Experimental results on gravity driven fully condensing flows in vertical tubes, their agreement with theory, and their differences with shear driven flows' boundary-condition sensitivities

This doctoral thesis presents the experimental results along with a suitable synthesis with computational/theoretical results towards development of a reliable heat transfer correlation for a specific annular condensation flow regime inside a vertical tube. For fully condensing flows of pure vapor (FC-72) inside a vertical cylindrical tube of 6.6 mm diameter and 0.7 m length, the experimental measurements are shown to yield values of average heat transfer co-efficient, and approximate length of full condensation. The experimental conditions cover: mass flux G over a range of 2.9 kg/m2-s ≤ G ≤ 87.7 kg/m2-s, temperature difference ∆T (saturation temperature at the inlet pressure minus the mean condensing surface temperature) of 5 ºC to 45 ºC, and cases for which the length of full condensation xFC is in the range of 0 < xFC < 0.7 m. The range of flow conditions over which there is good agreement (within 15%) with the theory and its modeling assumptions has been identified. Additionally, the ranges of flow conditions for which there are significant discrepancies (between 15 -30% and greater than 30%) with theory have also been identified. The paper also refers to a brief set of key experimental results with regard to sensitivity of the flow to time-varying or quasi-steady (i.e. steady in the mean) impositions of pressure at both the inlet and the outlet. The experimental results support the updated theoretical/computational results that gravity dominated condensing flows do not allow such elliptic impositions.

Is a General Theory of Socially Disapproved Violence Possible (or Necessary)?

A model of theoretical science is set forth to guide the formulation of general theories around abstract concepts and processes. Such theories permit explanatory application to many phenomena that are not ostensibly alike, and in so doing encompass socially disapproved violence, making special theories of violence unnecessary. Though none is completely adequate for the explanatory job, at least seven examples of general theories that help account for deviance make up the contemporary theoretical repertoire. From them, we can identify abstractions built around features of offenses, aspects of individuals, the nature of social relationships, and different social processes. Although further development of general theories may be hampered by potential indeterminacy of the subject matter and by the possibility of human agency, maneuvers to deal with such obstacles are available.

A connection between two nonparametric tests for perfect ranking in balanced ranked set sampling

In this note, we show that an extension of a test for perfect ranking in a balanced ranked set sample given by Li and Balakrishnan (2008) to the multi-cycle case turns out to be equivalent to the test statistic proposed by Frey et al. (2007). This provides an alternative interpretation and motivation for their test statistic.

Exploring double field theory

We consider a flux formulation of Double Field Theory in which fluxes are dynamical and field-dependent. Gauge consistency imposes a set of quadratic constraints on the dynamical fluxes, which can be solved by truly double configurations. The constraints are related to generalized Bianchi Identities for (non-)geometric fluxes in the double space, sourced by (exotic) branes. Following previous constructions, we then obtain generalized connections, torsion and curvatures compatible with the consistency conditions. The strong constraint-violating terms needed to make contact with gauged supergravities containing duality orbits of non-geometric fluxes, systematically arise in this formulation.

A Restriction-Centered Theory of Reasoning and Computation to materialize a Web 3.0

Researchers suggest that personalization on the Semantic Web adds up to a Web 3.0 eventually. In this Web, personalized agents process and thus generate the biggest share of information rather than humans. In the sense of emergent semantics, which supplements traditional formal semantics of the Semantic Web, this is well conceivable. An emergent Semantic Web underlying fuzzy grassroots ontology can be accomplished through inducing knowledge from users' common parlance in mutual Web 2.0 interactions [1]. These ontologies can also be matched against existing Semantic Web ontologies, to create comprehensive top-level ontologies. On the Web, if augmented with information in the form of restrictions andassociated reliability (Z-numbers) [2], this collection of fuzzy ontologies constitutes an important basis for an implementation of Zadeh's restriction-centered theory of reasoning and computation (RRC) [3]. By considering real world's fuzziness, RRC differs from traditional approaches because it can handle restrictions described in natural language. A restriction is an answer to a question of the value of a variable such as the duration of an appointment. In addition to mathematically well-defined answers, RRC can likewise deal with unprecisiated answers as "about one hour." Inspired by mental functions, it constitutes an important basis to leverage present-day Web efforts to a natural Web 3.0. Based on natural language information, RRC may be accomplished with Z-number calculation to achieve a personalized Web reasoning and computation. Finally, through Web agents' understanding of natural language, they can react to humans more intuitively and thus generate and process information.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

Applying prospect theory to predict new venture creation

Although prior research on new venture creation has identified several antecedents that differentiate entrepreneurs from non-entrepreneurs, scholars still have an incomplete understanding of the factors and decision processes that lead an individual to become an entrepreneur. By applying prospect theory, we introduce the reference point as an important antecedent of new venture creation. Testing our research model and hypotheses with entrepreneurs and employees, results show that entrepreneurs set more aspiring reference points and therefore find themselves more often in a perceived loss situation. Results are also robust when testing for entrepreneurial intention of business graduate students. According to prospect theory, the perceived loss triggers more risk-seeking behavior. Summing up, the reference point has a positive effect on new venture creation and differentiates entrepreneurs from nonentrepreneurs. We discuss theoretical and managerial implications of the findings and develop avenues for future research.

Applying the Fuzzy Analytical Hierarchy Process in Cognitive Cities

This paper introduces a mobile application (app) as the first part of an interactive framework. The framework enhances the inter-action between cities and their citizens, introducing the Fuzzy Analytical Hierarchy Process (FAHP) as a potential information acquisition method to improve existing citizen management en-deavors for cognitive cities. Citizen management is enhanced by advanced visualization using Fuzzy Cognitive Maps (FCM). The presented app takes fuzziness into account in the constant inter-action and continuous development of communication between cities or between certain of their entities (e.g., the tax authority) and their citizens. A transportation use case is implemented for didactical reasons.

A Conceptual Model to Combine Creativity Techniques with Fuzzy Cognitive Maps for Enhanced Knowledge Management

This chapter introduces a conceptual model to combine creativity techniques with fuzzy cognitive maps (FCMs) and aims to support knowledge management methods by improving expert knowledge acquisition and aggregation. The aim of the conceptual model is to represent acquired knowledge in a manner that is as computer-understandable as possible with the intention of developing automated reasoning in the future as part of intelligent information systems. The formal represented knowledge thus may provide businesses with intelligent information integration. To this end, we introduce and evaluate various creativity techniques with a list of attributes to define the most suitable to combine with FCMs. This proposed combination enables enhanced knowledge management through the acquisition and representation of expert knowledge with FCMs. Our evaluation indicates that the creativity technique known as mind mapping is the most suitable technique in our set. Finally, a scenario from stakeholder management demonstrates the combination of mind mapping with FCMs as an integrated system.

A Parametric Test of Perfect Ranking in Balanced Ranked Set Sampling

Many techniques based on data which are drawn by Ranked Set Sampling (RSS) scheme assume that the ranking of observations is perfect. Therefore it is essential to develop some methods for testing this assumption. In this article, we propose a parametric location-scale free test for assessing the assumption of perfect ranking. The results of a simulation study in two special cases of normal and exponential distributions indicate that the proposed test performs well in comparison with its leading competitors.

Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps

The new computing paradigm known as cognitive computing attempts to imitate the human capabilities of learning, problem solving, and considering things in context. To do so, an application (a cognitive system) must learn from its environment (e.g., by interacting with various interfaces). These interfaces can run the gamut from sensors to humans to databases. Accessing data through such interfaces allows the system to conduct cognitive tasks that can support humans in decision-making or problem-solving processes. Cognitive systems can be integrated into various domains (e.g., medicine or insurance). For example, a cognitive system in cities can collect data, can learn from various data sources and can then attempt to connect these sources to provide real time optimizations of subsystems within the city (e.g., the transportation system). In this study, we provide a methodology for integrating a cognitive system that allows data to be verbalized, making the causalities and hypotheses generated from the cognitive system more understandable to humans. We abstract a city subsystem—passenger flow for a taxi company—by applying fuzzy cognitive maps (FCMs). FCMs can be used as a mathematical tool for modeling complex systems built by directed graphs with concepts (e.g., policies, events, and/or domains) as nodes and causalities as edges. As a verbalization technique we introduce the restriction-centered theory of reasoning (RCT). RCT addresses the imprecision inherent in language by introducing restrictions. Using this underlying combinatorial design, our approach can handle large data sets from complex systems and make the output understandable to humans.

Workshop: ASSIP - Attempted Suicide Short Intervention Program. Theory, Practice, and 24 Months Follow-up RCT.

Background: ASSIP is a manualized brief therapy based on a model of suicide as goal-directed action, aimed at establishing a therapeutic alliance in a patient-oriented, collaborative approach. The main goals of the three-session program ASSIP are for patients to understand, from an observer’s position, patterns leading to a suicidal crisis, recognize triggers and warning signs, and to establish individual safety strategies for future suicidal crises. An ongoing therapeutic support is provided with regular letters over 24 months. Method: The study was conducted in a naturalistic setting. 120 Patients were randomly assigned to an intervention group (60 participants) treated with ASSIP combined with follow-up contact through letters, and a control group (60 participants) receiving a single session of clinical assessment. Both groups had treatment as usual. Patients completed a set of psychosocial and clinical questionnaires every six months over a period of 24 months. Results: In the ASSIP group 5 patients made a total of 5 reattempts, compared to 15 patients with 41 reattempts in the control group. The survival analysis yielded a significant difference with a Wald Chi2 of .000003. The ASSIP group had significantly lower suicidal ideation and fewer days of inpatient treatment compared to the control group. Higher scores in the Penn Helping Alliance Questionnaire were associated with lower suicidal ideation during follow-up. Conclusions: ASSIP is a highly effective brief therapy for patients with recent suicide attempts. Forming a strong therapeutic alliance is considered to be a major factor for outcome. ASSIP can be used with minimal training by experienced therapists. An English version of the manual will be published in May 2015.

Synchronizing Mind Maps with Fuzzy Cognitive Maps for Decision-Finding in Cognitive Cities

Synchronizing mind maps with fuzzy cognitive maps can help to handle complex problems with many involved stakeholders by taking advantage of human creativity. The proposed approach has the capacity to instantiate cognitive cities by including cognitive computing. A use case in the context of decision-finding (concerning a transportation system) is presented to illustrate the approach.

An Economic Theory of Mortgage Redemption Laws

Redemption laws give mortgagors the right to redeem their property following default for a statutorily set period of time. This paper develops a theory that explains these laws as a means of protecting landowners against the loss of non-transferable values associated with their land. A longer redemption period reduces the risk that this value will be lost but also increases the likelihood of default. The optimal redemption period balances these effects. Empirical analysis of cross-state data from the early twentieth century suggests that these factors, in combination with political considerations, explain the existence and length of redemption laws.