which brings out the organizational attainment from any training process. The findings further show that most organisation do not totally comply with the rules guiding manpower training process thereby making the process less effective on organizational performance, which may affect overall profitability. The study concludes that formulation and compliance of adequate rules and guidelines for manpower trainings will produce effective results for both employees and the organization at large. The study recommends that leaders of organizations, industries, and institutions must ensure total compliance on the part of both the employees and the organization to manpower training rules. Organizations and stakeholders should also ensure that strict policies on corporate compliance to manpower trainings form the heart of their cardinal mission.

Keywords: corporate compliance, manpower training, nexus, rules and guidelines

Procedia PDF Downloads 111

Authors: Chanchai Wimon, Polin Muangngam, Thannapat Nimsumram, Chanin Prombutra, Prasert Aengchuan, Perawat Boonpuek

Abstract:

Cleaning water containers is essential for drinking water production to prevent contamination and residual chemicals from washing liquid. Water distribution divisions in Thailand are facing a critical problem with residual contamination in 15-liter drinking water containers due to dust and residual chemicals (TDS value > 200) after normal washing. A thorough washing process is required before filling the purified water into each container. Unfortunately, the washing procedure and frequency do not align with the work instructions provided by the health department. The measured Total Dissolved Solids (TDS) value of the remaining water was found to range between 195–202, exceeding the standard TDS for excellent drinking water (50-190 ppm). This research uses the design of experiment technique in statistics to improve the washing process and reduce such contamination. Statistical data from our survey of the cleaning process is collected to identify affecting factors. Washing time and water volume are varied to test the efficiency of the washing process in reducing residual sediment in the water. The result indicates that cleaning the 15-liter container with 2 liters of tap water mixed with 15 milliliters of dishwashing liquid for 3.12 minutes per container is optimal, as the resulting TDS reduces to 189.75, falling within the standard value for good drinking water. This study result would benefit the drinking water industry in implementing a statistically evaluated cleaning procedure without conducting multiple trials, thus saving takt time and production costs.

Keywords: design of experiment, drinking water purification, quality management, production process reliability

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Authors: Budoor Al Abid

Abstract:

Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only.

Keywords: machine learning, adaptive, fuzzy logic, data mining

Procedia PDF Downloads 169