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The stock market is an important embodiment of a national economy and financial activities and has an important impact on a country, enterprises and individuals. Stock forecasting can allow investment institutions and investors to understand the trend of the stock market in advance, which is a challenging and meaningful study. First, through the impulse phenomenon of the stock market, this paper discusses the problem of stock price prediction with delay, and the impulse delay differential equation is established. Second, according to the difference between the differential and the difference, the nonlinear delay grey prediction model is established. Next, the model parameters are estimated and the solving steps are obtained. The nonlinear parameters and delay time are optimized by the particle swarm optimization algorithm. Finally, the new model is applied to the prediction of the Shanghai stock market and the Shenzhen stock market closing indexes; the results show that the new model can effectively predict stock prices, which is much better than the existing four grey models and a time series model.

In this study, porous calcium silicate (CaSiO3) scaffolds were prepared by 3D gel-printing (3DGP) method and Fe3O4 water-based magnetic fluids (WMFs) were prepared by phacoemulsification compound chemical coprecipitation method. Fe3O4 WMFs were coated on CaSiO3 scaffolds surface to prepare Fe3O4/CaSiO3 composite scaffolds. The effect of WMFs with different Fe3O4 concentrations on porous CaSiO3 scaffolds was studied. The composition and morphological characteristics of porous scaffolds were analyzed by using scanning electron microscope (SEM) and energy dispersive spectrometer (EDS) analysis. The magnetic properties were tested by vibrating sample magnetometer (VSM). The stability of Fe3O4 WMFs coatings and the degradability of composite scaffolds were tested by immersing them in simulated body fluid (SBF). The results show that when Fe3O4 concentration was 5.4% (w/v), the composite scaffolds had the highest saturation magnetization of 69.6 emu/g and the best stability in dynamic SBF. It is obviously that Fe3O4 WMFs coatings can be used for bone tissue engineering scaffolds repairing.

Synthesizing indoor scene layouts is challenging and critical, especially for digital design and gaming entertainment. Although there has been significant research on the indoor layout synthesis of rectangular-shaped or L-shaped architecture, there is little known about synthesizing plausible layouts for more complicated indoor architecture with both geometric and semantic information of indoor architecture being fully considered. In this paper, we propose an effective and novel framework to synthesize plausible indoor layouts in various and complicated architecture. The given indoor architecture is first encoded to our proposed representation, called InAiR, based on its geometric and semantic information. The indoor objects are grouped and then arranged by functional blocks, represented by oriented bounding boxes, using dynamic convolution networks based on their functionality and human activities. Through comparisons with other approaches as well as comparative user studies, we find that our generated indoor scene layouts for diverse, complicated indoor architecture are visually indistinguishable, which reach state-of-the-art performance.

The interplay between chirality and magnetism generates a distinct physical process, the magneto-chiral effect, which enables one to develop functionalities that cannot be achieved solely by any of the two. Such a process is universal with the breaking of parity-inversion and time-reversal symmetry simultaneously. However, the magneto-chiral effect observed so far is weak when the matter responds to photons, electrons, or phonons. Here we report the first observation of strong magneto-chiral response to excitons in a twisted bilayer tungsten disulfide with the amplitude of excitonic magneto-chiral (ExMCh) anisotropy reaches a value of ~4%. We further found the ExMCh anisotropy features with a spectral splitting of ~7 nm, precisely the full-width at half maximum of the excitonic chirality spectrum. Without an externally applied strong magnetic field, the observed ExMCh effect with a spontaneous magnetic moment from the ferromagnetic substrate of thulium iron garnet at room temperature is favorable for device applications. The unique ExMCh processes provide a new pathway to actively control magneto-chiral applications in photochemical reactions, asymmetric synthesis, and drug delivery., Systems where both time-reversal symmetry and inversion symmetry are broken can exhibit a magneto-chiral effect; however, this is usually weak. Here, Lan et al demonstrate a significantly enhanced excitonic magnetochiral effect in twisted bilayer tungsten disulphide on a ferromagnetic substrate.

Ultrathin and flexible electromagnetic shielding materials hold great potential in civil and military applications. Despite tremendous research efforts, the development of advanced shielding materials is still needed to provide additional functionalities for various artificial-intelligence-driven systems, such as tactile sensing ability. Herein, a layering design strategy is proposed to fabricate ultrathin Ti3C2Tx MXene-aramid nanofiber (MA) films by a layer-by-layer assembling process. Compared to that of randomly mixed films, the designed MA films exhibited a higher EMI shielding efficiency at an ultrathin thickness of 9 µm, which increased from 26.4 to 40.7 dB, owing to the additional multiple-interface scattering mechanism. Importantly, the novel MA films displayed strong EMI shielding ability even after heating/cooling treatments within a wide temperature range of −196 to 300 °C. Moreover, the same material displayed a tensile strength of 124.1 ± 2.7 MPa and a toughness of 6.3 ± 1.1 MJ·m−3, which are approximately 9.1 times and 45 times higher than those of pure MXene films, respectively. The MA film is also capable of detecting tactile signals via the triboelectric effect. A 2 × 4 tactile sensor array was developed to achieve an accurate signal catching capability. Therefore, in addition to the shielding performance, the manifestation of tactile perception by the MA films offers exciting opportunities in the fields of soft robotics and human-machine interactions.

Probabilistic linguistic term sets are used to express uncertain decision information in multiple attribute group decision making problems. For probabilistic linguistic multiple attribute group decision making (MAGDM) with weight determined by CRITIC (Criteria Importance Through Intercriteria Correlation) method, the probabilistic linguistic grey relational projection method is proposed in this paper. Firstly, the correlation coefficient among attributes and standard deviation of each attribute are utilized to compute the attributes weights. Then the most ideal alternative is decided by means of counting the grey relational projection (GRP) from probabilistic linguistic positive ideal solution and probabilistic linguistic negative ideal solution. In the end, a numerical example for site selection of hospital constructions is applied to further account for the extended method. The result demonstrates the availability of the proposed method and it can be used in other fields which refers to problems of selection.

Maintaining good quality of transient stability models for power system planning and operational analysis is of great importance. Identification and calibration of bad parameters using PMU measurements that work well for multiple events remains a challenging problem. In this letter, we present a novel parameter calibration method based on off-policy deep reinforcement learning (DRL) algorithm with maximum entropy, soft actor critic (SAC), to automatically tune incorrect parameter sets considering multiple events simultaneously, which can save tremendous labor efforts for maintaining model accuracy and complying with industry standards. The effectiveness of the proposed approach is verified through numerical experiments conducted on a realistic power plant model.

Autoencoder based methods are the majority of deep unsupervised outlier detection methods. However, these methods perform not well on complex image datasets and suffer from the noise introduced by outliers, especially when the outlier ratio is high. In this paper, we propose a framework named Transformation Invariant AutoEncoder (TIAE), which can achieve stable and high performance on unsupervised outlier detection. First, instead of using a conventional autoencoder, we propose a transformation invariant autoencoder to do better representation learning for complex image datasets. Next, to mitigate the negative effect of noise introduced by outliers and stabilize the network training, we select the most confident inliers likely examples in each epoch as the training set by incorporating adaptive self-paced learning in our TIAE framework. Extensive evaluations show that TIAE significantly advances unsupervised outlier detection performance by up to 10% AUROC against other autoencoder based methods on five image datasets.

Textures are encountered often on various common objects and surfaces. Many textures combine visual and tactile aspects, each serving important purposes; most obviously, a texture alters the object's appearance or tactile feeling as well as serving for visual or tactile identification and improving usability. The tactile feel and visual appearance of objects are often linked, but they may interact in unpredictable ways. Advances in high-resolution 3D printing enable highly flexible control of geometry to permit manipulation of both visual appearance and tactile properties. In this paper, we propose an optimization method to independently control the tactile properties and visual appearance of a texture. Our optimization is enabled by neural network-based models, and allows the creation of textures with a desired tactile feeling while preserving a desired visual appearance at a relatively low computational cost, for use in a variety of applications.

Enabling adaptivity on a uniform Cartesian grid is challenging due to its highly structured grid cells and axis-aligned grid lines. In this paper, we propose a new grid structure - the adaptive staggered-tilted (AST) grid - to conduct adaptive fluid simulations on a regular discretization. The key mechanics underpinning our new grid structure is to allow the emergence of a new set of tilted grid cells from the nodal positions on a background uniform grid. The original axis-aligned cells, in conjunction with the populated axis-tilted cells, jointly function as the geometric primitives to enable adaptivity on a regular spatial discretization. By controlling the states of the tilted cells both temporally and spatially, we can dynamically evolve the adaptive discretizations on an Eulerian domain. Our grid structure preserves almost all the computational merits of a uniform Cartesian grid, including the cache-coherent data layout, the easiness for parallelization, and the existence of high-performance numerical solvers. Further, our grid structure can be integrated into other adaptive grid structures, such as an Octree or a sparsely populated grid, to accommodate the T-junction-free hierarchy. We demonstrate the efficacy of our AST grid by showing examples of large-scale incompressible flow simulation in domains with irregular boundaries.

International audience; Digital predistorsion (DPD) is a commonly used approach to compensate for the power amplifiers (PA) nonlinearities and memory effects as well as to improve its power efficiency. To alleviate the restriction brought by high peak-to-average power ratio (PAPR) of input signals, crest factor reduction (CFR) is needed for higher efficiency. In modern communication systems, power of transmitted signals gets lower, which makes complexities of CFR and DPD become non-negligible. This paper proposes a new approach to realize a joint CFR and DPD model using neural networks (NN). The modeling accuracy is guaranteed by a new proposed augmented iterative learning control (AILC) algorithm for the NN training signals. Compared with conventional ILC, the proposed AILC is shown more robust according to simulation and experimental results. The proposed AILC-based NN-CFRDPD is experimentally evaluated on different test benches.

IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself. ARM big.LITTLE architecture is at the heart of prevalent commercial edge devices. It comprises of single-ISA heterogeneous cores grouped into multiple homogeneous clusters that enable power and performance trade-offs. All cores are expected to be simultaneously employed in inference to attain maximal throughput. However, high communication overhead involved in parallelization of computations from convolution kernels across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs pipelined design to split convolutional layers across clusters while limiting parallelization of their respective kernels to the assigned cluster. We develop a performance-prediction model that utilizes only the convolutional layer descriptors to predict the execution time of each layer individually on all permitted core configurations (type and count). Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in a 39% higher throughput than the highest antecedent throughput., Comment: Accepted to IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Traditionally, mudstones are speculated to be always deposited in the still water environment. However, recent studies demonstrate the significant role of sudden events in depositing large-scale mu...

IT companies need to monitor various Key Performance Indicators (KPIs) and detect anomalies in real time to ensure the quality and reliability of Internet-based services. However, due to the diversity of KPIs, the ambiguity and scarcity of anomalies and the lack of labels, anomaly detection for various KPIs has been a great challenge. Existing KPI anomaly detection methods have not explored the properties of anomalies in KPIs in detail to our best knowledge. Therefore, we explore anomalies in KPIs and recognize a common and important form of anomalies named abrupt changes , which often indicate potential failures in the relevant services. For abrupt changes in various KPIs, we propose DDCOL , an unsupervised online anomaly detection algorithm with parameter adaptation from the perspective of anomalies for the first time. We propose three techniques: high order ${D}$ ifference extraction and combination, ${D}$ ensity-based ${C}$ lustering with parameter adaptation and ${O}\text{n}{L}$ ine detection with subsampling ( DDCOL ). Compared with traditional statistical methods and unsupervised learning methods, extensive experimental results and analysis on a large number of public KPIs show the competitive performance of DDCOL and the significance of abrupt changes . Furthermore, we provide an interpretation for the promising results, which shows that DDCOL can be robust to KPI expected concept drifts, and obtain a good feature distribution of normal data in KPIs.

In this study, water mobility and oil qualities (color indexes, peroxide value, acid value, fatty acids) between continuous and intermittent hot air drying of fresh walnut were investigated based o...

The latest and upcoming mobile application processors, embedded in a myriad of consumer devices, are typically implemented as heterogeneous multiprocessor system-on-chips, comprised of various processing engines such as general-purpose cores with differing characteristics, GPUs, DSPs, nonprogrammable accelerators, and reconfigurable computing devices. The heterogeneity allows application kernels with specific requirements to be executed on processing core(s) ideally suited to perform that computation, resulting in a significantly improved performance and energy-efficiency. However, while heterogeneous computing is an attractive proposition in theory, considerable software support at all levels is essential to fully realize its promises. The system software needs to orchestrate the different on-chip compute resources in a synergistic manner with minimal engagement from the application developers, as well as fulfill the stringent power-thermal constraint in mobile computing. In this article, we present compiler time and runtime techniques to unleash the full potential of heterogeneous multiprocessors toward high-performance energy-efficient computing on consumer devices.

Many recent efforts on the electrode design for advanced Li-ion batteries (LIBs) are often devoted to increasing the gravimetric capacity, but little attention is paid to the volumetric capacity wh...

Band structure determines the motion of electrons in a solid, giving rise to exotic phenomena when properly engineered. Drawing an analogy between electrons and photons, artificially designed optical lattices indicate the possibility of a similar band modulation effect in graphene systems. Yet due to the fermionic nature of electrons, modulated electronic systems promise far richer categories of behaviors than those found in optical lattices. Here, we uncovered a strong modulation of electronic states in bilayer graphene subject to periodic potentials. We observed for the first time the hybridization of electron and hole sub-bands, resulting in local band gaps at both primary and secondary charge neutrality points. Such hybridization leads to the formation of flat bands, enabling the study of correlated effects in graphene systems. This work may provide a novel way to manipulate electronic states in layered systems, which is important to both fundamental research and application.

International audience; Neural networks (NN) are efficient techniques for behavioral modeling of power amplifiers (PA). This paper proposes a genetic algorithm to determine the optimal hyperparam-eters of the NN model for a PA. Different activation functions are compared. The necessary number of training epochs is also studied to get an optimal solution with a significantly reduced computational complexity. Experimental measurements on a PA with different signals validate the NN models determined by the proposed method.

Multi-functional metal surfaces with simultaneous superhydrophobic and antireflective properties have a wide range of industrial, medical, and defense applications. Rapid fabrication of such multifunctional surfaces on irregularly shaped metals and on a large scale has been a challenge. Here we demonstrate an approach for rapid, standoff fabrication of multi-functional surfaces with super-hydrophobic and antireflective properties on irregularly-shaped samples, by texturing them with femtosecond laser filaments in air, followed by chemical fluorination treatment. Two types of metals with a variety of curved surfaces have been processed at a standoff distance of 1 m: the type 304 stainless steel and the type Ti–6Al–4V titanium alloy. Through laser processing and fluorination, multi-functional surfaces with the water contact angle of over 150° and diffused reflectance below 9%, over the wavelength range from 240 to 2500 nm, have been realized on both metals. Our results suggest that this technique could enable cost-effective and large-scale fabrication of irregularly-shaped, multi-functional surfaces that can be useful for various applications.

An Ag/AgCl/NH2-UiO-66 hybridized photocatalyst was successfully constructed via facile solvothermal with UV reduction method for efficient photocatalytic Cr (VI) reduction. The photoelectrochemical data indicate that compared with the UiO-66, the charge separation and transfer efficiency of Ag/AgCl/NH2-UiO-66 heterojunction is significantly enhanced due to the introduction of amine functionalization and formation of inorganic-organic hybrid. The surface plasmon resonance (SPR) effect deriving from Ag nanoparticles (NPs) largely extends photo-response range whilst the separation efficiency of photo-generated electrons and holes is improved significantly. The synthesized Ag/AgCl/NH2-UiO-66 hybrid system shows ameliorated structural stability and superior photocatalytic activity for Cr (VI) reduction under visible light irradiation, which is 1.7 times higher than that of the bare UiO-66. Furthermore, the possible mechanism of Cr (VI) reduction is proposed by analyzing electron transfer path in the ternary Ag/AgCl/NH2-UiO-66 hybridized system.

International audience; This letter proposes a method to study the tradeoff between the power amplifier (PA) power efficiency and the corresponding digital predistortion (DPD) model complexity needed for its linearization. The linearization performance estimated with the adjacent channel power ratio (ACPR) is treated as a control variable. In order to study at different PA operating points (OPs), an algorithm is proposed to trace approximate Pareto fronts of the ACPR versus the DPD model number of coefficients. Crest factor reduction is applied when the backoff of the OP is less than the peak-to-average power ratio (PAPR) of the long-term evolution (LTE) signal. Experimental results on a PA with an LTE signal give interesting insights on the relationship between DPD complexity and PA power efficiency.

Many research efforts for advanced Li-ion batteries have been made to design new material with large capacity and long cycle life, but little attention has been paid to regulate the voltage platform until now. Although quite attractive for the binary Ge-based chalcogenides, challenge is that a large potential gap as well as incongruous reaction kinetics is typically found between their conversion step (>1.6 V) and alloying region (

With the increasing irreversible damage caused by air pollution, an early warning system to send warning information to human beings so that they can avoid more harm caused by air pollution is required. A reliable warning system can provide valuable information to protect mankind from the effects of pollution and can act as a tool that allows regulators to implement corresponding measures to reduce air pollution. However, the previous most valuable research studies were focused on pollution forecasting and the extent to which pollution affects health, and the aim of only a few studies was to analyze pollution from an application perspective and to construct a reasonable early warning system. In this study, an air pollution early warning system was constructed, which comprises two modules: an air pollution forecasting module and an air quality evaluation module. In the forecasting module, two denoising methods and a multi-objective optimization algorithm are integrated into a novel hybrid forecasting model. In the evaluation module, fuzzy synthetic evaluation is used to evaluate air quality objectively. To verify the performance of the proposed early warning system, hourly pollutants concentration data were used in a case study of three metropolises in China and three numeric simulation experiments were conducted. The simulation results show that the forecasting performance of the L2,1RF-ELM model used in this study is better than the traditional neural network, and the forecasting model proposed in this paper is better than the traditional statistical model ARIMA. Moreover,the early warning system performed well in terms of highly accurate forecasting and accurate evaluation in the three research areas.

Wind penetration is expected to reach substantially higher levels of penetration in the near future. A variable speed wind turbine generator, such as, the doubly-fed induction generator (DFIG) or the permanent magnet synchronous generator, does not naturally provide rotating inertia due to the converter interface, which leads to a reduction of the overall system inertial response. The replacement of the conventional generators which have governors also reduces primary frequency control capability. Multiple control strategies have been proposed to enable a DFIG to provide emulated inertial response through reducing rotor speed that can release some kinetic energy. However, this has no effect on primary settling frequency and may even bring a secondary frequency dip to the system due to rotor speed recovery. Moreover, the rotor speed is not guaranteed to stay in an allowable range during the reduction period. This paper proposes a new control strategy for DFIG to provide adequate inertial response with guaranteed rotor security. To mitigate the potential of the secondary frequency dip and improve the performance of primary frequency control, a dynamic demand control strategy is proposed to coordinate with DFIG control. The coordination strategy can provide fast frequency support from the time-scale of inertial response through primary frequency control. The proposed control can be realized in both small-scale and large-scale power systems.

Efficient detection of outliers from massive data with a high outlier ratio is challenging but not explicitly discussed yet. In such a case, existing methods either suffer from poor robustness or require expensive computations. This paper proposes a Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations. Specifically, it is worth highlighting the following aspects of LEOD: (1) Our framework exploits the low-rank structure embedded in the similarity matrix and considers inliers/outliers equally based on this low-rank structure, which facilitates us to encourage satisfying robustness with low computational cost later; (2) A novel re-weighting algorithm is derived as a new general solution to the constrained eigenvalue problem, which is a major bottleneck for the optimization process. Instead of the high space and time complexity (O((2n)2)/O((2n)3)) required by the classic solution, our algorithm enjoys O(n) space complexity and a faster optimization speed in the experiments; (3) A new alternative formulation is proposed for further acceleration of the solution process, where a cheap closed-form solution can be obtained. Experiments show that LEOD achieves strong robustness under an outlier ratio from 20% to 60%, while it is at most 100 times more memory efficient and 1000 times faster than its previous counterpart that attains comparable performance. The codes of LEOD are publicly available at https://github.com/demonzyj56/LEOD.

Water status and distribution in shiitake mushroom and the effects of drying temperature on water migration were investigated by low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance...

Cas13d, the type VI-D CRISPR-Cas effector, is an RNA-guided ribonuclease that has been repurposed to edit RNA in a programmable manner. Here we report the detailed structural and functional analysis of the uncultured Ruminococcus sp. Cas13d (UrCas13d)-crRNA complex. Two hydrated Mg2+ ions aid in stabilizing the conformation of the crRNA repeat region. Sequestration of divalent metal ions does not alter pre-crRNA processing, but abolishes target cleavage by UrCas13d. Notably, the pre-crRNA processing is executed by the HEPN-2 domain. Furthermore, both the structure and sequence of the nucleotides U(-8)-C(-1) within the repeat region are indispensable for target cleavage, and are specifically recognized by UrCas13d. Moreover, correct base pairings within two separate spacer regions (an internal and a 3′-end region) are essential for target cleavage. These findings provide a framework for the development of Cas13d into a tool for a wide range of applications., Cas13d is a class 2 type VI-D CRISPR-Cas RNA-guided RNase. Here the authors present the high-resolution crystal structure of the uncultured Ruminococcus sp. Cas13d (UrCas13d)-crRNA complex and by combining structural, mutational and biochemical studies provide mechanistic insights into the CRISPR-Cas13d system.

Interfacing nanomechanics with photonics and charge/spin-based electronics has transformed information technology and facilitated fundamental searches for the quantum-to-classical transition1–3. Utilizing the electron valley degree of freedom as an information carrier, valleytronics has recently emerged as a promising platform for developments in computation and communication4–7. Thus far, explorations of valleytronics have focused on optoelectronic and magnetic means8–16. Here, we realize valley–mechanical coupling in a resonator made of the monolayer semiconductor MoS2 and transduce valley information into mechanical states. The coupling is achieved by exploiting the magnetic moment of valley carriers with a magnetic field gradient. We optically populate the valleys and observe the resulting mechanical actuation using laser interferometry. We are thus able to control the valley–mechanical interaction by adjusting the pump-laser light, the magnetic field gradient and temperature. Our work paves the way for realizing valley-actuated devices and hybrid valley quantum systems. Transduction of valley information to mechanical states in a monolayer MoS2 resonator can be realized by optically pumping the valley carriers and applying an out-of-plane magnetic field gradient to induce a displacement-dependent valley splitting.

The chlor-alkali/polyvinyl chloride production chain is characterized by high levels of energy consumption and emissions. The sustainable development of this industry has become a growing concern in the face of finite resources and the increasing demand. In the production chain, environmental, energy, and economic aspects are intricately linked. Thus, it's important to investigate how this industry can develop sustainably by examining the interactions among these three aspects. This paper evaluates the chlor-alkali/polyvinyl chloride production chain by using an integrated method combining the driving force-pressure-state-impact-response framework and system dynamics. First, the driving force-pressure-state-impact-response framework is used to investigate the interactions between society and the environment in the development of the production chain. Selected indicators were defined to characterize each subsystem in order to evaluate these interactions quantitatively. Next, system dynamics models are developed to explore the interactions among the three subsystems via causality loops. Five scenarios of the development of chlor-alkali/polyvinyl chloride production chain were simulated to see the effect of different policies on the subsystems. The results show that the sustainable development scenario showed reductions of 52% in energy intensity and 88% in chemical oxygen demand load intensity compared to business as usual. This integrated methodology can also be used as a decision-support tool for the design, planning, and management of the production chains of other process industries.

Accurate real-time density monitoring is crucial in quality control and quality assurance during the asphalt concrete (AC) pavement construction process. Ground penetrating radar (GPR) technology has shown great potential in the continuous real-time density prediction of AC pavement. However, it is not accepted as a routine method by transportation agencies in the United States due to the lack of validation under field testing conditions. In this study, three field tests were performed using GPR to estimate AC pavement density. The Al-Qadi-Lahouar-Leng model was used to predict the density from GPR signals. The reference scan method was used to remove the effect of surface moisture during construction. The gradient descent-based non-linear optimization method was used to reconstruct the overlapped GPR signals result from the use of thin AC overlay, which has been widely implemented as an AC pavement rehabilitation technique. Digital filtering and other signal processing methods were used to de-noise the signal. GPR results using the proposed methods were compared with field core data and nuclear gauge results. The results show that the proposed methods were effective in estimating in-situ AC pavement density using GPR. Continuous density estimation by installing GPR on the roller is suggested to provide real-time compaction monitoring during the AC pavement construction process.

Laboratory determination of the mode II fracture toughness KIIC for rocks and rock-type materials remains a challenge task. One of the testing approaches is the compression of disk-type specimens, which is simple and convenient. However, although the notch in disk-type specimen is under mode II loading, the detailed studies reveal the drawbacks about interpretations of the disk-type specimens: (i) mode II fracture is only assumed without careful identifications about the actual fracture mode; or (ii) critical value of associated mode II loading is considered to be the fracture toughness KIIC regardless of the actual fracture mode. The widely accepted failure criterion for disk-type specimens is maximum tangential stress that actually is maximum tensile stress, such that the initiated fracture generally involves the opening displacements. Thus, it raises a serious doubt about the mode II fracture from the disk-type specimens for rock-type materials. In this study, a series of the disk-type specimens of the marble was tested, and digital image correlation (DIC) was used to obtain the displacements surrounding the notch where the fracture mode was identified. Experimental results show the initiated fractures from the specimens are the mode I fractures, suggesting that the assumption of the mode II fracture is not correct. In the discussion, the interpretation is provided for: (i) differences between the critical values of mode II loading from disk-type specimens without compression and from punch-through shear specimens under compression; (ii) application of the criterion of local symmetry; and (iii) characteristics for rocks or rock-type materials and other information.

Crack initiation and propagation in rock involve the development of a nonlinear zone around the crack tip called fracture process zone. The existence of fracture process zone influences the fracture behavior and therefore it must be carefully investigated. This zone is normally idealized as a cohesive zone whose mechanical properties are difficult to measure. In this study, a relatively simple approach is proposed to identify the properties of the cohesive zone and the length of the cohesionless crack in a sandstone specimen under three-point bending. Two types of specimens were tested: beams with center notch and with smooth boundary. The digital image correlation (DIC) was utilized to obtain the opening displacement around the position where the fracture initiated. For a small length of a horizontal section around the specimen center, the material is considered to be in the state of uniaxial tension and its behavior is interpreted by the cohesive zone model. The proposed approach discusses three types of opening displacement distribution patterns (I), (II), (III). Pattern (I) is related to tensile loading of the material, pattern (II) suggests the development of material softening within the cohesive zone and pattern (III) indicates the development of the cohesionless crack. These displacement patterns together with the appropriate reference loadings are used to identify the length of the cohesionless crack. For example, observation of pattern (III) is consistent with the development of the cohesionless crack when the reference loading is the beginning of experiment. Further, the proposed method is useful for measuring the critical crack opening displacement and the fracture energy.

Accurate and continuous monitoring of joint rotational motion is crucial for a wide range of applications such as physical rehabilitation [6, 85] and motion training [22, 54, 68]. Existing motion capture systems, however, either need instrumentation of the environment, or fail to track arbitrary joint motion, or impose wearing discomfort by requiring rigid electrical sensors right around the joint area. This work studies the use of everyday fabrics as a flexible and soft sensing medium to monitor joint angular motion accurately and reliably. Specifically we focus on the primary use of conductive stretchable fabrics to sense the skin deformation during joint motion and infer the joint rotational angle. We tackle challenges of fabric sensing originated by the inherent properties of elastic materials by leveraging two types of sensing fabric and characterizing their properties based on models in material science. We apply models from bio-mechanics to infer joint angles and propose the use of dual strain sensing to enhance sensing robustness against user diversity and fabric position offsets. We fabricate prototypes using off-the-shelf fabrics and micro-controller. Experiments with ten participants show 9.69° median angular error in tracking joint angle and its sensing robustness across various users and activities.

Water resource management is an important consideration for water-intensive industries, such as the chlor-alkali sector. Water footprint is a three-component measure of freshwater consumption over the life cycle of a product, and takes into account both direct and indirect water use. This work extends the water footprint concept for products by integrating water scarcity in geographic regions where the production takes place. A general framework for water footprint sustainability analysis (WFSA) for chemical production chains is thus developed. WFSA combines the water scarcity index and water footprint, and is applied at the level of geographic regions. Two indicators are then proposed to assess the sustainability of the product and of the industrial sector in a specific region of interest. Based on this framework, some policy recommendations are proposed to enhance the sustainability of the chlor-alkali sector.

The CPU-graphic processing unit (GPU) co-execution of computation kernels on heterogeneous multiprocessor system-on-chip can significantly boost performance compared to the execution on either the CPU or the GPU alone. However, engaging multiple on-chip compute elements concurrently at the highest frequency may not provide the optimal performance in a mobile system with stringent thermal constraints. The system may repeatedly exceed the temperature threshold necessitating frequency throttling and hence performance degradation. We present OPTiC, an analytical framework that given a computation kernel can automatically select the partitioning point and the operating frequencies for optimal CPU–GPU co-execution under thermal constraints. OPTiC estimates, through modeling, CPU and GPU power, performance at different frequency points as well as the performance impact of thermal throttling and memory contention. Experimental evaluation on a commercial mobile platform shows that OPTiC achieves an average 13.68% performance improvement over existing schemes that enable co-execution without thermal considerations.

Accurate prediction of the S-wave velocity of highly heterogeneous coal measure strata using high-resolution logging can effectively identify high-quality reservoirs. We have used multipole array sonic logs to predict the S-wave velocity of coal measure strata based on the conventional empirical method (CEM), multiple regression method (MRM), and rock-matrix modulus extraction (MME) method. Moreover, we used a complex multiple parameter iterative computational method of forward calculation and inversion in the MME method. Our results indicate that the MME method can effectively extract several rock modulus parameters. There are good binomial relationships between the extracted rock modulus parameters ([Formula: see text]) and between the extracted modulus parameters and the P-wave impedance ([Formula: see text]). The average relative errors of the S-wave velocities predicted by the CEM, MRM, and MME methods are 7.58%, 5.64%, and 2.31%, respectively. The MME method can effectively extract and couple effective information from different types of conventional well logs and perform high-precision S-wave time difference prediction.

Cancer cells can shed into the vasculature from tumors and form circulating tumor cells (CTCs), which circulate in the blood stream. Surface-enhanced Raman scattering (SERS) is a very suitable technology for CTC detection due to its high sensitivity. In this study, improved SERS-active magnetic nanoparticles were designed and constructed for CTC detection. Poly(ethyleneimine) (PEI)-stabilized superparamagnetic iron oxide nanoparticles (SPION-PEI) were synthesized by a solvothermal method. Negatively charged gold nanoparticles (AuNPs) were then self-assembled in situ on the surface of SPION-PEI. The obtained SPION-PEI@AuNPs were modified with 4-mercaptobenzoic acid (MBA, a Raman reporter molecule) and FA-conjugated rBSA (rBSA-FA) via Au–S bonds resulting in the composite nanoparticles SPION-PEI@AuNPs-MBA-rBSA-FA (SERS-active magnetic nanoparticles). SPION-PEI@AuNPs-MBA-rBSA-FA nanoparticles showed good specificity to HeLa cells and the limit of detection (LOD) was 1 cell per mL of blood, which is the best among the reported values. The linear relationship between the concentration of cancer cells and the SERS intensity was utilized for quantitative measurement of CTCs. The SPION-PEI@AuNPs-MBA-rBSA-FA was released from the CTCs via culturing with excess folic acid, and the released CTCs were further cultured for expansion and molecular phenotype analysis. The concentrations of CTCs in the blood of two first-stage clinical patients with cervical cancer were measured to be 6 ± 2 cells per 10 mL or 13 ± 5 cells per 10 mL by our SERS-active magnetic nanoparticles.

The development of photoactive nanomaterials with high biocompatibility for targeted tumor phototherapy is of great significance for antitumor applications; this study presents a novel phototherapeutic agent, the Ge/GeO2 complex, which shows broad photoabsorption in the near infrared (NIR) region. As a result, it can synchronously produce reactive oxygen species (ROS) and heat under NIR irradiation. After being loaded onto macrophages, Ge/GeO2 could be delivered to tumors in a targeted fashion. Combining the abovementioned merits together, macrophage-loaded Ge/GeO2 realized in vivo synergetic photothermal and photodynamic outcomes to completely remove solid tumors in mice via intravenous administration. In this study, B-ultrasonography was also employed to monitor the tumor evolution after phototherapy, revealing a sequential process of tumor necrosis, liquefaction/softening, and finally disappearance. In addition, Ge/GeO2 proposed in this study shows negligible cytotoxicity and hematotoxicity, especially after being loaded onto macrophages.

The parameter tuning optimization design is realized for an active disturbance rejection controller (ADRC) in combination with the improvement of the existing swarm intelligence algorithm. Taking the optimization design and application of ADRC as an example, this paper is focused on investigating the improvement of the hybrid algorithm composed of fish swarm algorithm and particle swarm optimization algorithm and its application in parameter tuning of ADRC. The main contents are as follows. First, the parameters that need to be tuned are determined based on the composition and principle of the ADRC. The module building technology of S-function is adopted to create the module library of ARDC in terms of the modular construction idea and a complete simulation example of ADRC is built in Simulink. Second, the parameters are improved according to the proposed hybrid algorithm composed of the artificial fish swarm algorithm and the standard particle swarm optimization algorithm, and the control performance is tested by the MATLAB simulation of the ADRC whose parameters are optimized by using the algorithm. Finally, the flight attitude control of the unmanned aerial vehicle (UAV) is taken as an application example, and the fixed-wing UAV is selected as the research object. Through the analysis of the experimental results, the effectiveness of the optimized design is verified for the ADRC in the attitude control of the UAV.

Static or discrete time prediction of flexible pavement asphalt concrete (AC) layer density and thickness utilizing the ground penetrating radar (GPR) has proved to be relatively accurate compared to other in-situ methods. However, the accuracy during continuous monitoring of construction has not been validated by real construction projects. This study investigated the continuous estimation of AC layer density and thickness using GPR at a construction site in Macomb, Illinois. The first test compared AC layer density and thickness for pavement segments built in two consecutive days. The second test evaluated the construction quality of pavement segments built utilizing two types of material transfer vehicles (MTV). The GPRan software and the proposed truncation algorithm were used to calculate AC layer thickness continuously. The AC layer density and thickness estimation results were compared to core data using the ALL model. The GPR estimation values were found to be reliable. Errors of thickness results between continuous GPR data and core data from two tests are approximate 3% and 6%, respectively. The density of the new pavement segments was larger than the old ones while the thickness was almost the same. The average error between continuous density predictions and core density was 3%. The two MTVs performed similarly based on layer thickness and density results.

The processing of ground-penetration radar (GPR) signals collected from thin asphalt concrete (AC) overlay is a challenging task due to the limitation of GPR signal resolution. In this study, a gradient descent based nonlinear optimization approach was developed to analyze GPR signals collected from thin AC overlays to estimate their thickness and density. Both finite difference time domain (FDTD) simulation and field tests were conducted to validate the proposed algorithm. The simulation showed that the accuracy of dielectric constant estimation increased after the nonlinear gradient descent method was applied. This resulted in a thickness estimation error of less than 1 mm. When nonlinear gradient descent was applied to field test measured signals, the average AC overlay thickness prediction and AC density estimation errors were 3 mm and 1.81%, respectively. This study demonstrates that the nonlinear gradient descent is an effective approach for estimating thin AC overlay thickness and density from GPR data.

Various types of memristor-based network circuits have been reported to imitate multifunctional associative learning, while at the price of complex control circuits. In this work, we construct a compact memristive network for associative learning, in which memristors serve as interconnected synapses to extract relevance and timing information. A spice-compatible dynamic model of memristor is established and all the learning functions of Pavlov's dog are successfully demonstrated through circuit simulation. By incorporating the actual characteristics of the memristor into the network, multiple functions such as associative learning, acquisition, backward conditioning, and extinction are completely implemented. Moreover, the spike timing coding scheme and learning ability versus signal interval (Δt) are in line with biological behavior, making this design a promising solution for future brain-inspired associative learning systems.

Catechin is a natural phenolic compound with various bioactivities. However, it is unstable under light and heat environments. Amylose can form a single helical hydrophobic cavity to encapsulate and protect bioactive compounds. In this work, we applied amylose inclusion complexes (IC) to encapsulate a lipophilized catechin, i.e., hexadecyl catechin (HC), to improve its photostability and thermal stability. The formation of amylose-HC IC was characterized using differential scanning calorimetry, X-ray diffraction, and Fourier transform infrared spectroscopy. The photostability and thermal stability studies showed that the retention of guest molecules in IC was 86.1% ± 5.1% and 87.4% ± 0.6%, respectively, which was significantly higher than that of the catechin, HC, and amylose-HC physical mixture groups. Moreover, the in vitro release profile of IC demonstrated a steady and complete release of catechin. The findings show the amylose encapsulation of catechin is a promising technique to preserve bioactive compounds in food.

In recent years, transdermal preparations have emerged as one of the most promising modes of administration. In particular, dissolving microneedles have attracted extensive attention because of their painlessness, safety, high delivery efficiency and easily operation for patients. This article mainly reviews the preparation methods, the types of matrix polymer materials, the content of dissolving microneedles performance testing, and the applications of dissolving microneedles. It is expected to lay a solid knowledge foundation for the in-depth study of the dissolving microneedles.

Microplastics exist widely in water environment. The microplastic distribution in sediments can better reflect the long-term microplastic pollution, especially the vertical distribution. However, the vertical microplastic distribution in sediments is diverse and unclear. This paper is the first study on vertical microplastic distribution in estuary sediment of Fuhe River, the main upstream river flowing to Baiyangdian Wetland in the northern China. The typical feature of Fuhe River is that the effluent of municipal wastewater treatment plants is its main water source. Microplastics in 15 sampling sites and different depths (0–50 cm) were examined. Results showed that the microplastic content decreased with the increase of sediment depth, and the highest content was 1049 ± 462 items/kg in the topmost sediment layer (0–5 cm). The particle size of microplastics was smaller in deeper sediment layers. The proportion of colored microplastics in deeper sediment layers was larger than that in shallower layers. Polyethylene (PE) and polypropylene (PP) were the main plastic polymer types in all sediment samples. The spatial distribution characteristics of microplastics in sediments were closely related to human activities, and the microplastic content was higher in the areas with more intense human activities. This study is helpful to understand the detailed distribution characteristics of microplastics in typical rivers in the northern China, and can provide guidance for reducing microplastic pollution.

Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.

The electrochemical synthesis of hydrogen peroxide (H2O2) using the oxygen reduction reaction (ORR) requires highly catalytic active, selective, and stable electrode materials to realize a green and efficient process. The present publication shows for the first time the application of a facile one-step bottom-up wet-spinning approach for the continuous fabrication of stable and flexible tubular poly(3,4-ethylene dioxythiophene) (PEDOT : PSS) and PEDOT : PSS/carbon nanotube (CNT) hollow fibers. Additionally, electrochemical experiments reveal the catalytic activity of acid-treated PEDOT : PSS and its composites in the ORR forming hydrogen peroxide for the first time. Under optimized conditions, the composite electrodes with 40 wt % CNT loading could achieve a high production rate of 0.01 mg/min/cm2 and a current efficiency of up to 54 %. In addition to the high production rate, the composite hollow fiber has proven its long-term stability with 95 % current retention after 20 h of hydrogen peroxide production.

For traditional asynchronous machine power generation systems, the installation of speed sensor not only increases the cost of the system, but also reduces the safety of the system. Aiming at this problem, a speed sensorless control method named Extended-State-Observer-Compensated Extended-Kalman-Filter(ESOC-EKF) is proposed. Based on the state space equations of asynchronous motor in the stationary coordinate system, this paper takes stator current, rotor flux, speed and motor electromagnetic torque as state variables, uses the detected stator current, voltage and the load disturbance signal observed by the Extended State Observer (ESO),and combine the Extended Kalman Filter(EKF) theory to obtain the optimal estimate of the state variable. Robustness and accuracy of the observation performance is improved with the introduction of load compensation, while the effectiveness of proposed method is verified by simulation results.

Facial expression recognition has become a newly-emerging topic in recent decades, which has important value in the field of human-computer interaction. In this paper, we present a deep learning based approach, named frequency neural network (FreNet), for facial expression recognition. Different from convolutional neural network in spatial domain, FreNet inherits the advantages of processing image in frequency domain, such as efficient computation and spatial redundancy elimination. First, we propose the learnable multiplication kernel and construct multiple multiplication layers to learn features in frequency domain. Second, a summarization layer is proposed following multiplication layers to further yield high-level features. Third, based on the property of discrete cosine transform (DCT), we utilize multiplication layers and summarization layer to construct the Basic-FreNet, which can yield high-level features on the widely used DCT feature. Finally, to further achieve better performance on Basic-FreNet, we propose the Block-FreNet in which the weight-shared multiplication kernel is designed for feature learning and the block sub-sampling is designed for dimension reduction. The experimental results show that the Block-FreNet not only achieves superior performance, but also greatly reduces the computational cost. To our best knowledge, the proposed approach is the first attempt to fill in the blank of frequency based deep learning model for facial expression recognition.