The Emergence of Partnership Networks in the Enterprise Application Development Industry - A Global Corporation Perspective

Currently, dramatic changes are happening in the IS development industry. The incumbent system developers (hubs) are embracing partnerships with less well established companies (spokes), acting in specific niches. This paper seeks to establish a better understanding of the motives for this strategy. Relying on existing work on strategic alliance formation, it is argued that partnering is particularly attractive, if these small companies possess certain capabilities that are difficult to obtain through other arrangements than partnering. Again drawing on the literature, three categories of capabilities are identified: the capability to innovate within their niche, the capability to provide a specific functionality that can be integrated with the incumbents’ systems, and the capability to address novel markets. These factors are analyzed through a case study. The case represents a market leader in the global IS development industry, which fosters a network of smaller partner firms. The study reveals that temporal dynamics between the identified factors are playing a dominant role in these networks. A cyclical partnership model is developed that attempts to explain the life cycle of a partnership within such a network.

The impact of neural invasion severity in gastrointestinal malignancies: a clinicopathological study.

OBJECTIVES Because neural invasion (NI) is still inconsistently reported and not well characterized within gastrointestinal malignancies (GIMs), our aim was to determine the exact prevalence and severity of NI and to elucidate the true impact of NI on patient's prognosis. BACKGROUND The union internationale contre le cancer (UICC) recently added NI as a novel parameter in the current TNM classification. However, there are only a few existing studies with specific focus on NI, so that the distinct role of NI in GIMs is still uncertain. MATERIALS AND METHODS NI was characterized in approximately 16,000 hematoxylin and eosin tissue sections from 2050 patients with adenocarcinoma of the esophagogastric junction (AEG)-I-III, squamous cell carcinoma (SCC) of the esophagus, gastric cancer (GC), colon cancer (CC), rectal cancer (RC), cholangiocellular cancer (CCC), hepatocellular cancer (HCC), and pancreatic cancer (PC). NI prevalence and severity was determined and related to patient's prognosis and survival. RESULTS NI prevalence largely varied between HCC/6%, CC/28%, RC/34%, AEG-I/36% and AEG-II/36%, SCC/37%, GC/38%, CCC/58%, and AEG-III/65% to PC/100%. NI severity score was uppermost in PC (24.9±1.9) and lowest in AEG-I (0.8±0.3). Multivariable analyses including age, sex, TNM stage, and grading revealed that the prevalence of NI was significantly associated with diminished survival in AEG-II/III, GC, and RC. However, increasing NI severity impaired survival in AEG-II/III and PC only. CONCLUSIONS NI prevalence and NI severity strongly vary within GIMs. Determination of NI severity in GIMs is a more precise tool than solely recording the presence of NI and revealed dismal prognostic impact on patients with AEG-II/III and PC. Evidently, NI is not a concomitant side feature in GIMs and, therefore, deserves special attention for improved patient stratification and individualized therapy after surgery.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

One-loop SQCD corrections to the decay of top squarks to charm and neutralino in the generic MSSM

In this article we calculate the one-loop supersymmetric QCD (SQCD) corrections to the decay u˜1→cχ˜01 in the minimal supersymmetric standard model with generic flavor structure. This decay mode is phenomenologically important if the mass difference between the lightest squark u˜1 (which is assumed to be mainly stoplike) and the neutralino lightest supersymmetric particle χ˜01 is smaller than the top mass. In such a scenario u˜1→tχ˜01 is kinematically not allowed and searches for u˜1→Wbχ˜01 and u˜1→cχ˜01 are performed. A large decay rate for u˜1→cχ˜01 can weaken the LHC bounds from u˜1→Wbχ01 which are usually obtained under the assumption Br[u˜1→Wbχ01]=100%. We find the SQCD corrections enhance Γ[u˜1→cχ˜01] by approximately 10% if the flavor violation originates from bilinear terms. If flavor violation originates from trilinear terms, the effect can be ±50% or more, depending on the sign of At. We note that connecting a theory of supersymmetry breaking to LHC observables, the shift from the DR¯¯¯¯¯ to the on-shell mass is numerically very important for light stop decays.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.

A Thalamic-Fronto-Parietal Structural Covariance Network Emerging in the Course of Recovery from Hand Paresis after Ischemic Stroke.

AIM To describe structural covariance networks of gray matter volume (GMV) change in 28 patients with first-ever stroke to the primary sensorimotor cortices, and to investigate their relationship to hand function recovery and local GMV change. METHODS Tensor-based morphometry maps derived from high-resolution structural images were subject to principal component analyses to identify the networks. We calculated correlations between network expression and local GMV change, sensorimotor hand function and lesion volume. To verify which of the structural covariance networks of GMV change have a significant relationship to hand function, we performed an additional multivariate regression approach. RESULTS Expression of the second network, explaining 9.1% of variance, correlated with GMV increase in the medio-dorsal (md) thalamus and hand motor skill. Patients with positive expression coefficients were distinguished by significantly higher GMV increase of this structure during stroke recovery. Significant nodes of this network were located in md thalamus, dorsolateral prefrontal cortex, and higher order sensorimotor cortices. Parameter of hand function had a unique relationship to the network and depended on an interaction between network expression and lesion volume. Inversely, network expression is limited in patients with large lesion volumes. CONCLUSION Chronic phase of sensorimotor cortical stroke has been characterized by a large scale co-varying structural network in the ipsilesional hemisphere associated specifically with sensorimotor hand skill. Its expression is related to GMV increase of md thalamus, one constituent of the network, and correlated with the cortico-striato-thalamic loop involved in control of motor execution and higher order sensorimotor cortices. A close relation between expression of this network with degree of recovery might indicate reduced compensatory resources in the impaired subgroup.

Virtual Patients in continuing medical education and residency training: a pilot project for acceptance analysis in the framework of a residency revision course in pediatrics.

AIM Virtual patients (VPs) are a one-of-a-kind e-learning resource, fostering clinical reasoning skills through clinical case examples. The combination with face-to-face teaching is important for their successful integration, which is referred to as "blended learning". So far little is known about the use of VPs in the field of continuing medical education and residency training. The pilot study presented here inquired the application of VPs in the framework of a pediatric residency revision course. METHODS Around 200 participants of a pediatric nephology lecture ('nephrotic and nephritic syndrome in children') were offered two VPs as a wrap-up session at the revision course of the German Society for Pediatrics and Adolescent Medicine (DGKJ) 2009 in Heidelberg, Germany. Using a web-based survey form, different aspects were evaluated concerning the learning experiences with VPs, the combination with the lecture, and the use of VPs for residency training in general. RESULTS N=40 evaluable survey forms were returned (approximately 21%). The return rate was impaired by a technical problem with the local Wi-Fi firewall. The participants perceived the work-up of the VPs as a worthwhile learning experience, with proper preparation for diagnosing and treating real patients with similar complaints. Case presentations, interactivity, and locally and timely independent repetitive practices were, in particular, pointed out. On being asked about the use of VPs in general for residency training, there was a distinct demand for more such offers. CONCLUSION VPs may reasonably complement existing learning activities in residency training.

Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.

The Application of Digital Rights Management Systems in the Music Industry - An Empirical Investigation

Technology advances in hardware, software and IP-networks such as the Internet or peer-to-peer file sharing systems are threatening the music business. The result has been an increasing amount of illegal copies available on-line as well as off-line. With the emergence of digital rights management systems (DRMS), the music industry seems to have found the appropriate tool to simultaneously fight piracy and to monetize their assets. Although these systems are very powerful and include multiple technologies to prevent piracy, it is as of yet unknown to what extent such systems are currently being used by content providers. We provide empirical analyses, results, and conclusions related to digital rights management systems and the protection of digital content in the music industry. It shows that most content providers are protecting their digital content through a variety of technologies such as passwords or encryption. However, each protection technology has its own specific goal, and not all prevent piracy. The majority of the respondents are satisfied with their current protection but want to reinforce it for the future, due to fear of increasing piracy. Surprisingly, although encryption is seen as the core DRM technology, only few companies are currently using it. Finally, half of the respondents do not believe in the success of DRMS and their ability to reduce piracy.