Finding Second-Order Clubs

Modeling data entities and their pairwise relationships as a graph is a popular technique to visualizing and mining information from datasets in a variety of fields such as social networks, biological networks, web graphs, and document networks. A powerful technique in this setting involves the detection of clusters. Clique, a subset of pairwise adjacent vertices, is often viewed as an idealized representation of a cluster. However, in the presence of errors in the data on which the graph is based, clique requirement may be too restrictive, resulting in small clusters or clusters that miss key members. Consequently, graph-theoretic clique generalizations based on the principle of relaxing elementary structural properties of a clique have been proposed in diverse fields to describe clusters of interest. For example, an s-club is a distance-based clique relaxation originally introduced in social network analysis to model cohesive social subgroups. In this dissertation, we consider low-diameter clusters that require another property like robustness, heredity, or connectedness (parameterized by r) to hold, in addition to the diameter. Specifically, we study s-clubs with side-constraints to make them less “fragile”, i.e., less susceptible to increase in the diameter if vertices (and edges) are deleted. The overall goal of this dissertation is to develop effective exact algorithms with an emphasis on s = 2, 3, 4 and low values of r to solve the maximum r-robust s-club and r-hereditary s-club problems on moderately large instances (around 10,000 vertices and less than 5% density). We analyze the complexity of the associated feasibility testing and optimization problems. Cut-like formulations are proposed for the maximum r-robust s-club problem with r ≥ 2 and s ∈ {2, 3, 4}. We explore preprocessing techniques and develop a graph decomposition approach for solving such problems. The computational benefits of each of the algorithmic ideas are empirically evaluated through our computational studies. Our approach permits us to solve problems optimally on very large and sparse real-life networks

Shipment sizing for autonomous trucks of road freight

Unprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks. Empirical data of estimated autonomous truck costs is collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction. Autonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network. The findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review. Logistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size. This study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks

ADJUSTING NETWORK PARAMETERS DYNAMICALLY TO ACCELERATE MESH NETWORK CONVERGENCE

Techniques are described to adaptively adjust network parameters by adjusting the network scale. On one side, a smooth Clear Channel Assessment (CCA) mechanism may adaptively adjust the network scale to take precautions for collision. On the other side, a Cyclic Redundancy Check (CRC) mechanism may also automatically adjust the network scale to detect the collision. This may speed up network formation and convergence significantly, and maintain the network effectively

DYNAMICAL LINK METRIC ADJUSTMENT USING CLASSIFICATION AND REGRESSION TREE (CART) AND SOFTWARE DEFINED NETWORKING (SDN) TECHNOLOGIES

Presented herein are techniques to dynamically switch between different link metric algorithms based on Classification And Regression Tree (CART) and Software Defined Networking (SDN) technologies

Perceived overqualification and deviant innovation behavior: The roles of creative self-efficacy and perceived organizational support

Employees’ perceived overqualification (POQ) is prevalent in organizations and has drawn increasing attention from both researchers and practitioners. Drawing from social cognitive theory, the purpose of this study is to extend existing understanding of the consequences of POQ by examining how and when POQ leads to deviant innovation behavior. This study hypothesizes that employees’ POQ indirectly impacts deviant innovation through enhanced creative self-efficacy (CSE), and that perceived organizational support (POS) strengthens this indirect relationship. Using data collected from 286 employees in China at two time points, this study found support for our hypotheses that POQ is positively related to CSE, and that CSE mediates the relationship between POQ and deviant innovation behavior. In addition, this study found that POS moderates the relationship between POQ and CSE, as well as the indirect effect of POQ on deviant innovation behavior via CSE. The theoretical and practical implications of our findings and future research directions are discussed

Physical detection of misbehavior in relay systems with unreliable channel state information

We study the detection 1 of misbehavior in a Gaussian relay system, where the source transmits information to the destination with the assistance of an amplify-and-forward relay node subject to unreliable channel state information (CSI). The relay node may be potentially malicious and corrupt the network by forwarding garbled information. In this situation, misleading feedback may take place, since reliable CSI is unavailable at the source and/or the destination. By classifying the action of the relay as detectable or undetectable, we propose a novel approach that is capable of coping with any malicious attack detected and continuing to work effectively in the presence of unreliable CSI. We demonstrate that the detectable class of attacks can be successfully detected with a high probability. Meanwhile, the undetectable class of attacks does not affect the performance improvements that are achievable by cooperative diversity, even though such an attack may fool the proposed detection approach. We also extend the method to deal with the case in which there is no direct link between the source and the destination. The effectiveness of the proposed approach has been validated by numerical results

Nanoscale domains in strained epitaxial BiFeO3 thin Films on LaSrAlO4 Substrate

BiFeO3 thin films with various thicknesses were grown epitaxially on (001) LaSrAlO4 single crystal substrates using pulsed laser deposition. High resolution x-ray diffraction measurements revealed that a tetragonal-like phase with c-lattice constant ~4.65 {\AA} is stabilized by a large misfit strain. Besides, a rhombohedral-like phase with c-lattice constant ~3.99 {\AA} was also detected at film thickness of ~50 nm and above to relieve large misfit strains. In-plane piezoelectric force microscopy studies showed clear signals and self-assembled nanoscale stripe domain structure for the tetragonal-like regions. These findings suggest a complex picture of nanoscale domain patterns in BiFeO3 thin films subjected to large compressive strains.Comment: 14 pages, 4 figure

Scutellarin regulates microglia-mediated TNC1 astrocytic reaction and astrogliosis in cerebral ischemia in the adult rats

Additional file 1: (A). Scutellarin at 0.54 mM did not elicit a noticeable reaction of GFAP/iNOS in TNC1. (B). iNOS mRNA expression in TNC1 astrocytes remained relatively unchanged at all time-points following treatment with BM, BM + L and CM; however, when incubated with CM + L for various time points, TNC1 showed a remarkable increase in iNOS peaking at 24 h. (C). Confocal images showing iNOS (C1-3) expression in TNC1 astrocytes incubated with different medium for 24 h. Compared with cells incubated in BM (C1) and BM + L (C2), TNC1 astrocytes incubated with CM + L (C3) were hypertrophic and showed a marked increase in iNOS immunofluorescence. Scale bars: 20 μm. DAPI—blue

Immunomodulatory role of estrogen in ischemic stroke: neuroinflammation and effect of sex

Although estrogen is predominantly related to the maintenance of reproductive functioning in females, it mediates various physiological effects in nearly all tissues, especially the central nervous system. Clinical trials have revealed that estrogen, especially 17β-estradiol, can attenuate cerebral damage caused by an ischemic stroke. One mechanism underlying this effect of 17β-estradiol is by modulating the responses of immune cells, indicating its utility as a novel therapeutic strategy for ischemic stroke. The present review summarizes the effect of sex on ischemic stroke progression, the role of estrogen as an immunomodulator in immune reactions, and the potential clinical value of estrogen replacement therapy. The data presented here will help better understand the immunomodulatory function of estrogen and may provide a basis for its novel therapeutic use in ischemic stroke