Shijiazhuang Tiedao University

In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.

Abstract Organic–inorganic hybrid perovskites are promising candidates for the next-generation solar cells. Many efforts have been made to study their structures in the search for a better mechanistic understanding to guide the materials optimization. Here, we investigate the structure instability of the single-crystalline CH 3 NH 3 PbI 3 (MAPbI 3 ) film by using transmission electron microscopy. We find that MAPbI 3 is very sensitive to the electron beam illumination and rapidly decomposes into the hexagonal PbI 2 . We propose a decomposition pathway, initiated with the loss of iodine ions, resulting in eventual collapse of perovskite structure and its decomposition into PbI 2 . These findings impose important question on the interpretation of experimental data based on electron diffraction and highlight the need to circumvent material decomposition in future electron microscopy studies. The structural evolution during decomposition process also sheds light on the structure instability of organic–inorganic hybrid perovskites in solar cell applications.

Privacy protection has recently received considerable attention in location-based services. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. In this paper, we consider the scenario where different location-based query requests are continuously issued by mobile users while they are moving. We show that most of the existing k-anonymity location cloaking algorithms are concerned with snapshot user locations only and cannot effectively prevent location-dependent attacks when users' locations are continuously updated. Therefore, adopting both the location k-anonymity and cloaking granularity as privacy metrics, we propose a new incremental clique-based cloaking algorithm, called ICliqueCloak, to defend against location-dependent attacks. The main idea is to incrementally maintain maximal cliques needed for location cloaking in an undirected graph that takes into consideration the effect of continuous location updates. Thus, a qualified clique can be quickly identified and used to generate the cloaked region when a new request arrives. The efficiency and effectiveness of the proposed ICliqueCloak algorithm are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against location-dependent attacks is small.

Piezoelectric nano-structures have been regarded as the next-generation piezoelectric material due to their inherent nano-sized piezoelectricity. This review summarizes the recent theoretical and experimental findings in piezoelectric nano-structures, including piezoelectric nanowires, nanoplates, nanobeams, nanofilms, nanoparticles, and piezoelectric heterogeneous materials containing piezoelectric nano-inhomogeneities. To begin, the types of piezoelectric nano-structured materials and the wide application of piezoelectric nano-structures in recent years are delineated. Next, the theoretical foundations including the definition of surface stress and electric displacement, the surface constitutive relations, the surface equilibrium equations, and nonlocal piezoelectricity, and their applications, are illustrated. Then, the effective mechanical and piezoelectric properties are depicted. Furthermore, the experimental investigations are classified, and some important observations are discussed. Finally, the perspectives and challenges for the future development of piezoelectric nano-structures are pointed out.

Based on theoretical analysis and experiments, this article proposes a new model for a magnetorheological (MR) damper. The proposed model with a smooth and concise form can interpret the bi-viscous and hysteretic behaviors of the MR damper very well. The parameters in the model have definite physical meanings. The bi-viscous and hysteretic behaviors can be characterized by two parameters 0 and A3. The proposed model makes it convenient to study the effects of the bi-viscous and hysteretic behaviors on the performance of a system with a MR damper. As one application of the model, a vibration isolation system with a MR damper is investigated, and the effects of bi-viscous and hysteretic behaviors on system performances are studied by numerical methods and theoretical analysis.

By kinetic control over the zeolite seed formation, we report the direct fabrication of hierarchical mesoporous zeolites using hexadecyl trimethyl ammonium bromide (CTAB) as the soft template in a conventional solution route. Nanometer-sized, subnanocrystal-type zeolite seeds with a high degree of polymerization are essential to prevent the formation of a separate amorphous mesoporous phase and the phase separation between the mesophase and zeolite crystals in the presence of CTAB and a certain amount of ethanol. The mechanisms for the formation of hierarchically porous zeolites in the solution process, including the effect of mother liquid aging, formation of subnanocrystal zeolite seeds and their self-assembly effect with CTAB, and the role of ethanol are proposed and discussed in detail. The prepared mesoporous ZSM-5 zeolite showed much higher catalytic activity than conventional counterparts for aldol condensations involving large molecules, especially in the synthesis of vesidryl.

Systematic calculations on the $\ensuremath{\alpha}$-decay energies $({Q}_{\ensuremath{\alpha}})$ and $\ensuremath{\alpha}$-decay half-lives of the superheavy nuclei (SHN) with $Z\ensuremath{\ge}100$ are performed by using 20 models and 18 empirical formulas, respectively. According to the comparisons between the calculated results and experimental data, it is shown that the WS4 mass model is the most accurate one to reproduce the experimental ${Q}_{\ensuremath{\alpha}}$ values of the SHN. Meanwhile it is found that the SemFIS2 formula is the best one to predict the $\ensuremath{\alpha}$-decay half-lives of the SHN because the parameters in this formula are from the experimental $\ensuremath{\alpha}$ emitter data of transuranium nuclei including SHN $(Z=92\text{--}118)$. In addition, the UNIV2 formula with fewest parameters and the VSS, SP and NRDX formulas with fewer parameters work well in prediction on the SHN $\ensuremath{\alpha}$-decay half-lives. Finally, the $\ensuremath{\alpha}$-decay half-lives of $Z=110\text{--}120$ isotopes are predicted within the above mentioned five formulas by inputting the $\mathrm{WS}4 {Q}_{\ensuremath{\alpha}}$ values. By analyzing the ${Q}_{\ensuremath{\alpha}}$ values and the $\ensuremath{\alpha}$-decay half-lives of this region, it is found that for $Z=110\text{--}114$ isotopes $N=162$ and $N=184$ are the submagic number and magic number, respectively. However, for the isotopes of $Z=116\text{--}120$ the submagic number is $N=178$.

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

1D nanomaterial-based heterojunctions with unique structures and outstanding physicochemical properties are divided into several types including type II heterojunction, p–n type heterojunction, Schottky junction, Z-type heterojunction, and S-scheme heterojunction.

The joint roughness coefficient (JRC), introduced in Barton (1973) represented a new method in rock mechanics and rock engineering to deal with problems related to joint roughness and shear strength estimation. It has the advantages of its simple form, easy estimation, and explicit consideration of scale effects, which make it the most widely accepted parameter for roughness quantification since it was proposed. As a result, JRC has attracted the attention of many scholars who have developed JRC-related methods in many areas, such as geological engineering, multidisciplinary geosciences, mining mineral processing, civil engineering, environmental engineering, and water resources. Because of such a developing trend, an overview of JRC is presented here to provide a clear perspective on the concepts, methods, applications, and trends related to its extensions. This review mainly introduces the origin and connotation of JRC, JRC-related roughness measurement, JRC estimation methods, JRC-based roughness characteristics investigation, JRC-based rock joint property description, JRC's influence on rock mass properties, and JRC-based rock engineering applications. Moreover, the representativeness of the joint samples and the determination of the sampling interval for rock joint roughness measurements are discussed. In the future, the existing JRC-related methods will likely be further improved and extended in rock engineering.

In recent years, the global climate has further deteriorated because of the excessive consumption of traditional energy sources. The replacement of traditional fossil fuels with limited reserves by alternative energy sources has become one of the main strategies to alleviate the increasingly serious environmental issues. As a sustainable and promising store of renewable energy, lithium-ion batteries have replaced other types of batteries for many small-scale consumer devices. Notwithstanding their worldwide applications, it has become abundantly clear that the design and fabrication of electrode materials is urgently required to adapt to meet the growing global demand for energy and the power densities needed to make electric vehicles fully commercially viable. To dramatically enhance battery performance, further advances in materials chemistry are essential, especially in novel nanomaterials chemistry. The construction of nanostructured cathode materials by reducing particle size can boost electrochemical performance. The present review is intended to provide readers with a better understanding of the unique contribution of various nanoarchitectures to lithium-ion batteries over the last decade. Nanostructured cathode materials with different dimensions (0D, 1D, 2D, and 3D), morphologies (hollow, core-shell, etc.), and composites (mainly graphene-based composites) are highlighted, aiming to unravel the opportunities for the development of future-generation lithium-ion batteries. The advantages and challenges of nanomaterials are also addressed in this review. We hope to simulate many more extensive and insightful studies on nanoarchitectonic cathode materials for advanced lithium-ion batteries with desirable performance.

This paper focuses on the problems existing in intrusion detection using neural network, including redundant information, large amount of data, long-time training, easy to fall into the local optimal. An intrusion detection method using deep belief network (DBN) and probabilistic neural network (PNN) is proposed. First, the raw data are converted to low-dimensional data while retaining the essential attributes of the raw data by using the nonlinear learning ability of DBN. Second, to obtain the best learning performance, particle swarm optimization algorithm is used to optimize the number of hidden-layer nodes per layer. Next, PNN is used to classify the low-dimensional data. Finally, the KDD CUP 1999 dataset is employed to test the performance of the method mentioned above. The experiment result shows that the method performs better than the traditional PNN, PCA-PNN and unoptimized DBN-PNN.

All solid-state lithium batteries (ASSLBs) employing inorganic solid electrolytes or solid polymer electrolytes are attracting increasing interests for electrochemical energy storage devices due to their advantages of high energy density, high safety, wide operating temperature range and long cycle life. However, the large interfacial resistance originated from the insufficient solid-solid contact at electrolyte/electrode interface hinders the development of ASSLBs. In addition, the interfacial stability and compatibility also greatly affect the electrochemical performance of batteries. To realize the ASSLB’s application requires significant research in solid electrolyte materials and solid electrolyte/electrode interfaces. This review summarizes the research and development in solid electrolyte materials and the interfaces of solid electrolyte/electrode, paying special attention to the challenges and progress for the studies of interface issues in ASSLBs. Based on the overview, we attempt to propose approaches to the issue by interface engineering and prospective developments of ASSLBs.

An innovative approach for damage assessment of a bridge deck is proposed with the measured dynamic response of a vehicle moving on top of a structure. The simply supported bridge deck is modeled as a Euler–Bernoulli beam. The moving vehicle serves as a smart sensor and force transducer in the structural system. The damage is defined as the flexural stiffness reduction in the beam finite element. The identification algorithm is based on dynamic response sensitivity analysis, and it is realized with a regularization technique from the measured vehicle acceleration measurement. Measurement noise, road surface roughness, and model errors are included in the simulations, and the results indicate that the proposed algorithm is computationally stable and efficient, and the identified results are acceptable and not sensitive to the different parameters studied.

A novel and facile post-grafting strategy has been developed to construct aromatic heterocycle-grafted graphitic carbon nitride photocatalysts.

Abstract Current research on quadrotor modeling mainly focuses on theoretical analysis methods and experimental methods, which have problems such as weak adaptability to the environment, high test costs, and long durations. Additionally, the PID controller, which is currently widely used in quadrotors, requires improvement in anti-interference. Therefore, the aforementioned research has considerable practical significance for the modeling and controller design of quadrotors with strong coupling and nonlinear characteristics. In the present research, an aerodynamic-parameter estimation method and an adaptive attitude control method based on the linear active disturbance rejection controller (LADRC) are designed separately. First, the motion model, dynamics model, and control allocation model of the quad-rotor are established according to the aerodynamic theory and Newton–Euler equations. Next, a more accurate attitude model of the quad-rotor is obtained by using a tool called CIFER to identify the aerodynamic parameters with large uncertainties in the frequency domain. Then, an adaptive attitude decoupling controller based on the LADRC is designed to solve the problem of the poor anti-interference ability of the quad-rotor and adjust the key control parameter b 0 automatically according to the change in the moment of inertia in real time. Finally, the proposed approach is verified on a semi-physical simulation platform, and it increases the tracking speed and accuracy of the controller, as well as the anti-disturbance performance and robustness of the control system. This paper proposes an effective aerodynamic-parameter identification method using CIFER and an adaptive attitude decoupling controller with a sufficient anti-interference ability.

This paper is a survey on the application of artificial neural networks in forecasting financial market prices. The objective of this paper is to appraise the potential of using artificial neural networks to predict the financial system, as it is reflected in many relevant articles. It will provide some guidelines and references for the research and implementation. This paper begins with an introduction to the theory of artificial neural networks. Subsequently it focuses on the forecast of stock prices and option pricing based on a non-linear ANN model. It proceeded with a presentation of the application of ANN in predicting exchange rates. The paper then reviewed the theoretical literature on the prediction of banking and financial crisis based on artificial neural networks. In general artificial neural network is a valuable forecast tool in financial economics due to the learning, generalization and nonlinear behavior properties. Finally it identifies a number of important opportunities for future research on the application of neural networks in financial economics.

Coal pores are the locations of coalbed methane occurrence and enrichment and the main storage space targeted in CO2 sequestration. The systematic investigation of the coal pore structure and its development is of great significance to provide insights into coalbed methane generation, coal mine gas outburst mechanisms, and coal seam CO2 sequestration. In this study, coal pore testing technologies and methods, pore classification methods, and coal pore structure characteristics and their main control factors were systematically investigated and summarized. The results show that direct test methods, indirect fluid injection test methods, and X-ray and spectroscopic methods have been used for the quantitative characterization of the pore structure. However, each testing method has limitations. Therefore, a comprehensive method for the quantitative characterization of the full-scale pore structure must be developed, especially for the accurate quantitative characterization of closed pores in coal. The in situ measurement of pores in coal is one of the future research trends. Classification methods of pores in coal mainly include the classification of the genetic pore type, pore size, and pore morphology. Metamorphism, tectonic deformation, and macerals are the main internal factors affecting the pore distribution in coal. The effect of tectonic deformation on the macromolecular structure and micro- and ultramicropores of coal must be further studied. In addition, molecular mechanics simulations, molecular dynamics simulations, and quantum chemistry calculations of the dynamic response of the macromolecular structure and micro- and ultramicropores during gas adsorption and desorption must be carried out.

A highly transmittable and pathogenic viral infection, COVID-19, has dramatically changed the world with a tragically large number of human lives being lost. The epidemic has created psychological resilience and unbearable psychological pressure among patients and health professionals. The objective of this study is to analyze investor psychology and stock market behavior during COVID-19. The psychological behavior of investors, whether positive or negative, toward the stock market can change the picture of the economy. This research explores Shanghai, Nikkei 225, and Dow Jones stock markets from January 20, 2020, to April 27, 2020, by employing principal component analysis. The results showed that investor psychology was negatively related to three selected stock markets under psychological resilience and pandemic pressure. The negative emotions and pessimism urge investors to cease financial investment in the stock market, and consequently, the stock market returns decreased. In a deadly pandemic, the masses were more concerned about their lives and livelihood and less about wealth and leisure. This research contributes to the literature gap of investors' psychological behavior during a pandemic outbreak. The study suggests that policy-makers should design a plan to fight against COVID-19. The government should manage the health sector's budget to overcome future crises.

Over the past couple of decades, as a new mathematical tool for addressing a number of tough problems, fractional calculus has been gaining a continually increasing interest in diverse scientific fields, including geotechnical engineering due primarily to geotechnical rheology phenomenon. Unlike the classical constitutive models in which simulation analysis gradually fails to meet the reasonable accuracy of requirement, the fractional derivative models have shown the merits of hereditary phenomena with long memory. Additionally, it is traced that the fractional derivative model is one of the most effective and accurate approaches to describe the rheology phenomenon. In relation to this, an overview aimed first at model structure and parameter determination in combination with application cases based on fractional calculus was provided. Furthermore, this review paper shed light on the practical application aspects of deformation analysis of circular tunnel, rheological settlement of subgrade, and relevant loess researches subjected to the achievements acquired in geotechnical engineering. Finally, concluding remarks and important future investigation directions were pointed out.