The Effectiveness of Firms' Strategic Orientations: Investigating Multiple Theoretical Perspectives

The strategic orientation of firms can take on many forms. Researchers most commonly distinguish between entrepreneurial, market, and learning orientations. In combination, strategic orientations represent a firm's value proposition in terms of the markets in which it operates, where it deploys its resources, and which behavioral patterns are established. This thesis provides insights into the effectiveness of strategic orientations by adopting multiple theoretical perspectives. The strategic orientations of entrepreneurial, market, learning, and innovation orientations are investigated in an isolated as well as interrelated manner. The first research article concentrates on entrepreneurial orientation as its conceptualization and operationalization is subject to several debates in the literature. This conceptual study shows how the challenges of the entrepreneurial orientation construct can be overcome in future research to arrive at a higher level of construct clarity. Thereby, the theoretical perspectives of entrepreneurial dominant logic and the theory of planned behavior are employed. The literature has predominantly focused on investigating the effectiveness of particular strategic orientations. Recently, scholars have stressed their synergetic impact on firm performance and, as such, the relevance of considering their combined role in creating superior value for firms. However, empirical research on their interrelatedness remains scant and dispersed, making it necessary to conduct further research on strategic orientations in an integrative manner. As such, the second research article demonstrates which interrelated roles are played by entrepreneurial, market, and learning orientations in their relationship to firm performance. The rich body of existing knowledge is synthesized by means of meta-analysis under the perspective of strategic coalignment as well as the resource-based view of the firm.

Design of irredundant multiple-level MOS networks for multiple-output and incompletely specified functions /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Rethinking Professional Practice: The Logic of Competition and the Crisis of Identity in Housing Practice

The relationship between professionalism, education and housing practice has become increasingly strained following the introduction of austerity measures and welfare reforms across a range of countries. Focusing on the development of UK housing practice, this article considers how notions of professionalism are being reshaped within the context of welfare retrenchment and how emerging tensions have both affected the identity of housing professionals and impacted on the delivery of training and education programmes. The article analyses the changing knowledges and skills valued in contemporary housing practice and considers how the sector has responded to the challenges of austerity. The central argument is that a dominant logic of competition has culminated in a crisis of identity for the sector. Although the focus of the article is on UK housing practice, the processes identified have a wider relevance for the analysis of housing and welfare delivery in developed economies.

Logic-Based Delivery of Site-Specifically-Modified Proteins from Gels through Engineered Biomacromolecular Architecture

Thesis (Master's)--University of Washington, 2016-08

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Neutrophil Chemotaxis in Multiple Chemoattractant Gradients

Chemotaxis, the phenomenon in which cells move in response to extracellular chemical gradients, plays a prominent role in the mammalian immune response. During this process, a number of chemical signals, called chemoattractants, are produced at or proximal to sites of infection and diffuse into the surrounding tissue. Immune cells sense these chemoattractants and move in the direction where their concentration is greatest, thereby locating the source of attractants and their associated targets. Leading the assault against new infections is a specialized class of leukocytes (white blood cells) known as neutrophils, which normally circulate in the bloodstream. Upon activation, these cells emigrate out of the vasculature and navigate through interstitial tissues toward target sites. There they phagocytose bacteria and release a number of proteases and reactive oxygen intermediates with antimicrobial activity. Neutrophils recruited by infected tissue in vivo are likely confronted by complex chemical environments consisting of a number of different chemoattractant species. These signals may include end target chemicals produced in the vicinity of the infectious agents, and endogenous chemicals released by local host tissues during the inflammatory response. To successfully locate their pathogenic targets within these chemically diverse and heterogeneous settings, activated neutrophils must be capable of distinguishing between the different signals and employing some sort of logic to prioritize among them. This ability to simultaneously process and interpret mulitple signals is thought to be essential for efficient navigation of the cells to target areas. In particular, aberrant cell signaling and defects in this functionality are known to contribute to medical conditions such as chronic inflammation, asthma and rheumatoid arthritis. To elucidate the biomolecular mechanisms underlying the neutrophil response to different chemoattractants, a number of efforts have been made toward understanding how cells respond to different combinations of chemicals. Most notably, recent investigations have shown that in the presence of both end target and endogenous chemoattractant variants, the cells migrate preferentially toward the former type, even in very low relative concentrations of the latter. Interestingly, however, when the cells are exposed to two different endogenous chemical species, they exhibit a combinatorial response in which distant sources are favored over proximal sources. Some additional results also suggest that cells located between two endogenous chemoattractant sources will respond to the vectorial sum of the combined gradients. In the long run, this peculiar behavior could result in oscillatory cell trajectories between the two sources. To further explore the significance of these and other observations, particularly in the context of physiological conditions, we introduce in this work a simplified phenomenological model of neutrophil chemotaxis. In particular, this model incorporates a trait commonly known as directional persistence - the tendency for migrating neutrophils to continue moving in the same direction (much like momentum) - while also accounting for the dose-response characteristics of cells to different chemical species. Simulations based on this model suggest that the efficiency of cell migration in complex chemical environments depends significantly on the degree of directional persistence. In particular, with appropriate values for this parameter, cells can improve their odds of locating end targets by drifting through a network of attractant sources in a loosely-guided fashion. This corroborates the prediction that neutrophils randomly migrate from one chemoattractant source to the next while searching for their end targets. These cells may thus use persistence as a general mechanism to avoid being trapped near sources of endogenous chemoattractants - the mathematical analogue of local maxima in a global optimization problem. Moreover, this general foraging strategy may apply to other biological processes involving multiple signals and long-range navigation.

Hilbert Space Valued Signal Plus Noise Models: Analysis of Structural Breaks under High Dimensionality and Temporal Dependence

This thesis is concerned with change point analysis for time series, i.e. with detection of structural breaks in time-ordered, random data. This long-standing research field regained popularity over the last few years and is still undergoing, as statistical analysis in general, a transformation to high-dimensional problems. We focus on the fundamental »change in the mean« problem and provide extensions of the classical non-parametric Darling-Erdős-type cumulative sum (CUSUM) testing and estimation theory within highdimensional Hilbert space settings. In the first part we contribute to (long run) principal component based testing methods for Hilbert space valued time series under a rather broad (abrupt, epidemic, gradual, multiple) change setting and under dependence. For the dependence structure we consider either traditional m-dependence assumptions or more recently developed m-approximability conditions which cover, e.g., MA, AR and ARCH models. We derive Gumbel and Brownian bridge type approximations of the distribution of the test statistic under the null hypothesis of no change and consistency conditions under the alternative. A new formulation of the test statistic using projections on subspaces allows us to simplify the standard proof techniques and to weaken common assumptions on the covariance structure. Furthermore, we propose to adjust the principal components by an implicit estimation of a (possible) change direction. This approach adds flexibility to projection based methods, weakens typical technical conditions and provides better consistency properties under the alternative. In the second part we contribute to estimation methods for common changes in the means of panels of Hilbert space valued time series. We analyze weighted CUSUM estimates within a recently proposed »high-dimensional low sample size (HDLSS)« framework, where the sample size is fixed but the number of panels increases. We derive sharp conditions on »pointwise asymptotic accuracy« or »uniform asymptotic accuracy« of those estimates in terms of the weighting function. Particularly, we prove that a covariance-based correction of Darling-Erdős-type CUSUM estimates is required to guarantee uniform asymptotic accuracy under moderate dependence conditions within panels and that these conditions are fulfilled, e.g., by any MA(1) time series. As a counterexample we show that for AR(1) time series, close to the non-stationary case, the dependence is too strong and uniform asymptotic accuracy cannot be ensured. Finally, we conduct simulations to demonstrate that our results are practically applicable and that our methodological suggestions are advantageous.

Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians.

We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourabilitymodel based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.

The Clinical Impact Of Cerebellar Grey Matter Pathology In Multiple Sclerosis.

The cerebellum is an important site for cortical demyelination in multiple sclerosis, but the functional significance of this finding is not fully understood. To evaluate the clinical and cognitive impact of cerebellar grey-matter pathology in multiple sclerosis patients. Forty-two relapsing-remitting multiple sclerosis patients and 30 controls underwent clinical assessment including the Multiple Sclerosis Functional Composite, Expanded Disability Status Scale (EDSS) and cerebellar functional system (FS) score, and cognitive evaluation, including the Paced Auditory Serial Addition Test (PASAT) and the Symbol-Digit Modalities Test (SDMT). Magnetic resonance imaging was performed with a 3T scanner and variables of interest were: brain white-matter and cortical lesion load, cerebellar intracortical and leukocortical lesion volumes, and brain cortical and cerebellar white-matter and grey-matter volumes. After multivariate analysis high burden of cerebellar intracortical lesions was the only predictor for the EDSS (p<0.001), cerebellar FS (p = 0.002), arm function (p = 0.049), and for leg function (p<0.001). Patients with high burden of cerebellar leukocortical lesions had lower PASAT scores (p = 0.013), while patients with greater volumes of cerebellar intracortical lesions had worse SDMT scores (p = 0.015). Cerebellar grey-matter pathology is widely present and contributes to clinical dysfunction in relapsing-remitting multiple sclerosis patients, independently of brain grey-matter damage.

Multiple Desmoid Tumors In A Patient With Gardner's Syndrome - Report Of A Case.

Desmoid tumor (DT) is a common manifestation of Gardner's Syndrome (GS), although it is a rare condition in the general population. DT in patients with GS is usually located in the abdominal wall and/or intra-abdominal cavity. We report a case of a 32 years-old female patient with familial adenomatous polyposis (FAP), who was already submitted to total colectomy and developed multiple DT, located in the abdominal wall and in the left breast. The patient underwent several surgical procedures, with a multidisciplinary team of surgeons. Wide surgical resections of the left breast and the abdominal wall tumors were performed in separate steps. Polypropylene mesh reconstruction and muscle flaps were needed to cover the defects of the thoracic and abdominal walls. After partial necrosis of the adipose-cutaneous flap in the abdomen that required a new skin graft, she had a satisfactory outcome with complete healing of the surgical incisions. DT is frequent in GS, however, breast localization is very rare, with few cases reported in the literature. Recurrence of DT is not negligible, even after a wide surgical resection. GS patients must be followed up closely, and clinical examination, associated with imaging studies, should be performed to detect any signs of tumor. DT represents one of the most significant causes of the morbidity and mortality that affects FAP patients following colectomy. In general, the surgical procedures to excise DT are highly complex, requiring a multidisciplinary team.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.

Unfavorable Outcomes During Treatment Of Multiple Sclerosis With High Doses Of Vitamin D.

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The Real-life Experience With Cardiovascular Complications In The First Dose Of Fingolimod For Multiple Sclerosis.

Fingolimod is a new and efficient treatment for multiple sclerosis (MS). The drug administration requires special attention to the first dose, since cardiovascular adverse events can be observed during the initial six hours of fingolimod ingestion. The present study consisted of a review of cardiovascular data on 180 patients with MS receiving the first dose of fingolimod. The rate of bradycardia in these patients was higher than that observed in clinical trials with very strict inclusion criteria for patients. There were less than 10% of cases requiring special attention, but no fatal cases. All but one patient continued the treatment after this initial dose. This is the first report on real-life administration of fingolimod to Brazilian patients with MS, and one of the few studies with these characteristics in the world.

Systemic Granulomatous Diseases Associated With Multiple Palpable Masses That May Involve The Breast: Case Presentation And An Approach To The Differential Diagnosis.

Palpable mass is a common complaint presented to the breast surgeon. It is very uncommon for patients to report breast mass associated with palpable masses in other superficial structures. When these masses are related to systemic granulomatous diseases, the diagnosis and initiation of specific therapy can be challenging. The purpose of this paper is to report a case initially assessed by the breast surgeon and ultimately diagnosed as granulomatous variant of T-cell lymphoma, and discuss the main systemic granulomatous diseases associated with palpable masses involving the breast.

Ten Years Of Proteomics In Multiple Sclerosis.

Multiple sclerosis, which is the most common cause of chronic neurological disability in young adults, is an inflammatory, demyelinating, and neurodegenerative disease of the CNS, which leads to the formation of multiple foci of demyelinated lesions in the white matter. The diagnosis is based currently on magnetic resonance image and evidence of dissemination in time and space. However, this could be facilitated if biomarkers were available to rule out other disorders with similar symptoms as well as to avoid cerebrospinal fluid analysis, which requires an invasive collection. Additionally, the molecular mechanisms of the disease are not completely elucidated, especially those related to the neurodegenerative aspects of the disease. The identification of biomarker candidates and molecular mechanisms of multiple sclerosis may be approached by proteomics. In the last 10 years, proteomic techniques have been applied in different biological samples (CNS tissue, cerebrospinal fluid, and blood) from multiple sclerosis patients and in its experimental model. In this review, we summarize these data, presenting their value to the current knowledge of the disease mechanisms, as well as their importance in identifying biomarkers or treatment targets.