Improved Lagrangian relaxation generation decision-support in presence of electric vehicles

<p><span>Decision making strategies for resources available in macro/micro scales have long been a critical argument. Among existing methods to address such a mixed-binary optimization model, Lagrangian relaxation (LR) found universal acceptance by many utilities, offering a fast and accurate answer. This paper aims at retrofitting the solution way of LR algorithm by dint of meta-heuristic cuckoo search algorithm (CSA). When integrating CSA into LR mechanism, a tighter duality gap is catered, representing more accurate feasible solution. The key performance of CSA exhibits a head start over other classical methods such as gradient search (GS) and Newton Raphson (NR) when dealt with the relative duality gap closure in LR procedure. Further, electric vehicles (EV) with its associated hard constraints are encompassed into model to imperiling the proposed CSA-LR if encountered with nonlinear fluctuation of duality gap. Simulation results show that the proposed CSA-LR model outperforms the solution quality with/without EV as compared with conventional NR-LR method.</span></p>

Current-Resistance Effects Inducing Nonlinear Fluctuation Mechanisms in Granular Aluminum Oxide Nanowires

The unusual superconducting properties of granular aluminum oxide have been recently investigated for application in quantum circuits. However, the intrinsic irregular structure of this material requires a good understanding of the transport mechanisms and, in particular, the effect of disorder, especially when patterned at the nanoscale level. In view of these aspects, electric transport and voltage fluctuations have been investigated on thin-film based granular aluminum oxide nanowires, in the normal state and at temperatures between 8 and 300 K. The nonlinear resistivity and two-level tunneling fluctuators have been observed. Regarding the nature of the noise processes, the experimental findings give a clear indication in favor of a dynamic random resistor network model, rather than the possible existence of a local ordering of magnetic origin. The identification of the charge carrier fluctuations in the normal state of granular aluminum oxide nanowires is very useful for improving the fabrication process and, therefore, reducing the possible sources of decoherence in the superconducting state, where quantum technologies that are based on these nanostructures should work.

Complexity Synchronization of Energy Volatility Monotonous Persistence Duration Dynamics

A new concept named volatility monotonous persistence duration (VMPD) dynamics is introduced into the research of energy markets, in an attempt to describe nonlinear fluctuation behaviors from a new perspective. The VMPD sequence unites the maximum fluctuation difference and the continuous variation length, which is regarded as a novel indicator to evaluate risks and optimize portfolios. Further, two main aspects of statistical and nonlinear empirical research on the energy VMPD sequence are observed: probability distribution and autocorrelation behavior. Moreover, a new nonlinear method named the cross complexity-invariant distance (CID) FuzzyEn (CCF) which is composed of cross-fuzzy entropy and complexity-invariant distance is firstly proposed to study the complexity synchronization properties of returns and VMPD series for seven representative energy items. We also apply the ensemble empirical mode decomposition (EEMD) to resolve returns and VMPD sequence into the intrinsic mode functions, and the degree that they follow the synchronization features of the initial sequence is investigated.

Non-Linear Dynamics Analysis of Protein Sequences. Application to CYP450

The nature of changes involved in crossed-sequence scale and inner-sequence scale is very challenging in protein biology. This study is a new attempt to assess with a phenomenological approach the non-stationary and nonlinear fluctuation of changes encountered in protein sequence. We have computed fluctuations from an encoded amino acid index dataset using cumulative sum technique and extracted the departure from the linear trend found in each protein sequence. For inner-sequence analysis, we found that the fluctuations of changes statistically follow a −5/3 Kolmogorov power and behave like an incremental Brownian process. The pattern of the changes in the inner sequence seems to be monofractal in essence and to be bounded between Hurst exponent [1/3,1/2] range, which respectively corresponds to the Kolmogorov and Brownian monofractal process. In addition, the changes in the inner sequence exhibit moderate complexity and chaos, which seems to be coherent with the monofractal and stochastic process highlighted previously in the study. The crossed-sequence changes analysis was achieved using an external parameter, which is the activity available for each protein sequence, and some results obtained for the inner sequence, specifically the drift and Kolmogorov complexity spectrum. We found a significant linear relationship between activity changes and drift changes, and also between activity and Kolmogorov complexity. An analysis of the mean square displacement of trajectories in the bivariate space (drift, activity) and (Kolmogorov complexity spectrum, activity) seems to present a superdiffusive law with a 1.6 power law value.

Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning

Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions. Considering the restriction of linear unmixing model, nonlinear unmixing algorithms find their applications in complex scenes. Kernel-based algorithms serve as important candidates for nonlinear unmixing as they do not require specific model assumption and have moderate computational complexity. In this paper we focus on the linear mixture and nonlinear fluctuation model. We propose a two-step kernel-based unmixing algorithm to address the case where a large spectral library is used as the candidate endmembers or the sparse mixture case. The sparsity-inducing regularization is introduced to perform the endmember selection and the candidate library is then pruned to provide more accurate results. Experimental results with synthetic and real data, particularly those laboratory-created labeled, show the effectiveness of the proposed algorithm compared with state-of-art methods.

Nonlinear Degradation of System Configuration During the Development of an Accident

This paper utilises a methodology named “Risk SituatiOn Awareness Provision” (RiskSOAP). RiskSOAP expresses the capability of a system to meet its safety objectives by controlling its processes and communicating threats and vulnerabilities to increase the situation awareness of its end-users and support their decision-making. In reality safety-related system features might be partially available or unavailable due to design incompleteness or malfunctions. Therefore, respectively, the availability and capability of RiskSOAP mechanisms might fluctuate over time. To examine whether changes in RiskSOAP values correspond to a system degradation, we used the results of a previous study that applied the RiskSOAP methodology to the Überlingen mid-air collision accident. Complementary to the previous application where the RiskSOAP was calculated for four milestones of the specific event, in this study we divided the accident further into seventeen time-points and we calculated the RiskSOAP indicator per time-point. The results confirmed that the degradation of the RiskSOAP capability coincided with the milestones that were closer to the mid-air collision, while the plotting of the RiskSOAP indicator against time showed its nonlinear fluctuation alongside the accident development.