Frustration effect and possibility of spin quantum liquid state in a tetrahedral spin ½ molecule

In this work, we studied the magnetic properties of a regularly tetrahedral molecule made of four spin-½, interconnected by exchange. For this purpose, we used the Heisenberg model and performed an exact resolution. In the case of a ferromagnetic coupling among the spins, the system orders itself under a magnetic field without displaying any net spontaneous magnetization. It behaves as a high spin-2 unique magnet.Under antiferromagnetic exchange, some exotic behaviour has been revealed due to the frustration governing the spin edifice. Thus, the magnetization is quantized into plateaus, which are separated by quantum phase transitions. In particular, we have argued the possibility of a quantum spin liquid (QSL) state that can occur at low temperature.This contribution intends mainly to bring single-molecular magnets to the front line of innovative nanoscale applications.     

Quantized-states-based adaptive control against unknown slippage effects of uncertain mobile robots with input and state quantization

An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.     

“Quad-band flexible magnesium zinc ferrite (MgZnFe2O4)-based double negative metamaterial for microwave applications”

This article presents vertically coupled, rectangular complementary split-ring resonator-shaped quad-band double-negative (DNG) metamaterial unit cells, that is, having both negative permittivity and permeability, which redirect negative refractive and also are not found in nature. The metamaterial is fabricated on magnesium zinc ferrite-based flexible microwave substrates, and the flexible substrates are chosen with two different concentrations of magnesium (Mg) denoted by Mg₃₀ and Mg₅₀ for 30% and 50% of Mg, which possess dielectric constants of 4.32 and 3.15 and loss tangents of 0.003 and 0.005, respectively. The proposed metamaterials are demonstrated by utilizing the CST microwave simulator, and their effective parameters are extracted according to the Nicolson-Ross-Wire method. With Mg_30, the prepared, flexible metamaterial shows measured resonances at 3.70 GHz, 7 GHz, 8.60 GHz, and 9.78 GHz, whereas with Mg₅₀ it shows the measured resonances at 4.10 GHz, 7.70 GHz, 9.33 GHz, and 10.62 GHz. Very good effective medium ratios (EMR) along with DNG properties are obtained, namely 6.5 and 5.85 for Mg₃₀ and Mg_50, respectively, with a physical dimension of 12.5 × 9.5 mm² for both of the unit cells_. Also, the electric field, magnetic field, and surface current distribution at different resonances and the polarization insensitivity at different polarization angles were observed. Thus, the designed new flexible substrate microwave materials based on DNG metamaterials are potential candidates for S-, C- and X-band applications, as well as for flexible microwave technologies.     

An infeasible-start framework for convex quadratic optimization,with application to constraint-reduced interior-point and other methods

Mathematical Programming - A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored...     

A Structural Entropy Measurement Principle of Propositional Formulas in Conjunctive Normal Form

The satisfiability (SAT) problem is a core problem in computer science. Existing studies have shown that most industrial SAT instances can be effectively solved by modern SAT solvers while random SAT instances cannot. It is believed that the structural characteristics of different SAT formula classes are the reasons behind this difference. In this paper, we study the structural properties of propositional formulas in conjunctive normal form (CNF) by the principle of structural entropy of formulas. First, we used structural entropy to measure the complex structure of a formula and found that the difficulty solving the formula is related to the structural entropy of the formula. The smaller the compressing information of a formula, the more difficult it is to solve the formula. Secondly, we proposed a λ-approximation strategy to approximate the structural entropy of large formulas. The experimental results showed that the proposed strategy can effectively approximate the structural entropy of the original formula and that the approximation ratio is more than 92%. Finally, we analyzed the structural properties of a formula in the solution process and found that a local search solver tends to select variables in different communities to perform the next round of searches during a search and that the structural entropy of a variable affects the probability of the variable being flipped. By using these conclusions, we also proposed an initial candidate solution generation strategy for a local search for SAT, and the experimental results showed that this strategy effectively improves the performance of the solvers CCAsat and Sparrow2011 when incorporated into these two solvers.     

Fluid sensing using microcantilevers: From physics-based modeling to deep learning

In-situ measurements of the viscosity and density of small volumes of liquids are required in several industrial applications. MEMS sensors deploying vibrating microstructures constitute an attractive alternative given the significant impact of the surrounding liquid on their dynamic behavior. In this work, we combine physics-based modeling approaches and deep learning techniques to simultaneously estimate the density and viscosity of liquids from the resonance frequencies and quality factors of immersed microcantilevers. The physics-based model is first validated by comparing the simulated resonance frequencies and quality factors of immersed microcantilevers to those obtained from experiments conducted on a large variety of liquids. Then, we use the simulations results to train deep neutral networks to learn the mapping from the data space to the parameter space. The deep learning method shows high prediction accuracy provided that there is enough independent input data, shows no bias in the predicted values, and provides the results instantaneously. The optimal accuracy in the estimation of the liquid viscosity and density is achieved when the first resonance frequency and corresponding quality factor are used as inputs.     

Modeling Influence of Raltegravir Intensification on Viral Dynamics: Stability and Hopf Bifurcation

In this paper, we propose an ordinary differential equation model with logistic target cell growth to describe influence of raltegravir intensifi- cation on viral dynamics. The basic reproduction number R 0 is established. The infection-free equilibrium E 0 is globally attractive if R 0 < 1, while virus is uniformly persistent if R 0 > 0. In addition, we find that Hopf bifurcation can occur at around the positive equilibrium within certain parameter ranges. Numerical simulations are performed to illustrate theoretical results.     

An individual-based modeling framework for infectious disease spreading in clustered complex networks

We introduce an individual-based model with dynamical equations for susceptible-infected-susceptible (SIS) epidemics on clustered networks. Linking the mean-field and quenched mean-field models, a general method for deriving a cluster approximation for three-node loops in complex networks is proposed. The underlying epidemic threshold condition is derived by using the quasi-static approximation. Our method thus extends the pair quenched mean-field (pQMF) approach for SIS disease spreading in unclustered networks to the scenario of epidemic outbreaks in clustered systems with abundant transitive relationships.We found that clustering can significantly alter the epidemic threshold, depending nontrivially on topological details of the underlying population structure. The validity of our method is verified through the existence of bounded solutions to the clustered pQMF model equations, and is further attested via stochastic simulations on homogeneous small-world artificial networks and growing scale-free synthetic networks with tunable clustering, as well as on real-world complex networked systems. Our method has vital implications for the future policy development and implementation of intervention measures in highly clustered networks, especially in the early stages of an epidemic in which clustering can decisively alter the growth of a contagious outbreak.