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Structural Retrofitting of a Reinforced Concrete Chimney

In this study, structural strengthening on Turkey's oldest iron and steel factory, Iskenderun Iron and Steel Co. Ltd. (ISDEMIR)'s No:1 Blast Furnace Aspiration Concrete Chimneys were examined. These reinforced concrete chimneys were built according to the Russian standard in 1975. For reinforced concrete chimney concrete and reinforcing rebar, Russian Norm standards have been translated into norms that used in Turkey. For years, industrial dynamic effects, occurring in the region through strong winds and heavy industrial dust circulation in the chimney consisted of visible damage to the chimney. For the determination of the current status of the chimney infrared detection devices were used for the location, identification of reinforcement steel bars. Tensile tests were used on reinforcing bars to determine the material properties of steel rebar. Compressive tests were performed on concrete core samples. After a detailed investigation a concrete jacketing method is utilized to strengthened the 59.2 m long chimney. This study will summarize details of the strengthening on the chimney.
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Placement Analysis using Data Mining

Education data mining is one of the growing fields of the present time as it grows many issues to improve system comes in the notice one of them is improvement of the placement. Placement is a very important issue for any educational organization. Every organization wants to improve its placement. Success of any educational institute is measured by the placed student of the organization. This paper actually deals with the application of neural network to the educational data to improve placement. In today’s world all organization faces one of the big problems is recruit right candidate for the suitable position, Organization ready to invest a huge amount to recruitment process but till now they failed to recruit. In this paper, we apply the data mining techniques for placement prediction. To predict the performance of a student’s is the great concern for the organization, as they seek knowledgeable, talented and qualified professionals to need to fill up their positions. According to the survey, the corporate companies spend a sum of 1800crores for choosing candidates to fill up their vacancies. The Majority of the companies recruiting the candidates via on-campus recruitment and to fill up them positions. Our method is very useful for corporate companies, consultancies. This method is the best way to get the right candidate at the right time in the corporate world. The industry gets the best talent candidates from different institutes/universities, and the students also get a chance to kick start their career with some of the best organization. But the students facing some difficulties in getting placements. To overcome the problem we apply the Improved Decision Tree classification algorithms on these data, we have predicted which students placed in Recruitment Drives. Corporate companies need only knowledgeable and skilled persons for the vacant position. To find that particularly skilled person there's a question that how can the companies identify them. In order to overcome this problem in this paper, we provide a complete solution to the recruitment process. Actual challenges appear when they will develop real-world software. Training develops confidence in whatever School, Colleges, Universities will train. But all corporate expect skilled, confidence with active persons. We are giving to find the right candidate for the right job in this development. After recruiting employees corporate feel much confident about their development. So as much as possible, clear them confusions and get new ideas about their project training and become confident about their work.
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Companies are progressively investing in practices aimed at improving the quality of management, with the main purpose of enabling them to operate competitively in the present market. For this, it is necessary to document the activities and information of the existing business processes in the organization, aiming at reducing time and cost in the elaboration of the particular model. In this context, the objective of this work is to develop a reference model of the Production Scheduling (PS) processes, an important module of production planning and control (PPC). The research methodology used in this work was divided into the following stages: study of PS and business process modeling, definition of reference model processes, choice of methodology and process modeling tool, development of reference model and prototype of the software and, finally, analysis of results. The modeling notation used was the BPMN, since it is considered a standard language in the field of process modeling. The prototype was developed through the Delphi interface in order to apply the model to support the implementation of business management programs. As results, from a formal documentation, the model proved to be a useful mechanism in the understanding of the processes raised and appropriate in the support to the implantation of production management tools.
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