First-principles study of the switching mechanism of [2]catenane molecular electronic devices

We present a first-principles study of the coherent charge transport properties of bistable [2]catenane molecular monolayers sandwiched between Au(111) electrodes. We find that conduction channels around the Fermi level are dominated by the two highest occupied molecular orbital levels from tetrathiafulvalene (TTF) and dioxynaphthalene (DNP) and the two lowest unoccupied molecular orbital levels from tetracationic cyclophane (CBPQT(4+)), and the OFF to ON switching results from the energetic shifts of these orbitals as CBPQT(4+) moves from TTF to DNP. We show that the superposition principle can be adopted for predicting the function of the composite device

Improving Depression Assessment & Management in Heart Failure Patients at the Pali Momi Medical Center

D.N.P.D.N.P. Thesis. University of Hawaiʻi at Mānoa 201

Focusing on the Value and Price of Digital Information Commodities

This study examines the value and price of digital information commodities, which are increasing in line with the advancing generalization of digital technology, via the Marxist labor theory of value, and explains how digital information commodities are produced, consumed and distributed based on this examination. We focus on the Marxist perspective, because it explains the inherent value and price of commodities in terms of the magnitude of social labor time involved, and through this, goes on to analyze the capitalist political and economic system as a whole. In this context, this study explains why these commodities are valueless goods due to the very innate characteristics of digital information commodities, and agrees with the adjacent assertion that the price of these commodities is a Marxist monopoly price. This connected analysis is supported by publications and numerous practical reviews, pinpointing how valueless digital information goods are commodified through state interventions such as intellectual property rights, and how these commodities are actually formed and maintained at a Marxist monopoly price. In the process of this analysis, we could see that mainstream media economics, ones characterized by actively embracing neoclassical economics, and to be more specific, those relying on the utility theory of value, as well as those advocating the arguments of the so-called ‘political economy of media’ which, while explaining the importance of knowledge and information via a Marxist labor theory of value, can yet be differentiated and contrasted from it. In other words, this study explains the digital media environment more fundamentally and concretely, centering on the fact that digital information commodities are of no value, unlike the existing approaches mentioned above, while also suggesting, via the knowledge gained, further implications for media and communication studies in general

Effective Memory Diversification in Legacy Systems

Memory corruption error is one of the critical security attack vectors against a wide range of software. Addressing this problem, modern compilers provide multiple features to fortify the software against such errors. However, applying compiler-based memory defense is problematic in legacy systems we often encounter in industry or military environments because source codes are unavailable. In this study, we propose memory diversification techniques tailored for legacy binaries to which we cannot apply state-of- the-art compiler-based solutions. The basic idea of our approach is to automatically patch the machine code instructions of each legacy system differently (e.g., a drone, or a vehicle firmware) without altering any semantic behavior of the software logic. As a result of our system, attackers must create a specific attack payload for each target by analyzing the particular firmware, thus significantly increasing exploit development time and cost. Our approach is evaluated by applying it to a stack and heap of multiple binaries, including PX4 drone firmware and other Linux utilities

Multimodal Epidemic Visual Analytics and Modeling

The risk of infectious disease increases due to various factors including the dense population, development of various transportations, urbanization, and abnormal weather conditions. Since the speed of epidemic spread is fast, it is necessary to respond quickly in order to prevent the high fatality rate. Therefore, a fast search for the highly accurate spreading model has to be focused on the proper analysis of disease spreading. There have been many studies to understand the disease spreading and the epidemic model is often used to analyze and predict the spread of infectious disease. However, it is limited to apply the epidemic model for the spread analysis because the model captures spreading changes only within the defined area. In this paper, we propose a framework for the disease spreading simulation with multimodal factors in the epidemic model and networks of possible spread routes. Our system provides an interactive simulation environment with the interregional disease spreading according to various spread parameters. Moreover, in order to understand the spreading directions, we extract vector fields over time and visualize the vector fields with the fatality of the disease. Therefore, users are able to understand the disease spreading phenomena and obtain appropriate models through our framework

Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

Molecular Dynamics Simulation Study on a Monolayer of Half [2]Rotaxane Self-Assembled on Au(111)

The self-assembled monolayer (SAM) structure of the tetrathiafulvalene-side half of the Stoddart−Heath type [2]rotaxane on Au(111) surface was investigated using molecular dynamics (MD) simulations. We find that the orientation of the cyclobis(paraquat-p-phenylene) (CBPQT) ring depends dramatically on the coverage, changing in order to obtain highly packed SAMs. The ring lies with its large hollow parallel to the surface at lower coverage (up to one CBPQT per 27 surface Au atoms with a footprint of 1.9 nm^2; 1/27) when free space is available around it, but as the coverage increases (up to one CBPQT per 12 surface Au atoms with a footprint of 0.9 nm^2; 1/12), it tilts completely around its axis and lies with its smaller side (paraquat or phenyl ring) parallel to the surface to accommodate the reduced area available. We find that the best packing densities correspond to one CBPQT per 12−18 surface Au atoms (1/18−1/12) with footprints in the range between 0.9 nm^2 and 1.3 nm^2

Superprotonic phase transition of CsHSO4: A molecular dynamics simulation study

The superprotonic phase transition (phase II --> phase I; 414 K) of cesium hydrogen sulfate, CsHSO4, was simulated using molecular dynamics with the "first principles" MSXX force field (FF). The structure, binding energy, and vibrational frequencies of the CsHSO4 monomer, the binding energy of the (H2SO4)2 dimer, and the torsion barrier of the HSO4- ion were determined from quantum mechanical calculations, and the parameters of the Dreiding FF for Cs, S, O, and H adjusted to reproduce these quantities. Each hydrogen atom was treated as bonded exclusively to a single oxygen atom (proton donor), but allowed to form hydrogen bonds to various second nearest oxygen atoms (proton acceptors). Fixed temperature-pressure (NPT) dynamics were employed to study the structure as a function of temperature from 298 to 723 K. In addition, the influence of several force field parameters, including the hydrogen torsional barrier height, hydrogen bond strength, and oxygen charge distribution, on the structural behavior of CsHSO4 was probed. Although the FF does not allow proton migration (i.e., proton jumps) between oxygen atoms, a clear phase transition occurred as demonstrated by a discrete change of unit cell symmetry (monoclinic to tetragonal), cell volume, and molar enthalpy. The dynamics of the HSO4- group reorientational motion also changed dramatically at the transition. The observation of a transition to the expected tetragonal phase using a FF in which protons cannot migrate indicates that proton diffusion does not drive the transition to the superprotonic phase. Rather, high conductivity is a consequence of the rapid reorientations that occur in the high temperature phase. Furthermore, because no input from the superprotonic phase was employed in these simulations, it may be possible to employ MD to hypothesize superprotonic materials