Numerical algorithms for dynamic traffic demand estimation between zones in a network

This paper presents numerical methods for dynamic traffic demand estimation between N zones in a network, where the zones are disjoint subsets of nodes of the network. Traffic is assumed to be generated or absorbed only in the zones and nowhere else in the network. Traffic volumes between zones over a fixed period of time are modeled as independent random variables with unknown means which it is desired to estimate. For each zone, the volume of all incoming and outgoing traffic is counted on a regular basis but no information about the origin or destination of the observed traffic is used. Procedures are suggested for a regular update of estimates of the N(N - 1) mean traffic demands between the zones on the basis of an incoming stream of the 2N traffic counts. The procedures are based on an exponential smoothing scheme and are reminiscent of the expectation maximization (EM) algorithm if smoothing is removed. Fast and reliable numerical algorithms, based on the conjugate gradient method, are presented for normal as well as for Poisson traffic demands. The Poisson case is linked with entropy maximization. Computational tests based on simulated data demonstrate both the numerical and statistical efficiency of the procedures.     

Secured secret sharing technique based on chaotic map and DNA encoding with application on secret image

This paper shows a secured key-based (k, n) threshold cryptography scheme, where key as well as secret data is shared among set of participants. In this scheme, each share has some bytes missing and these missing bytes can be recovered from a set of exactly k shares, but not less than k. Therefore, each share contains partial secret information which is also encrypted by DNA encoding scheme. Additionally, this paper introduces a concept, where each share information size is varied. More precisely, each share contains a different percentage of secret information and which is strongly dependent on the key. Moreover key sensitivity analysis and statistical analysis prove the robustness against different cryptanalysis attacks and thus ensures high acceptability for information security requirements.     

An analytical procedure for multi-site,multi-season streamflow generation using maximum entropy bootstrapping

Stochastic time series models are very useful in many environmental domains. In this paper, an analytical procedure for multi-site, multi-season streamflow generation using maximum entropy bootstrap stochastic model (M3EB) is developed that can implicitly preserve both the spatial and temporal dependence structure, in addition to the other statistical characteristics present in the historical time series. The proposed model is computationally less demanding and simple in terms of modeling complexity. The maximum entropy bootstrap (MEB) generates random samples from the empirical cumulative distribution function (ECDF) and rearranges the random series based on the rank ordering of the historical time series. The modeling structure of MEB implicitly satisfies the ergodic theorem (preservation of summary statistics) and guarantees the reproduction of the time dependent structure of an underlying process. The orthogonal transformation is used with M3EB to capture the spatial dependence present in the multi-site collinear data. The performance of M3EB is verified by comparing the statistical characteristics between the observed and synthetically generated streamflows. Three case studies from Colorado River Basin, USA; Red River Basin, USA and Canada; and Cauvery River Basin, India; are used to demonstrate the advantages of M3EB. The statistical measures adopted for evaluation of M3EB performance include monthly statistics (mean, standard deviation and skewness), temporal and spatial correlation, smoothing (flows other than present in historical data) and extrapolation (flows outside the range of historical data). The M3EB model shows (i) a high level of accuracy in preserving the statistics; and (ii) a high computational efficiency. Since M3EB can be used for multiple variable problems, the model can be easily extended to other environmental or hydroclimatic time series data.     

Entropic Contributions to Protein Stability

Thermodynamic stability is an important property of proteins that is linked to many of the trade-offs that characterize a protein molecule and therefore its function. Designing a protein with a desired stability is a complicated task given the intrinsic trade-off between enthalpy and entropy which applies for both the folded and unfolded states. Traditionally, protein stability is manipulated by point mutations which regulate the folded state enthalpy. In some cases, the entropy of the unfolded state has also been manipulated by means that drastically restrict its conformational dynamics such as engineering disulfide bonds. In this mini-review, we survey various approaches to modify protein stability by manipulating the entropy of either the unfolded or the folded states. We show that point mutations that involve elimination of long-range contacts may have a greater destabilization effect than mutations that eliminate shorter-range contacts. Protein conjugation can also affect the entropy of the unfolded state and thus the overall stability. In addition, we show that entropy can contribute to shape the folded state and yield greater protein stabilization. Hence, we argue that the entropy component can be practically manipulated both in the folded and unfolded state to modify protein stability.     

Design method of the layered active magnetic regenerator (AMR) for hydrogen liquefaction by numerical simulation

The design procedure of an active magnetic regenerator (AMR) operating between liquid nitrogen temperature and liquid hydrogen temperature is discussed with the selected magnetic refrigerants. Selected magnetic refrigerants (GdNi₂, Dy_0.85Er_0.15Al₂, Dy_0.5Er_0.5Al₂, and Gd_0.1Dy_0.9Ni₂) that have different transition temperatures are layered in an AMR to widen the temperature span. The optimum volume fraction of the layered refrigerants for the maximum COP with minimum volume is designed in a two-stage active magnetic regenerative refrigerator (AMRR) using one dimensional numerical simulation. The entropy generation in each stage of the AMR is calculated by the numerical simulation to optimize the proposed design. The main sources of the entropy generation in the AMR are pressure drop, convection and conduction heat transfers in the AMR. However, the entropy generation by the convective heat transfer is mostly dominant in the optimized cases. In this paper, the design parameters and the operating conditions such as the distribution of the selected refrigerants in the layered AMR, the intermediate temperature between two stages and the mass flow rate of heat transfer fluid are specifically determined to maximize the performance of the AMR. The proposed design method will facilitate the construction of AMR systems with various magnetic refrigerants and conditions such as AMR size, operating temperature range, and magnetic field variation.     

Ordering Method and Empirical Study on Multiple Factor Sensitivity of Group Social Attitudes Based on Entropy Theory

When studying the various factors affecting a group’s social attitudes, minor changes in a factor will easily cause changes to other factors due to their association and relevance to each other; therefore, such a factor is more sensitive, although there is a difference between sensitivity and importance. In order to comprehensively learn about the influence of multiple factors, explorations based on entropy theory have been conducted to determine the sensitivity of each factor, to specify the difference between the frequency and sensitivity priority of entropy theory, and to provide a method, a way of thinking, and a detailed basis for the resolution of actual problems.     

Large magnetocaloric effect in sintered ferromagnetic EuS

We present magnetocaloric effect measurements of the ferromagnetic semiconductor EuS in the vicinity of its ordering temperature. Single phase EuS powder was synthesized by CS₂ gas sulfurization of Eu₂O₃. A sintered compact with relative density over 95% was prepared by pulsed electric current sintering of the powder. Temperature and magnetic field dependence of the magnetization and specific heat were characteristic of a paramagnetic to ferromagnetic second order phase transition. The entropy change induced by an external magnetic field and the specific heat were both close to those of a single crystal. We obtained an entropy-temperature (S–T) diagram of the EuS sintered compact. Carnot cycle liquefaction of hydrogen using EuS was compared with several other materials, with results indicating that sintered EuS is an excellent magnetic refrigerant for hydrogen liquefaction.     

Color smoothing for RGB-D data using entropy information

RGB-D sensors are capable of providing 3D points (depth) together with color information associated with each point. These sensors suffer from different sources of noise. With some kinds of RGB-D sensors, it is possible to pre-process the color image before assigning the color information to the 3D data. However, with other kinds of sensors that is not possible: RGB-D data must be processed directly. In this paper, we compare different approaches for noise and artifacts reduction: Gaussian, mean and bilateral filter. These methods are time consuming when managing 3D data, which can be a problem with several real time applications. We propose new methods to accelerate the whole process and improve the quality of the color information using entropy information. Entropy provides a framework for speeding up the involved methods allowing certain data not to be processed if the entropy value of that data is over or under a given threshold. The experimental results provide a way to balance the quality and the acceleration of these methods. The current results show that our methods improve both the image quality and processing time, as compared to the original methods.     

An activated-state model for the prediction of solid-phase crystallization growth kinetics in dried lactose particles

Molecular-level understanding of solid-phase crystallization growth kinetics has the potential to improve the fundamental basis for understanding this process. This understanding has been developed here as part of an activated-state model, which accounts for the effects of the moisture content and the temperature on the crystallization rate. Water-induced crystallization (WIC) at different temperatures (15 °C, 25 °C, 40 °C) and a constant relative humidity (75%) environment has been used to analyze the changes in enthalpy, entropy and Gibbs free energy of activation for the solid-phase crystallization of lactose. WIC showed that, at higher temperatures, crystallization commences at lower moisture contents. The enthalpy and Gibbs free energy of activation increased during the crystallization process, which suggested that the binding energy needed for the formation of an activated complex increased as the moisture content decreased, making the formation of the activated complex more difficult. The entropy of activation, on the other hand, decreased with the decrease in the moisture content. From the activated rate equation, the energy of activation has been estimated to be 39 ± 2 kJ mol⁻¹, which is similar to the literature value (40 kJ mol⁻¹) for the solid-phase crystallization of lactose. The reaction rate constant at 25 °C is 1.4 × 10⁻⁴ s⁻¹, which is similar to the literature value of 1.3 × 10⁻⁴ s⁻¹. The WLF equation is inconsistent and hence unreliable in predicting the rates of crystallization at the glass-transition temperature from the analysis of experimental data at different temperatures.     

Identification of vulnerable lines in power grids with wind power integration based on a weighted entropy analysis method

Vulnerable overhead electricity lines are a cause of serious risk to power distribution grids as damage can be the cause of large scale blackouts and cascading failures. With the integration of large scale wind power into generating capacity, both the topology structure and the distribution characteristics of power flow in distribution grid have undergone various changes that have increased line vulnerability. A novel approach to identify vulnerable lines based on the weighted entropy analysis method is proposed in this paper. In this approach, an assessment index, named the incremental power flow entropy, is first developed, which is used to describe influences caused by variation of the lines' capability of carrying power flow transfers on the vulnerability of the lines themselves at the same aggregation level. A second assessment index, named structural importance, describes the structural changes of a power grid that are caused by the integration of wind-generated electric power. The two assessment indices then are merged into one index by using the entropy weight analysis method, which can assess the vulnerability of the lines from the two aspects of power flow transmission and structural links. Vulnerability analysis under different situations, such as with and without the integration of the wind farm, and sharp fluctuations in wind speed at the wind farm, were carried out on an IEEE 39-bus system integrated with a 75 MW wind farm. Simulation results verified that the proposed assessment index not only can identify the vulnerable lines in a power grid with wind farm integration but also accurately reflected the vulnerability of the internal lines of the wind farm itself.