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Summary With the development of high-angle/horizontal well technology, unconventional tight reservoirs with moderate/high resistivity have gradually become the focus of exploration and development. However, in moderate-high-resistance reservoir environments, distorted responses can be observed regularly in the array laterolog curves, which will lead to misjudgment for the oil-gas reservoirs. Therefore, the detection performance of the array lateral instrument powered by low-frequency alternating current urgently needs to be clarified and the true resistivity of the formation is necessary to be analyzed and extracted. In this paper, the reasons for distorted responses are investigated as well as the qualitative and quantitative assessments of the detection performance of low-frequency alternating current lateral instrument in moderate-high resistance formations. Moreover, a rapid inversion method is proposed based on the initial value grading optimization strategy to invert the anisotropic resistivities from the observed data with outliers. Numerical simulation results show that the current flow characteristics vary significantly across stratigraphic models and the distorted responses are primarily caused by the high-resistance characteristics and borehole enlargement. The combination of the initial value grading optimization strategy and the regularization inversion algorithm improves the accuracy of the inverted resistivities even further. Synthetic and field examples are analyzed to illustrate the detailed implementations and the performance of the proposed inversion method.

The all-inorganic perovskite CsPbIBr2 is a promising material in the photovoltaic (PV) field due to its acceptable optical bandgap and favorable air-stable phase stability. However, the conventional solution-processed poor-coverage thin films make it a challenging task to prepare high-performance solar cells. In this work, we introduce cadmium ions (Cd2+) into CsPbIBr2 perovskite and investigate how this doping treatment would affect the PV performance of the carbon-based CsPbIBr2 all-inorganic perovskite solar cells (FTO/SnO2/CsPbIBr2/carbon). We found that the crystallinity and morphology of CsPbIBr2 thin films are significantly improved after Cd2+ doping, leading to improved light absorption and carrier transportation with reduced carrier recombination in perovskites. The optimized device achieved the highest efficiency of 6.79% in comparison with 4.36% for the device without Cd2+ doping, an increase of 56% in efficiency. Meanwhile, the optimized device with Cd2+ doping exhibited enhanced stability (76% of initial efficiency after 10 hours) in ambient atmosphere. Furthermore, the whole fabrication process of these devices is performed at low temperatures, not more than 160 °C. The present work provides a simple and economical strategy for the fabrication of low-temperature-processed CsPbIBr2 solar cells and the improvement in their performance.

The marching-on-in-degree (MOD) method has been proposed for solving time-domain integral equations (TDIEs), and it uses the associated Laguerre functions as temporal basis and testing functions. Although the MOD scheme is assumed to be stable and accurate, there exists little investigation into the accuracy of the integral operations involving the associated Laguerre functions of high degree. It is shown in this paper that the numerical quadrature for integrating the highly oscillatory Laguerre functions may not be accurate and this may result in instability for the MOD solvers. In this paper, the highly oscillatory nature of the high-degree associated Laguerre functions is investigated first. Based on that, the numerical procedures are described which result in the inaccuracy of the numerical quadrature associated with the highly oscillatory nature for the higher order Laguerre functions. This can produce instability for the MOD solvers when a solution requires a high degree of the Laguerre functions. Hence, a novel Filon-type quadrature method and a stabilized MOD technique for time-domain magnetic field integral equations (TD-MFIE-SMOD) are presented in this paper. Some numerical results are presented to illustrate the validity of the proposed Filon-type quadrature rule resulting in stable solution of the TD-MFIE for transient scattering problems.

With the rapid development of deep learning, intelligent schemes have been gradually introduced to solve various inverse nonlinear problems. In this paper, we combine an efficient adaptive deep neural network (ADNN) framework with an adaptive modified Levenberg-Marquardt (AMLM) algorithm based on a three-layer inversion model to exact the formation resistivity and invasion depth from the measurements of array laterolog. The ADNN presented in this paper can achieve the 2D/3D fast forward modeling of array laterolog. The AMLM algorithm and a hierarchical inversion scheme are adopted to improve the anti-noise ability and convergence in complex logging environments, as well as achieve the fast and accurate reconstruction of longitudinal resistivity profiles in high-angle (HA)/horizontal (HZ) wells. Numerical simulations show that ADNN-based forward modeling only takes 0.021 s for each logging point, and the maximum relative error is less than 2%. The three-layer inversion model can eliminate the effect of the surrounding bed and improve the inversion accuracy in thinly layered formations. The AMLM inversion algorithm can effectively suppress the influence of noise, and takes only 10 steps to achieve convergence.

Railway border reloading station is an important node of the interconnectivity of international railway transport corridor, which overall carrying capacity is critical to keep the corridor unblocked. Compared with general technical stations, the organization of cargo reloading at the station definitely has an impact on transport efficiency and capacity in addition to arriving, departing and conventional technical operations. By analyzing the operation steps and characteristics of railway border reloading station, the concept of overall carrying capacity was defined and its composition was confirmed. Based on this, the operation chain analysis method of the overall carrying capacity of the border reloading station was developed. Then we conduct a case study of Erlian station. Five operation chains were built to calculate the overall carrying capacity and identify the bottleneck. The operation chain analysis method proposed in this paper overcomes the defect of not considering the impact of the reloading operation on station capacity in the existing general technical station transport capacity calculation method. It can comprehensively consider the facilities and equipment of the station and the current traffic flow, so as to accurately find the weak links among them and provide a basis for the improvement of the transportation organization. Therefore, the conclusion is more scientific and reasonable.

Under the current situation that MOOC are highly valued, how to guide online students to learn through MOOC has become a research topic. The traditional score calculation model has some problems. This paper proposes a MOOC score calculation model based on incidence matrix. Compared with the traditional model, it is more reasonable and can effectively promote students’ learning enthusiasm and reduce the phenomenon of sudden improvement in scores.

Dust greatly influences the light transmittance of solar panels and their corresponding photovoltaic (PV) performance. However, this critical issue (dust resistance for PV devices) has not been given much attention. In this work, a new self-cleaning coating is proposed to address this problem. The coating consists of patterned scallion-like zinc oxide (ZnO), which is hydrothermally grown on a colloidal monolayer template. The crystallization and fluorescence properties of the scallion-like ZnO are quite good. This self-cleaning coating can reduce the light reflection of the PV device as well as convert the ultraviolet (UV) photons into visible photons, thus reducing light-induced degradation of amorphous silicon PV devices. Also, the surface of this coating possesses superhydrophobic properties, with a water contact angle (CA) larger than 150° and a sliding angle (SA) less than 10°, after modification with heptadecafluorodecyltrimethoxysilane (HTMS). Most importantly, a predictive self-assembly method enabled by Monte Carlo (MC) simulation is developed to obtain a wafer-scale colloidal monolayer consisting of hexagonal-close-packed polystyrene (PS) spheres. This method combines spin-coating with thermal treatment which plays a key role in forming a high-quality colloidal monolayer. As commonly known, identifying the optimized self-assembly temperature through experiments is a great challenge and no studies are located in the literature investigating predictive self-assembly of the colloidal monolayer. Herein, we develop MC simulation to predict the optimized temperature of the colloidal monolayer self-assembly, which can effectively reduce experimental burden, and then fabricate a wafer-scale colloidal monolayer with high quality for the first time.

A spaser based on dark quadrupolar mode of a single metallic nanodisk coated with a layer of gain media is studied theoretically. The absorption efficiency of the metallic structure, the gain efficiency of the gain media, and the scattering efficiency of the whole nanosystem are calculated separately. It is found that the ratio of the absorption and the scattering intensities (RAS) of the dark quadrupolar mode depends strongly on the gain coefficient, which increases from 0.8 to 4.39 with gain coefficient reaches the threshold, by contrast the bright dipolar mode keeps its RAS unchanged at 0.16. This is attributed to that the gain media mainly amplifies quadrupolar eigenmode composition of the dark quadrupolar mode, thus the scattering loss caused by dipolar eigenmode composition is under effective control, which changes the RAS and leads to super low-threshold spaser. Our works may benefit the achievement of spaser system with low scattering loss.

Breast cancer is one of the most common malignancies among women in the world. Early detection and reliable monitoring are important factors in improving the survival rate and quality of life of cancer patients. The machine learning-based microwave tumor detection has received widespread attention because of its safety and cheapness. In current microwave breast cancer detection, most of the research is to determine the presence of tumors and few studies focus on monitoring the tumor development and estimate the size of breast cancer tumors. In this paper, by setting up an ultrawideband microwave detection system and collected sequence of microwave scattering signals of breast tissue, we estimate and monitor the tumor size by using the Long Short-Term Memory (LSTM) method. According to the Gompertz tumor growth model, we established 3D breast electromagnetic models to simulate the tumor growth process in Computer Simulation Technology (CST) electromagnetic simulation platform. The experimental results finally indicate that the LSTM algorithm can predict the size and growth trend of the tumor well and realize the monitoring of breast tumors.

Prosody detection is gaining increasingly popularity in the domain of prosody research because of its significance in Text to Sound, Computer-aided pronunciation training (CAPT), etc. Pitch accent is an important part of prosody and many recognition models of both static and dynamic have been investigated for automatic labeling it. Recently, artificial neural networks, especially Recurrent Neural Networks (RNNs) have been applied in pitch accent detection. However, traditional recurrent neural networks are unable to learn and remember over long sequences due to the issue of back-propagated error decay. To solve this problem, this paper investigates the use of Long Short-Term Memory (LSTM) neural networks for automatic pitch accent detection. This paper encodes lexical and syntactic features as binary variables and uses syllable-based acoustic features including syllable duration, syllable energy, features related to the fundamental frequency. Our experimental results show that LSTM-RNNs for pitch accent detection achieves an accuracy of 89.0%, which is better than the results of using classical detection methods by about 83.2%.

Foreign accent automatic identification has a key role in many speech systems, such as speech recognition, speaker identification, voice conversion, and immigration screenings, etc. English speakers exhibit dialectal differences or non-native accents on specific features of their speech, and these features can be used to identify the dialect or native language of the speaker. In this paper, we proposed the consonant phoneme based Extreme Learning Machine (ELM) recognition model for accent identification based on the different pronunciation of English consonant phonemes by Arab native speakers. Mel-Frequency Cepstrum Coefficients (MFCCs) and the normalized energy parameter along with their first and second derivatives are used as acoustic features and trained with ELMs, SVMs and DBN classifiers. ELM classifier showed fast learning, and better performance, based on KFold validation with an accuracy of 88% and standard deviation (σ=0.0167), 76% by SVM and 64% with DBN classifier respectively. Our proposed ELM and SVM model showed an 11%, 16% increase in accuracy respectively over the previous work model by using the same classifier on multiple words based acoustic model to identify regional accents.

To meet the demands on wideband unidirectional antennas in distinguishing ICH and IS stroke based on classification algorithm, a novel folded antenna is presented. The design of the antenna starts with a planar diplo antenna with bridge-shape ground. Then some slots are added to the ground to expand the bandwidth. Next, a folding technology is applied to the monopole to enhance its directivity while the overall size is reduced markedly at the same time. At last, a reflector and a pair of top-facing π-shape parasitic patches are applied to the antenna to increase the gain and decrease the voltage standing wave ratio (VSWR) at 1.7GHz. The final designed antenna has a dimensions of 0.293λ×0.107λ×0.053λ (λ is the wavelength at the lowest operating frequency). It has a measured reflection coefficient less than -10dB fractional bandwidth of 76% (1.1–2.45 GHz) and a peak gain of 4.5 dBi. In order to validate the effectiveness of the proposed antenna in the application, the proposed folded antenna is simulated with the human brain model and the result is classified by PCA and LDA classification algorithms. Finally, the accuracy of the final classification result is 100%, which indicates that the proposed folded antenna can be well applied to the brain stroke detection system.

Hot spots have been considered as a dominant role in surface enhancement Raman scattering (SERS). Here, we investigated the size and shape effect of Ag nanocubes in surface plasmon response and SERS by both experiment and theory simulation method. Silver nanocubes with abundant “hot spots” were synthesized by a polyol method and utilized as electrodes for achieving high SERS enhancement. The absorption wavelengths of the nanocubes were tunable by changing the edge lengths of the nanoparticles. Finite-difference time-domain (FDTD) simulations were employed to investigate the local E-field generation around silver (Ag) nanocubes indicating remarkable E-field enhancement at the edges and vertexes known as “hot spots”. Then, the SERS analyses demonstrated that the SERS activity of various Ag nanocubes using crystal violet (CV) as probe molecules showed a significant enhancement. The enhancement factors of SERS show strong sensitivity between the nanoparticle size and shape. Moreover, the enhancement for CV on Ag nanocubes was significantly higher than for Ag nanospheres. Therefore, the stable and high-performance of Ag nanocubes SERS system shows great application prospects in both scientific researches and practical application.

The maximum conductivity achievable in Al-doped ZnO thin films prepared by atomic layer deposition (ALD) is limited by the low doping efficiency of Al. To better understand the limiting factors for the doping efficiency, the three-dimensional distribution of Al atoms in the ZnO host material matrix has been examined on the atomic scale using a combination of high-resolution transmission electron microscopy (TEM) and atom probe tomography (APT). Although the Al distribution in ZnO films prepared by so-called "ALD supercycles" is often presented as atomically flat δ-doped layers, in reality a broadening of the Al-dopant layers is observed with a full-width-half-maximum of ∼2 nm. In addition, an enrichment of the Al at grain boundaries is observed. The low doping efficiency for local Al densities > ∼1 nm-3 can be ascribed to the Al solubility limit in ZnO and to the suppression of the ionization of Al dopants from adjacent Al donors.

Microwave imaging (MWI) based breast cancer detection has attracted widespread attentions because it has great advantages of non-ionization and low price. However, the current MWI systems require huge and expensive hardware, and complex signal calibration and present substantial challenges to the application of MWI system. For this reason, the embedded MW imaging (E-MWI) method is proposed for breast cancer detection. Firstly, the Software Define Radio (SDR) software is implemented on an embedded platform to reduce cost and miniaturize system. Secondly, an amplitude-based spatial domain confocal algorithm is used in E-MWI system to simplify the system calibration by avoiding signal phase acquisition. To verify the proposed method, experiments platform is set up using the E-MWI prototype system and a liquid breast phantom. The results show that the system successfully detects the target with different sizes.

In this work, we explore the performances of a low-temperature PEALD technology used to trim/clean/smooth and reshape ArF photoresist lines that could subsequently receive an in-situ spacer deposition required to build up any SAxP grating. Different gas mixtures (O2, N2, H2, Ar and combinations) are evaluated on both blanket and patterned wafers. Trim rate, line profile, surface roughness and chemical modification are characterized using ellipsometry, Fourier transform infrared spectroscopy and atomic force microscopy. The photoresist line roughness is measured from top down SEM imaging and the different contributors to the roughness determined from a Power Spectral Density (PSD) analysis. Few results obtained on EUV photoresist blanket wafers using similar plasma treatments will also be briefly presented.

Engineered metallic nanostructures, namely, plasmonic nanostructures, have broad application prospects. Herein, we develop a versatile and reliable method, which combines slope self-assembly colloidal crystal with metal deposition, to harvest the engineered metallic nanostructures. The method possesses the advantages of low-cost, high sample output and being compatible with industrial process. Colloidal semiconductor quantum dots (QDs) are integrated within pre-designed engineered metallic nanostructures. Impressed results (an approximate 15 fold increase of photoluminescence intensity), have been realized. Moreover, field distribution of the periodic metal nanostructures is simulated via finite-difference time domain (FDTD). Importantly, a new mechanism, in addition to conventional theory, is proposed to illustrate the large enhancement of fluorescence efficiency. Additionally, engineered fluorescence, including controlled emission linewidth, peak and intensity, is achieved through the coupling of engineered metallic nanostructures and QDs. It is demonstrated that plasmonic nanostructures and engineered fluorescence has the potential to provide promise for a range of applications, including solar cell , light-emitting diodes, and single-molecule studies.

In this paper, we innovatively propose a facile and controllable way to prepare a superhydrophobic Zn/ZnO film on a zinc substrate. In this approach, we soak the acid etched Zn substrate in pure warm water, resulting in the formation of a hierarchical configuration with micron-sized grooves and nanorods. The lotus-like hierarchical Zn/ZnO micro/nano structure can be acquired and tailored under a wide range of reaction conditions by controlling the reaction temperature and duration. The formation mechanism of the hierarchical structure has been discussed. The wettability of as-modified surfaces has been studied through three aspects: superhydrophobicity, oleophobicity (for oily liquids), and oil–water separation properties. What's more, the as-prepared superhydrophobic samples show good thermal stability (under 200 °C) and recoverability. This approach offers potential for industrial application of self-cleaning surfaces because of its features such as time-saving, low cost, easy preparation and so on.

Superhydrophobic surfaces were prepared on Cu substrates via a facile surface oxidation approach and subsequent chemical modification with low surface energy materials. In this paper, antiformin solution, as one of the most commonly used low cost disinfectants, is introduced for preparing bionic microstructures on copper surfaces for the first time. A short-time oxidation reaction soaking the Cu foil in antiformin liquid can result in the formation of a bean sprout-like structure. Moreover, regulating the morphology of the microstructure can be realized by changing the reaction duration and solution concentration under a wide range of experimental conditions. A possible growth mechanism is also proposed here. After being modified by stearic acid, all as-etched surfaces possess superhydrophobic properties, with a water contact angle (CA) larger than 150° and a sliding angle (SA) less than 10°. Simultaneously superoleophobic interfaces can be obtained after the etched surfaces are modified with heptadecafluorodecyltrimethoxysilane (HTMS). The resultant samples present good thermal stability at ambient temperatures below 150 °C. The same approach is also suitable for brass materials. This offers an effective and practical route for large scale industrial production of superhydrophobic materials.

The atomic layer deposition (ALD) process of hydrogen-doped indium oxide (In

This research presents a new methodology of developing the brain stroke localization algorithm by using the machine-learning based pattern recognition approach for foreign object classification. Due to the new algorithm requires less bandwidth from the system hardware level, the proposed method would be helpful to reduce the challenges of designing a high-performance UWB antenna array required from most microwave stroke detection system nowadays. The presented algorithm employs the interconnection point of two lines that intersect the stroke region inside a brain and evaluates the properties of the stroke by using Support Vector Machines (SVM) classification method; the SVM classifier is trained by the data set acquired from EM simulations as well as the optimized coefficients from PSO algorithm. The proposed methodology of brain stroke classification has been verified by simulated data. The performance and effectiveness demonstrated it's a promising algorithm to be installed in the next-generation microwave brain stroke detection system.

To enable effective signal detection of human microwave, it is important to design high performance ultra-wideband wearable antenna. Due to its intrinsic complexity, antenna optimization is a nonlinear problem that is expensive to simulate and optimize. In this paper, we propose to optimize tapered slot antenna (TSA) with an improved particle swarm optimization (PSO) algorithm. Comparing to the state-of-the-art approach that adopts embedded genetic algorithm(GA) in HFSS, our proposed approach requires less than 20% simulation time when the antenna bandwidth ranges from 1.8GHz to 5.0Ghz with gain of 9.8 dB. The optimization results have been adopted to guide the fabrication of the antenna for detecting brain strokes, confirming the feasibility of the proposed algorithm.

A monopole-like bowtie slot ultra wideband (UWB) antenna is proposed and designed for UWB applications. To increase the impedance bandwidth of ordinary monopole patch antenna modifications are made in the patch and ground plane geometry. The antenna is practically fabricated and measured. Simulated and measured results show a good agreement. The designed antenna offers compact size, operates across an ultra wideband (1.87 – 12.3 GHz), and exhibits good radiation characteristics.

This paper presents the design of a corrugated tapered slot antenna (TSA) with application to microwave imaging (MI) system for brain stroke detection. The antenna is excited by a stepped transmission line through slotline transition. Corrugations are used to reduce the size of the antenna and also improve its radiation performance. Simulated and measured results demonstrate that the proposed antenna operates across bandwidth spanning from 1.1–4.1 GHz, and achieves unidirectional radiation pattern with a broadside gain of 2.9 dB and front-to-back ratio of 14.6 dB.

Microwave remote sensing is a good candidate to be used in a real-time, low-cost and portable brain stroke detection system for its non-invasive, non-ionizing and cost-effective characteristics. Due to the heterogeneous and sophisticated texture of brain tissue and low dielectric contrast of the stroke, the conventional stroke imaging methods face significant challenges to achieve satisfactory results. This research presents a schema of identification and localization the blood block in a brain based on the comparison of microwave signal similarity. A realistic head model and a numerical analysis technique based on finite-difference time-domain (FDTD) are adopted in the analysis. The simulation results show the schema has blood localization accuracy with resolution of 10−3m.

NdFeB films with Nd compositions varied from 13.34 to 24.30 at% were deposited by DC gradient sputtering using targets Nd 12.5 Fe 71.5 B 16 and Nd. The hard magnetic properties, grain growth direction and magnetic domain structures were dramatically influenced by Nd composition. The samples with intermediate Nd concentrations exhibited optimal magnetic properties and microstructures, such as large squareness ratio over 0.9, large energy product up to 174 kJ/m 3 , and vertical domain structure. However, the samples with higher and lower Nd compositions showed almost isotropic loops. (0 0 l ) as main X-ray diffraction peaks in the optimal Nd composition region indicated most of Nd 2 Fe 14 B grains with c -axis perpendicular to the film plane, while NdFeB grains in other region are almost random growth. The good magnetic properties can be attributed to the vertical growth of Nd 2 Fe 14 B grains.