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Summary While there have been various lost circulation materials (LCMs) available in the market for treating fractures during the drilling of oil and gas wells, there is still a demand for a technology to seal large fractures. Considering limitations on the size of the particles that can be circulated through the drilling equipment, especially the bottomhole assembly, simply enlarging conventional LCM particles becomes ineffective for sealing large vugs and fractures. In this study, we use shape memory polymers (SMPs) to prepare programmed LCMs with various temporary shapes, which can transform to their permanent shapes with much larger dimensions as compared to their temporary shapes. A series of steps for thermomechanical programming of SMP is designed to trigger their expansion at the reservoir temperature. The dimensions of the programmed shapes can be an order of magnitude smaller than the ones for the original shapes, making their transport through the flowlines feasible, and bridging wide-opened fractures possible. The basic idea is that, after recovery, the SMP-based LCMs form an entangled network across a large width of fracture, and SMP particles recovered within the network, filling in the pores to form an effective sealing. We seek the capability of entangled ladders and interwoven fibers in forming a network across the fracture. A permeability plugging apparatus (PPA) is used to examine the efficiency of developed LCMs. The technique of 3D X-ray computed tomography (CT) is used to visualize the internal structure of formed plugs, enabling us to understand the mechanisms of bridging, plugging, and sealing.

The influence of cooling rate on the mechanical properties of resistance-welded continuous carbon fiber/poly-ether-ether-ketone (CF/PEEK) joints and crystallization behavior of PEEK in the heating elements (HEs) was investigated. 6 kinds of joints with different cooling rates were designed and prepared in resistance welding processes. A lap shear strength (LSS) improvement of 28% was reported for joints with lower cooling rates relative to joints without controlled cooling process, and the crystallinity degrees measured by differential scanning calorimetry (DSC) of lower-cooling-rate joints were also higher. The fatigue performances showed the same trend. The polarized optical microscope (POM) images showed that stainless steel (SS) wires in the HEs acted as heterogeneous nucleating agents, promoting high-density nucleation sites and accelerating formation of transcrystallinity at a cooling rate lower than 80 °C/min, and the transcrystallinity was hardly observed at higher cooling rates. The results of crystallization kinetics also showed that the Avrami exponent n reduced with cooling rate increasing, indicating that a two-dimensional growth of transcrystallinity dominated at lower cooling rates. It was suggested that qualities of resistance-welded joints were significantly affected by crystallinity and morphology which were both controlled by the cooling rate.

To evaluate the similarity of Huoling Shengji granule (HLG) and its placebo at both granules and solution status, the innovative methods that consist of intelligent sensory evaluation technologies and human sensory evaluation methods were developed based on critical quality attributes (CQAs) of granule. The CQAs for traditional Chinese medicine (TCM) placebo granule were mainly divided into three categories: formulation attributes, visual attributes, and attributes of taste and smell. In this investigation, the novel intelligent sensory evaluation technologies including the physical property testing apparatus, computer vision system, color card, and electronic tongue (E-tongue) were employed for characterization of CQAs of HLG and its placebo. Meanwhile, human sensory evaluation by test panels was used to description the HLG and its placebo in terms of appearance, color, taste, and smell. On that basis, the similarity of placebo to CQAs of HLG was assessed by calculating the angle cosine values. The intelligent and human sensory evaluation results showed that the similarity values of HLG and its placebo about the CQAs at granule and solution status were all close to 1, which means that the two preparations have high similarities. In this study, the established similarity evaluation methods based on the CQAs were convenient and reliable, which can be utilized to evaluate the similarity of TCM granule and their placebo at granule and solution status, and demonstrated to be well applied in placebo-controlled trials.

Atomically thin 2D materials provide an opportunity to investigate the atomic-scale details of defects introduced by particle irradiation. Once the atomic configuration of defects and their spatial distribution are revealed, the details of the mesoscopic phenomena can be unveiled. In this work, we created atomically small defects by controlled irradiation of gallium ions with doses ranging from 4.94 × 10

The effects of the microstructure and material composition on the formation kinetics of passive film and pitting behavior of super 13Cr stainless steel (S13Cr SS) were investigated and compared with those of 2Cr13 stainless steel (2Cr13 SS). The results indicated that the passive film formed on S13Cr SS surface quickly reached a stable state, which was attributed to two reasons. The small grain size coupled with the high dislocations density increased the number of nucleation sites for passive film formation, and the high material composition enhanced the Cr and Mo compound contents in the passive film. The passive film on S13Cr SS was more compact and thicker than that on 2Cr13 SS. The pit depth on S13Cr SS was relatively shallow after potentiodynamic polarization. Therefore, the protective properties of the passive film on S13Cr SS increased, and the pitting corrosion resistance of S13Cr SS was enhanced.

Box-type steel structure has been widely applied in the field of buildings and bridges because of its unique advantages. However, there are still many problems in field welding that need to be solved. In order to improve the level of automation and intellectualization in field welding of box girder, a new type of on-site all-position welding robot system is developed and designed in this paper. The convenience of on-site installation and debugging of the robot is realized by the scheme of permanent magnet adsorption and straight-arc combined track. The motion matching scheme of bilateral wheel-axle clamping makes the circumferential movement of the robot much smoother and more stable which makes the system with better overall force characteristics and load-carrying capacity. Then, a novel visual sensor based on combined laser structured lights is proposed and designed, which can realize several functions such as groove parameters detection, welding joint tracking and torch posture detection in field welding based on single detection image, so as to effectively improve the flexibility and intelligence of field welding robot. After that, based on the designed visual sensor and welding robot system, a visual servo-control system based on Visual Studio platform is established. Through the effective integration of detection image reading, image processing and calculation, image feature parameters transmission, motion control board call and feedback control signal output, the adjustment and control of welding torch's position and posture based on visual servo-control are realized. Furthermore, based on the kinematics calculation and the welding process requirements of the box girder, the welding process design is planned for the all-position welding of the box steel girder, which solved the technical problem of the rectangular fillet welding in the box steel butt welding (the welding torch position and posture transition control in right-angled corner of girth butt joint). Finally, all-position welding experiments of box steel girder were carried out. The results show that the welding robot based on visual servo-control can realize better control of weld formation in all-position welding of box steel girder.

Realization of wafer-scale single-crystal films of transition metal dichalcogenides (TMDs) such as WS2 requires epitaxial growth and coalescence of oriented domains to form a continuous monolayer. The domains must be oriented in the same crystallographic direction on the substrate to inhibit the formation of inversion domain boundaries (IDBs), which are a common feature of layered chalcogenides. Here we demonstrate fully coalesced unidirectional WS2 monolayers on 2 in. diameter c-plane sapphire by metalorganic chemical vapor deposition using a multistep growth process to achieve epitaxial WS2 monolayers with low in-plane rotational twist (0.09°). Transmission electron microscopy analysis reveals that the WS2 monolayers are largely free of IDBs but instead have translational boundaries that arise when WS2 domains with slightly offset lattices merge together. By regulating the monolayer growth rate, the density of translational boundaries and bilayer coverage were significantly reduced. The unidirectional orientation of domains is attributed to the presence of steps on the sapphire surface coupled with growth conditions that promote surface diffusion, lateral domain growth, and coalescence while preserving the aligned domain structure. The transferred WS2 monolayers show neutral and charged exciton emission at 80 K with negligible defect-related luminescence. Back-gated WS2 field effect transistors exhibited an ION/OFF of ∼107 and mobility of 16 cm2/(V s). The results demonstrate the potential of achieving wafer-scale TMD monolayers free of inversion domains with properties approaching those of exfoliated flakes.

A detection model of Internet of Things encrypted traffic based on edge intelligence is proposed in the paper, which can reduce the communication times of distributed Internet of Things gateways in the process of edge intelligence as well as the encrypted traffic detection model establishment time, in order to solve the problems that it is difficult to carry out efficient classification and accurate identification of the encrypted traffic of Internet of Things. In this paper, four new classification and identification methods for encrypted traffic are put forward, namely time-sequence behavior analysis, dynamic behavior analysis, key behavior analysis and two-round filtering analysis. The experimental results show that when the sample size is 1600, the encrypted traffic detection model establishment time is less than 100 seconds, and the accuracy of all the four new traffic classification methods is more than 92% and the recall rates of them are more than 83%.

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1&ndash, 4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (&le, 64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

Geosynchronous synthetic aperture radar (GEO SAR)-related technologies are being mature, and the first GEO SAR satellite is expected to launch in the next ten years. Under this circumstance, some equivalent experiments should be conducted at the current stage to validate some key characteristics or parameters, which could significantly increase the success possibility of the GEO SAR project. To validate the feasibility of GEO SAR imaging with long integration time, which is the most important and fundamental characteristic of GEO SAR, the first helicopter platform-based equivalent GEO SAR experiment with long integration time was performed in Qianxi County of China on May 22, 2019. The integration time of it is 80 s, which is carefully designed to maintain the consistence between itself and the integration time of the GEO SAR. Furthermore, the azimuth signal-to-noise ratio gain with long synthetic aperture time is analyzed. Moreover, the 2-D space-variant motion error introduced by the complex helicopter trajectory and the performances of different imaging algorithms are analyzed to choose the proper imaging algorithms; to overcome the flaws and unclarities of existing algorithms, some improvements are proposed to obtain the well-focused SAR image. What is more, the equivalence of this experiment is also analyzed detailedly to demonstrate the effectiveness of this experiment. At last, the imaging result with synthetic aperture time of 100 s and the comparison between itself and the optic photograph validate the success of this equivalent experiment and the feasibility of GEO SAR imaging with long integration time.

Esterification of free fatty acids in a low-grade oil with glycerol, the by-product of transesterification, has been proposed as one possible method to reduce the cost of raw materials for biodiesel production. The glycerolysis reaction recycles the byproduct glycerol to create a much more efficient and cost-effective process. However, the glycerolysis reaction involves challenges, including its requirement of high temperature. This study is the first to apply the deep eutectic solvent (DES) as a catalyst for glycerolysis reaction to convert free fatty acid (FFA) in vegetable oil deodorizer distillate (VODD) being a waste product from the oil refining process into glycerides. The effects of temperature, catalyst dosage, and time on the esterification efficiency of fatty acid conversion were explored. The reaction progress and product solubility were studied by FT-IR and GC. The results revealed that the reaction reached equilibrium at an approximate free fatty acid (FFA) conversion of 90%. The reaction was a second-order reversible reaction, using 8 wt% catalyst loading lowered activation energy to 58.80 J/mol. The optimum condition was determined as 160 °C, 5 wt% DES loading, and 10 min reaction time.

The corrosion behaviors of carbon steel and Ni-advanced weathering steel exposed to tropical marine atmosphere were investigated by using electrochemical impedance spectroscopy, x-ray diffraction, transmission electron microscope and x-ray photoelectron spectroscopy measurements. The results showed that Ni had almost no effect on corrosion kinetics and electrochemical performance of Ni-advanced weathering steel in mild atmosphere. Conversely, Ni-advanced weathering steel in marine atmosphere showed a significant superiority in improving corrosion resistance compared with carbon steel. Moreover, the mass formation of NiFe2O4 in the inner rust layer promoted the stability of the corrosion resistance improvement rate of Ni-advanced weathering steel with the increase in exposure time. Therefore, Ni-advanced weathering steel seemed to be more suitable for the harsh marine atmospheric environment.

Two welded joints applied for structural steel were exposed in the tropical marine atmospheric environment enduring 12 months in Thailand Trat to evaluate the corrosion resistance. The investigation results disclosed that the difference in microstructure resulted in uneven distribution of hardness and Volta potential in joints. For welds with a high content of Cr and Mn (W2), the hardness and Volta potential values of weld zone (WZ) were higher than those of another weld joint (W1), which was attributed to the addition of Mn and Cr in W2 fill metal. Meanwhile, the values of Ecorr of W2 fill metal were − 0.329 and − 0.601 V for rust and de-rusted conditions, respectively, which were more positive than that of other zones. Electrochemical impedance spectra (EIS) data presented that the corrosion resistance of heat-affected zone (HAZ) in both weld joints were more miserable than other zones in the same joint. In contrast, the inconspicuous corrosion steps between WZ and HAZ indicated a reasonable alloy composition design for both weld joints. The differences in corrosion resistance of subzones indicated that the local galvanic effect is majorly affected by material composition rather than microstructure.

Adaptive control of the resistance spot welding process has always been a hot issue in the field of resistance spot welding. Due to the significant difference in weldability of different materials, the basis of adaptive control is the identification of material stack-up (including material types, layer number of metal sheets, the thickness of metal sheet). The material stack-up identification method based on dynamic resistance was proposed for the first time in this article. Ten different types of material stack-up commonly used in the manufacture of automobile body-in-white were selected in the experiment, and a total of 550 dynamic resistance samples were collected. The dynamic resistance value was used as the feature, and the supervised classification algorithms including support vector machines, logical regression, and random forest were used to classify the dynamic resistance, to realize the recognition of material stack-up. The data preprocessing part, include signal acquisition, dynamic resistance calculation, filtering, and dimensionality reduction was introduced in detail. The results show that before dimensionality reduction, the classification accuracy of the random forest is the highest, reaching 93.9%. After dimensionality reduction, the classification performance of logistic regression is the best, and the accuracy is 96.97%. The requirement of adaptive control for the accuracy of material stack-up recognition can be satisfied.

The corrosion behavior of 2205 duplex stainless steel (2205 DSS) was investigated by atomization tests, which was performed in an S-bearing thin electrolyte layer used as the artificial industrial-...

Interfaces are often crucial determinants of the physicochemical properties of a material. As a result, the rational production and engineering of heterogeneities, and the resulting interfaces, can enhance the functionality of a material system. This is especially true of two dimensional (2D) materials, which are only a few atoms thick and thus sensitive to small perturbations of their surroundings. As a result, 2D materials and their heterostructures have been recently modified to function as catalysts, photodetectors, chemical sensors, memory, logic devices, single photon emitters, and more. In this review, we summarize the current understanding of functional interfaces in few-layered chalcogenide 2D systems, and address the following topics: The classification of interfaces by dimensionality and electronic structure, methods of creating 2D interfaces, characterization techniques and related challenges, applications of interfacial engineering in 2D systems, and finally a perspective on the future of this rapidly advancing field of study.

Incipient faults in electrical drive systems will evolve into faults or failures as time goes on. Successful detection and diagnosis of incipient faults can not only improve the safety and reliability but also provide optimal maintenance instructions for electrical drive systems. In this paper, an integration strategy of data-driven and deep learning-based method is proposed to deal with incipient faults. The salient advantages of the proposed method can be summarized as: (1) The moving average technique is firstly introduced into the canonical correlation analysis (CCA) framework, which makes the new residual signals more sensitive to incipient faults than the traditional CCA-based method; (2) Based on the defined residual signals, the new test statistics cooperating closely with Kullback–Leibler divergence (KLD) are proposed from the probability viewpoint, which can greatly improve the fault detectability; (3) It is of high computational efficiency because the estimation of probability density functions of residual signals is skilly avoided; (4) Based on the new developed test statistics, the fault matrices are defined and regarded as the input of convolutional neural network (CNN) whose feature extraction ability is highly improved compared with the traditional method, which helps to accurately diagnose of incipient faults; (5) The proposed method can be implemented without any priori knowledge on system information. Theoretical analysis and three sets of experiments on a practical electrical drive system demonstrate the effectiveness of the proposed method.

Carbon dots (CDs) have drawn increasing interests due to their unique optical properties and promising application in various fields. In this study, citric acid (CA) and 5-chloromethyl-8-hydroxyquinoline (LQ) were used to synthesize nitrogen-doped CDs as novel fluorescent probes using a one-step solvothermal route. The as-prepared CDs had strong blue-white fluorescence emission when excited at 405 nm wavelength with a quantum yield (QY) of 25%, behaving with high ion concentration stability. Water-soluble CDs with a 8-hydroxyquinoline structure on their surface could be used to detect Al3+ using a 'turn on' mechanism and trinitrophenol (TNP) using a 'turn off' mechanism with detection limits of 229 nM and 44.4 nM, respectively. Al3+ enhances the fluorescence of CDs by forming a coordination complex to generate a fluorescence synergistic role and limit CD nonradiative transition. TNP quenched the fluorescence with high selectivity and sensitivity, which was attributed to the inner filter effect and static quenching. These results indicated that these CDs with their unique 'turn on' and 'turn off' nature have potential application in the environmental protection field and in prevention of terrorist threats.

Existing effect analyses of antenna vibration (AV) on synthetic aperture radar (SAR) image, which are all aimed for low-earth-orbit SAR and based on the linear trajectory, second-order azimuth spectrum, and range time-invariant as well as azimuth time-variant antenna gain, are not feasible in geosynchronous SAR (GEO SAR), because GEO SAR has an obviously curved trajectory, high-order azimuth spectrum, and two-dimensional (2-D) time-variant antenna gain. To overcome these problems and analyze the effects of AV on GEO SAR image, a modified analysis is proposed in this paper. First, based on the curved trajectory, the high-order ideal echo signal model and the ideal point spread function (PSF) of GEO SAR are, respectively, established and deducted, and the AV that consists of antenna translational vibration (ATV) and antenna rotation motion (ARV) and the 2-D time-variant antenna gain in squint mode are also, respectively, modeled. Then, based on the series expansion, the paired echo theory, and the aforementioned models, the echo signal model affected by ATV, the corresponding PSF affected by ATV, the antenna gain model affected by ARV, and the corresponding PSF affected by ARV are obtained, and the effects of ATV and ARV on GEO SAR image are, respectively, analyzed in detail. Compared with existing effect analyses of AV on SAR image, the modified analysis considers the influences of curved trajectory, high-order azimuth spectrum, and 2-D time-variant antenna gain simultaneously, and it has much more accuracy and adaptability correspondingly. Finally, computer simulations validate the correctness of the proposed analysis.

An important issue in the mobile sink wireless sensor networks (MSWSNs) is sensor energy optimization. In order to alleviate the problem of unbalanced network load and high energy consumption in MSWSNs, we proposed a new data collection protocol in this paper: seamless clustering multi-hop routing protocol based on improved artificial bee colony algorithm (IABCP). Because of limited by the communication sensing range and intelligence of ordinary nodes, routing paths can only be constructed by crude methods. And the movement of the sink node will generate a large amount of energy consumption for locating the sink node. In order to solve this problem, we assign the task of routing table generation to the sink node, which will generate the routing table through the improved artificial bee colony algorithm. In addition, we adopt a new method to select cluster head (CH) nodes; node uses the average energy of the surrounding nodes and its own residual energy to calculate the claimed cluster head time. Moreover, we added a sub cluster head CH-β node. When the CH node reaches the number of replacement rounds, the CH-β node becomes CH directly. The simulation results show that our routing protocol is more robust compared with three other protocols.

The effect of Cu and Ni content on the corrosion behaviors of weathering steels in a corrosive atmosphere was investigated using electrochemical measurement techniques, such as electrochemical impedance spectroscopy and linear polarization resistance methods. Scanning electron microscopy with energy-dispersive spectrometry (EDS) and x-ray diffraction (XRD) were used to analyze the samples. The corrosion rates, which are above 2.0 mm a−1 at 48 h and approximately 1.12 mm a−1 at the range of 144-240 h, show that Cu and Ni slowed down the electrochemical reaction at the initial corrosion stage. With an increasing immersion time, the protective properties of the rust layer exhibited an increase in its impedance, and it was enhanced compared to that in the initial stage. The enrichment phenomenon of Cu and Ni elements in the inner rust layer was more evident in the 0.4Cu-0.4Ni and 0.7Cu-0.7Ni steels than that of 0.3Cu-0.3Ni steel after 240 h. The XRD and EDS results show that the inner rust layer contained compositions of CuO and Ni compounds, which filled up the pores located on the corrosion products contributing to the densification of the rust layer.

The evolution of the rust layers on carbon steel and weathering steel in high humidity and heat marine atmospheric environment was investigated by wet/dry cyclic acceleration corrosion tests in this study. The corrosion process of carbon steel and weathering steel was divided into two stages and the reasons for the changes in the corrosion rates of two steels were different. The composition phase of the inner rust layer of weathering steel was mainly goethite, whereas that of carbon steel was mainly akaganeite. Rust resistance (Rr) performed better than charge transfer resistance (Rt) in evaluating the protection performance of rust layer. As the corrosion proceeded, the evolution of the cathodic process of weathering steel was not obvious, whereas that of carbon steel was irregular.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive pulmonary disease that can cause fibrotic remodeling of the surrounding lung, thus leading to respiratory failure. Although IPF is the most common form of idiopathic interstitial pneumonia, the precise mechanisms underlying this condition remain unknown. In this study, we used total saponins of Panax notoginseng inhalation solution (TIS) to induce idiopathic bleomycin-induced pulmonary fibrosis in rats. The uniformity of delivery dose was investigated by analyzing the aerodynamic particle size distribution and drug stability. The potential of hydrogen potential of hydrogen (pH) of the inhalation solution was 7.0 and the solvent 0.9% NaCl solution, thus meeting physiological requirements for pulmonary drug administration. The delivery rate was 1.94 ± 0.16 mg·min−1 and the total dose was 17.40 ± 0.04 mg. TIS was composed of five key components: notoginsenoside R1, ginsenosides Rg1, ginsenosides Re, ginsenosides Rb1, and ginsenosides Rd. The mass median aerodynamic diameter (MMAD) for these five components were 3.62 ± 0.05 µm, 3.62 ± 0.06 µm, 3.65 ± 0.10 µm, 3.62 ± 0.06 µm, and 3.61 ± 0.05 µm, respectively. Fine particle fraction (FPF) was 66.24 ± 0.73%, 66.20 ± 0.89%, 66.07 ± 1.42%, 66.18 ± 0.79%, and 66.29 ± 0.70%, respectively. The MMAD for inhalation solutions needs to be 1–5 µm, which indicates that the components of TIS are suitable for inhalation. It is important to control the particle size of targeted drugs to ensure that the drug is delivered to the appropriate target tissue. In vitro experiments indicated that TIS exhibited high rates of deposition in lung tissue, thus indicating that pulmonary delivery systems may represent a good therapeutic option for patients.

A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. However, such an assumption is often violated in the real world due to non-stationarity of the environment or bias in sample selection. In this work, we consider a prevalent setting called covariate shift, where the input distribution differs between the training and test stages while the conditional distribution of the output given the input remains unchanged. Most of the existing methods for covariate shift adaptation are two-step approaches, which first calculate the importance weights and then conduct importance-weighted empirical risk minimization. In this paper, we propose a novel one-step approach that jointly learns the predictive model and the associated weights in one optimization by minimizing an upper bound of the test risk. We theoretically analyze the proposed method and provide a generalization error bound. We also empirically demonstrate the effectiveness of the proposed method.

Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In this paper, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudo-annotations. In this paper, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neural attention network for generating pseudo-annotations. Our decoupled attention structure could simultaneously identify the object regions and localize the discriminative parts, which generates high-quality pseudo-annotations in one forward path. The generated pseudo-annotations lead to the segmentation results that achieve the state of the art in weakly supervised semantic segmentation.

The nanostructured hybrid assembly has gained growing interest due to their unique electronic, optical and catalytic properties. Herein, a novel ratiometric fluorescent sensor was constructed by coupling blue-green emission carbon dots (CDs) to a block copolymer micelle-encapsulated red-emission CdSe/ZnS QDs through host-guest interaction. The carbon dots (CDs) were prepared by carbonization of β-cyclodextrin (β-CD) via a simple hydrothermal process. The amphiphilic diblock copolymer AZO-PNIPAM-b-P(St-co-MQ) containing azobenzene (AZO)-terminated hydrophilic poly(N-isopropylacrylamide) (PNIPAM) block and hydrophobic polystyrene (PS) block was synthesized by a typical RAFT polymerization. The 5-(2-methacryloylethyloxymethyl)-8- quinolinol (MQ) units were introduced into PS block, which could make CdSe/ZnS QDs to be embedded into the core of the block polymer micelles via coordination interaction in the self-assembling process. The as-fabricated β-cyclodextrin derived CDs-coupled block copolymer hybrid micelles loaded with CdSe/ZnS QDs as a fluorescence sensor exhibited dual emission peaks of CDs centered at 450 nm and CdSe/ZnS QDs at 620 nm with a large peak separation of 170 nm upon excitation at 360 nm. And the as-prepared hybrid micelles as a ratiometric fluorescence sensor can be applied to detect Hg2+ and Cu2+ with the detection limits down to 1.6 and 2.74 μM, respectively.

Existing ship inverse synthetic aperture radar (ISAR) imaging algorithms are not applicable, when the signal-to-noise ratio (SNR) is low, for the translational motion that cannot be well compensated by existing algorithms. To achieve ship ISAR imaging with low SNR, a ship ISAR imaging algorithm based on the generalized radon-Fourier transform (GRFT) is proposed in this paper. Considering not only the rotational motion but also the translational motion between the radar and the ship, the proposed algorithm uses the GRFT to simultaneously compensate the time-variant range envelopes and the Doppler phase. Thus, the signal coherence is fully utilized, and the coherent integration of the ship’s multicomponent echo signal is realized. Subsequently, to overcome the problem of the heavy computational load and improve the efficiency of the proposed algorithm, the scheme of cascaded GRFTs that consists of the coarse GRFT and the subsequent fine GRFT is adopted. The coarse GRFT with large search ranges and intervals is aimed at obtaining the real ranges of ship scatter points’ motion parameters. Based on the coarse GRFT result, the fine GRFT with small search ranges and intervals is performed to efficiently obtain the coherent integration result. Then, based on the coherent integration result, the constant false alarm rate (CFAR) detection is performed to obtain the desired scatter points and their amplitudes and motion parameters, and the multicomponent signal is reconstructed. Finally, based on the reconstructed multicomponent signal, the high-quality instantaneous ship ISAR image can be obtained. Computer simulations and experiment results validate the effectiveness of the proposed algorithm.

Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics and Hamiltonian Monte Carlo, are important methods for Bayesian inference. In large-scale settings, full-gradients are not affordable and thus stochastic gradients evaluated on mini-batches are used as a replacement. In order to reduce the high variance of noisy stochastic gradients, Dubey et al. [2016] applied the standard variance reduction technique on stochastic gradient Langevin dynamics and obtained both theoretical and experimental improvements. In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with the variance-reduced Langevin dynamics. Moreover, we apply the symmetric splitting scheme in our variance-reduced Hamiltonian Monte Carlo algorithms to further improve the theoretical results. The experimental results are also consistent with the theoretical results. As our experiment shows, variance-reduced Hamiltonian Monte Carlo demonstrates better performance than variance-reduced Langevin dynamics in Bayesian regression and classification tasks on real-world datasets., 25 pages

A pure nanostructured Ni coating was directly prepared by continuous cathode plasma electrolytic deposition (CPED) on aluminium alloy 6061. The microstructures and compositions of the as-deposited coatings were studied by scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS) and X-ray diffraction (XRD), followed by hardness and adhesion evaluations. Due to the plasma micro-arc and rapid quench, the Ni coatings prepared by CPED exhibit typically molten morphologies with nanocrystalline structures, which are capable of increasing the hardness values of the coatings. In addition, the adhesion abilities of the as-prepared coatings could be enhanced by metallurgical bonding between the coatings and the substrate. Moreover, the continuous CPED method is a high-efficiency approach to fabricate coatings with large areas.

Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the $l_{1}$ -penalized least absolute deviation ( $l_{1}$ -PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the $l_{1,2}$ -PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.

Molecular dynamic simulations have been carried out to investigate the crystallization mechanism during rapid cooling and isothermal relaxation for Zirconium (Zr) under a pressure of 100 GPa. The properties of the simulated system are analysed in terms of the system energy, the pair distribution function (PDF), and the largest standard cluster analysis. It is found that the so called topologically close-packed (TCP) local structures dominate in number in liquid, supercooled liquid and the final solid. Both solidification and relaxation crystallize into an A15 phase rather than the simple bcc or hcp crystals, the metastable and stable crystal of Zr under the ambient condition. Interestingly on PDF curves for liquids and super-liquids, left to the first major peak (at around r = 3.0 A) there is a mini-peak (at around r = 1.87 A) that decreases with the decrease of temperature and vanishes when crystallization ends. Further analysis reveals that the mini-peak originates from dynamical compact atomic pairs that are however almost nothing to do with TCP local structures. In addition, the multi characteristic lengths of the A15 unit (composed of Z12 and Z14 local structures with ratio of 1:3) result in the distinct split of the first and second peaks on the PDF curves.

Sinusoidal parameter estimation from signed measurements via majorization–minimization based RELAX

In this study, plasma electrolytic nitriding technology and an annealing treatment are used to modify titanium bipolar plates. Subsequently, the surface topography, corrosion resistance, and interfacial contact resistance of the nitrided samples are characterized. The results show that the nitrided layer consists of porous volcano-like micro-nano structure surface layer and the dense inner layer. Compared to the blank sample (87.9°±1.7°), the contact angle between the nitrided samples with annealing and deionized water significantly increases (104.7°±1.4° (nitrided for 15 min), 114.3 ± 3.0° (nitrided for 30 min)). The results of the electrochemical and atomic force microscopy analysis indicate that the nitrided samples with annealing have good corrosion resistance. The corrosion potential and corrosion current density of the nitrided samples with annealing are −0.016 V (Ag/AgCl) and 0.275 μA cm−2 (nitrided for 15 min) and 0.04 V (Ag/AgCl) and 0.195 μA cm−2 (nitrided for 30 min), respectively, which satisfy the requirement of DOE 2020 target (

We developed a simple way to prepare the polymer functionalized N-doping carbon dots (CDs) inspired by the mussel chemistry for white LED application. The N-doping CDs were decorated by catechol-terminated hydrophilic poly(poly(ethylene glycol) methyl ether methacrylate (PPEGMA) and hydrophobic polystyrene (PS) through Schiff base condensation/Michael addition reaction (CD@PEGMA and CD@PS), which owns two advantages: (1) polymer anchored CDs improved the optical properties of pure CDs; (2) hydrophobic PS could transfer hydrophilic CDs into nonpolar organic solvents to expand the application range of water-soluble CDs. The white-light bulk nanohybrid was obtained via in-situ polymerization of styrene monomers containing CD@PS as blue component and 5-(2-methacryloyl-ethyloxymethyl)-8-quinolinol (MQ) coordinated CdSe/ZnS quantum dots (QDs) as green and red components. The warm white light-emitting diode (WLED) was also fabricated based on the white bulk nanomaterials with CIE coordinate (0.32, 0.31) and the correlated color temperature 6250 K.

A defect-controlled approach for the nucleation and epitaxial growth of WSe2 on hBN is demonstrated. The WSe2 domains exhibit a preferred orientation of over 95%, leading to a reduced density of inversion domain boundaries (IDBs) upon coalescence. First-principles calculations and experimental studies as a function of growth conditions and substrate pretreatment confirm that WSe2 nucleation density and orientation are controlled by the hBN surface defect density rather than thermodynamic factors. Detailed transmission electron microscopy analysis provides support for the role of single-atom vacancies on the hBN surface that trap W atoms and break surface symmetry leading to a reduced formation energy for one orientation of WSe2 domains. Through careful control of nucleation and extended lateral growth time, fully coalesced WSe2 monolayer films on hBN were achieved. Low-temperature photoluminescence (PL) measurements and transport measurements of back-gated field-effect transistor devices fabricated on WSe...

The design of water-resistance and breathable materials applied to the protection of a historical silk textile has raised considerable interest for their highly practical potential. Thus, simple and functional composite coatings have been investigated and applied on Bombyx mori silk fabrics by spraying silk fibroin and a water soluble siloxane emulsion enriched with silica nanoparticles (12 nm). The layer of spraying silk fibroin on the surface of the silk fabric resulted in mesoscopic molecular network reconstruction by hydrogen bonds and crosslinking of ethylene glycol diglycidyl ether to improve the physical property of the silk fabric. By systematically investigating silica composite treatment, it was found that the sample treated with silica composite coatings possessed a good hydrophobic property, in which the static contact angles increased from 43.27° to 145.77° for uncoated and coated samples, respectively. As determined by Fourier transform-infrared spectroscopy analyses, hydrophobic components such as Si-O-Si, Si-O were successfully attached to the silk fabric. The scanning electron microscopy images and the energy-dispersive X-ray spectroscopy map point distribution images showed that the coating of the silica composite forms a uniform nano-scale structure, which improved the waterproof and breathable performance. Compared with uncoated fabric, the silica composite treatment was endowed with enhanced air permeability of 446.47 mm/s. After the abrasion and washing cycles, high durability of the coated fabric was demonstrated. Excellent hydrophobic capability could help silk fabric avoid the destruction of any harmful pollutant, such as light, bacteria, sewage and so on. Furthermore, the proposed relationship between the adhesive structure and the waterproof/breathable property is applicable in the design of functional silk textiles with different levels for protective performance.

Retinal ischemia-reperfusion (I/R) injuries are involved in the universal pathological processes of many ophthalmic diseases, including glaucoma, diabetic retinopathy, and retinal arterial occlusion. The reason is that the ischemia-reperfusion injury is accompanied by the abnormal accumulation of reactive oxygen species (ROS), which can cause damage to retinal ganglion cells (RGCs), promote their apoptosis, and finally lead to the irreversible loss of the visual field. RGCs are specialized projection neurons that are situated in the inner retinal surface of the eye, and they transmit visual images into certain areas of the brain in the form of action potentials. Therefore, any damage that affects the viability of RGCs can cause visual field defects or even irreversible vision loss. There is no effective drug treatment in clinical practice for the loss of the visual field that is caused by the oxidation and apoptosis of RGCs. Hence, finding a drug with neuroprotective and antioxidant functions is urgently needed. As a new type of nanomaterial, tetrahedral framework nucleic acids (tFNAs) exhibit outstanding biocompatibility and have been shown in our previous studies to participate in the positive regulation of cell behavior. In this experiment, we first established a cellular model of oxidative stress in RGCs with tert-butyl peroxide (TBHP). Then, we primarily explored the antioxidant and neuroprotective effects of tFNAs after TBHP-induced oxidative stress and the main mechanisms by which the tFNAs function. Our research showed that tFNAs could reduce the production of reactive oxygen species (ROS) in cells and protect the cells from oxidative stress by regulating intracellular oxidation-related enzymes. In addition, tFNAs could simultaneously improve oxidative stress-induced apoptosis significantly via affecting the expression of apoptosis-related proteins. Finally, we confirmed by western blotting that the mechanism by which tFNAs prevent damage caused by oxidative stress involves activating the Akt/Nrf2 pathway. Our findings provide new ideas for the prevention and treatment of a series of diseases that are caused by oxidative stress to RGCs.

We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains. Recent theories have suggested that pretrained language models acquire useful inductive biases through masks that implicitly act as cloze reductions for downstream tasks. While appealing, we show that the success of the random masking strategy used in practice cannot be explained by such cloze-like masks alone. We construct cloze-like masks using task-specific lexicons for three different classification datasets and show that the majority of pretrained performance gains come from generic masks that are not associated with the lexicon. To explain the empirical success of these generic masks, we demonstrate a correspondence between the Masked Language Model (MLM) objective and existing methods for learning statistical dependencies in graphical models. Using this, we derive a method for extracting these learned statistical dependencies in MLMs and show that these dependencies encode useful inductive biases in the form of syntactic structures. In an unsupervised parsing evaluation, simply forming a minimum spanning tree on the implied statistical dependence structure outperforms a classic method for unsupervised parsing (58.74 vs. 55.91 UUAS)., NAACL-HLT 2021

Aiming at the problem of excessive fluorine, iron, and manganese pollution in groundwater in mining areas, a serpentine-loaded hydroxyapatite (Srp/HAP) composite adsorbent was prepared by wet chemical coprecipitation. The preparation conditions of the Srp/HAP composite adsorbent were explored, Srp/HAP was microscopically characterized, and the adsorption performance and adsorption mechanism of the Srp/HAP composite adsorbent for F−, Fe2+ and Mn2+ were analyzed. The results showed that the optimal preparation conditions for the composite particles were as follows: solid–liquid ratio of Srp to calcium nitrate solution 20%, aging time 20 h, calcination temperature 180 °C, and calcination time 90 min. Compact Srp/HAP composite adsorbent particles were successfully prepared, and both the lamellar crimp structure of the Srp surface and the problem of HAP surface agglomeration were resolved. After loading, the specific surface area and pore volume of the particles significantly increased, and the surface pore structure improved, which is conducive to the simultaneous adsorption and removal of fluorine, iron and manganese. The optimal reaction conditions for Srp/HAP treatment of composite water samples with F−, Fe2+ and Mn2+ mass concentrations of 5 mg L−1, 20 mg L−1 and 5 mg L−1, respectively, are as follows: dosage of Srp/HAP 3 g L−1, pH 7, temperature 35 °C, and reaction time 150 min. Under these conditions, the removal rates of F−, Fe2+ and Mn2+ were 98.6%, 99.9% and 99.8%, respectively. The quasi-second-order kinetic model and Langmuir isothermal adsorption model described the adsorption process of F−, Fe2+ and Mn2+ by the composite particles well. The adsorption process includes both surface physical adsorption and chemical adsorption. Chemical adsorption is mainly characterized by ion exchange and surface complexation. The Srp/HAP composite particles can be used as an excellent adsorbent for the treatment of groundwater containing fluorine, iron and manganese ions in mining areas.

Coupling emitters with nanoresonators is an effective strategy to control light emission at the subwavelength scale with high efficiency. Low-loss dielectric nanoantennas hold particular promise for this purpose, owing to their strong Mie resonances. Herein, we explore a highly miniaturized platform for the control of emission based on individual subwavelength Si nanospheres (SiNSs) to modulate the directional excitation and exciton emission of two-dimensional transition metal dichalcogenides (2D TMDs). A modified Mie theory for dipole-sphere hybrid systems is derived to instruct the optimal design for desirable modulation performance. Controllable forward-to-backward intensity ratios are experimentally validated in 532 nm laser excitation and 635 nm exciton emission from a monolayer WS(2). Versatile light emission control is achieved for different emitters and excitation wavelengths, benefiting from the facile size control and isotropic shape of SiNSs. Simultaneous modulation of excitation and emission via a single SiNS at visible wavelengths significantly improves the efficiency and directionality of TMD exciton emission and leads to the potential of multifunctional integrated photonics. Overall, our work opens promising opportunities for nanophotonics and polaritonic systems, enabling efficient manipulation, enhancement and reconfigurability of light-matter interactions.

In this work, topology optimization was used to design a fixed-geometry fluidic diode of high diodicity, defined as the ratio of pressure drop of reverse flow to forward flow. Next, the fluidic diode was used in a valveless micropump. A three-dimensional and unsteady numerical analysis of fluid flow inside the micropump with the optimized diode was conducted by Computational Fluid Dynamics (CFD). Performance of a nozzle-diffuser type diode in the valveless micropump was also evaluated under the same conditions, for comparison. Both types of micropumps were fabricated and tested. The results show that the micropump with the designed fluidic diode can reach a flow rate of up to 33.5 ml/h, which is consistent with the simulation results and 2.2 times that of the nozzle-diffuser type micropump.

Single-layer heterostructures exhibit striking quasiparticle properties and many-body interaction effects that hold promise for a range of applications. However, their properties can be altered by intrinsic and extrinsic defects, thus diminishing their applicability. Therefore, it is of paramount importance to identify defects and understand 2D materials' degradation over time using advanced multimodal imaging techniques. Here we implemented a liquid-phase precursor approach to synthesize 2D in-plane MoS

Waterbomb structures are origami-inspired deformable structural components used in new types of robots. They have a unique radially deployable ability that enables robots to better adapt to their environment. In this paper, we propose a series of new waterbomb structures with square, rectangle, and parallelogram base units. Through quasi-static axial and radial compression experiments and numerical simulations, we prove that the parallelogram waterbomb structure has a twist displacement mode along the axial direction. Compared with the square waterbomb structure, the proposed optimal design of the parallelogram waterbomb structure reduces the critical axial buckling load-to-weight ratio by 55.4% and increases the radial stiffness-to-weight ratio by 67.6%. The significant increase in the radial stiffness-to-weight ratio of the waterbomb structure and decrease in the critical axial buckling load-to-weight ratio make the proposed origami pattern attractive for practical robotics applications.

Semi-supervised video object segmentation is a task of propagating instance masks given in the first frame to the entire video. It is a challenging task since it usually suffers from heavy occlusions, large deformation, and large variations of objects. To alleviate these problems, many existing works apply time-consuming techniques such as fine-tuning, post-processing, or extracting optical flow, which makes them intractable for online segmentation. In our work, we focus on online semi-supervised video object segmentation. We propose a GCSeg (Guided Co-Segmentation) Network which is mainly composed of a Reference Module and a Co-segmentation Module, to simultaneously incorporate the short-term, middle-term, and long-term temporal inter-frame relationships. Moreover, we propose an Adaptive Search Strategy to reduce the risk of propagating inaccurate segmentation results in subsequent frames. Our GCSeg network achieves state-of-the-art performance on online semi-supervised video object segmentation on Davis 2016 and Davis 2017 datasets. AI Singapore Ministry of Education (MOE) National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003), the NTU start-up grant, and the MOE Tier-1 research grants: RG126/17 (S), RG28/18 (S) and RG22/19 (S).

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non‐stationary multivariate process. For instance, future mean sea‐level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep‐landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors., Plain Language Summary: Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high‐impact events is crucial for an accurate assessment of climate‐related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still limited. In practice, studying compound events is a challenging task, which often requires interaction between experts across multiple disciplines. Recently, compound events were divided into four types to aid the framing of research on this topic, but guidelines on how to study these four types are missing. Here, we take a pragmatic approach and—focusing on case studies of different compound event types—illustrate how to address specific research questions that could be of interest to users. The results allow identifying recommendations for compound event analyses. Furthermore, through the case studies, we highlight the relevance that compounding effects have for the occurrence of landslides, flooding, vegetation impacts, and crop failures. The guidelines emerged from this work will assist the development of compound event analysis across disciplines and sectors., Key Points: Using case studies representative of four main compound event types we show how compound event‐related research questions can be tackled. We present user‐friendly guidelines for compound event analysis applicable to different compound event types. We demonstrate that compound events cause vegetation impacts, coastal flooding, landslides, and continental‐scale crop yield failures., European COST action DAMOCLES, NERC, Swiss National Science Foundation, Helmholtz Initiative and Networking Fund, Netherlands Organisation for Scientific Research (NWO), Fundação para a Ciência e a Tecnologia, Scientific Employment Stimulus 2017, Italian Ministry of University and Research, European Union's Horizon 2020 research and innovation programme, AXA Research Fund for support, Portuguese Foundation for Science and Technology

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultra-wideband (UWB) radar systems since RFI can severely degrade the radar imaging capability and target detection performance. In this paper, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology. This sampling strategy compares the signal to a known threshold varying with slow-time and therefore can be used to achieve a rather high sampling rate and quantization resolution with rather simple and affordable hardware. This paper establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we first model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We extend a majorization-minimization based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method based on the Alternating Direction Method of Multipliers (ADMM) methodology for the extended 1bMMRELAX algorithm to significantly improve its computational efficiency. Moreover, an ADMM-based sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.

Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques. We recently introduced a technique for one-bit UWB radar, which first uses a majorization-minimization method for RFI parameter estimation followed by a sparse method for radar echo recovery. However, this technique suffers from high computational complexity due to the need to estimate the parameters of each RFI source separately and iteratively. In this paper, we present a computationally efficient joint RFI mitigation and radar echo recovery framework to greatly reduce the computational cost. Specifically, we exploit the sparsity of RFI in the fast-frequency domain and the sparsity of radar echoes in the fast-time domain to design a one-bit weighted SPICE (SParse Iterative Covariance-based Estimation) based framework for the joint RFI mitigation and radar echo recovery of one-bit UWB radar. Both simulated and experimental results are presented to show that the proposed one-bit weighted SPICE framework can not only reduce the computational cost but also outperform the existing approach for decoupled RFI mitigation and radar echo recovery of one-bit UWB radar., Comment: arXiv admin note: text overlap with arXiv:2102.08987

Deep learning has been developed rapidly and widely used over the last decade. However, the concepts of adversarial example and adversarial attack are proposed, that is, adding some perturbations to the input of a deep learning model could easily change the prediction result. Deep learning-based NLP (natural language processing) classification algorithms also have this vulnerability. DeepWordBug algorithm is an advanced algorithm for generating adversarial examples, which can effectively deceive common NLP classification models. However, this algorithm needs to modify too many words to cheat NLP classification models, which limits its applications. In response to the shortcomings of DeepWordBug algorithm, this paper proposes an improving method to improve DeepWordBug. Drawing on the idea of Textfooler algorithm, the improved DeepWordBug adopts the method of dynamically adjusting the number of modified words, limits the maximum number of modified words. The new algorithm greatly reduces the number of words that need to be modified while ensuring the accuracy of NLP classification models as around 30%. It also ensures better practicality while maintaining transferability.

Surface functionalization of metallic and semiconducting 2D transition metal dichalcogenides (TMDs) have mostly relied on physi- and chemi-sorption at defect sites, which can diminish the potential applications of the decorated 2D materials, as structural defects can have substantial drawbacks on the electronic and optoelectronic characteristics. Here, we demonstrate a spontaneous defect-free functionalization method consisting of attaching Au single atoms to monolayers of semiconducting MoS2(1H) via S-Au-Cl coordination complexes. This strategy offers an effective and controllable approach for tuning the Fermi level and excitation spectra of MoS2 via p-type doping and enhancing the thermal boundary conductance of monolayer MoS2, thus promoting heat dissipation. The coordination-based method offers an effective and damage-free route of functionalizing TMDs and can be applied to other metals and used in single-atom catalysis, quantum information devices, optoelectronics, and enhanced sensing.

Modern software is bloated. Demand for new functionality has led developers to include more and more features, many of which become unneeded or unused as software evolves. This phenomenon, known as software bloat, results in software consuming more resources than it otherwise needs to. How to effectively and automatically debloat software is a long-standing problem in software engineering. Various debloating techniques have been proposed since the late 1990s. However, many of these techniques are built upon pure static analysis and have yet to be extended and evaluated in the context of modern Java applications where dynamic language features are prevalent. To this end, we develop an end-to-end bytecode debloating framework called JShrink. It augments traditional static reachability analysis with dynamic profiling and type dependency analysis and renovates existing bytecode transformations to account for new language features in modern Java. We highlight several nuanced technical challenges that must be handled properly and examine behavior preservation of debloated software via regression testing. We find that (1) JShrink is able to debloat our real-world Java benchmark suite by up to 47% (14% on average); (2) accounting for dynamic language features is indeed crucial to ensure behavior preservation---reducing 98% of test failures incurred by a purely static equivalent, Jax, and 84% for ProGuard; and (3) compared with purely dynamic approaches, integrating static analysis with dynamic profiling makes the debloated software more robust to unseen test executions---in 22 out of 26 projects, the debloated software ran successfully under new tests.