Au/ZnO and Au/Fe2O3 catalysts for CO oxidation at ambient temperature: comments on the effect of synthesis conditions on the preparation of high activity catalysts prepared by coprecipitation

The preparation of Au/ZnO and Au/Fe₂O₃ catalysts using two coprecipitation methods is investigated to determine the important factors that control the synthesis of high activity catalysts for the oxidation of carbon monoxide at ambient temperature. In particular, the factors involved in the preparation of catalysts that are active without the need for a calcination step are evaluated. The two preparation methods differ in the manner in which the pH is controlled during the precipitation, either constant pH throughout or variable pH in which the pH is raised from an initial low value to a defined end point. Non-calcined Au/ZnO catalysts prepared using both methods are very sensitive to pH and ageing time, and catalysts prepared at a maximum pH = 5 with a short ageing time (ca. 0–3 h) exhibit high activity. Catalysts prepared at higher pH give lower activity. However, all catalysts require a short operation period during which the oxidation activity increases. In contrast, the calcined catalysts are not particularly sensitive to the preparation conditions. Non-calcined Au/Fe₂O₃ catalysts exhibit high activity when prepared at pH ≥ 5. Calcined Au/Fe₂O₃ prepared using the controlled pH method retain high activity, whereas calcined catalysts prepared using the variable pH method are inactive. The study shows the immense sensitivity of the catalyst performance to the preparation methods. It is therefore not surprising that marked differences in the performance of supported Au catalysts for CO oxidation that are apparent in the extensive literature on this subject, particularly the effect of calcination, can be expected if the preparation parameters are not carefully controlled and reported.     

Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Physicochemical properties of irradiated and loaded LDPE films with polyfunctional monomers

Polyfunctional monomers (PFMs), namely, trimethylol propane trimethacrylate (TMPTMA), trimethylol propane triacrylate, ethylene glycol dimethacrylate, and diethylene glycol diacrylate were blended with low‐density polyethylene (LDPE) and exposed to different doses of EB irradiation. Fourier transform infrared and ultraviolet and UV–vis spectroscopy of the unirradiated, irradiated, unloaded, and PFMs‐loaded LDPE films were studied under various irradiation doses up to 300 kGy. The degree of crosslinking and oxidative degradation, as measured by the spectroscopic parameters, were dependent on both the irradiation dose and the type of loaded PFMs. For all of the loaded monomers, the extent of crosslinking increased at different rates as a function of irradiation dose. TMPTMA monomer was the most efficient in enhancing the crosslinking of LDPE films compared to the other loaded monomers. However, the unloaded LDPE film showed the least extent of crosslinking. In addition, the EB‐radiation‐induced changes, such as trans‐vinylene formation, a decrease in vinyl and vinylidene unsaturation; and carbonyl double‐bond formation and change in crystallinity were correlated. The importance of these results on the prediction of the role of polyfunctional monomers in the production of crosslinked polymers is discussed. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2025–2035, 2003     

Video compression using frame redundancy elimination and discrete cosine transform coefficient reduction

The need of human beings for better social media applications has increased tremendously. This increase has necessitated the need for a digital system with a larger storage capacity and more processing power. However, an increase in multimedia content size reduces the overall processing performance. This occurs because the process of storing and retrieving large files affects the execution time. Therefore, it is extremely important to reduce the multimedia content size. This reduction can be achieved by image and video compression. There are two types of image or video compression: lossy and lossless. In the latter compression, the decompressed image is an exact copy of the original image, while in the former compression, the original and the decompressed image differ from each other. Lossless compression is needed when every pixel matters. This can be found in autoimage processing applications. On the other hand, lossy compression is used in applications that are based on human visual system perception. In these applications, not every single pixel is important; rather, the overall image quality is important. Many video compression algorithms have been proposed. However, the balance between compression rate and video quality still needs further investigation. The algorithm developed in this research focuses on this balance. The proposed algorithm exhibits diversity of compression stages used for each type of information such as elimination of redundant and semi redundant frames, elimination by manipulating consecutive XORed frames, reducing the discrete cosine transform coefficients based on the wanted accuracy and compression ratio. Neural network is used to further reduce the frame size. The proposed method is a lossy compression type, but it can reach the near-lossless type in terms of image quality and compression ratio with comparable execution time.

     

价格已经提高到了过去几十年从未见过的水平

从海湾合作委员会(GCC)生活用纸制造业的前景来看,回顾2021第四季度,或许中东、北非和远东地区的每位参与者都对供应链困境感到惊讶,其中大部分原材料供应比以往任何时候都要困难得多。这对供需前景造成了严重的负面影响,由于缺乏供应,需求大幅增加。然而,这个消极因素很可能会产生市场的连锁反应。价格也提高到了过去几十年从未见过的水平。纸张供应商之间的价格战以前从未发生过,值得注意的是,尽管销售量减少,但利润率令人惊讶地满意。从海湾合作委员会(GCC)一些首次公开募股(IPO)公司公布的业绩,就能理解与一年前的同期相比业绩明显向好。     

Design of a printed log‐periodic dipole array antenna with high gain for millimeter‐wave applications

In this article, a V‐band printed log‐periodic dipole array (PLPDA) antenna with high gain is proposed. The antenna prototype is designed, simulated, fabricated, and tested. Simulation results show that this antenna can operate from 42 to 82 GHz with a fractional impedance bandwidth of 64.5% covering the whole V‐band (50–75 GHz). The antenna has a measured impedance matching bandwidth that starts from 42 to beyond 65 GHz with good agreement between the experimental and simulated results. At 50 and 65 GHz, the antenna has a measured gain of 10.45 and 10.28 dBi, respectively, with a gain variation of 2.6 dBi across the measured frequency range. The antenna prototype exhibits also stable radiation patterns over the operating band. It achieves side‐lobe suppression better than 17.26 dB in the H‐plane and better than 8.95 dB in the E‐plane, respectively. In addition, the cross‐polarization component is 18.5 dB lower than the copolarization with front‐to‐back ratio lower than 24.1 dB in both E‐ and H‐planes across the desired frequency range. Based on a comparison of performance among the reported work in the literature, we can say that the proposed PLPDA antenna is a proper candidate to be used in many applications at V‐band frequency. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:185–193, 2015.     

Reconstruction of a complex-valued object in double-passage coherent imaging through a random-phase screen

We consider the reconstruction of a complex-valued object that is coherently illuminated and viewed through the same random-phase screen. The reconstruction is based on two intensity measurements: the intensity of the Fourier transform of the image and the intensity of the Fourier transform of the image when modulated with an exponential filter. The illumination beam has a Gaussian intensity profile of arbitrary width, and the phase screen is assumed to be described by a Gaussian random process of large variance and arbitrary correlation length. Computer-simulated examples of the reconstruction of a two-dimensional complex object demonstrate that the reconstruction is robust.     

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results. Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling. The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard. The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset. There were four combinations of model and sampling technique. The F-measure for the AlexNet model was 0.807 while that for the DenseNet model was 0.808. Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852. Combination 3 was the AlexNet model and reservoir sampling. It had an average F-measure of 0.8545. Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017. Finally, combination 4 was the DenseNet model and reservoir sampling. It had an average F-measure of 0.8111. Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.