Many industries are transitioning to a digitalized world these days due to new opportunities and applications that come with it. This process has been accelerated in various ways due to the Covid-19 pandemic, to the point where many organizations have been able to digitalize some aspects of their daily tasks in a short period of time, ranging from using new technology and equipment like AI to utilizing social media and web-based platforms for forming virtual communities. We, as master's students with an engineering background, became interested in this topic and began reviewing literature to learn more about it. We noticed that the impact of digitalization on leadership in the healthcare industry has not been adequately addressed in previous academic work. We approached this topic as social constructionists with a relativistic perspective, and attempted to conduct a qualitative study with in-depth interviews with knowledgeable managers and employees in three different countries with different organizational cultures. This allowed us to examine how this process would affect both hierarchical and flat organizational management systems. Next, we went through open coding, trend identification, and templating. Following the theoretical framework and comparing the findings from different sample groups led to an interesting conclusion: Digitalization, as expected and as a support to our theoretical framework, helps organizations be more productive and deliver higher service quality, training and academic in this way helps all members, especially managers, do their jobs better, and there was no difference between the three different group samples. The final and most intriguing finding is that in the presence of digitalization, flat organizations become flatter thanks to the various tools that digitalization provides to the system to create a closer and more flat communication structure, whereas in hierarchical organizations, managers use the tools provided by this phenomenon to cement their position and power pyramid. To put it another way, digitalization alone will not be able to shape or even force the organizational structure to become flatter; all those virtual teams and web-based platforms will be used in the way that the current organization structure desires.

The amount of social text messages authored each day is huge and the information contained within is potentially very valuable. Software that can cluster and thereby help analyze these messages would consequently be helpful. This thesis explores several ways of clustering social text messages. Two algorithms and several setups with these algorithms have been tested and evaluated with the same data as input. Based on these evaluations, a comparison has been conducted in order to answer the question which algorithm setup is best suited for the task. The two clustering algorithms that have been the main subjects for the comparison are K-means and agglomerative hierarchical. All setups were run with 3-grams as well as with only single words as features. The evaluation measures used were intra-cluster distance, inter-cluster distance and silhouette value. Intra-cluster distance is the distance between data points in the same cluster while inter-cluster is the distance between the clusters. Silhouette value is another more general evaluation measure that is often used to estimate the quality of a clustering. The results showed that if running time is a high priority, using K-means without 3-grams is preferred. On the other hand, if the quality of the clusters is important and performance is less so, introducing 3-grams together with any of the two algorithms will suit your needs better. / Mängden sociala textmeddelanden som skrivs varje dag är enorm och informationen i dessa kan vara mycket värdefull. Mjukvara som kan klustra och på så sätt analysera dessa meddelanden kan därmed vara användbar. Denna avhandling utforskar flera sätt att klustra sociala textmeddelanden. Två algoritmer och flera konfigureringar med dessa algoritmer har testats och utvärderats med samma indata. Baserat på dessa utvärderingar har en jämförelse utförts för att kunna svara på frågan vilken av dessa konfigureringar som är bäst anpassad för sitt syfte. De två klustringsalgoritmerna som i första hand har jämförts är K-means och agglomerative hierarchical. Alla konfigureringar kördes både med och utan 3-gram som komplement till endast enstaka ord. Utvärderingsmetoderna som användes var intra-cluster distance, inter-cluster distance och silhouette value. Intra-cluster distance är avståndet mellan datapunkterna i samma kluster medan inter-cluster distance är avståndet mellan de olika klustrena. Silhouette value är annan, mer generell, utvärderingsmetod som ofta används för att uppskatta kvaliten på en klustring. Resultaten visade att K-means utan 3-gram är att föredra om kravet på körningstid inte är högst prioriterat. Å andra sidan, om kvaliten på klustringen är viktigare än prestandan på algoritmen, så bör 3-gram användas tillsammans med vilken som av de två algoritmerna.

In the quarter century since his death, Polanyi's political thought has received little attention. The few studies that are available tend to mis-represent the character of his political thought either by developing only one of its aspects, or worse by presenting his ideas in inappropriately ideological terms. In this thesis I attempt to remedy this situation and present a more accurate account ofPolanyi's political philosophy. Through the careful analysis offundamental aspects ofhis epistemology and ontology, and through a treatment oftheir relation to his political ideas, I present a comprehensive interpretation ofPolanyi's political thought, taking into account the full complexity ofhis philosophical understanding. I present Polanyi as a keen interpreter ofmodernity, whose political thought is characterized not only by its 'conservative' elements, as is argued by all previous interpreters, but also, and more importantly by its anti-and non-ideological quality. I maintain that crucial to the interpretation of Polanyi's political thought is the recognition of the important and hierarchical relationship between man's commitment to the discovery and upholding of the truth ofa transcendent source oforder experienced in reality, and the role of traditions and standards in scientific, intellectual and political life. I maintain that the mis-representative accounts ofPolanyi's political philosophy offered by previous interpreters are due in large part to a failure to recognize the importance of this relationship. / Thesis / Master of Arts (MA)

The goal of an optimal control algorithm is to improve the performance of a system. For a stochastic system, a typical optimal control method minimizes the mean (first cumulant) of the cost function. However, there are other statistical properties of the cost function, such as variance (second cumulant) and skewness (third cumulant), which will affect the system performance. In this dissertation, the work on the statistical optimal control are presented, which extends the traditional optimal control method using cost cumulants to shape the system performance. Statistical optimal control will allow more design freedom to achieve better performance. The solutions of statistical control involve solving partial differential equations known as Hamilton-Jacobi-Bellman equation. A numerical method based on neural networks is employed to find the solutions of the Hamilton-Jacobi-Bellman partial differential equation. Furthermore, a complex problem such as multiple satellite control, has both continuous and discrete dynamics. Thus, a hierarchical hybrid architecture is developed in this dissertation where the discrete event system is applied to discrete dynamics, and the statistical control is applied to continuous dynamics. Then, the application of a multiple satellite navigation system is analyzed using the hierarchical hybrid architecture. Through this dissertation, it is shown that statistical control theory is a flexible optimal control method which improves the performance; and hierarchical hybrid architecture allows control and navigation of a complex system which contains continuous and discrete dynamics. / Electrical and Computer Engineering

Having a linear representation of magnitude across scales is essential in understanding many scientific concepts (Tretter, et al., 2006a) and is predictive of a range of mathematical achievement tests (Siegler & Booth, 2004). Despite the importance of understanding magnitude and scale, people have substantial difficulty comparing magnitudes outside of human perception (e.g., Jones, et al., 2008). The present work aims to examine the way people learn to represent and reason about large magnitudes through the development of two science of learning activities based on hierarchical alignment activity and corrective feedback. The hierarchical alignment activity utilizes several analogical reasoning principles: hierarchical alignment, progressive alignment, structural alignment, and multiple opportunities to make analogies. Study 1 examines the effectiveness of hierarchical alignment by contrasting it with a conventional activity that uses all the analogical reasoning principles described above except for hierarchical alignment. Study 2 examines a corrective feedback activity, based on the same analogical reasoning principles used in study 1, except, using corrective feedback instead of progressive alignment and hierarchical alignment. Thus, study 2 examines the necessity of hierarchical and progressive alignment. That both activities were successful in developing linear representations of geologic time (and for study 1, astronomical distances), suggests that multiple opportunities to make analogies through structural alignment are key components in developing analogies for learning magnitude. There appears to be an additive benefit of including hierarchical alignment (i.e., practice aligning magnitude relations across scales) in analogies for learning about magnitudes. Corrective feedback may also be a useful strategy in learning about scale information. Pedagogical implications are discussed. Both activities were based on the hypothesis that magnitudes at scales outside human perception are represented and reasoned about in the same way as magnitudes at human scales. The Category Adjustment Model (Huttenlocher, et al., 1988) suggests magnitude at human scales is stored as a hierarchical combination of metric and categorical information. People may use category boundaries to help make estimations in lieu of precise metric information. Variation in estimation, therefore, occurs because of imprecision of category boundaries (Shipley & Zacks, 2008; Zacks & Tversky, 2001). The current studies provided salient category boundaries to develop a more linear representation of magnitude. Thus, the effectiveness of the hierarchical alignment activity and the corrective feedback activity supports the hypothesis that people use hierarchically organized categorical information when making estimations across scales and across dimensions; and that providing people with more salient category boundary information improves estimation. Similarities and differences among temporal, spatial, and abstract line estimations are identified. Theoretical implications, including the potential application of the Category Adjustment Model to mental number lines, are discussed. / Psychology

This dissertation focuses on estimation and selection methods for a simple linear model with two levels of variation. This model provides a foundation for extensions to more levels. We propose new regularization criteria for model selection, subset selection, and variable selection in this context. Regularization is a penalized-estimation approach that shrinks the estimate and selects variables for structured data. This dissertation introduces a procedure (HM-ALASSO) that extends regularized multilevel-model estimation and selection to enforce principles of fixed heredity (e.g., including main effects when their interactions are included) and random heredity (e.g., including fixed effects when their random terms are included). The goals in developing this method were to create a procedure that provided reasonable estimates of all parameters, adhered to fixed and random heredity principles, resulted in a parsimonious model, was theoretically justifiable, and was able to be implemented and used in available software. The HM-ALASSO incorporates heredity-constrained selection directly into the estimation process. HM-ALASSO is shown to enjoy the properties of consistency, sparsity, and asymptotic normality. The ability of HM-ALASSO to produce quality estimates of the underlying parameters while adhering to heredity principles is demonstrated using simulated data. The performance of HM-ALASSO is illustrated using a subset of the High School and Beyond (HS&B) data set that includes math-achievement outcomes modeled via student- and school-level predictors. The HM-ALASSO framework is flexible enough that it can be adapted for various rule sets and parameterizations. / Statistics

No / This paper presents Reconfigurable Manufacturing System (RMS) characteristics through comparison with conventional manufacturing systems in order to address a design strategy towards a RMS. The strategy is considered as apart of a RMS design loop to achieve a reconfigurable strategy over its implementation period. As another part of the design loop, a reconfiguration link between market and manufacturing is presented in order to group products into families (reconfiguring products) and then assign them to the required manufacturing processes over configuration stages. In particular, the Analytical Hierarchical Process (AHP) is employed for structuring the decision making process for the selection of a manufacturing system among feasible alternatives based on the RMS study. Manufacturing responsiveness is considered as the ability of using existing resources to reflect new environmental and technological changes quickly. The AHP model highlights manufacturing responsiveness as a new economic objective along with classical objectives such as low cost and high quality. The forward-backward process is then proposed to direct and control the design strategy under uncertain conditions during its implementation period. The proposed hierarchy is generic in structure and could be applicable to many firms by means of restructuring the criteria. This work is based on a case study in a manufacturing environment. Expert Choice software (Expert Choice 1999) is applied to examine the structure of the proposed model and achieve synthesise/ graphical results considering inconsistency ratios. The results are examined by monitoring sensitivity analysis while changing the criteria priorities. Finally, to allocate available resources to the alternative solutions, a (0-1) knapsack formulation algorithm is represented.

Recollection is a pattern completion process that enables retrieval of arbitrarily associated information following minimal study. These attributes enable recollection to support retrieval of many kinds of mnemonic representations, from highly associative contextual information to very specific low-level representations. However, recollection is typically studied in the context of declarative memory tasks, in which participants exhibit recollection by explicitly reporting on the recollected information. Is it the case that recollection is limited to declarable representations, or is it a more general process that occurs for any representation? Two experiments and a novel analysis technique are presented to answer this question. The results suggest that recollection is not limited to declarable representations. These results argue against theories of recognition memory that restrict the representational input allowed to mnemonic processes; mnemonic processes in general may act on arbitrary representations.

Despite the profound impact of deep learning across various domains, supervised model training critically depends on access to large, high-quality datasets, which are often challenging to identify. To address this, we introduce <b>H</b>ierarchical <b>B</b>ayesian <b>D</b>ataset <b>S</b>election (<b>HBDS</b>), the first dataset selection algorithm that utilizes hierarchical Bayesian modeling, designed for collaborative data-sharing ecosystems. The proposed method efficiently decomposes the contributions of dataset groups and individual datasets to local model performance using Bayesian updates with small data samples. Our experiments on two benchmark datasets demonstrate that HBDS not only offers a computationally lightweight solution but also enhances interpretability compared to existing data selection methods, by revealing deep insights into dataset interrelationships through learned posterior distributions. HBDS outperforms traditional non-hierarchical methods by correctly identifying all relevant datasets, achieving optimal accuracy with fewer computational steps, even when initial model accuracy is low. Specifically, HBDS surpasses its non-hierarchical counterpart by 1.8% on DIGIT-FIVE and 0.7% on DOMAINNET, on average. In settings with limited resources, HBDS achieves a 6.9% higher accuracy than its non-hierarchical counterpart. These results confirm HBDS's effectiveness in identifying datasets that improve the accuracy and efficiency of deep learning models when collaborative data utilization is essential. / Master of Science / Deep learning technologies have revolutionized many domains and applications, from voice recognition in smartphones to automated recommendations on streaming services. However, the success of these technologies heavily relies on having access to large and high-quality datasets. In many cases, selecting the right datasets can be a daunting challenge. To tackle this, we have developed a new method that can quickly figure out which datasets or groups of datasets contribute most to improving the performance of a model with only a small amount of data needed. Our tests prove that this method is not only effective and light on computation but also helps us understand better how different datasets relate to each other.

This study examined whether measures of social capital were significant predictors of enrollment in postsecondary education for students from a low SES background. Results take the form of two articles. The first article addresses enrollment in four-year institutions of postsecondary education, and the second article addresses enrollment in two-year institutions of postsecondary education. The research questions for this study were: 1. Does probability of enrollment in a four-year postsecondary institution or a two-year postsecondary institution for low SES students differ by mean school SES? 2. Does probability of enrollment in a four-year postsecondary institution or a two-year postsecondary institution for low SES students differ by school locale? 3. When controlling for contextual or environmental variables and student background characteristics, are low SES students with higher levels of social capital more likely to enroll in a four-year postsecondary institution or a two-year postsecondary institution than low SES students with lower levels of social capital? 4. When controlling for contextual or environmental variables, background characteristics, and level of social capital does probability of enrollment in a four-year institution of postsecondary education or a two-year postsecondary institution vary by race for low SES students? When controlling for school level variables, academic achievement and preparation, and select background characteristics, low SES students with higher levels of social capital are more likely to enroll in a four-year college. Students whose parents expected them to obtain more education and those students who obtained more information about attending college were more likely to enroll in a four-year university. In the analysis of enrollment in four-year institutions of postsecondary education, African American low SES students were three times more likely to enroll in a four-year college or university than low SES Caucasian students. Only one measure of social capital, information acquisition, was significantly related to enrollment in a two-year institution of postsecondary education. No significant variability in probability of enrollment in a two-year institution of postsecondary education was observed by either of the school level variables used. Race was not a significant factor when controlling for background characteristics and the measures of social capital used in this study. / Ph. D.