Invenio@HGF: Collaborative repository infrastructure

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Collaborative Customer Relationship Management in Business-to-Business Firm's Strategy

The objective of this thesis is to study the presence of collaborative customer relationship management in a firm’s strategy. In addition the thesis explains specific implementations of collaborative CRM, and CRM in general, by each case company. The sample consists of five Finnish business-to-business companies through applying multiple-case study method. The data is collected through face-to-face interviews with employees knowledgeable of the case company’s CRM processes. The qualitative data is analyzed through coding and shows that two out of five case companies have adopted and are using collaborative CRM in their strategy and operations. These case companies see collaborative CRM as an important driver for the company, through customer focus and market orientation. The rest of the case companies are either in the process of moving towards collaborative CRM or have given little consideration to it. The results show that collaborative CRM is in use, and that each company modifies it to meet their exact aspirations. The major challenge in the process is to fully grasp the importance of a shared vision that can translate into collaborative efforts in CRM and business strategy.

Kuali OLE: A Collaborative ILS Partnership

Presentation of Robert H. McDonald at the Library Network Days, October 22, 2014 in Helsinki. – Esitys Kirjastoverkkopäivillä 22.10.2014 Helsingissä

Metabolism of diglycine and triglycine by non-filtering kidneys

We have studied the metabolism of diglycine and triglycine in the isolated non-filtering rat kidney. Kidneys from adult male Wistar Kyoto rats weighing 250-350 g were perfused with Krebs-Henseleit solution containing either 1 mM diglycine or triglycine. The analysis of the peptide residues and their components was performed using an amino acid microanalyzer utilizing ion exchange chromatography. Diglycine was degraded to a final concentration of 0.09 mM after 120 min (91%); this degradation occurred predominantly during the first hour, with a 56% reduction of the initial concentration. The metabolism of triglycine occurred similarly, with a final concentration of 0.18 mM (82%); during the first hour there was a 67% reduction of the initial concentration of the tripeptide. Both peptides produced glycine in increasing concentrations, but there was a slightly lower recovery of glycine, suggesting its utilization by the kidney as fuel. The hydrolysis of triglycine also produced diglycine, which was also hydrolyzed to glycine. The results of the present study show the existence of functional endothelial or contraluminal membrane peptidases which may be important during parenteral nutrition.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Clinical assessment of the effect of digital filtering on the detection of ventricular late potentials

Ventricular late potentials are low-amplitude signals originating from damaged myocardium and detected on the body surface by ECG filtering and averaging. Digital filters present in commercial equipment may interfere with the ability of arrhythmia stratification. We compared 40-Hz BiSpec (BI) and classical 40- to 250-Hz band-pass Butterworth bidirectional (BD) filters in terms of impact on time domain variables and diagnostic properties. In a transverse retrospective age-adjusted case-control study, 221 subjects with sinus rhythm without bundle branch block were divided into three groups after signal-averaged ECG acquisition: GI (N = 40), clinically normal controls, GII (N = 158), subjects with coronary heart disease without sustained monomorphic ventricular tachycardia (SMVT), and GIII (N = 23), subjects with heart disease and documented SMVT. Conventional variables analyzed from vector magnitude data after averaging to 0.3 µV final noise were obtained by application of each filter to the averaged signal, and evaluated in pairs by numerical comparison and by diagnostic agreement assessment, using conventional and optimized thresholds of normality. Significant differences were found between BI and BD variables in all groups, with diagnostic results showing significant disagreement between both filters [kappa value of 0.61 (P<0.05) for GII and 0.31 for GIII (P = NS)]. Sensitivity for SMVT was lower with BI than with BD (65.2 vs 91.3%, respectively, P<0.05). Filters provided significantly different numerical and diagnostic results and the BI filter showed only limited clinical application to risk stratification of ventricular arrhythmia.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.

Physicochemical and sensory changes in aged sugarcane spirit submitted to filtering with activated carbon filter

Sugarcane spirit is a drink considered as a national symbol of Brazil. It is produced by large producers and by about 30 thousand small and medium home-distilling producers dispersed throughout the country. The copper originating from the home-distillers can become a serious problem since at high concentrations in beverages it may cause serious human health problems. Therefore, the objective of this study was to investigate the influence of the activated carbon used in commercial filters on the physicochemical and sensory characteristics of aged sugarcane spirit. Analyses of copper, dry extract, alcoholic degree, higher alcohols, volatile acids, aldehydes, esters, furfural, and methanol were performed. The sensory evaluation was performed by seven selected trained judges, who analyzed the yellow color, woody aroma and flavor, and intensity of alcoholic aroma and flavor of the cane spirit before and after the filtration process. The sensory tests were carried out using a 9 cm non-structured intensity scale. A reduction was observed in all compounds analyzed physicochemically, except for the esters, which increased after filtration. This increase is probably due to the esterification of the alcohols and acids present. According to the sensory results obtained, a reduction was observed in the intensity of the yellow color, aroma, and wood flavor characteristics, the major characteristics of the aging process.

Variational Ensemble Kalman Filtering in Hydrology

The current thesis manuscript studies the suitability of a recent data assimilation method, the Variational Ensemble Kalman Filter (VEnKF), to real-life fluid dynamic problems in hydrology. VEnKF combines a variational formulation of the data assimilation problem based on minimizing an energy functional with an Ensemble Kalman filter approximation to the Hessian matrix that also serves as an approximation to the inverse of the error covariance matrix. One of the significant features of VEnKF is the very frequent re-sampling of the ensemble: resampling is done at every observation step. This unusual feature is further exacerbated by observation interpolation that is seen beneficial for numerical stability. In this case the ensemble is resampled every time step of the numerical model. VEnKF is implemented in several configurations to data from a real laboratory-scale dam break problem modelled with the shallow water equations. It is also tried in a two-layer Quasi- Geostrophic atmospheric flow problem. In both cases VEnKF proves to be an efficient and accurate data assimilation method that renders the analysis more realistic than the numerical model alone. It also proves to be robust against filter instability by its adaptive nature.

Rigid body dynamics and Kalman filtering

X-ray computed log tomography has always been applied for qualitative reconstructions. In most cases, a series of consecutive slices of the timber are scanned to estimate the 3D image reconstruction of the entire log. However, the unexpected movement of the timber under study influences the quality of image reconstruction since the position and orientation of some scanned slices can be incorrectly estimated. In addition, the reconstruction time remains a significant challenge for practical applications. The present study investigates the possibility to employ modern physics engines for the problem of estimating the position of a moving rigid body and its scanned slices which are subject to X-ray computed tomography. The current work includes implementations of the extended Kalman filter and an algebraic reconstruction method for fan-bean computer tomography. In addition, modern techniques such as NVidia PhysX and CUDA are used in current study. As the result, it is numerically shown that it is possible to apply the extended Kalman filter together with a real-time physics engine, known as PhysX, in order to determine the position of a moving object. It is shown that the position of the rigid body can be determined based only on reconstructions of its slices. However, the simulation of the body movement sometimes is subject to an error during Kalman filter employment as PhysX is not always able to continue simulating the movement properly because of incorrect state estimation.

Designing gamification for collaborative learning in group work

Computer Supported Collaborative Learning (CSCL) is a teaching and learning approach which is widely adopted. However there are still some problems can be found when CSCL takes place. Studies show that using game-like mechanics can increase motivation, engagement, as well as modelling behaviors of players. Gamification is a rapid growing trend by applying the same mechanics. It refers to use game design elements in non-game contexts. This thesis is about combining gamification concept and computer supported collaborative learning together in software engineering education field. And finally a gamified prototype system is designed.

The functional fit between collaborative software and work systems:Qualification of work system needs to software functionality

The study develops an approach that tries to validate software functionality to work systems needs in SMEs. The formulated approach is constructed by using a SAAS based software i.e., work collaboration service (WCS), and SMEs as the elements of study. Where the WCS’s functionality is qualified to the collaboration needs that exist in operational and project work within SMEs. For this research constructivist approach and case study method is selected because the nature of the current study requires an in depth study of the work collaboration service as well as a detailed study of the work systems within different enterprises. Four different companies are selected in which fourteen interviews are conducted to gather data pertaining. The work systems method and framework are used as a central part of the approach to collect, analyze and interpret the enterprises work systems model and the underlying collaboration needs on operational and project work. On the other hand, the functional model of the WCS and its functionality is determined from functional model analysis, software testing, documentation and meetings with the service vendor. The enterprise work system model and the WCS model are compared to reveal how work progression differs between the two and make visible unaddressed stages of work progression. The WCS functionality is compared to work systems collaboration needs to ascertain if the service will suffice the needs of the project and operational work under study. The unaddressed needs provide opportunities to improve the functionality of the service for better conformity to the needs of enterprise and work. The results revealed that the functional models actually differed in how operational and project work progressed within the stages. WCS shared similar stages of work progression apart from the stages of identification and acceptance, and progress and completion stages were only partially addressed. Conclusion is that the identified unaddressed needs such as, single point of reference, SLA and OLA inclusion etc., should be implemented or improved within the WCS at appropriate stages of work to gain better compliance of the service to the needs of the enterprise an work itself. The developed approach can hence be used to carry out similar analysis for the conformance of pre-built software functionality to work system needs with SMEs.