Master of Science / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / The shortage of clean, usable water is a global problem (Millennium Ecosystem Assessment, 2005). As much as 80% of the world’s population has been reported to be in areas of high water security risk due to a convergence of factors, such as watershed disturbance, pollution, water resource development and biotic factors (Voeroesmarty et al., 2010). Water reuse technologies are a potential solution to this problem. However, implementation of treatment technologies for improved water reuse require rapid, effective monitoring techniques capable of insuring treatment quality. Fluorescence spectroscopy has shown potential for wastewater treatment monitoring due to its sensitivity, selectivity, and capacity to be employed in-situ. Online fluorescence data and full fluorescence excitation-emission matrices coupled with parallel factor analysis (PARAFAC) were employed to evaluate the treatment performance of a membrane bioreactor (MBR) at Fort Riley, KS. Specific research goals were to evaluate the effectiveness of fluorescence for monitoring wastewater treatment and to determine the contamination detection limit of fluorescence techniques in a non-potable reuse scenario. Study results revealed a two-stage startup period, the first 60 days indicated membrane cake layer formation and the first 90 days showed signs of oxic tank maturation. Fluorescence was found to be effective at monitoring carbon concentration trends throughout the MBR system, showed preferential removal of protein-like dissolved organic matter (DOM), and an increase in biodegradation of DOM as the oxic tank matured. A ratio of the humic-like fluorescent components to the protein-like fluorescent components correlated to TOC removal (R² = .845, p < .001). Also, fluorescence was able to detect contamination in the effluent at the 0.74-1.24 mg C/L level using two wavelength pairs, indicating that effective real-time monitoring for contamination can be accomplished with minimal instrumentation and post-processing of data.

China has continued the economic reform and open door policy over 30 years with many great achievements, such as the second largest GDP, the largest import and export economy with the largest infrastructural investment in the world. On the other hand, the conflicts and risks the firms especially for small and medium sized manufacturing enterprises (SMEs) have faced are extremely serious and more acute due to the economy growth and increasing social wealth, especially in Yangtze River Delta, in the general context of ever increasing cost such as labour, land and higher customers’ expectations such as the quality of product. These serious problems are challenges for the competitiveness of SMEs in Yangtze River Delta. This research aims to investigate and improve the competitiveness of SMEs by the main variables such as enterprise’s resources, product’s competitive issues and innovation activities related barriers. To achieve the aim, the research employed a mixed method of quantitative and qualitative approaches to build the competitiveness’s belief network model by Bayesian Belief Networks and analyze the factors of the most important variables by the SPSS software. Secondly, 36 entrepreneurs of small and medium sized manufacturing enterprises in Yangtze River Delta have been carefully selected to participate in the questionnaire survey and face to face interviews. All participants are entrepreneurs who have run enterprise for at least three years. Five kinds of resources, competitive issues and innovation have been identified as the variables of competitiveness. The findings of research are mainly related to the three aspects which are general view of variables; barriers to innovation activity and importance of variables for improving the competitiveness; and the factor analysis of quality management practices. Firstly, the general condition of financial resource is the worst in resource sector of SMEs; Dependability is the best performance in competitive issues of SMEs; Lack of finance is generally identified the biggest barrier to innovation of SMEs. Secondly, the Physical resource in resource sector and Quality in competitive issues sector are the most important variables for improving the competitiveness of SMEs after BBN assessment; Lack of technical experts is the most serious barrier when the SMEs are really focusing on the innovation according to the BBN assessments. Thirdly, the factor analyses have identified the key independent factors explaining the quality management practices in these SMEs. Finally, these findings can help the SMEs build variables’ impact tables based on the outputs from the conditional assessment of BBNs to make more efficient and effective decisions when they try to improve the enterprise competitiveness, with detailed recommendations. At the same time, the importance and factors of good quality management practices have also been argued to help the entrepreneurs improve the quality performance and their enterprise competitiveness.

The focus of the projects presented here was to develop possible solutions to three issues commonly encountered during chemometric analysis of comprehensive two-dimensional liquid chromatography diode array detector (LCxLC-DAD) data. The focus of the first project was to determine a means of performing background correction that removed two background ridges. The methods of simply subtracting out a mean blank sample, singular value decomposition based background correction (SVD-BC) and asymmetrically weighted least squares (AWLS) were compared. AWLS was found to be the only background correction technique to fully remove the ridges. However, AWLS was also found to attenuate the peak intensity by approximately 25% due to over fitting of the background at the lower wavelengths. The focus of the second project was the investigation of five common interpolation strategies for the reconstruction of the sampled first dimension peak. The interpolation strategy that best reproduced the original first dimension retention time was Gaussian fitting. This was expected given that the simulated data set was generated using a Gaussian model for the peak shape. An algorithm, semi-automated alignment method (SAAM), was then developed that allowed for each peak to be aligned independently of the other peaks in the data set. SAAM was validated using both simulated and experimental data. The simulated results indicated that SAAM produced percent recoveries close to 100%. SAAM was also compared to iterative key set factor analysis-alternating least squares (IKSFA-ALS) for the analysis of phenytoin in a waste water treatment plant effluent. SAAM produced a concentration of 26±3 ppb compared to 39±9 ppb from IKSFA-ALS. While these results are very different, the result produced by SAAM is still within the experimental error of the reference 2D-LC/MS/MS method, 42±19. Finally, SAAM was compared to two existing literature methods. A mixture of simulated and experimental data sets was used to measure the accuracy and precision of the results. SAAM was found to be impacted less by intra- and inter-sample retention time shifting then PARAFAC2. SAAM and shifted candecomp/PARAFAC were found to produce very similar results. However, SAAM was found to experience some difficulty producing accurate and precise results with some of the experimental data sets.

The purpose of this research was to develop and test an instrument to measure or quantify the construct of inner strength. Inner strength was synthesized into a theory based on twelve years of qualitative data from direct participant quotes, defined by Roux et al. (2002) as a central human resource that promotes well-being and healing. The inner strength questionnaire (ISQ) has undergone extensive psychometric evaluation which resulted in several versions. The third version had 37-items and was believed to have four subscales that mirrored the theoretical themes that emerged through the qualitative data. The sample was composed of 281 women with a variety of chronic health conditions including breast cancer, multiple sclerosis, diabetes, and heart disease. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were employed to assess the structure of the ISQ. Internal consistency was used to assess the reliability of each hypothesized subscale and the entire ISQ. Convergent and discriminant validity, were analyzed using the multitrait-multimethod by Campbell and Fiske (1959). For convergent validity, the entire instrument and each of the subscales was correlated with similar theoretical subscales of the Mastery of Stress Instrument (Younger, 1993). For discriminant validity, the Center for Epidemiological Studies in Depression (CESD) was correlated with the entire ISQ. Both the EFA and CFA revealed a four factor model. The four factors were labeled 1) mental, 2) connectedness, 3) knowing and searching, 4) physical. The reliability for all items on the ISQ was 0.91. The reliabilities for each of the subscales were 0.85 for mental, 0.96 for connectedness, 0.85 for knowing and searching, and 0.83 for physical. The multitrait-multimethod matrix revealed correlation coefficients for the relationship between the ISQ and MSI to be 0.55 and a correlation coefficient of -0.19 between the ISQ and the CESD.A four factor model is supported by the statistical data. Through face-to-face interviewing and input from content expert reviewers, ten items were eliminated from the 37-item (version 3) instrument to form a new version of the instrument. The new instrument supports the metasynthesis generated by Roux (2002) except for the fifth theme, entitled "new normal", which was found to be a consequence of having inner strength, not a component of inner strength.

Manufacturing organisations have placed significant attention to the potential of industrial human-robot collaboration (HRC) as a means for enhancing productivity and product quality. This concept has predominantly been seen from an engineering and safety aspect, while the human related issues tend to be disregarded. As the key human factors relevant to industrial HRC have not yet been fully investigated, the research presented in this thesis sought to develop a human factors tool to enable the successful implementation of industrial HRC. First, a theoretical framework was developed which collected the key organisational and individual level human factors by reviewing comparable contexts to HRC. The human factors at each level were investigated separately. To identify whether the organisational human factors outlined in the theoretical framework were enablers or barriers, an industrial exploratory case study was conducted where traditional manual work was being automated. The implications provided an initial roadmap of the key organisational human factors that need to be considered as well as the critical inter-relations between them. From the list of individual level human factors identified in the theoretical framework, the focus was given on exploring the development of trust between human workers and industrial robots. A psychometric scale that measures trust specifically in industrial HRC was developed. The scale offers the opportunity to system designers to identify the key system aspects that can be manipulated to optimise trust in industrial HRC. Finally, the results were gathered together to address the overall aim of the research. A human factors guidance tool was developed which provides practitioners propositions to enable successful implementation of industrial HRC.

Title: Analysis of customer satisfaction in Aquapark Příbram Objectives: The aim of this thesis is to conduct content and statistical analysis of data collected directly from customers of Aquapark Příbram. Customers of the public were a research sample with the highest priority. As secondary sample were established trainers of the sports clubs using the Aquapark Příbram. Methods: Data collected from the primary sample were gained via CERM-CSQ based questionnaire Data collected from the secondary sample were obtained by structured interview. This structure was based on our questionnaire. Factor and correlation analysis were used to process quantitative data. Results: The content analysis showed the biggest drawbacks of service quality. According to the statistical analysis some service quality attributes have bigger influence on customer satisfaction than the others. Above mentioned drawbacks belong to those more influential attributes. On the basis of the results obtained certain suggestions were made to increase customer satisfaction. Key words: customer satisfaction, service quality, Aquapark Příbram, model CSQ, factor analysis

In spite of the wealth of research in IS security, there is very little understanding of what actually makes an IS security program successful within an organization. Success has been treated generally as a separate entity from IS security altogether; a great deal of research has been conducted on the "means to the end", while limited research has been focused on truly understanding what the end actually is. The problem compelling this research is that previous studies within the IS security domain do not adequately consider what factors contribute towards IS security success within the organizational context, and how the factors interact. This study built upon Shannon and Weaver (1949) and Mason (1978) to develop a model for predicting IS security success within an organization. A considerable body of information systems security literature was organized based on their findings. Core dimensions of information system security success were identified and operationalized within a model for predicting success with IS security initiatives. The model was empirically validated in a three-phase approach using survey methodology. First, the survey was tested for validity and reliability using an expert panel and pilot study. Next, the survey was administered to a sample, with the results analyzed using Confirmatory Factor Analysis and Structural Equation Modeling techniques. Initial analysis of the measurement model generated through Confirmatory Factor Analysis showed mixed fit. Factor loadings and average variance extracted calculations resulted in the selection of low performing items for removal; after revision, the revised measurement model showed improved fit for all measures. Structural Equation Modeling analysis was conducted on three structural models with varying levels of mediation. Based on the analysis of fit and comparison indices, the model depicting partial mediation was determined to be the best variation of the IS security success model. This study is the first known instance of an empirically tested IS security success model and should provide many avenues for future study, as well as providing practitioners a fundamental roadmap for success within their organizational IS security programs.

In der Metakontrastmaskierung wird die Sichtbarkeit des ersten Stimulus (Target) durch das Auftreten eines zweiten Stimulus (Maske) reduziert. Zwei Maskierungsfunktionen (MF) treten hauptsächlich auf: Typ A, wenn die Sichtbarkeit mit ansteigender SOA zumimmt, und Typ-B, wenn die Sichtbarkeit in kurzer und langer SOA hoch ist und auf ein Minimum in mittlerer SOA abfällt. In fünf Studien wurde systematisch untersucht welchen Einfluss experimentelle Parameter auf das Auftreten der MF haben. Je länger die Maske im Verhältnis zum Target präsentiert wird, desto weiter verschiebt sich das Minimum der MF hin zu kürzerer SOA und desto mehr ähnelt sie einer Typ-A-MF (monoton ansteigend). Die Maskierung ist in kleinen Stimuli stärker als in großen Stimuli, sowohl im Zentrum als auch in der Peripherie. Bei beiden Stimulusgrößen findet bei der kürzesten SOA die stärkste Maskierung statt. MF unterscheiden sich nicht, wenn sich die Vorhersagbarkeit der Präsentationsorte der Stimuli unterscheidet. Scheinbewegungen werden in langen SOAs eher wahrgenommen als in kurzen SOAs. Darüber hinaus wurden inter-individuelle Unterschiede gefunden, die Einblicke in die Mechanismen erlauben, die in der Metakontrastmaskierung beteiligt sind. Einige Versuchspersonen zeigen eine Typ-A-MF und berichten Scheinbewegungen in der Abfolge der Stimuli wahrzunehmen, hauptsächlich in langen SOAs. Andere zeigen eine Typ-B-MF und berichten negative Nachbilder in Form des Tagets im Inneren der Maske wahrzunehmen, hauptsächlich in kurzen SOAs. Typ-A- und Typ-B-Versuchspersonen unterscheiden sich in ihrer Top-Down-Verarbeitung der Stimuli, nicht aber in ihrer Bottom-Up-Verarbeitung. Zwei Prozesse stellten sich heraus, die in der Verarbeitung der Metakontraststimuli beteiligt sein könnten. Nach der Integrations-Segregations-Theorie steht die getrennte (segregierte) Wahrnehmung zweier Stimuli, die Scheinbewegungen ermöglicht, mit dem Segregationsprozess in Zusammenhang. Dagegen steht die gleichzeitige (integrierte) Wahrnehmung zweier nacheinander folgender Stimuli mit dem Integrationsprozess in Zusammenhang. Da Prozess 1 stark mit einer Typ-A-MF verknüpft ist, einhergehend mit der Wahrnehmung von Scheinbewegungen, könnte dieser Prozess dem Segregationsprozess entsprechen. Da Prozess 2 stark mit einer Typ-B-MF verknüpft ist, einhergehend mit der Wahrnehmung negativer Nachbilder, könnte dieser Prozess dem Integrationsprozess entsprechen. Es wird angenommen, dass diese beiden Prozesse an der bewussten Wahrnehmung des Targets in der Metakontrastmaskierung beteiligt sind.

Thesis advisor: Rudolph Hon / This study characterizes the current state of water quality of surface streams and rivers in the eastern New England region. A set of water quality data for nine rivers, part of the USGS National Water-Quality Assessment (NAWQA) Program was statistically evaluated to identify natural and anthropogenic persistent influential factors on water quality in surface waters. Binary analysis and multivariate analysis, mainly Principal Component Analysis (PCA) and Factor Analysis (FA) were applied to determine the least number of independent relationships among multiple chemical components in the data set. Statistical results show that in eight of the nine rivers included in this study, four principal components can explain about 80% of the total variance of the original data. The most significant contributing factors can be identified with: (1) chemical weathering; (2) road salt applications; (3) nutrient cycling; and (4) agricultural/waste water. / Thesis (MS) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Geology and Geophysics.

Structural equation models combine factor analysis models and multivariate regression models to estimate associations between observed variables and unobserved variables. The main achievement of this Capstone Project is the understanding of structural equation models and application of the models to real-world data. In this report, we reviewed structural equation models and several prerequisite topics. We performed a simulation study to compare maximum likelihood structural equation model estimation versus two-stage sequential estimation using multiple linear regression and maximum likelihood factor analysis. The simulation study confirmed that confidence intervals produced by structural equation models are valid and those obtained by two-stage sequential estimation are largely inaccurate. We applied structural equation models to an educational data comparing the efficacy of teaching conditions on learning scientific inquiry skills among 177 middle school students in Fitchburg, Massachusetts using a computer simulated science microworld. Application of structural equation models to the educational data showed that there were no significant differences in test score gains between three learning conditions, while controlling for latent factors measured by survey responses.