Nonkinetic changes in heat-treated glasslike carbons part I: Pore size and bulk dimensions

Small-angle X-ray scattering measurements have been used to characterize the radius of gyration R_g of the pores in a glasslike carbon heat treated between 1000 and 2800°C for periods up to 150 hours. Measurements were also made of the irreversible bulk dimensional changes Δl. In both cases, a nonkinetic increase occurs when the heat treatment temperature is increased stepwise to a higher value. The relative changes ΔR_g/R_g and Δl/l exhibit a similar three-region behavior: an initial increase with the annealing temperature up to 1600°C, a plateau up to about 2200°C, and thereafter an increase again. However, the maximum dilatation Δl/l is scarcely 3% whereas the increase in ΔR_g/R_g is twofold. The first region is attributed to a gas pressure mechanism, and the highest-temperature region results from strain relief due to the mismatch of the thermal expansion coefficients in the directions perpendicular and parallel to the layers in graphitic materials. The intermediate region is explained by a combination of both mechanisms.     

CF4和惰性气体混合物的输运性质的计算

The present work is concerned with determining the viscosity, diffusion, thermal diffusion factor and thermal conductivity of five equimolar binary gas mixtures including: CF4-He, CF4-Ne, CF4-Ar, CF4-Kr, CF4-Xe from the principle of corresponding states of viscosity by the inversion technique. The Lennard-Jones (12-6) model potential is used as the initial model potential. The calculated interaction potential energies obtained from the inversion procedure is employed to reproduce the viscosities, diffusions, thermal diffusion factors, and thermal conductivities. The accuracies of the calculated viscosity and diffusion coefficients were 1% and 4%, respectively.     

Novel Fabrication of Photoactive CuO/HY Zeolite as an Efficient Catalyst for Photodecolorization of Malachite Green

Topics in Catalysis - Copper oxide loaded on HY zeolite (CuO/HY) catalyst was prepared via a facile electrochemical method, and its photoactivity was evaluated by the decolorization of malachite...     

Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

Non-Orthogonal Multiplex Access (NOMA) can be deployed in Unmanned Aerial Vehicle (UAV) networks to improve spectrum efficiency. Due to the broadcasting feature of NOMA-UAV networks, it is essential to focus on the security of the wireless system. This paper focuses on maximizing the secrecy sum rate under the constraint of the achievable rate of the legitimate channels. To tackle the non-convexity optimization problem, a reinforcement learning-based alternative optimization algorithm is proposed. Firstly, with the help of successive convex approximations, the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization tools. Afterwards, through plenty of explorations of the wireless environment, the Q-learning networks approach the optimal location transition strategy of the UAV, even without the wireless channel state information.     

A study of Cr(VI) and NH4+adsorption using greensand (glauconite) as a low‐cost adsorbent from aqueous solutions

This paper investigates the removal of Cr(VI) and NH⁴⁺ from aqueous solutions using greensand (glauconite). The effects of adsorbent dosage, contact time, initial Cr(VI) and NH⁴⁺ concentrations, and pH were investigated in batch experiments. The results show that these parameters influenced Cr(VI) and NH⁴⁺ removal using glauconite considerably. Pseudo‐first‐order, pseudo‐second‐order and intraparticle diffusion kinetic models were applied to explain the kinetic data, and the pseudo‐second‐order model achieved good agreement. The equilibrium isotherm data are coordinated with the Freundlich, Langmuir models, Temkin and Dubinin–Radushkevich; the Freundlich model proved most best suitable. The removal efficiency of Cr(VI) and NH₄⁺ were 12.21 mg/g (54% at pH 3) and 19.24 mg/g (77.08% at pH 7). All in all, the results showed that the adsorption process on glauconite could be used as an effective method for removing Cr(VI) and NH⁴⁺ from aqueous solutions.     

Correlation,Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

Correlation tracker is computation intensive (if the search space or the template is large), has template drift problem, and may fail in case of fast maneuvering target, rapid changes in its appearance, occlusion suffered by it and clutter in the scene. Kalman filter can predict the target coordinates in the next frame, if the measurement vector is supplied to it by a correlation tracker. Thus, a relatively small search space can be determined where the probability of finding the target in the next frame is high. This way, the tracker can become fast and reject the clutter, which is outside the search space in the scene. However, if the tracker provides wrong measurement vector due to the clutter or the occlusion inside the search region, the efficacy of the filter is significantly deteriorated. Mean-shift tracker is fast and has shown good tracking results in the literature, but it may fail when the histograms of the target and the candidate region in the scene are similar (even when their appearance is different). In order to make the overall visual tracking framework robust to the mentioned problems, we propose to combine the three approaches heuristically, so that they may support each other for better tracking results. Furthermore, we present novel method for (1) appearance model updating which adapts the template according to rate of appearance change of target, (2) adaptive threshold for similarity measure which uses the variable threshold for each forthcoming image frame based on current frame peak similarity value, and (3) adaptive kernel size for fast mean-shift algorithm based on varying size of the target. Comparison with nine state-of-the-art tracking algorithms on eleven publically available standard dataset shows that the proposed algorithm outperforms the other algorithms in most of the cases.     

Significantly enhanced electrocatalytic activity of copper for hydrogen evolution reaction through femtosecond laser blackening

In this work, we report on the creation of a black copper via femtosecond laser processing and its application as a novel electrode material. We show that the black copper exhibits an excellent electrocatalytic activity for hydrogen evolution reaction (HER) in alkaline solution. The laser processing results in a unique microstructure: microparticles covered by finer nanoparticles on top. Electrochemical measurements demonstrate that the kinetics of the HER is significantly accelerated after bare copper is treated and turned black. At ?0.325 V (v.s. RHE) in 1 M KOH aqueous solution, the calculated area-specific charge transfer resistance of the electrode decreases sharply from 159 Ω cm² for the untreated copper to 1 Ω cm² for the black copper. The electrochemical surface area of the black copper is measured to be only 2.4 times that of the untreated copper and therefore, the significantly enhanced electrocatalytic activity of the black copper for HER is mostly a result of its unique microstructure that favors the formation and enrichment of protons on the surface of copper. This work provides a new strategy for developing high-efficient electrodes for hydrogen generation.     

Adaptive QoS scheduling in wireless cellular networks

Quality of service (QoS) has been always controversial in resource shared networks. Scheduling as a packet prioritizing mechanism at Data Link Layer (DLL) contributes to QoS guarantee provisioning significantly. In this paper, a novel packet scheduler is developed in wireless cellular networks. The proposed scheme provides QoS-guaranteed service for the applications running on the sensor nodes in all the three aspects of QoS, i.e. data rate, packet loss and packet delay with regard to jitter simultaneously. We establish a three-dimensional space with certain basis vectors for QoS and introduce the efficient point of performance in terms of QoS provisioning in that space. Then we develop a generalized metric, the QoS-deviation, which is the Euclidean distance between the QoS work point of flows and the QoS efficient point in the proposed space. Based on this metric, a novel scheduling approach, namely AQDC, is designed which makes it possible to tune the trade-off between QoS provisioning and throughput optimization in an adaptive manner depending on the current Cell QoS-deviation level (CDL). Furthermore, we also develop another scheduler, namely ARTC, which is the residual-time version of the AQDC scheduler. Finally, a QoS-deviation-based CAC policy will be introduced which can be applied to all schedulers without any consideration about their structure and can be employed in cellular packet switched networks.     

Nanoscopic volume trapping and transportation using a PANDA ring resonator for drug delivery

A novel design of nanoscopic volume transmitter and receiver for drug delivery system using a PANDA ring resonator is proposed. By controlling some suitable parameters, the optical vortices (gradient optical fields/wells) can be generated and used to form the trapping tools in the same way as the optical tweezers. By using the intense optical vortices generated within the PANDA ring resonator, the nanoscopic volumes (drug) can be trapped and moved (transport) dynamically within the wavelength router or network. In principle, the trapping force is formed by the combination between the gradient field and scattering photons, which is reviewed. The advantage of the proposed system is that a transmitter and receiver can be formed within the same system (device), which is called a transceiver, which is available for nanoscopic volume (drug volume) trapping and transportation (delivery).     

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.