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Avian nidopallium caudolaterale (NCL), a functional analogue of mammalian prefrontal cortex, is thought to be participated to goal-directed behavior. However, few studies so far investigated local field potential (LFP) properties within this area. In this study, we recorded the LFP activity from the NCL of six pigeons when they performed a goal-directed decision-making task in a plus-maze. Spectral analysis revealed a significant LFP-power increase in the gamma-band (40–60 Hz) during the decision-making process. Moreover, the LFP activity in the gamma-band was modulated by the behavioral outcomes of pigeons. It could decode effectively the motion directions of animals. These results indicate that the gamma rhythm of LFP recorded from the NCL correlates with the goal-directed behavior of pigeons.

Herein, we prepared autoclaved lightweight aggregates containing quartz tailings (QT) and determined their effect upon mechanical properties of concrete. QT, by pelletizing and autoclaving, were directly mixed with quicklime, cement and fine river sand to prepare lightweight aggregates, denoted as autoclaved QT aggregate (QTA). The prepared QTA were then used as coarse aggregates in manufacturing concrete. QT were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and laser particle size distribution analyzer; whislt the CaO/SiO2 ratio of QTA was investigated. The compressive strength and the structure efficiency of concrete containing the preapred QTA were thereafter determined. Results exhibit that QTA with the CaO/SiO2 ratio of 0.4 has the highest cylinder compressive strength of 20.6 MPa. The 28-d compressive strength of the QTA concrete is 74 MPa, retaining 95% of that of crushed stone (CS) concrete. The oven dry density of QTA concrete however dips 19% relative to the counterpart (CS concrete). It results in a 37.88 MPa·m3/t structure efficiency of QTA concrete, or 24% higher than the counterpart. The research is expected to provide an effective appraoch to reduce solid wastes paricularly QT.

Purpose – The purpose of this paper is to develop a potential nanoparticles lubricant additive for solving the problem of the insolubility and stable dispersity; the complex nanoparticles with core-shell structures are less studied in the field. Therefore, this paper determines novel complex nanoparticles and their tribological properties. Design/methodology/approach – According to the conventional preparation method, the complex nanoparticle styrene/calcium borate (PS/O-CaB) was synthesized. The microstructures of the as-obtained samples were characterized by X-ray diffraction infrared spectroscopy and thermogravimetric analysis. Tribological properties of PS/O-CaB used as lubricating oil additive were evaluated on four-ball tribometer. The worn surface of the steel ball was investigated by a three-dimensional non-contact surface profilometer and X-ray photoelectron spectroscopy (XPS). Findings – The results of the structure characteristic indicate two different crystalline forms, namely, Ca2B6O11 and Ca2B2O5, and the average size of calcium borate nanoparticles in PS/O-CaB is about 20-40 nm. Moreover, the good tribological properties are due to a wear resistance film containing both depositions and the tribochemical reaction products which comprise B2O3, FeB and Fe2O3. Originality/value – Novel complex nanoparticles with core-shell structure (PS/O-CaB) were successfully prepared. Moreover, the PS/O-CaB shows excellent tribological capacity such as load-carrying, friction-reducing and antiwear property.

Considering path optimization problem on mobile robot, an experimental platform based on NAO robot is set up to build navigation map and get optimal path under unknown environment. Based on simultaneous localization and mapping (SLAM) theory, the sonar sensor of the NAO robot is adopted to detect distances between robot and obstacles and between robot and end landmark. K-means algorithm is used to classify the obstacle and calculate the accurate distances, and a sonar sensor and a camera are used to distinguish the obstacle and the landmark. Then, Q-learning algorithm is adopted to build navigation map for the robot. After that, bi-directional parallel search strategy and simulate annealing algorithm are proposed to improve traditional artificial bee colony algorithm, and the improved artificial bee colony algorithm is introduced to find optimal path in the navigation map. Finally, the experimental results show that the built navigation map based the robot can find an optimal path from the origin to the landmark avoiding the obstacles and a faster convergence speed can be obtained by the improved artificial bee colony algorithm.

At present, the technology of the traditional relay controller and honeywell digital controller are adopted in many combustion machine control systems. But the two technologies show some shortcomings in practice. A new combustion machine control system based on PLC was presented in which the intelligent PID control algorithm was adopted. In the new proposed system, the temperature is controlled by the analog outputs. The practical application results show that the proposed system with properties of high accuracy, good stability and high reliability. The design of software and hardware of the new system is introduced in this paper.

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. This paper presents an approach, based on Particle Swarm Optimization (PSO) algorithm, to effectively address the assembly line balance problem with the complicating factors of parallel workstations. The method of linearly decreasing the inertia weight of the PSO (LDWPSO) is used in this paper to compare with the standard PSO. Experimental results show that the LDWPSO algorithm is superior to the standard PSO on some assembly line balancing problem.

A novel particle swarm optimization (NPSO) algorithm with dynamically changing inertia weight based on fltness and iterations was presented for improving the performance of the Particle Swarm Optimization algorithm. The new algorithm was tested with three benchmark functions. The experimental results show that the swarm can escape from local optimum, and it also can speed up the convergence of particles to improve the performance.

This paper presents an improved method of fabric defects inspection based on local entropy. The adaptive threshold was used for judging the defects of different pictures. It is good for reducing the error due to the change of the background and the subjective factor. The Tyler expand is also used to simplify the formula of the local entropy to accelerate the speed of calculation. In addition, noise in pictures is filtered out and the shape of the defects remains the same after the filtering by opening function of mathematical morphology. Final experiment results shows that this method has the advantage of high identification and good anti-noise ability.

A method of character recognition based on Hough transform and RBF neural network is proposed through research on weight accumulation algorithm of Hough transform. According to the feature of characters’ structure by using the duality of point-line Hough transform was done. In this method, the number of the points on the same line in parameter space and the position coordinates of the elements in image mapping space were taken to RBF neural network recognition system as characteristic input vector. It reduced the dimension of character feature vector and reflected the overall distribution of character lattice and the essential feature of character shape. The simulation results indicated there were some merits in this improved method: capability of recognition is strong, the quantity of calculation is small, and the speed of calculation is quick.