People with AN are more likely than controls to report an impoverished social network before the onset of the illness (e.g. having no close friends in childhood; [33, 35] fewer social activities [36] and less social support [37]). In part, this relates to temperamental characteristics, as people with EDs are inhibited and shy with internalising problems [38]. Loneliness, feelings of inferiority and high levels of social anxiety [39] are also reported.
social network analysis for startups epub 13
In cases ascertained in adolescence, social problems predated the illness in 20% of cases and informed the prognosis, such that cases with social deficits prior to onset at age 15, were found to be impaired 18 years later at follow-up [6, 40]. Experiences of teasing, bullying and criticism, often pertaining to weight/shape and eating, are found before the illness [41]. During the illness social networks are often reduced [42, 43]. In addition, a sense of inferiority in relationship to others can persist post recovery [44]. Thus, a wide variety of behavioural features support the model, in that an avoidant social phenotype is of relevance to the onset and prognosis. In the next section we examine the form of this social phenotype in more detail. A variety of experimental paradigms have been used to examine factors that may underpin social avoidance in people with EDs, [45] including attention, emotional expression and interpretation and theory of mind.
Accurate reading of the intentions and emotions of others is necessary for effective social communication. People with AN have impairments in this domain. A systematic review and meta analysis concluded that people with AN have impairments in recognizing facial emotions, [51]. In contrast, people with BN have little or no impairment in facial emotion recognition [52], and may be better than healthy controls at recognizing negative emotions [53]. In AN, interpreting emotional meaning from the voice [54, 55], body movement [56] and from films [54] is also impaired. For the most part, these impairments are less marked after recovery, suggesting that they may be starvation, state effects. There is little evidence that these traits are part of an inherited vulnerability as they are not present in first degree relatives (twins, parents) (Kanakam et al. submitted Goddard et al. submitted).
Abstract:This research investigated influential factors on changes in networks of startups through a qualitative exploratory case study approach. Based on interviews with founders in Germany and selected stakeholders in entrepreneurial networks combined with a network mapping approach, we developed a framework of influential factors on network changes. In essence, this framework categorizes factors into sustainable resource acquisition, knowledge and skill acquisition, interpersonal factors, and interorganizational factors. Overall, our research contributes to a better understanding of factors that impact network changes by providing a construct with potential for theoretical standardization. In addition, this research offers important managerial implications.Keywords: startup; entrepreneurship; entrepreneurial firm; network; network changes; influential factors; ties; relationships
Quantum algorithms can speed-up the big data analysis exponentially [40]. Some complex problems, believed to be unsolvable using conventional computing, can be solved by quantum approaches. For example, the current encryption techniques such as RSA, public-key (PK) and Data Encryption Standard (DES) which are thought to be impassable now would be irrelevant in future because quantum computers will quickly get through them [41]. Quantum approaches can dramatically reduce the information required for big data analysis. For example, quantum theory can maximize the distinguishability between a multilayer network using a minimum number of layers [42]. In addition, quantum approaches require a relatively small dataset to obtain a maximally sensitive data analysis compared to the conventional (machine-learning) techniques. Therefore, quantum approaches can drastically reduce the amount of computational power required to analyze big data. Even though, quantum computing is still in its infancy and presents many open challenges, it is being implemented for healthcare data.
Quantum computing is picking up and seems to be a potential solution for big data analysis. For example, identification of rare events, such as the production of Higgs bosons at the Large Hadron Collider (LHC) can now be performed using quantum approaches [43]. At LHC, huge amounts of collision data (1PB/s) is generated that needs to be filtered and analyzed. One such approach, the quantum annealing for ML (QAML) that implements a combination of ML and quantum computing with a programmable quantum annealer, helps reduce human intervention and increase the accuracy of assessing particle-collision data. In another example, the quantum support vector machine was implemented for both training and classification stages to classify new data [44]. Such quantum approaches could find applications in many areas of science [43]. Indeed, recurrent quantum neural network (RQNN) was implemented to increase signal separability in electroencephalogram (EEG) signals [45]. Similarly, quantum annealing was applied to intensity modulated radiotherapy (IMRT) beamlet intensity optimization [46]. Similarly, there exist more applications of quantum approaches regarding healthcare e.g. quantum sensors and quantum microscopes [47].
The evolution of FinTech has unfolded in three stages, summarized in Table 1. The first, which we call FinTech 1.0, occurred from 1866 to 1967, when the financial services industry remained largely analogue despite being heavily interlinked with technology. The next period, FinTech 2.0, extended from 1968 to 2008, an era characterized by the development of digital technology for communications and transactions and thus the growing digitization of finance. Since 2009, in the period we call FinTech 3.0, new startups and established technology, ecommerce, and social media companies have begun to deliver financial products and services directly to the public as well as to businesses, including banks.11
MIT maintains an open-campus policy along with an "open network."[82][138] Two days after Swartz's death, MIT President L. Rafael Reif commissioned professor Hal Abelson to lead an analysis of MIT's options and decisions relating to Swartz's "legal struggles."[139][140] To help guide the fact-finding stage of the review, MIT created a website where community members could suggest questions and issues for the review to address.[141][142]
The sentinel laboratory network is considered to be stable, and exists since more than 30 years. It nevertheless comprises several limitations. Firstly, if the network is representative at national level, its geographical repartition at provincial level is uneven. For example, the coverage of East Flanders and Flemish Brabant is high (between 80 and 90 %), whereas Namur, Liege and Limburg have a lower coverage (below 50 %). The reported incidence may thus be underestimated in the provinces with a lower coverage. Secondly, it gives a partial picture of the incidence, as laboratory tests are not recommended for patients presenting with an erythema migrans. Thirdly, a window period of undetectable antibodies should be considered if the blood analysis occurs within the first three weeks after the tick bite. Fourthly, laboratory tests only confirm the presence of anti-Borrelia antibodies, which does not necessarily means that the patient is suffering from Lyme disease: it could also be due to a previous symptomatic or asymptomatic Borrelia infection. Fifthly, the database may content some duplicates, as a patient is considered to be eligible for reinfection after a one-year period. However, as the database is repetitively used with the same parameters, this phenomenon should not have any impact on its global trend, which is of interest and regularly followed in public health. 2ff7e9595c
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