Tip 2 diyabetes mellitus tanılı 18-64 yaş arası yetişkinlerde beslenme okuryazarlığı ve öz etkililiğin diyabet ve öz bakım aktivitelerine etkisi

xiii, 101 sayfa29 cm. 1 CDÖzet:TİP 2 DİYABETES MELLİTUS TANILI 18-64 YAŞ ARASI YETİŞKİNLERDE BESLENME OKURYAZARLIĞI VE ÖZ ETKİLİLİĞİN DİYABET ÖZ BAKIM AKTİVİTELERİNE ETKİSİ Giriş ve Amaç: Tanımlayıcı ve analitik tipteki bu araştırmanın amacı bir aile sağlığı merkezine kayıtlı 18-64 yaş arası Tip II Diyabetes Mellitus tanılı yetişkinlerin beslenme okuryazarlığı ve öz etkililiğin diyabet öz bakım aktivitelerine etkisinin incelenmesidir. Gereç-Yöntem: Araştırma İzmir iline bağlı merkez Karabağlar ilçesi 13 No'lu Bahçelievler Aile Sağlığı Merkezinde 200 Tip 2 Diyabetli Birey ile yürütülmüştür. Veri toplamada sosyodemografik ve sağlık özelliklerini içeren Bilgi Formu, Beslenme Okuryazarlığı Ölçeği, Diyabet Öz Bakım Ölçeği (DÖBÖ) ve Diyabet Öz Etkililik Ölçeği (DÖÖ) kullanılmıştır. Veriler Aralık 2018-Mayıs 2019 tarihleri arasında toplanmıştır. Veriler SPSS 25.0 programında analiz edilmiştir. Bulgular: Araştırmaya katılanların 65'i kadın (n:130), yaş ortalaması 52,9±9,34'dür. Bireylerin beslenme okuryazarlığı 74,5'inde yeterli, 24,9'unda sınırda, 2'sinde yetersizdir. Lise ve üniversite mezunlarında beslenme okuryazarlığı yeterli, ilkokul/ ortaokul mezunlarında sınırlıdır (x221,44p0.000). Diyabete ilişkin eğitim almayanların Genel Beslenme Bilgisi düzeyleri yetersiz/sınırlıdır (X210,11p0,006). DÖBÖ ölçek puan ortalamaları 88,03±14,77, DÖÖ toplam ölçek puan ortalamaları 69,87±16,60'dır. DÖÖ ile DÖBÖ toplam ölçek puanları arasında pozitif yönde, güçlü, ileri (r0,73p<0.01)DÖÖ ile Yetişkinlerde Beslenme Okuryazarlığı Değerlendirme Aracı (YBOYDA) toplam ölçek puanları arasında pozitif yönde, zayıf, ileri düzeyde (r0,27p<0.01) bir ilişki olduğu belirlenmiştir. Sonuç: Araştırma sonuçlarına göre diyabetli bireylerin eğitimlerinde beslenme okuryazarlık düzeylerine göre verilecek eğitimlerin planlanması ve yürütülmesi bireylerin öz etkililiklerini ve öz bakım aktivitelerini artırma yönünde olumlu katkı sağlayacaktır.Summary:THE RELATIONSHIP AMONG NUTRITION LITERACY, SELF EFFICACY ANDSELF CARE ACTİVİTİES AS REGARDS ADULTS AGED BETWEEN 18-64 YEARS OLD DIAGNOSED WITH TYPE 2 DIABETES MELLITUS Introduction and Objective: This descriptive and analytical study was aimed at investigating the relationship between nutrition literacy, self efficacy and self care activities in 18 to 64 years old adults diagnosed with Type II Diabetes Mellitus in a Family Health Center in İzmir. Materials and Methods: The study was carried out in Bahçelievler Family Health Center (No. 13) in Karabağlar district of İzmir. The sample size of the study was 200 people. The study data were collected between December 2018 and May 2019 using the Personal Information Form questioning the socio-demographic and health characteristics of the participants, Adults Nutrition Literacy Assessment Scale (ANLAS), Diabetes Self-Care Scale (DSCS), and Diabetes Self Efficacy Scale (DSES). Datas were analyzed in SPSS 25.0 version. Results: The mean age of the participants was 52.9 ± 9.34years. Of them, 65 (n: 130) were female. While the nutrition literacy level was adequate in 74.5 of the participants, it was at borderline in24.9 and inadequate in 2. While the senior high school and university graduates had an adequate nutrition literacy level, primary / junior high school graduates had borderline nutrition literacy levels (x2 21.44p 0.000).General Nutritional Knowledge levels of those who did not receive training about diabetes were inadequate or at the borderline (X2 10.11p 0.006). The mean scores the participants obtained from the overall DSCS and DSES were 88.03 ± 14.77 and 69.87 ± 16.60 respectively. There was a positive, strong, significant relationship between the scores obtained from the DSCS and DSES (r 0.73p <0.01) and a positive, weak, significant relationship between the scores obtained from the DSES and (r 0.27p <0.01). Conclusion: The study results indicated that the planning of the content of the training to be given to individuals with diabetes according to their nutrition literacy levels would improve their self efficacy and self care activities

Bilinear modeling of batch processes. Part III: Parameter Stability

A paramount aspect in the development of a model for a monitoring system is the so-called parameter stability. This is inversely related to the uncertainty, i.e., the variance in the parameters estimates. Noise affects the performance of the monitoring system, reducing its fault detection capability. Low parameters uncertainty is desired to ensure a reduced amount of noise in the model. Nonetheless, there is no sound study on the parameter stability in batch multivariate statistical process control (BMSPC). The aim of this paper is to investigate the parameter stability associated to the most used synchronization and principal component analysis-based BMSPC methods. The synchronization methods included in this study are the following: indicator variable, dynamic time warping, relaxed greedy time warping, and time linear expanding/compressing-based. In addition, different arrangements of the three-way batch data into two-way matrices are considered, namely single-model, K-models, and hierarchicalmodel approaches. Results are discussed in connection with previous conclusions in the first two papers of the series.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Authors also acknowledge the anonymous reviewers for their comments to improve the article.González Martínez, JM.; Camacho Páez, J.; Ferrer, A. (2014). Bilinear modeling of batch processes. Part III: Parameter Stability. Journal of Chemometrics. 28(1):10-27. https://doi.org/10.1002/cem.2562S1027281Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22(5), 299-308. doi:10.1002/cem.1113Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22(10), 533-547. doi:10.1002/cem.1179González-Martínez J Vitale R de Noord O Ferrer A Does synchronization matter in bilinear batch process monitoring?García-Muñoz, S., Kourti, T., MacGregor, J. F., Mateos, A. G., & Murphy, G. (2003). Troubleshooting of an Industrial Batch Process Using Multivariate Methods. Industrial & Engineering Chemistry Research, 42(15), 3592-3601. doi:10.1021/ie0300023Zarzo, M., & Ferrer, A. (2004). Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(1), 15-27. doi:10.1016/j.chemolab.2003.11.009Wallace D Prosensus multivariate v12. 02 2010Louwerse, D. J., Tates, A. A., Smilde, A. K., Koot, G. L. M., & Berndt, H. (1999). PLS discriminant analysis with contribution plots to determine differences between parallel batch reactors in the process industry. Chemometrics and Intelligent Laboratory Systems, 46(2), 197-206. doi:10.1016/s0169-7439(98)00185-3Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Kaistha, N., & Moore, C. F. (2001). Extraction of Event Times in Batch Profiles for Time Synchronization and Quality Predictions. Industrial & Engineering Chemistry Research, 40(1), 252-260. doi:10.1021/ie990937cRamsay, J. O., & Silverman, B. W. (1997). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xKassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412González-Martínez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2860Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Gurden, S. P., Westerhuis, J. A., Bijlsma, S., & Smilde, A. K. (2000). Modelling of spectroscopic batch process data using grey models to incorporate external information. Journal of Chemometrics, 15(2), 101-121. doi:10.1002/1099-128x(200102)15:23.0.co;2-vKourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1999). Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13(3-4), 397-413. doi:10.1002/(sici)1099-128x(199905/08)13:3/43.0.co;2-iNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Wold, S., Kettaneh, N., Fridén, H., & Holmberg, A. (1998). Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 331-340. doi:10.1016/s0169-7439(98)00162-2Chen, J., & Liu, K.-C. (2002). On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 57(1), 63-75. doi:10.1016/s0009-2509(01)00366-9Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2005). Fault detection properties of global, local and time evolving models for batch process monitoring. Journal of Process Control, 15(7), 799-805. doi:10.1016/j.jprocont.2005.02.001Lennox, B., Montague, G. A., Hiden, H. G., Kornfeld, G., & Goulding, P. R. (2001). Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering, 74(2), 125-135. doi:10.1002/bit.1102Ündey, C., Ertunç, S., & Çınar, A. (2003). Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis. Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Camacho, J., & Picó, J. (2006). Multi-phase principal component analysis for batch processes modelling. Chemometrics and Intelligent Laboratory Systems, 81(2), 127-136. doi:10.1016/j.chemolab.2005.11.003Rännar, S., MacGregor, J. F., & Wold, S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 41(1), 73-81. doi:10.1016/s0169-7439(98)00024-0Camacho, J., Picó, J., & Ferrer, A. (2009). The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter? Analytica Chimica Acta, 642(1-2), 59-68. doi:10.1016/j.aca.2009.02.001Van Sprang, E. N. ., Ramaker, H.-J., Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2002). Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18), 3979-3991. doi:10.1016/s0009-2509(02)00338-xLei, F., Rotbøll, M., & Jørgensen, S. B. (2001). A biochemically structured model for Saccharomyces cerevisiae. Journal of Biotechnology, 88(3), 205-221. doi:10.1016/s0168-1656(01)00269-3Camacho J González-Martínez J Ferrer A Multi-phase (MP) toolbox 2013 http://mseg.webs.upv.es/Software.htmlCamacho, J., Picó, J., & Ferrer, A. (2008). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22(11-12), 632-643. doi:10.1002/cem.115

Multi-synchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms

Batch synchronization has been widely misunderstood as being only needed when variable trajectories have uneven length. Batch data are actually considered not synchronized when the key process events do not occur at the same point of process evolution, irrespective of whether the batch duration is the same for all batches or not. Additionally, a single synchronization procedure is usually applied to all batches without taking into account the nature of asynchronism of each batch, and the presence of abnormalities. This strategy may distort the original trajectories and decrease the signal-to-noise ratio, affecting the subsequent multivariate analyses. The approach proposed in this paper, named multisynchro, overcomes these pitfalls in scenarios of multiple asynchronisms. The different types of asynchronisms are effectively detected by using the warping information derived from synchronization. Each set of batch trajectories is synchronized by appropriate synchronization procedures, which are automatically selected based on the nature of asynchronisms present in data. The novel approach also includes a procedure that performs abnormality detection and batch synchronization in an iterative manner. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate the performance of the proposed approach in a context of multiple asynchronisms.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Part of this research work was carried out during an internship of the corresponding author at Shell Global Solutions International B.V. (Amsterdam, The Netherlands). The authors also thank the anonymous referees for their comments, which greatly helped to improve the text.González Martínez, JM.; De Noord, O.; Ferrer, A. (2014). Multi-synchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms. Journal of Chemometrics. 28(5):462-475. https://doi.org/10.1002/cem.2620S462475285Kourti, T. (2009). Multivariate Statistical Process Control and Process Control, Using Latent Variables. Comprehensive Chemometrics, 21-54. doi:10.1016/b978-044452701-1.00013-2Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Kourti, T. (2003). Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications. Annual Reviews in Control, 27(2), 131-139. doi:10.1016/j.arcontrol.2003.10.004Lakshminarayanan S Gudi R Shah S Monitoring batch processes using multivariate statistical tools: extensions and practical issues. 1996 241 246Zarzo, M., & Ferrer, A. (2004). 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Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Neogi, D., & Schlags, C. E. (1998). Multivariate Statistical Analysis of an Emulsion Batch Process. Industrial & Engineering Chemistry Research, 37(10), 3971-3979. doi:10.1021/ie980243oKourti, T., Lee, J., & Macgregor, J. F. (1996). Experiences with industrial applications of projection methods for multivariate statistical process control. Computers & Chemical Engineering, 20, S745-S750. doi:10.1016/0098-1354(96)00132-9Duchesne, C., Kourti, T., & MacGregor, J. F. (2002). Multivariate SPC for startups and grade transitions. AIChE Journal, 48(12), 2890-2901. doi:10.1002/aic.690481216Zhang, Y., Dudzic, M., & Vaculik, V. (2003). Integrated monitoring solution to start-up and run-time operations for continuous casting. 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Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xSrinivasan, R., & Qian, M. S. (2005). Off-line Temporal Signal Comparison Using Singular Points Augmented Time Warping. Industrial & Engineering Chemistry Research, 44(13), 4697-4716. doi:10.1021/ie049528tSrinivasan, R., & Sheng Qian, M. (2006). Online fault diagnosis and state identification during process transitions using dynamic locus analysis. Chemical Engineering Science, 61(18), 6109-6132. doi:10.1016/j.ces.2006.05.037Srinivasan, R., & Qian, M. (2007). Online Temporal Signal Comparison Using Singular Points Augmented Time Warping. Industrial & Engineering Chemistry Research, 46(13), 4531-4548. doi:10.1021/ie060111sChen, J., & Liu, J. (2000). Post analysis on different operating time processes using orthonormal function approximation and multiway principal component analysis. Journal of Process Control, 10(5), 411-418. doi:10.1016/s0959-1524(00)00016-0Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. doi:10.1109/tassp.1978.1163055Nielsen, N.-P. V., Carstensen, J. M., & Smedsgaard, J. (1998). Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography A, 805(1-2), 17-35. doi:10.1016/s0021-9673(98)00021-1Pravdova, V., Walczak, B., & Massart, D. L. (2002). A comparison of two algorithms for warping of analytical signals. Analytica Chimica Acta, 456(1), 77-92. doi:10.1016/s0003-2670(02)00008-9Tomasi, G., van den Berg, F., & Andersson, C. (2004). Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemometrics, 18(5), 231-241. doi:10.1002/cem.859Kassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412Gollmer, K., & Posten, C. (1996). Supervision of bioprocesses using a dynamic time warping algorithm. Control Engineering Practice, 4(9), 1287-1295. doi:10.1016/0967-0661(96)00136-0Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2003). Dynamic time warping of spectroscopic BATCH data. Analytica Chimica Acta, 498(1-2), 133-153. doi:10.1016/j.aca.2003.08.045Fransson, M., & Folestad, S. (2006). Real-time alignment of batch process data using COW for on-line process monitoring. Chemometrics and Intelligent Laboratory Systems, 84(1-2), 56-61. doi:10.1016/j.chemolab.2006.04.020González-Martínez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2869González-Martínez, J. M., Westerhuis, J. A., & Ferrer, A. (2013). Using warping information for batch process monitoring and fault classification. Chemometrics and Intelligent Laboratory Systems, 127, 210-217. doi:10.1016/j.chemolab.2013.07.003Kourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778González-Martínez, J. M., Camacho, J., & Ferrer, A. (2013). Bilinear modeling of batch processes. Part III: parameter stability. Journal of Chemometrics, 28(1), 10-27. doi:10.1002/cem.2562Camacho, J., & Ferrer, A. (2014). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects. Chemometrics and Intelligent Laboratory Systems, 131, 37-50. doi:10.1016/j.chemolab.2013.12.003Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Lei, F., Rotbøll, M., & Jørgensen, S. B. (2001). A biochemically structured model for Saccharomyces cerevisiae. Journal of Biotechnology, 88(3), 205-221. doi:10.1016/s0168-1656(01)00269-3Camacho J González-Martínez JM Ferrer A Multi-phase (MP) toolbox 2013 http://mseg.webs.upv.es/Software.htmlUMETRICS SIMCA 13.0.3 Umea, Sweden 2013 [email protected] www.umetrics.comGonzález-Martínez, J. M., Vitale, R., de Noord, O. E., & Ferrer, A. (2014). Effect of Synchronization on Bilinear Batch Process Modeling. Industrial & Engineering Chemistry Research, 53(11), 4339-4351. doi:10.1021/ie402052

Using warping information for batch process monitoring and fault classification

This paper discusses how to use the warping information obtained after batch synchronization for process monitoring and fault classification. The warping information can be used for i) building unsupervised control charts or ii) fault classification when a rich faulty batches database is available. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate the proposal.This research work was supported by the Spanish government (Ministry of Science and Innovation, MICINN) under project DPI2011-28112-C04-02. We gratefully acknowledge Associate Professor Jose Camacho for providing the simulation scheme of the fermentation process of Saccharomyces cerevisiae cultivation.Gonzalez-Martinez, J.; Westerhuis, J.; Ferrer Riquelme, AJ. (2013). Using warping information for batch process monitoring and fault classification. Chemometrics and Intelligent Laboratory Systems. 127:210-217. https://doi.org/10.1016/j.chemolab.2013.07.003S21021712

Siyasi iktidarların kazanılması veya kaybedilmesinde propagandanın rolü

Bu tezin, veri tabanı üzerinden yayınlanma izni bulunmamaktadır. Yayınlanma izni olmayan tezlerin basılı kopyalarına Üniversite kütüphaneniz aracılığıyla (TÜBESS üzerinden) erişebilirsiniz.ABSTRACT The project, "The role of Propaganda In Gaining or Loosing the Political Power" has been prepared regarding to increasing importance of propaganda in todays world. In the project primarly; the concepts of power, political power and propaganda has been explained. Historical development of propaganda, the relations of public opinion and propaganda, political advertising and propaganda has been studied. In the explanation of the use of mass media in propaganda, the effects of mass media on social structure and popüler culture has been also studied. In the same chapter, the effects of mass media on political behavior, political socialization and political culture has been explained. At the end of the chapter, the relations of these concepts with political propaganda has been discussed. The types of propaganda, according to the characteristics of political systems has been explained. Then the types of propaganda in democratic and non-democratic systems has been studied by models. The success and the characteristics of propagandic activities of the Welfare Party, which has been the first party in December 24 1995 elections in Turkey has been explained. The first three chapters of the project has been prepared by theorical information. In the last chapter the project has been questioned by an Area Research which has been applied to 300 voters, from different social and economical status in Istanbul between January 23-30 1998.ABSTRAKT Günümüzde siyasal propagandanın artan önemi dikkate alınarak 'Siyasi iktidarların Kazanılması veya Kaybedilmesinde Propagandanın Rolü" konulu çalışma hazırlanmıştır. Çalışmada öncelikle iktidar, siyasi iktidar ve propaganda kavramı üzerinde durulmuştur. Propagandamının tarihsel gelişimi, kamuoyu ve propaganda ve siyasal reklamcılık ilişkileri ele alınmıştır. Propaganda faaliyetle¬ rinde kitle iletişim araçlarından yararlanma konusu irdelenirken, kitle iletişim araçlarının toplumsal yapı üzerindeki etkileri, popüler kültürün oluşumu incelenmiştir. Daha sonra kitle iletişim araçlarının siyasal davranış üzerindeki etkileri, siyasal kültürün oluşumu ve siyasal toplumsallaşma sürecinde kitle iletişim araçlarının etkileri ayrı ayrı ele alınmıştır. Bunların siyasal propaganda ile ilişkileri tartışılmıştır. Siyasal sistemlerin özelliğine göre propaganda biçimleri incelenmiş, demokratik ve demokratik olmayan sistemlerde propaganda biçimleri örnekleriyle ele alınmıştır. 24 Aralık 1995 tarihli Genel Seçiminde birinci parti olan Refah Partisi'nin başarısında yürüttüğü propaganda faaliyetinin önemi ve bu faaliyetlerin özellikleri incelenmiştir. ilk üç bölümde kuramsal bilgilerin aktarıldığı çalışma, 23-30 Ocak tarihleri arasında istanbul il sınırlan içinde yaşayan, seçme yeterliliğine sahip farklı sosyo-ekonomik düzeylerde 300 seçmenin katıldığı alan araştırmasıyla sulanmıştı

Troubleshooting an Industrial Batch Process for the Manufacturing of Specialty Chemicals using Data Analytics

The troubleshooting of an industrial, multi-unit, multi-phase batch process for the manufacturing of specialty chemicals is considered in this study. The investigated problem is inconsistency in the final product quality, leading to the need of applying \u201ccorrections\u201d to some batches with consequent significant increase of the processing time. Product quality information is scarce and available only for the last unit in the process flow diagram. It is shown that, by coupling the use of multivariate statistical methods to engineering understanding, one can step back in the process flow diagram to identify the unit wherein the problem originates, and to single-out the root-cause of the fault. For the process under investigation, fault isolation led to reduction of the cycle times and increase of productivity