Mechanochemical sulfidization of a mixed oxide-sulphide copper ore by co-grinding with sulfur and its effect on the flotation efficiency

Mechanochemical sulfidization of a mixed sulfide/oxide copper ore by co-grinding with sulfur and additives including Mg(NO₃)₂ and Fe(NO₃)₃ salts and iron, aluminum and magnesium powders was investigated for the first time. Also, the influence of sulfidization during the wet-milling process was examined on the separation efficiency and recovery of copper in detail. The results demonstrated that co-grinding with sulfur solely had the best flotation performance at the value of 0.5 wt.% and it was attributed to the possible existence of S-O bonding on copper oxides surfaces. In addition, adding magnesium nitrate salt, magnesium powder, iron nitrate salt and aluminum powder as additive associated with 0.5 wt% sulfur into ball milling caused the flotation improvement at the amounts of 0.2 wt%, 0.2 wt%, 0.5 wt% and 0.5 wt%, respectively. Also, the effect of grinding time and sulfidization pH with 0.5 wt% sulfur solely was determined and pHs of 7.5 to 8.5 gave the best results. The highest recovery (75.76%) and separation efficiency (63.44%) were achieved at pH of 7.5 and 8.5, respectively.     

ECFGM: enriched control flow graph miner for unknown vicious infected code detection

Vicious codes, especially viruses, as a kind of impressive malware have caused many disasters and continue to exploit more vulnerabilities. These codes are injected inside benign programs in order to abuse their hosts and ease their propagation. The offsets of injected virus codes are unknown and their targets usually are latent until they are executed and activated, what in turn makes viruses very hard to detect. In this paper enriched control flow graph miner, ECFGM in short, is presented to detect infected files corrupted by unknown viruses. ECFGM uses enriched control flow graph model to represent the benign and vicious codes. This model has more information than traditional control flow graph (CFG) by utilizing statistical information of dependent assembly instructions and API calls. To the best of our knowledge, the presented approach in this paper, for the first time, can recognize the offset of infected code of unknown viruses in the victim files. The main contributions of this paper are two folds: first, the presented model is able to detect unknown vicious code using ECFG model with reasonable complexity and desirable accuracy. Second, our approach is resistant against metamorphic viruses which utilize dead code insertion, variable renaming and instruction reordering methods.     

Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

An Efficient Finite Difference Method for The Time‐Delay Optimal Control Problems With Time‐Varying Delay

In this paper, an efficient finite difference method is presented for the solution of time‐delay optimal control problems with time‐varying delay in the state. By using the Pontryagin's maximum principle, the original time‐delay optimal control problem is first transformed into a system of coupled two‐point boundary value problems involving both delay and advance terms. Then the derived system is converted into a system of linear algebraic equations by using a second‐order finite difference formula and a Hermite interpolation polynomial for the first‐order derivatives and delay terms, respectively. The convergence analysis of the proposed approach is provided. The new scheme is also successful for the optimal control of time‐delay systems affected by external persistent disturbances. Numerical examples are included to demonstrate the validity and applicability of the new technique. Some comparative results are included to illustrate the effectiveness of the proposed method.     

Evolution of quantum correlations in the open quantum systems consisting of two coupled oscillators

The open quantum systems consisting of coupled and uncoupled asymmetric oscillators are considered with an initial quantum-dot trapped-ion coherent state. The quantum correlations between spatial modes of this trapped ion are examined to find their dependence on the temperature, asymmetric parameter, dissipation coefficient and the magnetic field. It is observed that the discord of the initial state is an increasing function of the asymmetric parameter and the magnetic field. Moreover, in the case of two uncoupled modes, entanglement and discord are decreasing functions of temperature and the dissipation coefficient. However, as the temperature and dissipation coefficient increase, the discord fades out faster. In the case of two coupled modes, as the temperature and dissipation coefficient increase, the sudden death of the entanglement and fade out of the discord happen sooner; moreover, as the magnetic field increases, the entanglement sudden death and the discord fade out time occur sooner. Also, with the increase in the asymmetric parameter, the entanglement sudden death is postponed. In addition, in the asymmetric system, appreciable discord can be created in the temperature range 0–10 K, while appreciable entanglement can be created in the temperature range 0–5 mK. Finally, it is observed that non-monotonic evolution of quantum correlations is due to coupling of modes.     

A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints

In the present paper, the dynamic facilities layout problem is studied in presence of ambiguity of information flow. Product demand (and consequently material flow) is defined as fuzzy numbers with different membership functions. The problem is modeled in fuzzy programming. Three models of expected value, chance-constrained programming and dependent-chance programming and two hybrid intelligent algorithms are then presented. At the end, efficiency of algorithms for solving fuzzy models of dynamic facilities layout is shown through some numerical examples.     

Experience classification for transfer learning in traffic signal control

In recent years, due to the drastic rise in the number of vehicles and the lack of sufficient infrastructure, traffic jams, air pollution, and fuel consumption have increased in cities. The optimization of timing for traffic lights is one of the solutions for the mentioned problems. Many methods have been introduced to deal with these problems, including reinforcement learning. Although a great number of learning-based methods have been used in traffic signal control, they suffer from poor performance and slow learning convergence. In this paper, a transfer learning-based method for traffic signal control has been proposed. Multi-agent system has also been used for modelling the traffic network and transfer learning has been used to make reinforcement learning agents transfer their experience to each other. Furthermore, a classifier has been utilized to classify the transferred experiences. The results show that using the proposed method leads to a significant improvement on average delay time and convergence time of the learning process.

     

Vibrational investigation of the spinning bi-dimensional functionally graded (2-FGM) micro plate subjected to thermal load in thermal environment

Microsystem Technologies - This article presented a numerical method for discovering the free vibration of a spinning bi-dimensional functionally graded materials (FGM) micro circular plate exposed...     

Cylindrical functionally graded shell model based on the first order shear deformation nonlocal strain gradient elasticity theory

Microsystem Technologies - In this article, a cylindrical functionally graded shell model is developed in the framework of nonlocal strain gradient theory for the first time. For this purpose, the...     

Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. An innovative data-driven FDD methodology has been presented in this paper on the basis of a distributed configuration of three adaptive neuro-fuzzy inference system (ANFIS) classifiers for an industrial 440 MW power plant steam turbine with once-through Benson type boiler. Each ANFIS classifier has been developed for a dedicated category of four steam turbine faults. A preliminary set of conceptual and experimental studies has been conducted to realize such fault categorization scheme. A proper selection of four measured variables has been configured to feed each ANFIS classifier with the most influential diagnostic information. This consequently leads to a simple distributed FDD system, facilitating the training and testing phases and yet prevents operational deficiency due to possible cross-correlated measured data effects. A diverse set of test scenarios has been carried out to illustrate the successful diagnostic performances of the proposed FDD system against 12 major faults under challenging noise corrupted measurements and data deformation corresponding to a specific fault time history pattern.