Influence of pores arrangement on stability of photonic structures during sintering

Discrete Element Method (DEM) has been used for numerical investigation of sintering-induced structural deformations occurring in inverse opal photonic structures. The influence of the initial arrangement of template particles on the stability of highly porous inverse opal α-Al₂O₃ structures has been analyzed. The material transport, densification, as well as formation of defects and cracks have been compared for various case studies. Three different stages of defects formation have been distinguished starting with local defects ending with intrapore cracks. The results show that the packing of the template particles defined during the template self-assembly process play a crucial role in the later structural deformation upon thermal exposure. The simulation results are in very good agreement with experimental data obtained from SEM images and previous studies by ptychographic X-ray tomography.     

Hydroxyapatite/bioactive glass functionally graded materials (FGM) for bone tissue engineering

In this work, HA/bioactive glass Functionally Graded Materials (FGMs) are obtained for the first time by means of Spark Plasma Sintering (SPS). Two series of highly dense 5 layered products, namely FGMS1 and FGMS2, are prepared under optimized SPS conditions, i.e. 1000 °C/2 min/16 MPa and 800 °C/2 min/50 MPa, respectively, using a die with varying cross section.Results arising from XRD, SEM, mechanical and biological characterization in SBF, evidence that lower temperature and higher-pressure levels used for FGMS2 samples provide better materials in terms of microstructure, compactness, hardness, elastic modulus and in vitro bioactivity. Indeed, a fully sintered and crack-free microstructure with no crystallisation at the top layer (100% bioactive glass) is correspondingly produced.The obtainment of such FGMs is quite promising, since it permits to vary the relative volume fractions of the two constituents and, consequently, tailor the biological response for specific clinical applications.     

聚酯类增塑剂的合成与应用研究进展

综述了国内外聚酯增塑剂的合成及应用的最新研究进展，重点介绍了超支化、生物基、石油基聚酯增塑剂的合成与制备方法以及不同类型聚酯增塑剂在聚氯乙烯(PVC)、淀粉、聚乳酸、聚氨酯弹性体与橡胶制品中的最新应用研究进展；并对聚酯增塑剂未来发展方向做了展望。     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.     

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

Investigations on grinding process of woven ceramic matrix composite based on reinforced fiber orientations

Fiber orientations play the decisive role in grinding process of woven ceramic matrix composites, but the influence of woven fibers in grinding process is not clear. This paper studies the surface quality and grinding force by comparing different woven surfaces. Through a series of experiments in optimized sampling conditions, we analyze characteristics of the material surface topography height, wave distribution and surface support properties in details. And we find some outstanding characteristics of the surface microstructure. We also study the influence of grinding processing parameters on surface microstructure. The results show that machining surface which contains more parallel fibers is rougher and more keenness than gauss surface. Grinding wheel speed and depth of cut have great influence on surface topography and surface support properties. And it is discovered that grinding forces are also highly dependent on fiber orientations. The mechanism of the grinding phenomena is also analyzed in this paper according to knowledge of fracture mechanics and mechanical damage phenomenology. The research obtained will be an important technical support on improving the processing quality of woven ceramic matrix composites.     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

Science fiction reduces the eeriness of android robots: A field experiment

As suggested by the uncanny valley hypothesis, robots that resemble humans likely elicit feelings of eeriness. Based on the psychological model of meaning maintenance, we expected that the uncanny valley experience could be mitigated through a fictional story, due to the meaning-generating function of narratives. A field experiment was conducted, in which 75 participants interacted with the humanlike robot Telenoid. Prior to the interaction, they either read a short story, a non-narrative leaflet about the robot, or they received no preliminary information. Eeriness ratings were significantly lower in the science fiction condition than in both other conditions. This effect was mediated by higher perceived human-likeness of the robot. Our findings suggest that science fiction may provide meaning for otherwise unsettling future technologies.     

New nanocomposite based on poly(lactic-co-glycolic acid) copolymer and magnetite. Synthesis and characterization

The study presents the preparation of the new magnetic nanocomposite based on PLGA and magnetite. The PLGA used to obtain the magnetic nanocomposites was synthesized by the copolymerization of lactic acid with glycolic acid, in the presence of tin octanoate [Sn(Oct)₂] as catalyst, by polycondensation procedure. Magnetite was obtained by co-precipitation from aqueous salt solutions FeCl₂/FeCl₃. The particles size of magnetite was 420 nm, and the saturation magnetization 62.78 emu/g, while the PLGA/magnetite nanocomposite size was 864 nm and the saturation magnetization 39.44 emu/g. The magnetic nanocomposites were characterized by FT-IR, DLS technique, SEM, VSM and simultaneous thermal analyses (TG–FTIR–MS). The polymer matrix PLGA acts as a shell and carrier for the active component, while magnetite is the component which makes targeting possible by external magnetic field manipulation. Based on the gases resulted by thermal degradation of PLGA copolymer, using the simultaneous analysis TG–FTIR–MS, a possible degradation mechanism was proposed.