Value co-creation in bio-based chemicals value network

The purpose of this Master’s thesis is to study value co-creation in emerging value network. The main objective is to examine how value is co-created in bio-based chemicals value network. The study provides insights to different actors’ perceived value in the value network and enlightens their motivations to commit to the collaborative partnerships with other actors. Empirical study shows that value co-creation is creation of mutual value for both parties of the relationship by combining their non-competing resources to achieve a common goal. Value co-creation happens in interactions, and trust, commitment and information sharing are essential prerequisites for value co-creation. Value co-creation is not only common value creation, but it is also value that emerges for each actor because of the co-operation with the other actor. Even though the case companies define value mainly in economic terms, the other value elements like value of the partnership, knowledge transfer and innovation are more important for value co-creation.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.

Pressure dependence of spin fluctuations in metallic glasses Zrâ â Niâ â ,Zrâ â (Nix/Feâ â â -x)â â and /Zrx/feâ â â -x

The superconducting transition temperature Tc of metallic glasses ZrxFelOO-x (x=80, 75), Zr75(NixFelOO-x)25 (x=75, 50, 25), and CU2SZr75 were measured under quasi-hydrostatic pressure up to 8 OPa (80kbar). The volume (pressure) dependence of the electron-phonon coupling parameters Aep for CU25Zr75 was calculated using the McMillan equatio11. Using this volume dependence of Aep and the modified McMillan equation which incorporates spin-fluctuations, the volume dependence of the spin fluctuation parameter, Asf, was determined in Zr75Ni25, ZrxFelOO-x , a11d Zr75(NixFelOO-x)25. It was found that with increasing pressure, spinfluctuations are suppressed at a faster rate in ZrxFe lOO-x and Zr75(NixFelOO-x)25, as Fe concentration is increased. The rate of suppression of spin-fluctuations with pressure was also found to be higher in Fe-Zr glasses than in Ni-Zr glasses of similar composition.

The diffusion of Co⠶溩 Zrâ â Tiâ â alloy /|nS. Kumar. -- 260 St Catharines [Ont.] : Dept. of Physics, Brock University,

The diffusion of Co60 in the body centered cubic beta phase of a ZrSOTi SO alloy has been studied at 900°, 1200°, and 1440°C. The results confirm earlier unpublished data obtained by Kidson17 • The temperature dependence of the diffusion coefficient is unusual and suggests that at least two and possibly three mechanisms may be operative Annealing of the specimen in the high B.C.C. region prior to the deposition of the tracer results in a large reduction in the diffusion coefficient. The possible significance of this effect is discussed in terms of rapid transport along dislocation network.

Pressure dependence of superconducting properties of MgB2 /

We prepared samples of MgB2 and ran sets of experiments aimed for investigation of superconducting properties under pressure. We found the value of pressure derivative of the transition temperature -1.2 ± 0.05 K/GPa. Then, using McMillan formula, we found that the main contribution to the change of the transition temperature under the pressure is due to the change in phonon frequencies. Griineisen parameter was calculated to be 7g = 2.4. Our results suggest that MgB2 is a conventional superconductor.

Pressure dependence of superconductivity in amorphous Ni x Zr 100-x alloys

Pressure variations of the superconducting transition temperature Ic of a series of amorphous NixZr 1 OO-x alloys have been studied under quasmydrostatic pressures upto 8 G Pa. For amorphous samples having Ni-concentration less than 40%, i)Tc/dP is positive in sign and it decreases non linearly with increase in I. whereasdTcldP is negative in sign for Ni concentration of 45%. Comparison with the Hall coefficient (I) and the thermoelectric power (2) results for the same amorphous alloys leads to the conclusion that s-d hybridization nature of the d-band (Nil plays a central role in the sign reversal behaviour. Application of pressures greater than 2 G Pa to Ni20ZrgO led to the formation of a new phase, w-Zr. which retains its form after the pressure is released.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Riffing on a Theme: Faculty Experiences With Service-Learning in a Food Security Research Network in Ontario

While service-learning is often said to be beneficial for all those involved—students, community members, higher education institutions, and faculty members—there are relatively few studies of the attraction to, and effect of, service-learning on faculty members. Existing studies have tended to use a survey design, and to be based in the United States. There is a lack of information on faculty experiences with service-learning in Ontario or Canada. This qualitative case study of faculty experiences with service-learning was framed through an Appreciative Inquiry social constructionist approach. The data were drawn from interviews with 18 faculty members who belong to a Food Security Research Network (FSRN) at a university in northern Ontario, reports submitted by the network, and personal observation of a selection of network-related events. This dissertation study revealed how involvement with service-learning created opportunities for faculty learning and growth. The focus on food security and a commitment to the sustainability of local food production was found to be an ongoing attraction to service-learning and a means to engage in and integrate research and teaching on matters of personal and professional importance to these faculty members. The dissertation concludes with a discussion of the FSRN’s model and the perceived value of a themed, transdisciplinary approach to service-learning. This study highlights promising practices for involving faculty in service-learning and, in keeping with an Appreciative Inquiry approach, depicts a view of faculty work at its best.

Using Network Analysis to Understand and Advance Falls Prevention Services and Programs

The purpose of this study was to understand referral linkages that exist among falls prevention agencies in a southern Ontario region using network analysis theory. This was a single case study which included fifteen individual interviews. The data was analyzed through the constant comparative approach. Ten themes emerged and are classified into internal and external factors. Themes associated with internal factors are: 1) health professionals initiating services; 2) communication strategies; 3) formal partnerships; 4) trust; 5) program awareness; and 6) referral policies. Themes associated with external factors are: 1) client characteristics; 2) primary and community care collaboration; 3) networking; and 4) funding. Recommendations to improve the referral pathway are: 1) electronic database; 2) electronic referral forms; 3) educating office staff; and 4) education days. This study outlined the benefit of using network analysis to understand referral pathways and the importance of implementing strategies that will improve falls prevention referral pathways.