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As a three-way approximation of fuzzy sets, shadowed sets has attracted extensive attention in recent years. A fundamental issue in the process of constructing shadowed sets is the interpretation and determination of the threshold pair (α,β), and the uncertainty consistency, that is, the consistency of fuzzy entropy. However, there may be a large fuzzy entropy loss between a fuzzy set and its corresponding game theoretic shadowed sets (GTSS), and the GTSS model is also accompanied by a large time cost when the precision of (α,β) is improved. Therefore, the fuzzy-entropy-based game theoretic shadowed sets (FeGTSS) is proposed in this paper from the perspective of fuzzy entropy loss. First, based on the compromise principle of game theory, the fuzzy entropy loss of shadowed sets is analyzed. Second, in the process of calculating (α,β), the optimal game strategy is searched based on the dichotomy algorithm. Third, the FeGTSS model is extended and discussed based on the analysis of different data distribution types. Finally, the rationality and validity of the FeGTSS model are illustrated through instances and experimental analysis.

Benefitting from the strong feature extraction capability of deep learning, infrared and visible image fusion has made a great progress. Since infrared and visible images are obtained by different sensors with different imaging mechanisms, there exists domain discrepancy, which becomes stumbling block for effective fusion. In this paper, we propose a novel self-supervised feature adaption framework for infrared and visible image fusion. We implement a self-supervised strategy that facilitates the backbone network to extract features with adaption while retaining the vital information by reconstructing the source images. Specifically, we preliminary adopt an encoder network to extract features with adaption. Then, two decoders with attention mechanism blocks are utilized to reconstruct the source images in a self-supervised way, forcing the adapted features to contain vital information of the source images. Further, considering the case that source images contain low-quality information, we design a novel infrared and visible image fusion and enhancement model, improving the fusion method’s robustness. Experiments are constructed to evaluate the proposed method qualitatively and quantitatively, which show that the proposed method achieves the state-of-art performance comparing with existing infrared and visible image fusion methods. Results are available at https://github.com/zhoafan/SFA-Fuse.

For abnormal detection of time series data, the supervised anomaly detection methods require labeled data. While the range of outlier factors used by the existing semi-supervised methods varies with data, model and time, the threshold for determining abnormality is difficult to obtain, in addition, the computational cost of the way to calculate outlier factors from other data points in the data set is also very large. These make such methods difficult to practically apply. This paper proposes a framework named LSTM-VE which uses clustering combined with visualization method to roughly label normal data, and then uses the normal data to train long short-term memory (LSTM) neural network for semi-supervised anomaly detection. The variance error (VE) of the normal data category classification probability sequence is used as outlier factor. The framework enables anomaly detection based on deep learning to be practically applied and using VE avoids the shortcomings of existing outlier factors and gains a better performance. In addition, the framework is easy to expand because the LSTM neural network can be replaced with other classification models. Experiments on the labeled and real unlabeled data sets prove that the framework is better than replicator neural networks with reconstruction error (RNN-RS) and has good scalability as well as practicability.

Recent progress in bioresorbable stents (BRSs) has provided a promising alternative for treating coronary artery disease. However, there is still lack of BRSs with satisfied compression and degradation performance for pediatric patients with congenital heart disease, leading to suboptimal therapy effects. Here, we developed a mechanically self-reinforced composite bioresorbable stent (cBRS) for congenital heart disease application. The cBRS consisted of poly(p-dioxanone) monofilaments and polycaprolactone/poly(p-dioxanone) core-shell composite yarns. Interlacing points in cBRS structure were partially bonded, offering the cBRS with significantly higher compression force compared to typical braids and remained good compliance. The suitable degradation profile of the cBRS can possibly preserve vascular remodeling and healing process. In addition, the controllable structural organization provides a method to customize the performance of the cBRS by altering the proportion of different components in the braids. The in vivo results suggested the cBRS supported the vessel wall similar to that of metallic stent. In both abdominal aorta and iliac artery of porcine, cBRS was entirely endothelialized within 1 month and maintained target vessels with good patency in the 12-month follow-up. The in vivo degradation profile of the cBRS is consistent with static degradation results in vitro. It is also demonstrated that there is minimal impact of pulsatile pressure of blood flow and variation of radial force on the degradation rate of the cBRS. Moreover, the lumen of cBRS implanted vessels were enlarged after 6 months, and significantly larger than the vessels implanted with metallic stent in 12 months., Graphical abstract Image 1, Highlights • By using hybridized braiding technique, we developed a braided composite bioresorbable stent (cBRS) with mechanically self-reinforced properties and two-stage degradation profile. • The cBRS consisted showed comparable radial force to metallic stents and excellent flexibility. • After stents implantation in porcine artery for 12 months, significantly larger lumen area was observed in cBRS implanted vessels compared with that of Wallstent implanted vessels. • The controllable design principle of this braided composite stent provide the method to customize bioresorbable stents for clinical requirements. • The bioresorbable stent developed in this study have comparable strength to metallic stents and excellent compliance.

Currently, transdisciplinary integration has become increasingly close, and has gradually become the source of innovation. At the same time, petroleum engineering technologies demand more new technologies like functional materials and electronic information technologies. In order to effectively promote technological innovation and development of the petroleum engineering, it is important to continuously monitor, analyze and evaluate the latest development of the technologies outside of the oil and gas industry. This paper combines qualitative analysis of onsite demands, application cases, technical characteristics, and quantitative analysis of literature metrology, patent evaluation, technology maturity, to evaluate the application prospects of densified wood, liquid metal and poly (thioctic acid) in the field of petroleum engineering, and specific transdisciplinary suggestions are put forward. It is recommended to carry out pre-research work for the potential application of functional materials in the petroleum engineering, and it is expected to introduce new materials for downhole tools, new antennas for downhole instruments, extend long-term effectiveness of downhole plugging, and improve drilling efficiency.

Based on the granular computing and three-way decisions theory, the sequential three-way decisions (S3WD) model implements the idea of progressive computing. However, almost S3WD models are established based on labeled information system, and there is still a lack of S3WD model for processing unlabeled information system (UIS). In this paper, to solve the issue of given accepted number for UIS, a data-driven sequential three-way decisions (DDS3WD) model is proposed. Firstly, from the perspective of similarity computed by TOPSIS, a general three-way decisions model for UIS based on decision risk is presented and its shortcomings are analyzed. Then, a concept of optimal density difference is defined to establish the DDS3WD model for UIS by updating attributes. Finally, the related experiments show that DDS3WD is feasible and effective for dealing with UIS under the condition of given accepted number of objects.

Information granule is the basic element in granular computing (GrC), and it can be obtained according to the granulation criterion. In neighborhood rough sets, current uncertainty measures focus on computing the knowledge granulation of single granular space and have two main limitations: (i) neglecting the structural information of boundary regions and (ii) the inability to reflect the difference between neighborhood granular spaces with the same uncertainty for approximating a target concept. Firstly, a fuzziness-based uncertainty measure for neighborhood rough sets is introduced to characterize the structural information of boundary regions. Moreover, from the perspective of distance, based on the idea of density peaks, we present a fuzzy-neighborhood-granule-distance- (FNGD-) based method to discover the relationship between granules in a granular space. Then, to characterize the difference between granular spaces for approximating a target concept, we present the fuzzy neighborhood granular space distance (FNGSD) and fuzzy neighborhood boundary region distance (FNBRD). FNGD, FNGSD, and FNBRD are hierarchically organized from fineness to coarseness according to the semantics of granularity, which provide three-layer perspectives in the neighborhood system.

With the increasing interest in exploring the deep-space environment, the problems of health and safety of astronauts' bone induced by microgravity warrants an investigation. Recent studies have discovered that several microRNAs (miRNAs) have played a vital role in osteoblast differentiation and bone formation under microgravity, whereas the in-depth signaling pathway mechanisms are not yet completely understood. Here, we performed the hind limb unloading (HLU) mice model and 3D clinostat-random position machine (RPM) to simulate the effects of microgravity on bone at animal and cellular level, respectively. Firstly, we screened the different expressed miRNAs under simulated microgravity by miRNA sequencing, we then identified the highest different expressed miRNA (miR-138–5p) that was up-regulated under simulated microgravity and inhibited osteoblast differentiation in MC3T3-E1 cells. Moreover, we analyzed miR-138's targets during osteogenic differentiation and we found that these targets regulated osteogenic differentiation through Wnt/β-catenin signaling, a key signaling pathway in controlling osteoblast differentiation. The association between miR-138–5p and β-catenin activity was determined by a luciferase reporter assay. Further experiments confirmed that miR-138–5p suppressed β-catenin expression and β-catenin activity. Moreover, β-catenin overexpression attenuated osteoblast differentiation reduction induced by increased miR-138–5p level in miR-138–5p overexpression osteoblastic cells. In addition, inhibition of miR-138–5p counteracted the negative effects of simulated microgravity on osteoblast differentiation and β-catenin expression in MC3T3-E1 cells. Our findings demonstrated that a signaling pathway mechanism of miR-138–5p in regulating osteoblast differentiation under simulated microgravity, which may reveal a novel mechanism for astronauts' bone loss induced by microgravity and provide a potential therapeutic target for disused osteoprosis.

Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) for recognizing unseen classes. However, the projection function is difficult to generalize the relationship description between the feature space and multi-semantic embedding spaces(all kinds of class names are embedded as different vectors in terms of the various semantic interpretation, for example attributes, word vectors, global vectors and hierarchical embedding), which have the diversity characteristic for detailing the different semantic information of the same class. To deal with this issue, we present a novel method to ZSL based on learning class label autoencoder with structure refinement(CLASR). CLASR can not only build a scalable framework for adapting to multi-semantic embedding spaces, but also utilize the encoder-decoder paradigm for constraining the bidirectional projection between the feature space and the class label space. Moreover, CLASR can focus on the fusion model between the relationship of feature classes (visual feature classes structure) and the relevance of the semantic classes (semantic classes structure) for improving zero-shot classification by structure refinement. The CLASR solution can provide both unseen class labels and the relation of the different classes structure(visual feature and semantic classes structure) that can encode the intrinsic structure of classes. Extensive experiments demonstrate the CLASR outperforms the state-of-art methods on four benchmark datasets, which are AwA, CUB, Dogs and ImNet-2.

The geographical locations of smart devices can help in providing authentication information between multimedia content providers and users in 5G networks. The IP geolocation methods can help in estimating the geographical location of these smart devices. The two key assumptions of existing IP geolocation methods are as follows: (1) the smallest relative delay comes from the nearest host; (2) the distance between hosts which share the closest common routers is smaller than others. However, the two assumptions are not always true in weakly connected networks, which may affect accuracy. We propose a novel street-level IP geolocation algorithm (Corr-SLG), which is based on the delay-distance correlation and multilayered common routers. The first key idea of Corr-SLG is to divide landmarks into different groups based on relative-delay-distance correlation. Different from previous methods, Corr-SLG geolocates the host based on the largest relative delay for the strongly negatively correlated groups. The second key idea is to introduce the landmarks which share multilayered common routers into the geolocation process, instead of only relying on the closest common routers. Besides, to increase the number of landmarks, a new street-level landmark collection method called WiFi landmark is also presented in this paper. The experiments in one province capital city of China, Zhengzhou, show that Corr-SLG can improve the geolocation accuracy remarkably in a real-world network.

A universal fusion framework for handling multi-realm image fusion reduces the cost of manual selection in varied applications. Addressing the generality of multiple realms and the sensitivity of specific realm, we propose a novel universal framework for multi-realm image fusion through learning realm-specific and realm-general feature representations. Shared principle network, adaptive realm feature extraction strategy and realm activation mechanism are designed for facilitating high generalization of across-realm and sensitivity of specific-realm simultaneously. In addition, we present realm-specific no-reference perceptual metric losses based on the edge details and contrast for optimizing the learning process, making the fused image exhibit more specific appearance. Moreover, we collect a new multi-realm image fusion dataset (MRIF), consisting of infrared and visual images, medical images and multispectral images, to facilitate our training and testing. Experimental results show that the fused image obtained by the proposed method achieves superior performance compared with the state-of-the-art methods on MRIF and the other three datasets including infrared and visual images, medical images and remote sensing images, respectively.

Silicon integrated lead-free oxide thin film capacitors with high energy storage density (Wre), high efficiency (η) and good thermal stability have great application potential in modern communication fields. Here, 1 mol% SiO2-doped Ba(Zr0.35Ti0.65)O3 (BZTS) thin film capacitors are integrated on Si and HfO2 buffered Si substrates by using a radio-frequency magnetron sputtering system. It is found that the energy storage performances are significantly improved by inserting an HfO2 buffer layer (about 13.5 nm) between the BZTS layer and the Si substrate. The improved Wre of the BZTS/HfO2 thin film capacitors can be up to 93.48 J cm−3 at room temperature, which is about 65% higher than that of the film without the HfO2 buffer layer, and the η is ∼ 71.44%. Moreover, the introduction of the HfO2 buffer layer leads to a superior thermal stability in a wide temperature range from −100 °C to 200 °C with a very small change rate of ∼3.39%. Under different irradiations with doses of He+ from 1 × 1012 ions per cm2 to 7 × 1015 ions per cm2 and neutrons from 5 × 1012 ions per cm2 to 1 × 1014 ions per cm2, the BZTS thin film capacitors with the HfO2 buffer layer show ultra-stable energy storage performance. Our research provides an effective strategy for the integration of high performance BZTS thin film capacitors on Si substrates, and our results demonstrate potential of the Si integrated thin film capacitors for the application in nuclear technology, space stations, satellites, radiation centers and other harsh environments.

With a good balance between tracking accuracy and speed, kernelized correlation filter (KCF)-based trackers have now become dominant approaches in the field of online object tracking. However, a KCF-based tracker updates templates by linearly combining historical objects, and its performance inevitably degrades when the accumulated error causes the model to look more similar to the background than the foreground. To address this model drifting problem, we propose an incremental kernel principal component analysis (IKPCA)-based KCF (IKPCA-KCF) tracking scheme. In IKPCA-KCF, IKPCA technology is used to incrementally learn the target features to alleviate the model drifting problem. Moreover, reduced-set (RS) expansions are adopted to compress the historical target samples to maintain a constant template update rate. Additionally, KCF-based trackers use the response map generated by the correlation filter to determine the target location. However, when the target leaves the field of view, using the maximum response value to reflect the target’s position information will certainly induce tracking errors. To further solve the tracking stability problem, a simple and effective nearest neighbor classifier is used to confirm the tracking target during the tracking process. Extensive experimental results demonstrate that the proposed IKPCA-KCF tracker is competitive with state-of-the-art trackers on eight challenging video datasets.

Dielectric capacitors, which have the characteristics of greater power density, have received extensive research attention due to their application prospects in pulsed power devices. Film capacitors are easier to integrate into circuits due to their smaller size and higher energy storage density compared to other dielectric capacitor devices. Recently, film capacitors have achieved excellent energy storage performance through a variety of methods and the preparation of multilayer films has become the main way to improve its energy storage performance. In this review, we have summarized several control optimization mechanisms, such as heterojunction effect, interfacial ‘dead-layer’ and space-charges effect, modulating the distribution of electric field and polarization, multilayer film interface design and interface blocking effects, and interlayer coupling, for multilayer films by recalling previous studies on multilayer films. The outlook and future research direction of multilayer films are also discussed in this article.

Power point tracking (PPT) is one of the necessary functions of the wind turbine to optimize the use of wind energy. PPT is a condition that needs to be completed after the grid is connected, which can be achieved by tracking the optimal rotation speed of the output of the wind turbine and the optimum torque and power output of the hydraulic system. Based on a fixed displacement pump speed control, an optimal PPT strategy with the active disturbance rejection control (ADRC) method is proposed, and the control objective is to maximize the energy conversion of the system. This paper sets out to (i) establish a hydraulic wind turbine grid-connected affine nonlinear mathematical model, based on the ADRC method and a fixed displacement pump speed output control, (ii) design a nonlinear tracking differentiator and extended state observer and nonlinear state error feedback control law, and (iii) achieve optimal PPT under different wind speeds. Simulations were model by MATLAB/Simulink, where the system inputs signals of different wind speeds and analyses control system stability and robustness. Simulation results show that the input power was greater with a fixed displacement pump speed .

Vague set, as well as intuitionistic fuzzy set, is an extended model of fuzzy sets. On the basis of fuzzy sets, vague sets describe the membership degree of a vague concept by using an interval value instead of a single value. To a certain degree, vague sets have a more powerful ability to process fuzzy information than fuzzy sets. Thus, when characterizing a target concept by vague sets, identifying methods to make scientific and reasonable decisions has become an essential issue. However, existing decision methods always focus on the decisions based on fuzzy concepts, and research on how to make three-way decisions based on vague concepts is still lacking. Therefore, in this paper, the concept of rough vague sets is proposed to construct a rough approximation framework of vague concepts. Then, the fuzziness of the existing approximation approaches is analyzed. Next, improved step-vague set model which is a better approximation approach than existing approaches and the algorithm used to search for a improved step-vague set are proposed. Furthermore, based on the improved step-vague sets, probabilistic rough vague sets and a three-way approximation model with shadowed sets are introduced. Finally, several illustrative examples and relative experiment are listed to verify the effectiveness and significance of the proposed models.

Currently, implantable fibrous medical devices still suffer from invisibility under current clinical imaging techniques. To address this problem, 2, 3, 5-triiodobenzoic acid (TIBA) was recruited as a contrast agent, and then a set of iodinated poly( p-dioxanone) (PPDO) fibers was fabricated via melt-spinning hybrid blends of PPDO with TIBA (PPDO/TIBA). The impact of TIBA content on the rheological behavior of blends was evaluated firstly. The physical, chemical, and thermal properties of PPDO/TIBA fibers were investigated accordingly by SEM, FTIR, DSC, and TGA. Moreover, the radiopaque property of PPDO/TIBA hybrid fibers as a potential radio-opacifying platform for medical devices was verified in vitro and in vivo. Finally, the accumulated release results of the hybrid fibers during in vitro degradation indicate the continual X-ray visibility of the hybrid fibers maintains for 22 days. This intriguing iodinated platform may pave the way for constructing fibrous materials with in-situ X-ray tracking property.

Strontium titanate (SrTiO3, ST) is an incipient ferroelectric material. Generally, a one-dimensional (1D) ST polycrystal is difficult to obtain because of its high aspect ratio and directional orientation. We prepared a 1D ST polycrystal from a layered H2Ti4O9·H2O (HT) single crystal via a solvothermal soft chemical process. The 1D ST polycrystal fabricated from ST nanocrystals has a [010] crystal axis, implying that it is a mesocrystal. The ST nanocrystals were grown not only on the surfaces but also in the interlayers of the original HT matrix. The formation mechanism of the ST mesocrystal is based on a topochemical mesocrystal conversion process. ST ceramics fabricated from ST mesocrystal powder present a lossy capacitor response and excellent energy-storage capabilities owing to the presence of locally oriented ST nanocrystals with a lattice mismatch in the rod-like ST mesocrystals. The investigations performed in this study can set a new benchmark for the development of new electric ST-based materials.

Natural gas resources in the Qaidam Basin are abundant and of various types. On the basis of the natural gas carbon isotope distribution characteristics, the western Qaidam Basin mainly comprises o...

A novel online two-dimensional supercritical fluid chromatography/reversed-phase liquid chromatography-triple-quadrupole mass spectrometry (2D SFC/RPLC-QQQ MS) method based on a vacuum solvent evaporation interface was developed for lipid profiling in human plasma, in which lipid classes were separated by the first-dimension SFC and different lipid molecular species were further separated by the second-dimension RPLC. All separation condition parameters were carefully optimized, and their influence on the chromatographic behavior of lipids is discussed. Finally, the recoveries of 11 lipid standards were all more than 88% for the interface. Besides, the limit of detection for these lipid standards was on the order of nanograms per milliliter, and the relative standard deviations of the peak area and retention time ranged from 1.54% to 19.85% and from 0.00% to 0.10%, respectively. The final 2D SFC/RPLC-QQQ MS method allowed the identification of 370 endogenous lipid species from ten lipid classes, including diacylglycerol, triacylglycerol, ceramide, glucosylceramide, galactosylceramide, lactosylceramide, sphingomyelin, acylcarnitine, phosphatidylcholine, and lysophosphatidylethanolamine, in human plasma within 38 min, which was used for screening potential lipid biomarkers in breast cancer. The 2D SFC/RPLC-QQQ MS method is a potentially useful tool for in-depth studies focused on complex lipid metabolism and biomarker discovery. Graphical Abstract.

Effective detection of infrared (IR) moving small targets in complex cluttered environments plays a key role in IR search and track systems for self-defense or attacks. In this letter, an IR moving small-target detection algorithm utilizing a spatiotemporal consistency of motion trajectories is proposed. First, feature points are densely sampled and tracked using the dense optical flow algorithm to compute dense trajectories. Second, suspected trajectories are deleted by utilizing the moving characteristics of the target. Third, under the assumption that each small target is defined as a compact space region, a binary image is created depending on the image coordinates of the trajectory points, from which salient contours are extracted as candidate target regions. Finally, a coding mechanism for contour numbering is introduced, and the moving targets are distinguished from the backgrounds by the temporal consistency of contour codewords. Several experiments were conducted, and their results demonstrate that our proposed method can detect small moving IR targets with higher detection rate, lower false alarm rate, and less running time compared with the state-of-the-art methods.

Our aim was to apply a composite braided biodegradable stent (CBBS) made from poly p-dioxanone (PPDO) and polycaprolactone (PCL) as an alternative to metallic stents for the treatment of pediatric endovascular disease. CBBS properties after adjunctive post-dilatation were assessed using radial force testing. CBBS degradation was assessed using in vitro measurements. Self-expandable CBBSs (8 × 20 mm) were implanted in abdominal aortas with an oversizing ratio of 1.1–1.4 (group A, n = 12) and in common iliac arteries with an oversizing ratio >1.4 (group B, n = 12). Self-expandable metal WALLSTENTs (8 × 21 mm) were implanted in common iliac arteries with an oversizing ratio >1.4 and served as controls (group C, n = 12). Artery evaluations including angiography and histological examinations were performed at 1, 4, 6 and 12 months after stent implantation. Eight millimeter CBBSs delivered in 8Fr sheaths with adjunctive post-dilatation had properties similar to those of metallic benchmark stents and were degraded in 12 months, with mild to moderate inflammation-induced neointimal hyperplasia and vessel restenosis. Post-dilatation and oversizing are suggested when using CBBSs for polymeric strut tissue embedding and optimal wall apposition, but an overextended ratio should be avoided because of the induction of less-desirable neointimal hyperplasia. Mid-term outcomes of CBBSs with adjunctive post-dilatation were better than those of WALLSTENTs in a swine endovascular disease model.

Refractive index (RI) sensing is a promising technique in a variety of different scientific and industrial applications. In this paper, we theoretically and experimentally propose a newly-designed RI sensor. The proposed sensor consists of a particular long-period fiber grating inscribed in a two-mode fiber (LPFG-TMF) coated with tunable graphene oxide (GO), and the special structure allows an extremely high sensitivity in the transition region. The mode coupling between the LP11 core mode and the sixth-order cladding mode in the LPFG-TMF offers an apparently higher sensitivity feature. And the sensitivity is higher than that of a single-mode fiber-based LPFG (LPFG-SMF), which originates from the coupling between the core mode and the sixth-order cladding mode. Furthermore, the ultrasonic treatment time of GO, which impacts on the properties and performance of RI sensing, is also studied and investigated. Proof-of-concept results demonstrate significantly that comparing with the bare LPFG-TMF (621.66 nm/RIU) and LPFG-SMF coated with GO ultra-sonicated 5 hours (323.68nm/RIU), the LPFG-TMF coated with GO ultra-sonicated 5 hours performs optimized sensitivity of 11605.79 nm/RIU when surrounding RI ranges from 1.4558 to 1.4577. With its premium ability to operate high-sensitivity measurements, the RI sensing is expected to be useful for broad applications across the chemical fields.

While ``ideal'' Weyl states have been realized in bosonic systems, their existence in fermionic systems has been elusive. New experiments provide long-sought evidence for such a state.

Spin-valley locking in monolayer transition metal dichalcogenides has attracted enormous interest, since it offers potential for valleytronic and optoelectronic applications. Such an exotic electronic state has sparsely been seen in bulk materials. Here, we report spin-valley locking in a Dirac semimetal BaMnSb2. This is revealed by comprehensive studies using first principles calculations, tight-binding and effective model analyses, angle-resolved photoemission spectroscopy measurements. Moreover, this material also exhibits a stacked quantum Hall effect (QHE). The spin-valley degeneracy extracted from the QHE is close to 2. This result, together with the Landau level spin splitting, further confirms the spin-valley locking picture. In the extreme quantum limit, we also observed a plateau in the z-axis resistance, suggestive of a two-dimensional chiral surface state present in the quantum Hall state. These findings establish BaMnSb2 as a rare platform for exploring coupled spin and valley physics in bulk single crystals and accessing 3D interacting topological states., Valley dependent spin polarization called spin-valley locking appears in absence of magnetism but it is limited to rare examples of transition metal dichalcogenides. Here, the authors report evidence of spin-valley locking and stacked quantum Hall effect in a bulk Dirac semimetal BaMnSb2.

This paper aims to better analyze the crack resistance of lignin fiber reinforced SMA-13 (LFSMA-13) asphalt mixtures, with and without polymer anti-rut agent (ARA), under different aging degrees. IDEAL-CT test and Fourier transform infrared (FTIR) spectroscopy were utilized to analyze the relationships between the crack resistance of LFSMA-13, with and without ARA, and the parameters of the FTIR spectrum of the asphalt extracted from the test samples. A convenient testing method to predict the anti-crack ability of the mixtures in a road was also derived in this study. The test samples were prepared using the specifications listed by AASHTO. The fracture formation work (Winitial) and cracking index (CTIndex) in the IDEAL-CT test were adopted to reflect the cracking ability of the asphalt mixtures in both the crack formation stage and the crack propagation stage. The peak areas of the FTIR spectrum were utilized to reveal the chemical properties of the asphalt material inside the SMA-13 asphalt mixtures, with and without ARA under different aging degrees. Grey correlation analysis was adopted to choose the most suitable FTIR spectrum parameters to derive the prediction models of Winitial and CTIndex under different aging degrees. After conducting a series of tests, the results showed that the aging process could well affect the crack resistance of the test samples and the peak areas of the asphalt extracted from the mixtures. The FTIR parameters selected from the grey correlation analysis could be used to well predict the anti-crack ability of the asphalt mixtures.

Robust blind watermarking has become a vital means of copyright protection, and this paper presents a new optimal robust and blind watermarking method of grayscale images based on intertwining logistic map and a variant of particle swarm optimization (PSO) in a hybrid domain. In the proposed approach, firstly a host image is decomposed by discrete wavelet transform, and discrete cosine transform (DCT) is applied to insensitive LH and HL subbands according to human visual model. Then, optimum frequency spectra in the DCT domain are chosen to form a feature matrix for improving the robustness and transparency of watermark. Finally, a shuffled watermark image using the chaotic logistic map is inserted by modifying the largest singular values of a feature matrix pair in the singular value decomposition domain. An improved version of PSO is employed to perform multi-dimensional optimization for selection of the most qualified DCT coefficients and estimation of watermark embedding strength in terms of their significant influence on imperceptibility and robustness. The security of the proposed method is provided by intertwining logistic map. Experimental results on standard test images indicate that PSO searches efficiently optimal values of watermark embedding strength and the most suitable DCT subbands, and the proposed watermarking algorithm performs much better than the other compared schemes in imperceptibility and robustness objectives.

Robust zero-watermarking is an effective and distortion-free technique for copyright protection and has become a research hotspot in the field of digital watermarking. In the face of weak robustness and security of existing schemes, this paper presents a novel robust and secure color image zero-watermarking algorithm based on majority voting pattern and hyper-chaotic encryption. In the proposed algorithm, firstly an original color image is decomposed by one-level discrete wavelet transform (DWT) and the corresponding low-frequency components of three channels are partitioned into blocks. Then, to resist strong attacks, we construct a distinguishable robust binary feature matrix extracted from all blocks and color components by using a combination of Frobenius norm in the singular value decomposition (SVD) domain and majority voting pattern. To promote security, a binary copyright logo is confused and diffused by hyper-chaotic Lorenz system. Finally, it performs a bitwise exclusive-or operation on the binary feature matrix and the encrypted copyrighted logo to obtain a zero-watermark signal. Experimental results indicate that in addition to the high-level security, the proposed zero-watermarking algorithm also has a stronger robustness against common geometric and non-geometric attacks, including rotation, scaling, filtering, JPEG compression and noise addition, compared with some existing typical zero-watermarking and traditional watermarking methods.

Granular computing is an umbrella term to cover a series of theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. Rough sets, as one of the main concrete models of granular computing, has attracted considerable attention and has been successfully applied to numerous kinds of fields. To show the basic ideas and principles of granular computing from the perspective of rough sets, the main models, uncertainty measures and applications of rough sets are surveyed in the paper.

Alzheimer’s disease (AD) is one of the most common diseases in the world. It is a neurodegenerative disease that can cause cognitive impairment and memory deterioration. In recent years, the number of the elderly population is increasing, and the incidence of elderly diseases has increased significantly. The most representative of these diseases is Alzheimer’s disease. According to some data, the average survival time of Alzheimer’s disease patients is only 5.5 years, which is the “fourth killer” that endangers the health of the elderly after cardiovascular diseases, cerebrovascular diseases and cancer. According to conservative estimates of the International Federation of Alzheimer’s Diseases, the number of Alzheimer’s disease patients worldwide will increase to 75.62 million by 2030; by 2050, the number of patients will reach 135.46 million. Therefore, it is urgent to classify the course of Alzheimer’s disease. In this paper, support vector machine (SVM) model method is used to classify and predict different disease processes of Alzheimer’s disease based on structural brain magnetic resonance imaging (MRI) imaging data, so as to help the auxiliary diagnosis of the disease. In this paper, the extracted MRI data and the SVM model are combined to obtain more accurate classification prediction results. The accuracy of classification and prediction is the best. According to the predicted results, the data characteristics related to diseases can be determined, which can provide a basis for clinical and basic research, etiology and pathological changes.

Bioresorbable cardiovascular stents are usually subjected to dynamic mechanical loads in vivo, which results in a different degradation profile compared to that measured under a static in vitro immersion test. In this study, a dynamic test platform was used to mimic cyclic pulsatile pressure and compared the changes in polymer properties of poly(p-dioxaone)/polycaprolane (PPDO/PCL) braided composite bioresorbable stents (BCBRSs) with static loading and non-loaded environments. The results showed static compressive load accelerated the changes in the hydrolysis process and crystallinity for polymers while the pulsatile pressure increased surface corrosion of the stent struts. Moreover, the degradation rate under dynamic loading was attenuated due to the mitigation of viscous flow of molecule chains and autocatalysis process, compared with that under static loading and non-loaded conditions.

Reactive oxygen species (ROS) are crucial molecules in cancer therapy. Unfortunately, the therapeutic efficiency of ROS is unsatisfactory in clinic, primarily due to their rigorous production conditions. By taking advantage of the intrinsic acidity and overproduction of H2O2 in the tumor environment, we have reported an ROS nanoreactor based on core-shell-structured iron carbide (Fe5C2@Fe3O4) nanoparticles (NPs) through the catalysis of the Fenton reaction. These NPs are able to release ferrous ions in acidic environments to disproportionate H2O2 into •OH radicals, which effectively inhibits the proliferation of tumor cells both in vitro and in vivo. The high magnetization of Fe5C2@Fe3O4 NPs is favorable for both magnetic targeting and T2-weighted magnetic resonance imaging (MRI). Ionization of these NPs simultaneously decreases the T2 signal and enhances the T1 signal in MRI, and this T2/T1 switching process provides the visualization of ferrous ions release and ROS generation for the supervision of tumor curing. These Fe5C2@Fe3O4 NPs show great potential in endogenous environment-excited cancer therapy with high efficiency and tumor specificity and can be guided further by MRI.

Recent face detectors based on deep convolutional neural networks (DCNN) have substantially improved the detection performance in the wild. However, the detection speed is still the biggest bottleneck, which hinders the practical deployment of these face detectors on resource-limited platforms. To address this issue, the paper proposes a Proposal Pyramid Network (PPN) to generate face candidates extremely fast, which reduces the major computational complexity in cascaded DCNN face detectors. PPN is a lightweight fully convolutional network with multiple branches, which takes a single image as input and generates face proposals with different scales in terms of separate branches simultaneously. In this manner, it avoids the traditional image pyramid structure and thus achieves a very fast processing speed. To evaluate the effectiveness of our proposed method, a three-stage cascaded DCNN face detector is realized based on PPN. Extensive experimental results on several public benchmarks show that the proposed face detector achieves comparable accuracy to the-state-of-arts while the detection speed reaches 60 FPS with an i5 CPU, which significantly exceeds previous DCNN-based face detectors. (The source code is available at https://github.com/zhaofan0622/PPN ).

The present work investigated the effect of the addition of V2O5 and MnCO3 on the microstructure and magnetic properties of Ni-Zn ferrite ceramic samples prepared by a conventional ceramic sintering method at different temperatures. With this aim, a series of samples were prepared by varying the loadings of V2O5-MnCO3 (un-doped, 0.4–0.1 wt%, 0.1–0.4 wt%, 0.2–0.3 wt%, 0.3–0.2 wt%, and 0.5–0.1 wt%, denoted as sample 1, sample 2, sample 3, sample 4, sample 5, and sample 6, respectively). The initial permeability and power loss of the different Ni-Zn ferrites were investigated with respect to the sintering temperature. The V2O5 and MnCO3 dopants significantly improved the initial permeability and power loss characteristics of the Ni-Zn ferrite at frequencies ≥0.5 MHz. When sintered at 1100 °C, sample 2 showed a maximum initial permeability of 931.23 H/m at a frequency of 1 MHz combined with a minimum power loss of 339.2 kW/m3. Co-doping with V2O5 and MnCO3 also resulted in the sintered samples with larger average grain sizes and higher density, while the sintering temperature of Ni-Zn ferrites was significantly reduced.

To prepare a desired negative differential resistance (NDR) device by standard complementary-metal-oxide-semiconductor (CMOS) process, a photoelectric dual control NDR device with a PNP bipolar-junction-transistor (BJT) and an NPN BJT was designed and fabricated by using the Si-base standard 0.18 μm CMOS process without any process modification and a special substrate. In order to reduce the valley current under optical control, a metal mask was added to the NDR device. The results show that the device exhibits good NDR characteristics under either voltage-control or photo-control. Under voltage-control, a low volley current (0.23 pA) and a high peak-to-valley current ratio (1.4 × 1010) are obtained at less than 1 V. Under photo-control, the two parameters obtained at less than 0.5 V, are 37 nA and 4827, respectively. Also, the device displays fine S-type NDR characteristics and nice maintaining response function under photo-control. These superior photoelectric NDR characteristics endow the device with greatly potential application in the photoelectric logic circuits.

Wound infection is a significant burden on public health. Most present antibacterial agents are typically toxic and devoid of long-term durability. We reported an antimicrobial microcapsule with Chinese nutgall (CN) encapsulated, which was a plant-derived extraction. It is biocompatible and has been used in traditional medicine systems. Sodium alginate (SA) and chitosan worked as shells. The promise of the design is to adopt biocompatible natural polymers and electrostatic attractive chitosan and SA form stable shells to keep long-term release of CN. The results exhibited microcapsules with integrated performance of biocompatibility, long-term durability (inhibition rate of 98.99% against S. aureus after 12 h and 100% after 12 h, 99.61% against E. coli after 6 h and 100% after 12 h), high antibacterial efficacy (with S. aureus inhibition zones of 7.67 mm and E. coli inhibition zones of 5.27 mm) and ease of storage (-20°C for more than 60 h). Their successful fabrication may provide new insights into application of traditional cotton gauze in a sustainable and multifunctional form.

Tibetan mural samples from the Jiazhaer mountain cave were studied using enzyme-linked immunosorbent assays (ELISA) and immunofluorescence microscopy (IFM). Samples containing protein binders were first identified using ELISA, and then IFM was used to determine the location of protein binders. Using these methods, we discovered gelatin and casein in samples from wall murals, distributed in both red and black pigments. We excluded the possibility of contamination by conducting further experiments where simulated samples were spiked with milk. We conclude that both gelatin and casein were used as binders in the pigments of the Tibetan Buddhist murals in the Jiazhaer (Transliteration from Tibetan) mountain cave. This is the first evidence of casein being used as a binder in Chinese mural pigments.

In vivo chemical analysis is an important aspect in exploring brain functions. Brain function realization depends on multiple chemical molecules working together. The determination of one single chemical substance is difficult to clarify the signal transduction and disease occurrence. Thus, it is particularly important to simultaneous determination of multiple biological species in the brain. In this study, we designed and constructed a dual-functional electrochemical biosensor for simultaneous detection of glutamate and Ca2+. Pt-microelectrode, jointly decorated by GluOx and Pt NPs, served as an amperometric recording channel for glutamate. Meanwhile, the all-solid-state Ca2+ selective microelectrode (Ca-ISME) was employed as a potentiometric recording channel for Ca2+. The dual function microelectrode (DFME) biosensor was developed for simultaneous monitoring glutamate and Ca2+ without crosstalk between the signals. The DFME biosensor showed good selectivity towards common interferences, as well as low detection limits (0.5 μM for glutamate and 1 μM for Ca2+), which met the needs for in vivo analysis. Finally, the developed biosensor was successfully applied to real-time monitoring of glutamate and Ca2+ in live rat brain during spreading depression (SD) and ischemia processes. This study provided a simple and effective approach for in vivo analysis of two or more chemical substances.

Numerous recent face detectors based on convolutional neural networks (CNNs) have significantly improved the detection performance. However, CNNs usually have a huge number of parameters which lead to very low detection speeds. To address this issue, this paper proposes a fast CNNs cascade face detector with multi-task learning and network acceleration techniques. In particular, the first stage of the detector is an elaborately designed fully convolutional network with a novel pyramid architecture, which can generate multi-scale face proposals efficiently with no more than twice image resizing operations. Several network compression and acceleration techniques including multi-layer merging and knowledge distilling are adopted to further improve inference speed. In addition, online and offline hard sample mining are jointly utilized to further strengthen the power of networks. The experimental results on challenging FDDB show that the proposed face detector is comparable in performance with the-state-of-arts while the speed reaches astonishing 165 fps on Titan GPU.

Using a customized thermogravimetric analyzer, the characteristics of the simultaneous calcination/sulfation reaction of limestone (the simultaneous reaction) under oxy-fuel circulating fluidized bed (CFB) boiler conditions were investigated. The results were compared with the calcination-then-sulfation reaction (the sequential reaction) that has been widely adopted by previous investigators. The sample mass in the simultaneous reaction was higher than that in the sequential reaction. With the increase of SO2 concentration (0–0.9%), the mass difference between the two reaction scenarios increased; while with the increase of temperature (890–950 °C), the difference became smaller. Calcination in the presence of SO2 was slower than that without SO2. With the increase of SO2 concentration, the pore volume of the calcined CaO decreased, and the effectiveness factors of the calcination reaction also declined. This indicates when CaSO4 forms, the pores in CaO were filled or blocked, thus increasing the internal resistance to CO2. Because the simultaneous process is the real one in CFB boilers, and it shows different characteristics from the sequential reaction, all investigations of CaO sulfation in CFB should follow this approach. Also in this work, the effects of SO2 concentration, temperature and H2O on the simultaneous reaction were studied. The sulfation ratio in the simultaneous reaction increased with higher SO2 concentration. Compared with that in the absence of H2O, 8% H2O in flue gas significantly improved sulfation. In the tested range (890–950 °C), the optimum temperature for sulfation was around 890 °C. The sulfation rate in the mass-loss stage was higher than that in the fast sulfation stage, which is likely due to the continuous generation of nascent CaO in this stage.

Computed tomography (CT) has an excellent performance in detecting dense structure, such as bones and implants, while magnetic resonance (MR) provides high-resolution information for soft issues. To obtain sufficient and accurate information for diagnosis, we propose a CT and MR image fusion method via adaptive structure decomposition to combine the complementary information. First, on the basis of different scales of issues, we adaptively decompose the source images into sub-bands (bands of small, middle, and large issues) by a spectral total variation method. Second, based on the interpretability of sub-bands, for the small scale and middle scale of issues, we extract the edge information from the sub-bands and design the fusion weight by the local edge energy. And for the large scale of issues, we design the fusion weight by the local intensity energy. Third, we reconstruct the fused image. The experimental results demonstrate the superiority of the proposed method on both subjective and objective assessments.

Polymeric bioresorbable stents (BRSs) can eliminate the long-term stent restenosis by degrading after vascular remolding and have been recommended for the congenital heart disease treatment. However, the mechanical weakness remains one of main inferiorities of their applications. So, the aim of this study was to develop mechanically reinforced bioresorbable stents (MRBSs) based on poly(p-dioxanone) (PPDO) monofilaments and braiding technology. Axial runners were introduced and MRBSs showed greatly higher compression force and relatively lower viscous performance, as well as longer mechanical stability during degradation, compared with controls. Besides, stent compression behaviors were analyzed experimentally and numerically to investigate their deformation mechanisms. The results showed increased contacting points and friction force among yarns in MRBSs. Also, the skeleton formed in MRBSs attributed to higher yarn bending degree, strain energy and better structure stability during compression. Combined with the non-linear PPDO material stress-strain ratio and thermodynamic theory, yarn based stent compression modes were discussed. In addition, the autocatalysis and nonrandom chain scission degradation behaviors of MRBSs were revealed.

Vague sets, as well as intuitionistic fuzzy sets, are extensions of fuzzy sets. Based on fuzzy sets, vague sets generalize the membership degree from a single value to an interval value. Vague sets have a more powerful ability to process fuzzy information than fuzzy sets to some degree. In addition as we all know, human cognition is usually a gradual process. As a result, in a given multi-granularity space, how to characterize a vague concept and further measure its uncertainty, become a hot issue worth studying. However, the uncertainty of vague sets in rough approximation spaces is still lacking relative studies. Therefore, in order to more effectively excavate knowledge from vague sets, this paper focuses on the uncertainty of vague sets and reveals its hidden rules. First, change rules of the average fuzziness of the vague value with changing its truth membership degree and false membership degree are discussed. Second, in rough approximation spaces, the uncertainty of vague sets, i.e., the uncertainty of average-step-vague sets are analyzed. Then, its change rules with changing granularity are analyzed and discussed. Next, to better approximate a vague concept, change rules of uncertainty for approximation sets of vague sets with changing knowledge granularity are discussed. Finally, several illustration examples are listed to verify the obtained conclusions. These rules are in accordance with human cognitive mechanisms in multi-granularity knowledge spaces.

The monodisperse CoFe 2 O 4 nanoparticles were synthesized by a modified chemical coprecipitation method. Coating SiO 2 on the surface of the CoFe 2 O 4 nanoparticles was carried out to keep single domain particles non-interacting with cubic magnetocrystalline anisotropy. The Curie temperatures ( T c ) of the monodisperse CoFe 2 O 4 nanoparticles can be accurately measured because the SiO 2 shells prevented the aggregation and growth of nanoparticles at high temperature. The magnetic properties of the CoFe 2 O 4 @SiO 2 nanoparticles with core-shell structure in a wide temperature range (300～950 K) were investigated. It is remarkable that the coercive field ( H c ) of CoFe 2 O 4 nanoparticles increased from about 760 Oe to 1806 Oe after being coated with SiO 2 , which increased by 137.6% compared to the uncoated samples at 300 K. The saturation magnetization ( M s ) of the CoFe 2 O 4 @SiO 2 nanoparticles is 34.59 emu/g, which is about 52% of the naked CoFe 2 O 4 nanoparticles value (66.51 emu/g) at 300 K. The hysteresis loops of the CoFe 2 O 4 @SiO 2 nanoparticles showed an orderly magnetic behavior at high temperature, such as the M s , remanence magnetization ( M r ) and H c decreased as temperature increasing, being equal to zero near T c . This is a good indication that the CoFe 2 O 4 @SiO 2 nanoparticles are suitable for a wide variety of technological applications at high temperature.

Different microbial aggregates show substantial differences in morphology, and extracellular polymer substances have been confirmed to play a key role in the formation of aggregates. In this study, three different microbial aggregates and their exopolysaccharides were compared. The results show that the granular sludge was largest in size and the most compact in shape. Biofilms with a certain thickness had the next greatest density, and flocculent sludge, with the smallest particle size, was the loosest. The extended Derjaguin-Landau-Verwey-Overbeek analysis shows that hydrogen bonding, hydrophobic and electrostatic interactions affect the aggregation of microorganisms. A comparison of exopolysaccharides shows that granular sludge exopolysaccharides show the highest hydrophobicity (38.08%) and lowest surface charge (−20.5 mV), followed by biofilm exopolysaccharides (27.9% and −24.8 mV respectively). The results of Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy show that the contents of hydrophilic and hydrophobic functional groups and charged functional groups of exopolysaccharides affect the above properties of exopolysaccharides, thereby affecting microbial aggregation. In addition, the hydrogen bond content of exopolysaccharides in granular sludge (19.3%), biofilm (19.2%) and activated sludge (18.9%) decreased sequentially. This also affects the cross-linking of microbial exopolysaccharides to form hydrogels. Finally, the results of confocal laser scanning microscopy showed that, different from the other two aggregates, the extracellular α-polysaccharides of granular sludge are mainly distributed in the nucleus, which is more conducive to aggregation. The research results of this thesis provide a new understanding of the differences in the aggregation morphology of different aggregates from the perspective of exopolysaccharides.

This article contends that vendors are now pushing into the small to mid-size organizational markets with simplified, less-risk, less-reward systems while consultants have project experience and have thorough contracts isolating their role. However, SMEs that adopted ERP on-premise solutions before are now facing a dilemma: continuing with ERP on-premise upgrades or switch to on-demand solutions. This article contains data surveyed from Chief Information Officers (CIO) of SMEs with respect to indicators of ERP adoptions. Cost, reduced demand for own IT resources, outage/accessibility and performance were found to be the most critical and important factors to assess ERP adoptions for SMEs.

The block transform video coder of H.265/HEVC has been formulated for a more flexible content representation to satisfy various implementation demands. Here three coefficient-scanning methods of diagonal, horizontal and vertical scan are employed for mapping a 2-D array to a 1-D vector for further reducing redundancy in entropy coding. However, the fixation scanning pattern does not fully exploit the correlation among quantized coefficients and the coding redundancy still exists. In this paper, a new adaptive coefficient scanning (ACS) method is proposed for effective H.265/HEVC entropy coding. Here the characteristic of syntax elements of quantized coefficients is first studied and then the related context probability of symbol is estimated through combining local property. Guided by the principle of entropy coding, the scanning approach is established for higher coding performance. Experimental results demonstrate that a reduction of about 3.6%–4.2% in bit-rate can be observed with almost no loss in coding complexity.

We use magnetotransport in dual-gated magnetic topological insulator heterostructures to map out a phase diagram of the topological Hall and quantum anomalous Hall effects as a function of the chemical potential (primarily determined by the back gate voltage) and the asymmetric potential (primarily determined by the top gate voltage). A theoretical model that includes both surface states and valence band quantum well states allows the evaluation of the variation of the Dzyaloshinskii-Moriya interaction and carrier density with gate voltages. The qualitative agreement between experiment and theory provides strong evidence for the existence of a topological Hall effect in the system studied, opening up a new route for understanding and manipulating chiral magnetic spin textures in real space., Comment: Minor corrections to original submission

Accurate and real-time traffic prediction is essential for smart cities. However, due to the diversity of traffic point of interest in the city, the spatial correlation of urban traffic is extremely complicated. The use of ordinary spatial correlation feature extraction methods cannot cope with this complex spatial correlation. In addition, traffic flow changes also have temporal correlation. The traditional time series feature extraction method can’t extract the temporal correlation of traffic data well. Therefore, we propose a ConvLSTM-Inception Network(CL-IncNet) model for traffic prediction. The CL-IncNet uses the Inception module to extract the multi-granularity spatial correlation and uses the Inception module’s stack to extract the indirectly affected spatial correlation. In terms of extracting time-related features, we chose ConvLSTM structure. Compared with LSTM and GRU, this structure has fewer parameters and can guarantee the spatial relative relation of output information. The experimental results on the NYC-Taxi dataset show that our method outperforms four well-known baseline results.