Scale-space effect and scale hybridization in image intelligent recognition of geological discontinuities on rock slopes

Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.

Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

Different El Niño Flavors and Associated Atmospheric Teleconnections as Simulated in a Hybrid Coupled Model

A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Niño flavors,namely the Eastern-Pacific(EP)and Central-Pacific(CP)types,and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM.The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific,including the amplitude and spatial patterns of sea surface temperature(SST),zonal wind stress,and precipitation anomalies.An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events,respectively.Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events,the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter.In particular,the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere,while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American(PNA)pattern.As a result,different climatic impacts exist in North American regions,with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño,respectively.This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.

Hybrid low salinity water and surfactant process for enhancing heavy oil recovery

Combining low salinity water (LSW) with surfactants has an enormous potential for enhancing oil recovery processes. However, there is no consensus about the mechanisms involved, in addition to the fact that several studies have been conducted in model systems, while experiments with rocks and reservoir fluids are scarce. This study presents a core-flooding experiment of LSW injection, with and without surfactant, using the core and heavy oil samples obtained from a sandstone reservoir in southeastern Mexico. The effluents and the crude oil obtained at each stage were analyzed. The study was complemented by tomographic analysis. The results revealed that LSW injection and hybrid process with surfactants obtained an increase of 11.4 percentage points in recovery factor. Various phenomena were caused by LSW flooding, such as changes in wettability and pH, ion exchange, mineral dissolution, detachment of fines and modification of the hydrocarbon profile. In the surfactant flooding, the reduction of interfacial tension and alteration of wettability were the main mechanisms involved. The findings of this work also showed that the conditions believed to be necessary for enhanced oil recovery with LSW, such as the presence of kaolinite or high acid number oil, are not relevant.

Colorless to black switching with high contrast ratio via the electrochemical process of a hybrid organic-inorganic perovskite

Colorless‐to‐black switching has attracted widespread attention for smart windows and multifunctional displays because they are more useful to control solar energy.However,it still remains a challenge owing to the tremendous difficulties in the design of completely reverse absorptions in transmissive and colored states.Herein,we report on an electrochemical device that can switch between colorless and black by using the electrochemical process of hybrid organic–inorganic perovskite MAPbBr_(3),which shows a high integrated contrast ratio of up to 73%from 400 to 800 nm.The perovskite solution can be used as the active layer to assemble the device,showing superior transmittance over the entire visible region in neutral states.By applying an appropriate voltage,the device undergoes reversible switching between colorless and black,which is attributed to the formation of lead and Br_(2)in the redox reaction induced by the electron transfer process in MAPbBr_(3).In addition,the contrast ratio can be modulated over the entire visible region by changing the concentration and the applied voltage.These results contribute toward gaining an insightful understanding of the electrochemical process of perovskites and greatly promoting the development of switchable devices.

基于MOOC的“高分子化学”线上线下混合式教学模式改革与研究

“高分子化学”是材料类专业的重要课程,理论体系严谨,知识点众多。传统教学方式难以引起学生兴趣,学习自主性较弱。通过构建基于MOOC的线上线下混合式教学,以问题为驱动,强化过程考核,学生参与度和自主学习效率得到了提高。由近年来本校高分子材料与工程专业学生的课程目标达成度评价数据发现,线上线下混合式教学和考核模式的改革产生了显著成效。

基于时频域联合数据辅助MIMO均衡算法的多维度复用千公里级相干传输系统

基于少模光纤的模式复用系统利用单根纤芯中的正交模式实现各独立信道的并行传输,能够有效提升系统容量,解决单模光纤系统的容量危机.本文联合使用模式复用、波分复用与偏振复用技术,基于六模渐变型光纤实现了80个波长通道下32 Gbaud四模双偏振16阶正交振幅调制信号的1000 km相干传输,并在接收端使用时频域联合数据辅助8×8多输入多输出最小均方算法,以补偿由光纤传输导致的模式耦合及模间色散等线性损伤.与传统的纯时域或纯频域多输入输出最小均方均衡算法相比,该算法分别实现了57.1%的收敛速度提升与25.1%的收敛误差下降.经过1000 km少模光纤传输后,80通道每个模式及偏振态的误码率均低于开销为20%的软判决前向纠错编码阈值2.4×10^(-2),系统净速率达68.2 Tbit/s.

Hybrid Neural Network Model for RH Vacuum Refining Process Control

A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.

SYNTHESIS OF MESOPOROUS POLY(STYRENE-co-MALEIC ANHYDRIDE)/SILICA HYBRID MATERIALS VIA A NONSURFACTANT-TEMPLATED SOL-GEL PROCESS

Mesoporous poly(styrene-co-maleic anhydride)/silica hybrid materials have been prepared. The synthesis was achieved by the HCl-catalyzed sol-gel reactions of tetraethyl orthosilicate (TEOS) and styrene-maleic anhydride copolymer in the presence of 3-aminopropyl triethoxysilane (APTES) as a coupling agent and citric acid as a nonsurfactant template or pore-forming agent, followed by ethanol extraction. Characterization results from nitrogen sorption isotherms and powder X-ray diffraction indicate that polymer-modified mesoporous materials with large specific surface areas (e.g. 900 m(2)/g) and pore volumes (e.g. 0.6 cm(3)/g) could be prepared. As the citric acid concentration is increased, the specific surface areas, pore volumes and pore diameters of the hybrid materials increase.

High-quality manufacturing method ofcomplicated castings based on multi-materialhybrid moulding process

A multi-material hybrid patternless moulding process for complicated castings has been proposed. Moulding sands used in the hybrid moulding process include silica sand, ceramic sand, chromite sand, zircon sand, and steel shot sand. Experimental method was used to study the effects of moulding sands on the temperature field, mechanical properties, and dimensional precision of the iron castings. Under the condition that the wall thickness on different sides of the casting is the same, when the wall thickness is greater than 10 mm, the heat storage capacity of the moulding sands from strong to weak is steel shot sand, zircon sand, chromite sand, ceramic foundry sand, and silica sand. Tensile strength of the obtained castings from high to low is zircon sand, chromite sand, steel shot sand, ceramic sand, and silica sand. Contraction rate of the obtained castings from high to low is steel shot sand, zircon sand, chromite sand, silica sand, and ceramic sand. Therefore, steel shot sand and zircon sand can be used as chilled sand, and even can be used instead of cold iron when the casting wall thickness is greater than 10 mm. Zircon sand and chromite sand can be used to obtain high mechanical properties, and silica sand and ceramic sand can be selected to obtain high dimensional precision of the castings. Finally, a typical iron casting piece was tested by experiment using the hybrid moulding process. Excellent performances of iron castings confirm the feasibility of the hybrid moulding process.

PERFORMANCE EVALUATION METHOD FOR BUSINESS PROCESS OF MACHINERY MANUFACTURER BASED ON DEA/AHP HYBRID MODEL

A set of indices for performance evaluation for business processes with multiple inputs and multiple outputs is proposed, which are found in machinery manufacturers. Based on the traditional methods of data envelopment analysis （DEA） and analytical hierarchical process （AHP）, a hybrid model called DEA/AHP model is proposed to deal with the evaluation of business process performance. With the proposed method, the DEA is firstly used to develop a pairwise comparison matrix, and then the AHP is applied to evaluate the performance of business process using the pairwise comparison matrix. The significant advantage of this hybrid model is the use of objective data instead of subjective human judgment for performance evaluation. In the case study, a project of business process reengineering （BPR） with a hydraulic machinery manufacturer is used to demonstrate the effectiveness of the DEA/AHP model.

A comparative study on microstructural evolution and surface properties of graphene/CNT reinforced Al6061-SiC hybrid surface composite fabricated via friction stir processing

A comparative study on the surface properties of Al-SiC-multi walled carbon nanotubes (CNT) and Al-SiC-graphene nanoplatelets (GNP) hybrid composites fabricated via friction stir processing (FSP) was documented. Microstructural characterization reveals a more homogeneous dispersion of GNPs in the Al matrix as compared to CNTs. Dislocation blockade by SiC and GNP particles along with the defect-free interface between the matrix and reinforcements is also observed. Nanoindentation study reveals a remarkable ~207% and ~27% increment in surface nano-hardness of Al-SiC-GNP and Al-SiC-CNT hybrid composite compared to as-received Al6061 alloy, respectively. On the other hand, the microhardness values of Al-SiC-GNP and Al-SiC-CNT are increased by ~36% and ~17% relative to as-received Al6061 alloy, respectively. Tribological assessment reveals ~56% decrease in the specific wear rate of Al-SiC-GNP hybrid composite, whereas it is increased by ~122% in Al-SiC-CNT composite. The higher strength of Al-SiC-GNP composite is attributed to the mechanical exfoliation of GNPs to few layered graphene (FLG) in the presence of SiC. Also, various mechanisms such as thermal mismatch, grain refinement, and Orowan looping contribute significantly towards the strengthening of composites. Moreover, the formation of tribolayer by the squeezed-out GNP on the surface is responsible for the improved tribological performance of the composites. Raman spectroscopy and various other characterization methods corroborate the results.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.

Machining performance on hybrid process of abrasive jet machining and electrical discharge machining

To develop a hybrid process of abrasive jet machining (AJM) and electrical discharge machining (EDM),the effects of the hybrid process parameters on machining performance were comprehensively investigated to confirm the benefits of this hybrid process.The appropriate abrasives delivered by high speed gas media were incorporated with an EDM in gas system to construct the hybrid process of AJM and EDM,and then the high speed abrasives could impinge on the machined surface to remove the recast layer caused by EDM process to increase the efficiency of material removal and reduce the surface roughness.In this study,the benefits of the hybrid process were determined as the machining performance of hybrid process was compared with that of the EDM in gas system.The main process parameters were varied to explore their effects on material removal rate,surface roughness and surface integrities.The experimental results show that the hybrid process of AJM and EDM can enhance the machining efficiency and improve the surface quality.Consequently,the developed hybrid process can fit the requirements of modern manufacturing applications.

Hybrid Process of Reactive Distillation and Pervaporation for the Production of Tert-amyl Ethyl Ether

In this study, a reactive distillation column in which chemical reaction and separation occur simultaneously is applied for the synthesis of tert-amyl ethyl ether （TAEE） from ethanol （EtOH） and tert-amyl alcohol （TAA）. Pervaporation, an efficient membrane separation technique, is integrated with the reactive distillation for enhancing the efficiency of TAEE production. A user-defined Fortran subroutine of a pervaporation unit is developed, allowing the design and simulation of the hybrid process of reactive distillation and pervaporation in Aspen Plus simulator. The performance of such a hybrid process is analyzed and the results indicate that the integration of the reactive distillation with the pervaporation increases the conversion of TAA and the purity of TAEE product, compared with the conventional reactive distillation.

Influence of addition of Gr_p/Al_2O_(3p )with SiC_p on wear properties of aluminum alloy 6061-T6 hybrid composites via friction stir processing

Aluminum alloy base surface hybrid composites were fabricated by incorporating with mixture of （SiC＋Gr） and （SiC＋Al2O3） particles of 20 μm in average size on an aluminum alloy 6061-T6 plate using friction stir processing （FSP）. Microstructures of both the surface hybrid composites revealed that SiC, Gr and Al2O3 are uniformly dispersed in the nugget zone （NZ）. It was observed that the addition of Gr particles rather than Al2O3 particles with SiC particles, decreases the microhardness but immensely increases the dry sliding wear resistance of aluminum alloy 6061-T6 surface hybrid composite. The observed microhardness and wear properties are correlated with microstructures and worn micrographs.

Effect of Refrigerant on the Performance of a C3/MRC Liquefaction Process

The Mixed Refrigerant(MR)component is an important factor influencing the performances of natural gas lique-faction processes.However,there is a lack of systematic research about the utilization of propane pre-cooled(C3/MRC).In this paper,this mixed refrigerant cycle liquefaction process is simulated using the HYSYS software and the main influential parameters involved in the process are varied to analyze their influence on the liquefaction rate and power consumption.The results show that an effective way for lowering the power consumption of the compressor consists of reducing the flow through the compressor through optimization of the percentage of mixed refrigerant.The power consumption of the compressor in the hybrid refrigeration process is affected by both flow and pressure ratios.Its specific power consumption can be reduced by increasing the flow and decreasing the pressure ratio at the same time.The increase in refrigerant pressure at the high-pressure end can significantly mitigate the energy loss of the heat exchanger and compressor.

Hybrid Micro-forming Processes and Quality Evaluation of Micro-double Gear

Micro-gear is an important actuating component used widely in the micro electro mechanical systems(MEMS) devices.The technologies of micro-forming and precision assembly are urgently developed to manufacture the micro-double gear with central shaft.In the paper,a novel hy-brid-forming process with two kinds of piercing method have been proposed to manufacture the micro-double gear using micro forming technology.The tests of hybrid forming process were carried out with two steps and the micro-double gear was successfully manufactured with good surface quality.The results also show that the hybrid micro-forming process with central piercing method can improve the defects of inclining shaft generated by double-ended piercing method.The quality evaluation of micro-double gear was conducted with surface roughness,micro-hardness and impact tests.The results show that the micro-double gear with good mechanical properties can meet the requirements of application for milli-machines.

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

The text classification process has been extensively investigated in various languages,especially English.Text classification models are vital in several Natural Language Processing(NLP)applications.The Arabic language has a lot of significance.For instance,it is the fourth mostly-used language on the internet and the sixth official language of theUnitedNations.However,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic language.In general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the features.In this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is proposed.The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text.The first step in the proposed model is to pre-process the input data to transform it into a useful format.The Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature vectors.Next,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic text.In the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s novelty.The proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

Hybrid femtosecond laser three-dimensional micro-and nanoprocessing:a review

The extremely high peak intensity associated with ultrashort pulse width of femtosecond(fs)lasers enabled inducing nonlinear multiphoton absorption in materials that are transparent to the laser wavelength.More importantly,focusing the fs laser beam inside the transparent materials confined the nonlinear interaction to within the focal volume only,realizing three-dimensional(3D)micro/nanofabrication.This 3D capability offers three different processing schemes for use in fabrication:undeformative,subtractive,and additive.Furthermore,a hybrid approach of different schemes can create much more complex 3D structures and thereby promises to enhance the functionality of the structures created.Thus,hybrid fs laser 3D microprocessing opens a new door for material processing.This paper comprehensively reviews different types of hybrid fs laser 3D micro/nanoprocessing for diverse applications including fabrication of functional micro/nanodevices.