Experimental estimation of discord in an antiferromagnetic Heisenberg compound $$\hbox {Cu}(\hbox {NO}_{3})_{2}\cdot 2.5\,\hbox {H}_{2}\hbox {O}$$

Temperature-dependent static magnetic susceptibility and heat capacity data were employed to quantify quantum discord in copper nitrate \((\hbox {CN, Cu}(\hbox {NO}_{3})_{2}\cdot 2.5\, \hbox {H}_{2}\hbox {O})\) which is a spin 1/2 antiferromagnetic Heisenberg system. With the help of existing theoretical formulations, quantum discord, mutual information, and purely classical correlation were estimated as a function of temperature using the experimental data. The experimentally quantified correlations estimated from susceptibility and heat capacity data are consistent with each other, and they exhibit a good match with theoretical predictions. Violation of Bell’s inequality was also checked using the static magnetic susceptibility as well as heat capacity data. Quantum discord estimated from magnetic susceptibility as well as heat capacity data is found to be present in the thermal states of the system even when the system is in a separable state.     

Efficient controller synthesis for a fragment of \hbox {MTL}_{0, \infty }

In this paper we offer an efficient controller synthesis algorithm for assume-guarantee specifications of the form $\varphi _1 \wedge \varphi _2 \wedge \cdots \wedge \varphi _n \rightarrow \psi _1 \wedge \psi _2 \wedge \cdots \wedge \psi _m$ . Here, $\{\varphi _i,\psi _j\}$ are all safety-MTL $_{0, \infty }$ properties, where the sub-formulas $\{\varphi _i\}$ are supposed to specify assumptions of the environment and the sub-formulas $\{\psi _j\}$ are specifying requirements to be guaranteed by the controller. Our synthesis method exploits the engine of Uppaal-Tiga and the novel translation of safety- and co-safety-MTL $_{0, \infty }$ properties into under-approximating, deterministic timed automata. Our approach avoids determinization of Büchi automata, which is the main obstacle for the practical applicability of controller synthesis for linear-time specifications. The experiments demonstrate that the chosen specification formalism is expressive enough to specify complex behaviors. The proposed approach is sound but not complete. However, it successfully produced solutions for all the experiments. Additionally we compared our tool with Acacia+ and Unbeast, state-of-the-art LTL synthesis tools; and our tool demonstrated better timing results, when we applied both tools to the analogous specifications.     

The preparation of organic light-emitting diode encapsulation barrier layer by low-temperature plasma-enhanced chemical vapor deposition: a study on the $$\hbox {SiO}_\mathrm{x}\hbox {N}_\mathrm{y}$$ film parameter optimization

This study prepared \(\hbox {SiO}_\mathrm{x}\hbox {N}_\mathrm{y}\) film by using plasma-enhanced chemical vapor deposition in organic light-emitting diode (OLED) encapsulation to prevent the invasion of moisture and oxygen for longer light-emitting lifetime of OLED components. It applied high density inductively coupled plasma for the coating of film on polyethersulfone, silicon and glass substrate, and discussed the relevance between process parameters and quality characteristics including coating uniformity, coating thickness and moisture permeation. This study used Taguchi method to plan the experiment and calculated the optimal parameters of each quality, used technique for order preference by similarity to ideal solution and grey relational analysis to determine the optimal parameter of all qualities. The back-propagation neural network was combined with Levenberg–Marquardt algorithm to construct the simulation and prediction system. Based on the quality optimization design, the single layer film’s moisture permeation rate was 0.02 g/m\(^{2}\)/day, the maximum coating thickness reached 420 nm, and the fastest rate was 21 nm/min, which was higher than the industrial standard specification (10 nm/min) by 110 %.     

High granular and short term time series forecasting of $$\hbox {PM}_{2.5}$$ PM 2.5 air pollutant - a comparative review

Forecasting time series has acquired immense research importance and has vast applications in the area of air pollution monitoring. This work attempts to investigate the abilities of various existing techniques when applied for short term, high granular time series forecasting of PM_2.5. More specifically, a comparative study has been provided, taking into account both popularly used models and lesser-used models in this area. The study has been carried out considering ten well defined models that are ARIMA (auto-regressive integrated moving average), SARIMA (seasonal ARIMA), SES (single exponential smoothing), DES (double exponential smoothing), TES (triple exponential smoothing), ANN (artificial neural network), DT (decision tree), kNN (k-nearest neighbor), LSTM (long short-term memory) and MCFO (markov chain first order). A framework has been built that categories the models, implements them under identical execution environment and forecasts succeeding values. Implementation has been carried out over five data sets of real-world air pollution time series, that are collected from five differently located government setup monitoring stations over a period of 1 year (July 2018-June 2019). Rigorous statistical analysis has been performed that yields an insight to the nature and variability of these time series data. Forecasting has been carried out on short term basis, focusing on high granularity whereas, three different lengths of forecast horizon (1 day, 1 week, and 1 month) have been tested. Eventually, the models have been compared in terms of their associated performance measuring units namely, RMSE (root mean of squared error), MAE (mean absolute error) and MAPE (mean absolute percentage error). The comparative results verified with multiple datasets show that all the models posses less error for a shorter forecast horizon, where LSTM providing the best performance. Superiority of machine learning and deep learning models are found in case of longer length of forecast horizon with kNN achieving best accuracy whereas, significant performance degradation of ARIMA is found for longer forecast horizon. Moreover, TES, DT, kNN, LSTM, MCFO are found to be well adopted in relation with shape and variability of the data. Note that the performance on various length of high granular forecast horizon have been studied over multiple datasets that give an added value to this work.

     

Dynamics of the spherical 3- \mathit{U\underline{P}S/}S parallel mechanism with prismatic actuators

Recursive relations in kinematics and dynamics of the symmetric spherical 3- parallel mechanism having three prismatic actuators are established in this paper. Controlled by three forces, the parallel manipulator is a 3-DOF mechanical system with three parallel legs connecting to the moving platform. Knowing the position and the rotation motion of the platform, we develop first the inverse kinematics problem and determine the position, velocity, and acceleration of each manipulator’s link. Further, the inverse dynamic problem is solved using an approach based on the principle of virtual work, but it has been verified using the results in the framework of the Lagrange equations with their multipliers. Finally, compact matrix relations and graphs of simulation for the input forces and powers are obtained.     

Inductive Synthesis of an Automaton from Its Specification in the Logical Language \mathfrak{L}

An approach to the synthesis of an automaton specified by a formula of the logical language is proposed. This approach makes it possible to construct the automaton inductively, i.e., according to the structure of the formula, starting from automata corresponding to subformulas represented in the form of conjunction or disjunction of literals.     

\mathcal{MD}-HBase: design and implementation of an elastic data infrastructure for cloud-scale location services

The ubiquity of location enabled devices has resulted in a wide proliferation of location based applications and services. To handle the growing scale, database management systems driving such location based services (LBS) must cope with high insert rates for location updates of millions of devices, while supporting efficient real-time analysis on latest location. Traditional DBMSs, equipped with multi-dimensional index structures, can efficiently handle spatio-temporal data. However, popular open-source relational database systems are overwhelmed by the high insertion rates, real-time querying requirements, and terabytes of data that these systems must handle. On the other hand, key-value stores can effectively support large scale operation, but do not natively provide multi-attribute accesses needed to support the rich querying functionality essential for the LBSs. We present the design and implementation of $\mathcal {MD}$ -HBase, a scalable data management infrastructure for LBSs that bridges this gap between scale and functionality. Our approach leverages a multi-dimensional index structure layered over a key-value store. The underlying key-value store allows the system to sustain high insert throughput and large data volumes, while ensuring fault-tolerance, and high availability. On the other hand, the index layer allows efficient multi-dimensional query processing. Our optimized query processing technique accesses only the index and storage level entries that intersect with the query region, thus ensuring efficient query processing. We present the design of $\mathcal {MD}$ -HBase that demonstrates how two standard index structures—the K-d tree and the Quad tree—can be layered over a range partitioned key-value store to provide scalable multi-dimensional data infrastructure. Our prototype implementation using HBase, a standard open-source key-value store, can handle hundreds of thousands of inserts per second using a modest 16 node cluster, while efficiently processing multi-dimensional range queries and nearest neighbor queries in real-time with response times as low as few hundreds of milliseconds.     

Branching Time Logics \mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha } with Operations Until and Since Based on Bundles of Integer Numbers, Logical Consecutions, Deciding Algorithms

This paper is intended as an attempt to describe logical consequence in branching time logics. We study temporal branching time logics $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ which use the standard operations Until and Next and dual operations Since and Previous (LTL, as standard, uses only Until and Next). Temporal logics $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ are generated by semantics based on Kripke/Hinttikka structures with linear frames of integer numbers $\mathcal {Z}$ with a single node (glued zeros). For $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ , the permissible branching of the node is limited by α (where 1≤α≤ω). We prove that any logic $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ is decidable w.r.t. admissible consecutions (inference rules), i.e. we find an algorithm recognizing consecutions admissible in $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ . As a consequence, it implies that $\mathcal {BTL}^{\mathrm {U,S}}_{\mathrm {N},\mathrm {N}^{-1}}(\mathcal {Z})_{\alpha }$ itself is decidable and solves the satisfiability problem.     

An upper bound on the minimum number of monomials required to separate dichotomies of {-1, 1}n

It is known that any dichotomy of {-1, 1}n can be learned (separated) with a higher-order neuron (polynomial function) with 2n inputs (monomials). In general, less than 2n monomials are sufficient to solve a given dichotomy. In spite of the efforts to develop algorithms for finding solutions with fewer monomials, there have been relatively fewer studies investigating maximum density (Pi(n)), the minimum number of monomials that would suffice to separate an arbitrary dichotomy of {-1, 1}n . This article derives a theoretical (upper) bound for this quantity, superseding previously known bounds. The main theorem here states that for any binary classification problem in {-1, 1}n (n > 1), one can always find a polynomial function solution with 2n -2n/4 or fewer monomials. In particular, any dichotomy of {-1, 1}n can be learned by a higher-order neuron with a fan-in of 2n -2n/4 or less. With this result, for the first time, a deterministic ratio bound independent of n is established as Pi(n)/2n < or = 0 75. The main theorem is constructive, so it provides a deterministic algorithm for achieving the theoretical result. The study presented provides the basic mathematical tools and forms the basis for further analyses that may have implications for neural computation mechanisms employed in the cerebral cortex.     

折扣{0-1}背包问题的简化新模型及遗传算法求解

当前折扣{0-1}背包问题（D{0-1}KP）模型将折扣关系作为一个新的个体,导致求解过程必需采取修复法对个体编码进行修复,求解方式较少。针对求解方法单一的问题,通过改变模型中二进制的编码表达方式,提出折扣关系不在个体编码中的表达方法。首先,设定对任意折扣关系,当且仅当所涉及个体编码值同时为1（即其乘积为1）时,折扣关系成立,据此建立简化折扣{0-1}背包问题（SD{0-1}KP）模型;然后,针对SD{0-1}KP模型,基于杰出者保留策略（EGA）,结合贪心策略（GRE）,提出改进遗传算法——第一遗传算法（FG）;最后,再结合罚函数法,提出求解SD{0-1}KP高精度罚函数法——第二遗传算法（SG）。结果表明,SD{0-1}KP能够完全覆盖D{0-1}KP问题领域,与FirEGA相比,所提出的两类算法在求解速度方面优势明显,且SG算法首次引入罚函数法,有效地丰富了该问题的求解算法。     

Robust Control of an LUSM-Based $Xhbox{--}Yhbox{--}theta$ Motion Control Stage Using an Adaptive Interval Type-2 Fuzzy Neural Network

The robust control of a linear ultrasonic motor based $Xhbox{--}Yhbox{--}theta$ motion control stage to track various contours is achieved by using an adaptive interval type-2 fuzzy neural network (AIT2FNN) control system in this study. In the proposed AIT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms are derived using the Lyapunov stability theorem to train the parameters of the IT2FNN online. Furthermore, a robust compensator is proposed to confront the uncertainties including the approximation error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of lumped uncertainty in the robust compensator, an adaptive lumped uncertainty estimation law is also investigated. In addition, the circle and butterfly contours are planned using a nonuniform rational B-spline curve interpolator. The experimental results show that the contour tracking performance of the proposed AIT2FNN is significantly improved compared with the adaptive type-1 FNN. Additionally, the robustness to parameter variations, external disturbances, cross-coupled interference, and frictional force can also be obtained using the proposed AIT2FNN.      

用仿真确定银行业务窗口的最佳利用状态

利用CSIMAN仿真技术，建立一个银行的业务窗口仿真系统，并根据CSIMAN仿真系统中的资源利用率NR(I)和实体排队长度NQ(I)，确定业务窗口的最佳利用状态。     

The Optimisation of Renewable Energy Sources in an Electrical Power System by Use of Simulation and Deterministic Planning Models

This paper presents two models which have been developed to study the operational and economic impact of integrating renewable power sources in a large scale electricity grid. The first of these models simulates the hour-by-hour operation of the grid and how conventional power plant is scheduled to accommodate renewable power sources in order to meet the total demand. The second model is a deterministic model model which assesses the economics of renewable power sources alongside conventional forms of power generation. Results are presented showing how the two models can be used together in order to study the operational and economic impact of wind and photovoltaic power when integrated into the electrical grid systems of England and Wales, the Republic of Ireland and Portugal.