Nucleon Mass Splitting at Finite Isospin Chemical Potential

We investigate nucleon mass splitting at finite isospin chemical potential in the frame of two flavor Nambu--Jona-Lasinio model. It is analytically proved that, in the phase with explicit isospin symmetry breaking the proton mass decreases and the neutron mass increases linearly in the isospin chemical potential.Comment: 3 pages and no figure

Wireless sensor network aided search and rescue in trails

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on August 29, 2007)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2006.Dissertations, Academic -- University of Missouri--Columbia -- Computer science.In recent years, wireless sensor networks have been used in applications of data gathering and target localization across large geographical areas. In this thesis, we study the issues involved in applying wireless sensor networks to search and rescue of lost hikers in trails and focus on the optimal placement of sensors and access points such that the cost of search and rescue is minimized. Particularly, we address two problems: a) how to identify the lost hiker position as accurately as possible, i.e., obtain a small search region containing the lost hiker; and (b) how to search efficiently in search regions for different trail topologies and search agent capabilities. We study the problem of achieving smaller search regions with different problem attributes. For simpler trail topologies, we propose theoretical models that consider both efficiency and accuracy criteria and present analytical results. For complicated graph topologies, we develop efficient heuristic algorithms with various heuristics. In addition, we analyze the difference of single hiker and multiple hiker scenarios with different hiking dynamics. After access point deployment is decided, the actual cost of search in individual search regions can be computed. We analyze four different types of search and rescue agents, present algorithms to find the optimal search pathes for each one of them, and compute their search costs. The algorithms are developed based on solving Chinese Postman problems. Next, we present extensive experimental results to compare the performances of different methods and examine the accuracy of the mathematical models. A very fast heuristic method, divide-merge, is shown to outperform all others and finds near-optimal solutions. We also shows the effects of the graph topologies and number of access points on the solution qualities. Generally speaking, more access points lead to smaller search regions. Further improvement by moving the access points from vertices to edges is easily achieved when the number of access points is large or/and the average degree of vertices is small. Finally, we extend our results by relaxing the assumption of the uniform distribution of the hiker missing probability. We analyze the problem complexity and present a general solution

The resilience of interdependent transportation networks under targeted attack

Modern world builds on the resilience of interdependent infrastructures characterized as complex networks. Recently, a framework for analysis of interdependent networks has been developed to explain the mechanism of resilience in interdependent networks. Here we extend this interdependent network model by considering flows in the networks and study the system's resilience under different attack strategies. In our model, nodes may fail due to either overload or loss of interdependency. Under the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The resilience of interdependent SF networks is found in our simulation much smaller than single SF network or interdependent SF networks without flows.Comment: 5 pages, 4 figure

Epigenetic Down-Regulation of Sirt 1 via DNA Methylation and Oxidative Stress Signaling Contributes to the Gestational Diabetes Mellitus-Induced Fetal Programming of Heart Ischemia-Sensitive Phenotype in Late Life.

Rationale: The incidence of gestational diabetes mellitus (GDM) is increasing worldwide. However, whether and how GDM exposure induces fetal programming of adult cardiac dysfunctional phenotype, especially the underlying epigenetic molecular mechanisms and theranostics remain unclear. To address this problem, we developed a late GDM rat model. Methods: Pregnant rats were made diabetic on day 12 of gestation by streptozotocin (STZ). Experiments were conducted in 6 weeks old offspring. Results: There were significant increases in ischemia-induced cardiac infarction and gender-dependent left ventricular (LV) dysfunction in male offspring in GDM group as compared to controls. Exposure to GDM enhanced ROS level and caused a global DNA methylation in offspring cardiomyocytes. GDM attenuated cardiac Sirt 1 protein and p-Akt/Akt levels, but enhanced autophagy-related proteins expression (Atg 5 and LC3 II/LC3 I) as compared to controls. Ex-vivo treatment of DNA methylation inhibitor, 5-Aza directly inhibited Dnmt3A and enhanced Sirt 1 protein expression in fetal hearts. Furthermore, treatment with antioxidant, N-acetyl-cysteine (NAC) in offspring reversed GDM-mediated DNA hypermethylation, Sirt1 repression and autophagy-related gene protein overexpression in the hearts, and rescued GDM-induced deterioration in heart ischemic injury and LV dysfunction. Conclusion: Our data indicated that exposure to GDM induced offspring cardiac oxidative stress and DNA hypermethylation, resulting in an epigenetic down-regulation of Sirt1 gene and aberrant development of heart ischemia-sensitive phenotype, which suggests that Sirt 1-mediated signaling is the potential therapeutic target for the heart ischemic disease in offspring

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

Ubiquitous decision making processes in accounting outsourcing- Case study of four Finnish small and medium sized enterprises

This study focuses on the state-of the art of and decision making process in the outsourcing of accounting in Finnish firms. My aim is to explore what tools and criteria have been used, what has been the role of each stakeholder group, and what other factors could influence the decision making. Moreover I intend to discover how the new IT technology like cloud computing and social media has transformed the management style in this particular process. A research among 4 Finnish small and medium sized enterprises has been made in the form of interviews. All the results are collected and classified into 3 categories. The analysis of these results is made from 3 aspects: culture, technology and service. In the context of cultural dimensions, I find controversial intuitive results to the Finnish culture. According to the results, top management is generally the decision maker and employees are rarely involved in the process. New technologies and devices enable the evolution of IT outsourcing channel, hence transform the business management style. Most companies are active in deploying advanced IT systems by making outsourcing contracts instead of in-house development. When selecting the service provider, companies usually consider what are their own demands and requirements, how expertise the service provider is, and the capability of system updating. A general framework of the mechanisms of integration and coordination of service processes is used to describe the efficiency and development of case firms. The framework, which assume that the different type of services differ inherently by the delivery strategies, is adopted from the model of service and channels to show the efficiency of outsourcing of accounting services among the case firms. The research results could help managers of accounting service providers or consultants to identify the key issues, focus on the strategic solutions and achieve edges over the competitors in the decision making processes

What do seller manipulations of online product reviews mean to consumers?

There is growing evidence that consumers are influenced by online product reviews when making a variety of purchase decisions. Firms are therefore tempted to monitor and manipulate online product reviews on the company\u27s website or forum to influence consumer perceptions by anonymously posting positive reviews, hiding or deleting unfavorable reviews, or offering rewards to consumers who post favorable reviews. Our review of the literature has revealed a surprising shortage of work directed at the development of an integrative theoretical framework or rigorous empirical studies on the effectiveness and the exact impact of such activities on the payoffs to various parties. This study fills a void in the online marketing and information manipulation literature by studying consumers\u27 suspicion, awareness and evaluation of specific manipulation tactics through in-depth interviews with 16 experienced online shoppers in China. We adopt a grounded theory approach to analyze the qualitative data and end up with a series of research propositions (research framework) for further testing and verification. The findings about consumers\u27 views of online manipulations would provide valuable insights to industry associations and policy makers on whether and how to regulate online manipulation activities to ensure the healthy development of the e-commerce