Comparison of the Parameters of UWB Signals for Comparing the Characteristics of the Human Visual System and Matrix Receivers

Journal of Communications Technology and Electronics - A quantitative comparison of the spectral characteristics of the human visual system and matrix photodetectors is carried out. Criteria for a...     

Use of Nuclear Energy in Outer-Space Systems

Atomic Energy - The history and direction of further development of space nuclear energetics are examined. The functional diagram and principle of operation of a promising, powerful,...     

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of...     

Laboratory Studies to Determine Suitable Chemicals to Improve Oil Recovery from Garzan Oil Field

Garzan oil field is located at the south east of Turkey. It is a mature oil field and the reservoir is fractured carbonate reservoir. After producing about 1% original oil in place (OOIP) reservoir pressure started to decline. Waterflooding was started in order to support reservoir pressure and also to enhance oil production in 1960. Waterflooding improved the oil recovery but after years of flooding water breakthrough at the production wells was observed. This increased the water/oil ratio at the production wells. In order to enhance oil recovery again different techniques were investigated. Chemical enhanced oil recovery (EOR) methods are gaining attention all over the world for oil recovery. Surfactant injection is an effective way for interfacial tension (IFT) reduction and wettability reversal. In this study, 31 different types of chemicals were studied to specify the effects on oil production. This paper presents solubility of surfactants in brine, IFT and contact angle measurements, imbibition tests, and lastly core flooding experiments. Most of the chemicals were incompatible with Garzan formation water, which has high divalent ion concentration. In this case, the usage of 2-propanol as co-surfactant yielded successful results for stability of the selected chemical solutions. The results of the wettability test indicated that both tested cationic and anionic surfactants altered the wettability of the carbonate rock from oil-wet to intermediate-wet. The maximum oil recovery by imbibition test was reached when core was exposed 1-ethly ionic liquid after imbibition in formation water. Also, after core flooding test, it is concluded that considerable amount of oil can be recovered from Garzan reservoir by waterflooding alone if adverse effects of natural fractures could be eliminated.     

Diluted chemical bath deposition of CdZnS as prospective buffer layer in CIGS solar cell

In this study, dilute chemical bath deposition technique has been used to deposit CdZnS thin films on soda-lime glass substrates. The structural, morphological, optoelectronic properties of as-grown films have been investigated as a function of different Zn²⁺ precursor concentrations. The X-ray diffractogram of CdS thin-film reveals a peak corresponding to (002) plane with wurtzite structure, and the peak shift has been observed with the increase of the Zn²⁺ concentration upon formation of CdZnS thin film. From morphological studies, it has been revealed that the diluted chemical bath deposition technique provides homogeneous distribution of film on the substrate even at a lower concentration of Zn²⁺. Optical characterization has shown that the transparency of the film is influenced by Zn²⁺ concentration and when the Zn²⁺ concentration is varied from 0 M to 0.0256 M, bandgap values of resulting films range from 2.42 eV to 3.90 eV while. Furthermore, electrical properties have shown that with increasing zinc concentration the resistivity of the film increases. Finally, numerical simulation validates and suggests that CdZnS buffer layer with composition of 0.0032 M Zn²⁺ concentration would be a promising candidate in CIGS solar cell.     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Modeling of Ignition and Combustion of a Coflowing Hydrogen Jet in a Supersonic Air Flow

Combustion, Explosion, and Shock Waves - Results of a numerical study of mixing, ignition, and combustion of a cold hydrogen jet propagating along the lower wall of a channel parallel to a...     

Three dimensional reconstruction of brain tumor along with space occupying in lesions

Multimedia Tools and Applications - The three-dimensional models of brain tumors serve as diagnostic assistance for physicians, surgeons, and radiologists. The proposed system establishes an...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.