Complex networks are composed of a large number of interacting nodes. Examples of complex networks include the topology of the Internet, connections between websites or web pages in the World Wide Web (WWW), and connections between participants in social networks.Due to their ubiquity, modeling complex networks is importantfor answering many research questions that cannot be answered without a mathematical model. For example, mathematical models of complex networks can be used to find the most vulnerable nodes to protect during a virus attack in theInternet, to predict connections between websites in the WWW, or to find members of different communities insocial networks. Researchers have analyzed complex networksand concluded that they are distinguished from other networks by four specific statistical properties. These four statistical properties are commonly known in this field as: (i) thesmall world effect,(ii) high average clustering coefficient, (iii) scale-free power law degree distribution, and (iv) emergence of community structure. These four statistical properties are further described later in this dissertation. Mostmodels used to generate complex networks attempt to produce networks with these statistical properties. Additionally, most of these network models generate homogeneous complex networks where all the networknodes are considered to have the same properties. Homogenous complex networks neglect the heterogeneous nature ofthe nodes in many complexnetworks. Moreover, somemodels proposed for generating heterogeneous complexnetworks are not general as they make specific assumptions about the properties of the network.Including heterogeneity in the connection algorithm of a modelwould makeitmore suitable for generating the subset of complex networks that exhibit selective linking.Additionally, all modelsproposed, to date, for generating heterogeneous complex networks do not preserve all four of the statistical properties of complexnetworks stated above. Thus, formulation of a model for the generation of general heterogeneous complex networkswith characteristics that resemble as much as possible the statistical properties common to the real-world networks that have received attention from the research community is still an open research question. In this work, we propose two new types of models to generate heterogeneous complex networks. First, we introduce the Integrated Attribute Similarity Model (IASM). IASM uses preferential attachment(PA) to connect nodes based on a similarity measure for node attributes combined with a node's structural popularity measure. IASM integrates the attribute similarity measure and a structural popularity measure in the computation of the connection function used to determine connectionsbetween each arriving (newly created) node and the existing(previously created or old) network nodes. IASM is also the first model known to assign an attribute vector having more than one element to each node, thus allowing different attributes per node in the generated complex network. Networks generated using IASM have a power law degree distribution and preserve the small world phenomenon. IASM models are enhanced to increase their clustering coefficient using a triad formation step (TFS). In a TFS, a node connects to the neighbor of the node to which it was previously connected through preferential attachment, thus forming a triad. The TFS increases the number of triads that are formed in the generated network which increases the network's average clustering coefficient. We also introduce a second novel model,the Settling Node Adaptive Model (SNAM). SNAM reflects the heterogeneous nature of connectionstandard requirements for nodes. The connectionstandard requirements for a noderefers to the values of attribute similarity and/or structural popularityof old node ythat node new xwould find acceptable in order to connect to node y.SNAM is novel in that such a node connection criterion is not included in any previous model for the generation of complex networks. SNAM is shown to be successful in preserving the power law degree distribution, the small world phenomenon, and the high clustering coefficient of complex networks. Next,we implement a modification to the IASM and SNAM models that results in the emergence of community structure.Nodes are classified into classes according to their attribute values. The connection algorithm is modified to include the class similarity values between network nodes. This community structure model preservesthe PL degree distribution, small world property, and does not affect average clustering coefficient values expected from both IASM and SNAM. Additionally, the model exhibits the presence of community structure having most of the connections made between nodes belonging to the same class with only a small percent of the connections made between nodes of different classes. We perform a mathematical analysis of IASM and SNAM to study the degree distribution for networks generated by both models. This mathematical analysis shows that networks generated by both models have a power law degree distribution. Finally, we completed a case study to illustrate the potential value of our research on the modeling of heterogeneous complex networks. This case study was performed on a Facebook dataset. The case study shows that SNAM, with some modifications to the connection algorithm, is capable of generating a network with almost the same characteristics as found for the original dataset. The case study providesinsight on how the flexibility of SNAM's connection algorithm can be an advantagethat makes SNAM capable of generating networks with different statistical properties. Ideas for future research areas includestudyingthe effect of using eigenvector centrality, instead of degree centrality, on the emergence of community structure in IASM; usingthe nodeindex as an indication for its order of arrival to the network and distributing added connections fairly among networknodes along the life of the generated network; experimenting with the nature of attributesto generatea more comprehensive model; and usingtime sensitive attributes in the models, where the attribute can change its value with time, / Ph. D.

Recently, hyperthermia has been investigated as an alternate therapy for the treatment of tumors. The present project explored the feasibility of preferential hyperthermia as a method of treating deep seated tumors. The overall goal of this research was to determine theoretically if preferential heating could be used to attain the desired thermal dose (DTD) for a two cm diameter tumor. The simulations in this work show that, when using a single silicon carbide insert, the model cannot provide enough energy for an entire 2 cm diameter tumor to receive the DTD. However, when using an enhanced design model with multiple (4) silicon carbide inserts, the DTD could be attained in a tumor up to 3.5 cm in diameter. This study involved using the commercially available software package ANSYS 7.0 program to model a spherical 2 cm tumor, assuming the tumor is located in deep tissue with a constant perfusion rate and no major blood vessels nearby. This tumor was placed in the center of a cube of healthy tissue. To achieve the preferential heating of the tumor, a silicon carbide insert was placed in the center of the tumor and microwave energy was applied to the insert (in the form of volumetric heating). The thermal modeling of this system was based on the Pennes Bioheat equation with a maximum temperature limitation of of 80 ºC. The Thermal Dose Analyzer software program was used to evaluate the results of the thermal simulations (from ANSYS) to determine if the DTD had been attained. Additional enhanced design models were also examined. These models include 2 cm and 4 cm tumors with four silicon carbide inserts symmetrically placed about the tumor and a 4 cm tumor model using a single silicon carbide insert with antennae attached to the insert to increase energy distribution to the tumor. The simulations show that only the enhanced design cases with four silicon carbide inserts can achieve the DTD for an entire 2 cm tumor. / Master of Science

The thesis is focused on import of goods to the Czech Republic in preferential and non-preferential systems from third world countries. The aim of this diploma thesis is to make recommendations which should be used as a useful tool for companies with business plan to import goods to the Czech Republic in both systems. There are also described documents used for proving in non-preferential and preferential system which are the source for setting the customs tariff. At the end of the thesis the differences of import caused by different origin of goods are evaluated.

The Generalized System of Preferences (GSP) is a unilateral trade liberalization program in which developed countries offer non-reciprocal tariff reductions (tariff preferences) on certain products imported from designated developing and least developed countries. GSP is considered an important tool in the World Trade Organization's approach to development. This dissertation--composed of three essays--explores whether low-income countries have achieved an increased access to high-income markets as a result of these non-reciprocal tariff preferences offered to their exports. The first essay provides an overview of the GSP program. The second essay presents an evaluation of the GSP program by considering the products and markets where low-income countries' exports are concentrated. Using a theoretically consistent gravity equation for primary and processed agri-food trade over the period 1962-2010, the results illustrate that the GSP program and modifications of it have delivered significant positive effects in developing countries' exports to developed country markets in agricultural trade but not necessarily so in non-agricultural goods. The third essay develops two theoretically founded novel indices to measure preference margins offered by high-income countries to low-income countries through tariff reduction. One index captures the restrictions bilateral tariff rates impose on market access conditions of a country as compared to the most favored nation rate, called the Exponential Trade Restrictiveness Index (ETRI). The other index captures the relative ease with which a country can access foreign markets compared to its competing suppliers, called the Exponential Relative Preferential Margin (ERPM). Then, these two bilateral indices are used to develop a model of sector-based bilateral trade to re-evaluate the Generalized System of Preferences (GSP) in terms of relative market access preferences. The results show that the GSP has increased relative market accessibility for low-income countries and in turn boosted exports from these countries by 26 to 28 percent. / Ph. D.

Master of Arts / Department of Economics / Peri da Silva / Preferential Trade Agreements (PTAs) are known to facilitate liberalization with respect to only a few trading partners and thus they have been a topic of debate for the past two decades especially because their effect on most favored nation (MFN) tariffs is known to be ambiguous. We provide insights for analyzing whether the PTAs indeed hamper or support multilateral liberalization. Using product level official and actual tariffs we provide evidence from the United States (US) import data that the stumbling block effect on the US MFN bound tariffs is present only for goods that receive full preference in books or in actual. However, my dataset does not statistically support the stumbling block hypothesis in the case of Applied tariffs.

No description available.

Thesis advisor: Robert Murphy / Given the increasing proliferation of preferential trade agreements, this work seeks to investigate the economic, political, and cultural relationships that may be built from the common foundation of a trade agreement. It evaluates the experiences of the European Union and the North American Free Trade Agreement and makes predictions and suggestions for future preferential trading partners, as well as analyzing the potential impact that PTAs will have on globalization and international relations. / Thesis (BA) — Boston College, 2004. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: International Studies. / Discipline: College Honors Program.

<p>Preferential processing favouring threatening information has received increased attention because cognitive formulations have placed increased emphasis on its role as a key cognitive factor underlying vulnerability to and maintenance of anxiety disorders. The present dissertation comprises four empirical studies within the area of preferential processing. Two different outcome measures were used to index preferential processing of threat-related information: Skin conductance responses (SCRs) were used in Studies I, II, and III. The emotional Stroop task was used in Study IV. The main focus has been on preferential processing of threat-related information that occurs outside awareness, thus <i>preferential preattentive processing</i>. Study I investigated the role of traumatic combat experience with regard to preferential processing among UN soldiers following a presentation of threat-related pictures. Results indicated that soldiers with combat experience consistently reacted with lower SCRs compared to soldiers without combat experience. One issue addressed in the individual studies was the association between preferential preattentive processing and trait anxiety. Studies II, III, and IV showed that elevated levels of trait anxiety promote preferential preattentive processing of negatively valenced information, whereas elevated levels of social desirability generally prevent preferential preattentive processing of negatively valenced information. Study II highlighted the importance of including the social desirability factor when studying effects of trait anxiety on preferential processing. In addition, Studies III and IV explored the relationship between preferential processing and emotional vulnerability. The main findings support the notion of preferential preattentive processing of threat representing an underlying predisposition to heightened emotional vulnerability in response to stressful events.</p>

This paper examines the effect of uncertain initial soil moisture on hydrologic response at the plot scale (1 m2) and the catchment scale (3.6 km2) in the presence of threshold transitions between matrix and preferential flow. We adopt the concepts of microstates and macrostates from statistical mechanics. The microstates are the detailed patterns of initial soil moisture that are inherently unknown, while the macrostates are specified by the statistical distributions of initial soil moisture that can be derived from the measurements typically available in field experiments. We use a physically based model and ensure that it closely represents the processes in the Weiherbach catchment, Germany. We then use the model to generate hydrologic response to hypothetical irrigation events and rainfall events for multiple realizations of initial soil moisture microstates that are all consistent with the same macrostate. As the measures of uncertainty at the plot scale we use the coefficient of variation and the scaled range of simulated vertical bromide transport distances between realizations. At the catchment scale we use similar statistics derived from simulated flood peak discharges. The simulations indicate that at both scales the predictability depends on the average initial soil moisture state and is at a minimum around the soil moisture value where the transition from matrix to macropore flow occurs. The predictability increases with rainfall intensity. The predictability increases with scale with maximum absolute errors of 90 and 32% at the plot scale and the catchment scale, respectively. It is argued that even if we assume perfect knowledge on the processes, the level of detail with which one can measure the initial conditions along with the nonlinearity of the system will set limits to the repeatability of experiments and limits to the predictability of models at the plot and catchment scales.

Preferential processing favouring threatening information has received increased attention because cognitive formulations have placed increased emphasis on its role as a key cognitive factor underlying vulnerability to and maintenance of anxiety disorders. The present dissertation comprises four empirical studies within the area of preferential processing. Two different outcome measures were used to index preferential processing of threat-related information: Skin conductance responses (SCRs) were used in Studies I, II, and III. The emotional Stroop task was used in Study IV. The main focus has been on preferential processing of threat-related information that occurs outside awareness, thus preferential preattentive processing. Study I investigated the role of traumatic combat experience with regard to preferential processing among UN soldiers following a presentation of threat-related pictures. Results indicated that soldiers with combat experience consistently reacted with lower SCRs compared to soldiers without combat experience. One issue addressed in the individual studies was the association between preferential preattentive processing and trait anxiety. Studies II, III, and IV showed that elevated levels of trait anxiety promote preferential preattentive processing of negatively valenced information, whereas elevated levels of social desirability generally prevent preferential preattentive processing of negatively valenced information. Study II highlighted the importance of including the social desirability factor when studying effects of trait anxiety on preferential processing. In addition, Studies III and IV explored the relationship between preferential processing and emotional vulnerability. The main findings support the notion of preferential preattentive processing of threat representing an underlying predisposition to heightened emotional vulnerability in response to stressful events.