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Large domain wall (DW) conductivity in an insulating ferroelectric plays an important role in the future nanosensors and nonvolatile memories. However, the wall current was usually too small to drive high-speed memory circuits and other agile nanodevices requiring high output-powers. Here, a large domain-wall current of 67.8 μA in a high on/off ratio of ~4460 was observed in an epitaxial Au/BiFeO3/SrRuO3 thin-film capacitor with the minimized oxygen vacancy concentration. The studies from read current-write voltage hysteresis loops and piezo-response force microscope images consistently showed remaining of partially unswitched domains after application of an opposite poling voltage that increased domain wall density and wall current greatly. A theoretical model was proposed to explain the large wall current. According to this model, the domain reversal occurs with the appearance of head-to-head and tail-to-tail 180° domain walls (DWs), resulting in the formation of highly conductive wall paths. As the applied voltage increased, the domain-wall number increased to enhance the on-state current, in agreement with the measurements of current-voltage curves. This work paves a way to modulate DW currents within epitaxial Au/BiFeO3/SrRuO3 thin-film capacitors through the optimization of both oxygen vacancy and domain wall densities to achieve large output powers of modern domain-wall nanodevices.

In present work, a novel heavy density matrix alloy (Ni–W–Co) was produced through vacuum induction melting (VIM) and vacuum arc remelting (VAR). The as-cast microstructure, element segregation, and mechanical properties were characterized by optical microscopy, scanning electron microscopy, electron probe microanalysis, and universal tensile testing machine. Results indicated that the present matrix alloy displays a considerable purity and chemical homogeneity. The microstructure exhibits typical dendritic features, which is composed of austenite single-phase structure entirely. The elements Ni and Co are evenly distributed in the matrix, while W shows a distinct segregation trend in the as-cast ingot. The tensile properties of the as-cast alloy at room temperature show that the maximum ultimate tensile strength and fracture elongation are up to 560Mpa and 55.79%, respectively. Tensile fracture morphologies reveal the presence of complex quasi cleavage and ductile fractures. The true stress-plastic strain curves display distinct linear deviation in the region of uniform plastic deformation and exhibit two groups of positive and negative slopes at the lower and higher strains, respectively. The instantaneous work hardening rate-true strain curves depict typical “three stages” hardening characteristics.

The failure criterion of rocks is a critical factor involved in reliability design and stability analysis of geotechnical engineering. In order to accurately evaluate the triaxial compressive strength of rocks under different confining pressures, a nonlinear empirical strength criterion based on Mohr-Coulomb criterion was proposed in this paper. Through the analysis of triaxial test strength of 11 types of rock materials, the feasibility and validity of proposed criterion was discussed. For a further verification, six typical strength criteria were selected, and the prediction results of each criterion and test results were statistically analyzed. The comparative comparison results show that the prediction results obtained by applying this new criterion to 97 conventional triaxial compression tests of 11 different rock materials are highly consistent with the experimental data. Statistical analysis was executed to assess the application of the new criterion and other classical criteria in predicting the failure behavior of rock. This proposed empirical criterion provides a new reference and method for the determination of triaxial compressive strength of rock materials.

Multifunctional LiNbO3 material plays an important role in domain wall microelectronics and nonlinear optoelectronics. However, the material is hard and relatively inert, and hence is quite difficultly etched. A new oblique method to etch LiNbO3 memory cells at the surface of X-cut bulk crystals was proposed in this study. The process includes mask fabrication, oblique etching, and wet corrosion cleaning. The etching angle highly approaches 83° to achieve better polarization retention than others with etching angles of 0° and 70°. Meantime, the measured domain switching hysteresis loops become more symmetrical along the voltage axis than others with the presence of a strong imprint field. The horizontal electric field simulation along the polar Z axis exhibits the enhanced nucleating fields of the domains located at two edges of each memory cell in contact to Pt electrodes under the same applied voltage. The depolarization field was better screened in the memory with a higher etching angle. The new oblique method can improve the performance of the ferroelectric domain wall memory significantly.

Large domain wall conductivity in insulating ferroelectric thin films provides a new idea for non-destructive readout of domain information of a nonvolatile ferroelectric domain wall memory. However, there are several challenges hindering its development, including the excessive leakage current and insufficient on-state read current. A lot of experimental and theoretical data have referred to the accumulation of oxygen vacancies at the domain wall regions of ferroelectric thin films as the extrinsic contribution of the wall conduction. In this work, we found that the wall current varied with the angle between the applied electric field and the initial polarization, and that the wall current could be increased to 1.9 μA for a bismuth ferrite thin-film device with the reduced oxygen vacancies in contrast to the previous value of 2 nA for the device with rich oxygen vacancies. The oxygen vacancy concentration can be controlled through the thermal annealing of the sample at two atmospheres of 5% hydrogen mixed with argon gas and pure oxygen gas. For the former, the enhanced oxygen vacancies within the annealed sample at the reduced atmosphere decreased the domain wall current by over a few orders of magnitude, in contrast to the significant enhancement of the wall current for the sample in the latter annealed at the oxidizing atmosphere. The change of oxygen vacancy concentration was implied from the Fe2+⇔Fe3+ transition by the X-ray photoelectron spectroscopy analysis. This change confirmed the rigid domain wall conduction mechanism as well as the extrinsic adverse defect compensation of the wall current, paving the way to the exploration of high-power domain-wall nanodevices with sufficient output currents.

We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. The problem of TST for an early and microscopic tumor is interpreted from the computational perspective with nanorobots being the “natural” computing agents, the high-risk tissue being the search space, the tumor targeted being the global optimal solution, and the tumor-triggered biological gradient field (BGF) providing the aided knowledge for fitness evaluation of nanorobots. This natural computation process can be seen as on-the-fly path planning for nanorobot swarms with an unknown target position, which is different from the traditional path planning methods. Our previous works are focusing on the TST for a solitary lesion, where we proposed the weak priority evolution strategy (WP-ES) to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumors, which can be seen as a multimodal optimization problem for the “natural” computation. To overcome this issue, we propose a sequential targeting strategy (Se-TS) to complete TST for the multiple lesions with the assistance of nanorobot swarms, which are maneuvered by the external actuating and tracking devices according to the WP-ES. The Se-TS is used to modify the BGF landscape after a tumor is detected by a nanorobot swarm with the gathered BGF information around the detected tumor. Next, another nanorobot swarm will be employed to find the second tumor according to the modified BGF landscape without being misguided to the previous one. In this way, all the tumor lesions will be detected one by one. In other words, the paths of nanorobots to find the targets can be generated successively with the sequential modification of the BGF landscape. To demonstrate the effectiveness of the proposed Se-TS, we perform comprehensive simulation studies by enhancing the WP-ES based swarm intelligence algorithms using this strategy considering the realistic in-body constraints. The performance is compared against that of the “brute-force” search, which corresponds to the traditional systemic tumor targeting, and also against that of the standard swarm intelligence algorithms from the algorithmic perspective. Furthermore, some in vitro experiments are performed by using Janus microparticles as magnetic nanorobots, a two-dimensional microchannel network as the human vasculature, and a magnetic nanorobotic control system as the external actuating and tracking system. Results from the in silico simulations and in vitro experiments verify the effectiveness of the proposed Se-TS for two representative BGF landscapes.

This letter proposes a hybrid microwave medical imaging (MMI) approach to reconstruct the inner structure of the human body with improved accuracy on mapping the dielectric permittivities. Specifically, the results of the quantitative and qualitative MMI are employed to act as the a priori information for each other through an iterative process. Hence, the hybrid MMI approach combines the advantages from both the quantitative and qualitative algorithms to visualize simultaneously the inner body structure with clear tissue boundaries and the positions of strong scatterers. This proposed approach is evaluated by considering the medical scenario of bone fracture detection. The resultant image shows that the relative error of the reconstructed permittivity can drop by 33% for the scanned body area when compared to the one generated by the conventional quantitative MMI, whereas the position of a bone fracture can still be precisely indicated.

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

Molten reduction to release the value metals are the necessary step in the copper slag recovery process. How to effectively choose and utilize the reducing agents has an extremely important significance. In the research, molten reductions with the CO-H2-Ar mixed gases are investigated. De-oxidation amounts and de-oxidation rates maintain stability first and then decrease with the increase of CO/H2 ratios. They are all much higher than those of pure CO or H2 due to the water-gas shift reaction. The magnetite phase decreased gradually with the decrease of CO/H2 ratios, and some pure ferrous phase can generate in the slag as the ratios are less than 1:1. Otherwise, the apparent rate constants, the reaction orders and the activation energies for the various CO/H2 ratios are also measured. The results illustrate that the reaction process will vary with the variety of mixed gas compositions.

As the most abundant green and renewable resource in nature, cellulose has received the researchers’ attention because of its excellent properties. In this work, waste paper cellulose with high length-to-diameter ratio and rich hydroxyl groups was utilized as template for the preparation of Cu-MOF/PSS hybrid material (Cu-MOF: Cu3(BTC)2, BTC = 1,3,5-benzenetricarboxylate; PSS: Polymer polystyrene sulfonate), where the Cu-MOF was well-threaded by PSS polymers. Afterwards, the obtained cellulose-based Cu-MOF/PSS hybrid material (WP@PSS@Cu-MOF) was used for Li+ ion extraction in brine solution and some adsorption experiments were carried out to evaluate its adsorption behavior. The results indicated that it had an obvious selectivity and high adsorption capacity for Li+ ion. It also confirmed that the introduction of PSS into Cu-MOF matrix greatly improved the selectivity and adsorption capacity for Li+ ions due to the synergy between the size-sieving effects from Cu-MOF and special binding affinity from PSS polymer. However, it was found that an excessive PSS addition easily led to the residual Cu(OH)2 phase and negatively affected the formation of Cu-MOF crystals, causing the reduction of capturing sites for Li+ ions. As a whole, this work provided a green and simple strategy to utilize low-cost biomass as template material to fabricate some absorbents that have great potential for applications in the adsorption and separation fields.

As a new type of bolt, the polycystic aeration bolt has a broad application prospect in soft soil area; however, its design and production are still in the stage of constant exploration and improvement. From the perspective of bolt material and engineering cost, the important components of the polycystic aeration bolt were analyzed by combination with the existing bolt models, and a new structural design scheme of a kind of polycystic aeration bolt which can be used in practical engineering was presented in this paper. Then, the stress and failure mode of this bolt were discussed, and the theoretical equation of the bearing capacity was derived by using the elastic-plastic theory. In addition, the assembly and fabrication technology of this bolt in practical foundation pit engineering was described in detail. Finally, the field pull-out test of the polycystic aeration bolt was carried out, and the test results were compared with those of the conventional grouting bolt, which indicated that this new bolt has a greater advantage in bearing capacity than the conventional grouting bolt, verifying the feasibility of the structural design scheme of the polycystic aeration bolt proposed in this paper.

A 60-GBaud intensity modulation and direct detection discrete Fourier transform spread (DFT-S) discrete multi-tone (DMT) transmission system employing low-density parity check- (LDPC-) encoded probabilistic shaping 16-ary quadrature amplitude modulation (PS-16QAM) is proposed and experimentally demonstrated. In contrast to classical probability-oriented PS schemes such as arithmetic distribution matching (ADM) and constant composition distribution matching (CCDM), the proposed one is performed based on a novel bit-weighted distribution matching (BWDM) algorithm having the advantage of simpler operations along with lower computation and hardware complexity, which merely relies on bit-class processing. As the key content of the BWDM, the bit weight intervention operation is utilized to enlarge the probability of bit ‘0’ in the first two out of four serial binary sequences before PS-16QAM mapping. Different from the labels indicating probability distribution in ADM and CCDM, the end product of the BWDM is a group of bits mapped from input data source, which can be seamlessly compatible with the following LDPC-based forward error correction. In our experiments, system performance is respectively investigated with three LDPC code rates (3/4, 5/6, and 9/10) as well as three PS parameter values ( k = 4, 5, and 7), which leads to various symbol probability distribution. Compared with standard uniformly-distributed 16QAM, up to 0.407-dB shaping gain can be achieved to approach Shannon limit in the low signal-to-noise ratio region. Moreover, the experimental results show that appreciable receiver power sensitivity improvement can be obtained by the PS-16QAM DMT based on BWDM over 2-km standard single-mode fiber for short-reach optical interconnection. Additionally, the reference-based pre-equalization is equipped to compensate for the high frequency loss by ultra-wide signal bandwidth.

The unstable PbSe quantum dot (QD) surface requires tedious and complicated synthetic protocols and renders them substantially underdeveloped compared to PbS QDs. Here, we describe a direct synthes...

Bolt is popular in the reinforcement of geotechnical engineering, which can significantly improve the strength and stability of jointed rock mass. For bolted joint, the bolting area is a certain scope instead of the entire joint surface; therefore, it is necessary to study the effect of bolt influencing area on the shear strength of rock joints. In this paper, a series of laboratory direct shear tests were executed on the bolted joints to explore the influence of bolts on the joint shear strength, as well as the influencing area of bolt. Via successively changing bolting angle and bolt number, the shear stress-shear displacement curves of bolted joints were recorded and the variation law of shear strength was analyzed. Based on the assumption of the circular influencing area of bolt, the influence coefficient m (defined as the diameter ratio of the influencing area to the bolt) was introduced to establish the theoretical calculation model of the shear strength of bolted joint, which was verified by test results. Furthermore, the value of m was changed, and the shear strengths of bolted joints under different bolting condition were calculated to compare with the test results. The average relative error Eave was selected to determine the optimal value of m under the corresponding bolting condition, and it tends to sufficiently small values under the case of m > 30 for one-bolted joint and m > 25 for two-bolted joint, as well as m > 20 for three-bolted joint, which demonstrates that m can be applied to effectively calculate the actual influencing area of bolt.

Converting solar energy into chemical energy by artificial photosynthesis is promising in addressing the issues of the greenhouse effect and fossil fuel crisis. Herein, a novel photocatalyst, i.e. CdS/TiO2 hollow microspheres (HS), were dedicatedly designed to boost overall photocatalytic efficiency. TiO2 nanoparticles were in-situ decorated on the inside and outside the shell of CdS HS, ensuring close contact between TiO2 and CdS. The CdS/TiO2 HS with abundant mesopores inside of the shell boost the light absorption via multiscattering effect as well as accessible to reactions in all directions. The heterojunction was scrutinized and the charge transfer across it was revealed by in-situ irradiated X-ray photoelectron spectroscopy (ISI-XPS). Ultimately, the charge transfer in this composite was determined to follow step-scheme mechanism, which not only facilitates the separation of charge carriers but also preserves strong redox ability. Benefited from the intimate linkage between CdS and TiO2 and the favorable step-scheme heterojunction, enhanced photocatalytic CO2 reduction activity was accomplished. The CH4 yield rate of CdS/TiO2 reaches 27.85 μmol g–1 h–1, which is 145.6 and 3.8 times higher than those of pristine CdS and TiO2, respectively. This work presents a novel insight into constructing step-scheme photocatalytic system with desirable performance.

Current techniques of patterned material deposition require separate steps for patterning and material deposition. The complexity and harsh working conditions post serious limitations for fabrication. Here, we introduce a single-step and easy-to-adapt method that can deposit materials in-situ. Its methodology is based on the semiconductor nanoparticle assisted photon-induced chemical reduction and optical trapping. This universal mechanism can be used for depositing a large selection of materials including metals, insulators and magnets, with quality on par with current technologies. Patterning with several materials together with optical-diffraction-limited resolution and accuracy can be achieved from macroscopic to microscopic scale. Furthermore, the setup is naturally compatible with optical microscopy based measurements, thus sample characterisation and material deposition can be realised in-situ. Various devices fabricated with this method in 2D or 3D show it is ready for deployment in practical applications. This method will provide a distinct tool in material technology., Complexity and harsh working conditions pose limitations for fabrication of patterned materials. Here the authors report a single-step method for in situ deposition of materials that is based on semiconductor nanoparticle assisted photon-induced chemical reduction and optical trapping.

The problem of recommending new items to users (often referred to as item cold-start recommendation) remains a challenge due to the absence of users’ past preferences for these items. Item features from side information are typically leveraged to tackle the problem. Existing methods formulate regression methods, taking item features as input and user ratings as output. These methods are confronted with the issue of overfitting when item features are high-dimensional, which greatly impedes the recommendation experience. Availing of high-dimensional item features, in this work, we opt for feature selection to solve the problem of recommending top-N new items. Existing feature selection methods find a common set of features for all users, which fails to differentiate users’ preferences over item features. To personalize feature selection, we propose to select item features discriminately for different users. We study the personalization of feature selection at the level of the user or user group. We fulfill the task by proposing two embedded feature selection models. The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users. Experimental results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features that are crucial to top-N recommendation and hence improving performance.

Slope stability has been the research focus in the field of geotechnical engineering. Both the asynchronous decay speeds and distinct stability contributions of cohesion c and friction φ during slope instability have been evidenced. In this study, based on linear softening model and weighted average hypothesis, a modified double-reduction method is established. The research includes: 1) the asynchronism between decay speeds of c and φ are described by adopting different slopes in linear softening model for c and tanφ, in which case the respective reduction factors in strength reduction method Fc and Fφ are solved. 2) The distinct slope stability contributions of c and φ is readily linked with the different influences to safety factor, and therefore, introducing the equivalent influence angle θe (defined as the slope angle at which c and φ share identical contributions to stability), as well as its determination method. 3) According to weighted average hypothesis that the overall safety factor Fs is the weighted average of Fc and Fφ, the contribution scaling factor μ (defined as the weighted ratio of Fc and Fφ is proposed, which promotes the solution of respective weighted coefficients wc and wφ of two reduction factors by combining θe, achieving a new double-reduction method. 4) The validity of this method is verified via comprehensive comparison with existing double-reduction methods of practical slope examples.

We propose and experimentally demonstrate a wavelength-division-multiplexing passive optical network (WDM-PON) employing low-density parity-check (LDPC) coded probabilistic shaping 8-level pulse amplitude modulation (PS-PAM8). In such a splitter-based WDM-PON system with direct detection, a novel probabilistic shaping (PS) method based on a bit-weighted distribution matching (BWDM) for near-Gaussian distribution (NGD) is proposed for the first time. For typical PS schemes based on typical arithmetic distribution matching (ADM) and constant composition distribution matching (CCDM), refining intervals continuously is needed to find the target symbol probability, which will necessarily introduce a plenty of additional redundant bits in the process of the target symbol probability distribution (SPD) emulation from data source. Different from ADM and CCDM, the PS scheme based on BWDM is realized by a simple bit-weight intervention algorithm with low hardware and calculation complexity, which only includes weight-marking bit insertion and bit inversion processes for adjustable SPD kurtosis. The experimental results show that by employing the proposed PS-PAM8, the receiver power sensitivity and system fiber nonlinear effect tolerance can be dramatically improved compared with the unshaped signal. Thus, the WDM-PON employing PS-PAM8 is of cost-effective advantage, which can serve more available optical network units due to the enlarged system loss budget for the optical distribution network.

In this work, the ZnO/Cu2−xS composite material is prepared based on Al doped zinc oxide glass substrate and applied as counter electrode (CE) in quantum dot sensitized solar cell. The ZnO/Cu2−xS CEs with the different Cu/S ratios of Cu2−xS (2-x = 1.73–1.48) can be controlled by immersing Cu/ZnO in different concentrations of S2- solution. As a result, the solar cell fabricated by Cu1.62S/ZnO CE exhibits the best photoelectric conversion efficiency of 5.35% under 1 sun illumination, which is higher than that of Cu1.62S CE (4.22%). On the basis of the result of solid-state current density–voltage test and transient photovoltage measurements, the formation of p-n heterojunction and the improving electrocatalytic activity in composite CEs accounts for the enhanced photoelectric conversion efficiency. These results are coincide with the outcomes of the impedance analysis, Tafel polarization measurement and cyclic voltammetry test for the symmetrical cell. Consequentially, the ZnO/Cu2−xS composite counter electrode is a promising material and the applications in solar cells.

Mobile edge computing is a new paradigm that can enhance the computation capability of end devices and alleviate communication traffic loads during transmission. Mobile edge computing is highly useful for emerging resource-hungry mobile applications. However, a key challenge for mobile edge computing systems is multiple resources allocation between Mobile Edge Clouds (MECs) and End Users (EUs), especially for multiple heterogeneous MECs and EUs. To address this problem, we propose a Stackelberg game-based framework in which EUs and MECs act as followers and leaders, respectively. The proposed framework aims to compute a Stackelberg equilibrium solution in which each MEC achieves the maximum revenue while each EU obtains utility-maximized resources under budget constraints. We decompose the multiple resources allocation and pricing problem into a set of subproblems in which each subproblem only considers a single resource type. The Stackelberg game framework is constructed for each subproblem wherein each player (i.e., an EU) can selfishly maximize its utility by selecting an appropriate strategy in the strategy space. We prove the existence of the subgame Stackelberg equilibrium and develop algorithms to determine the Stackelberg equilibrium for each resource type, including an optimal demand computation algorithm, to determine the best resource demand strategy for an EU and an iterative algorithm to find an equilibrium price. The equilibrium solutions of all subgames constitute the equilibrium solution of the original problem. We also conduct simulation experiments of our game, such as numerical data for the Stackelberg equilibrium, numerical data for the convergence of the Stackelberg equilibrium, and numerical data as the system size increases. Finally, we demonstrate that an EU with idle resources can play the role of an MEC.

Due to the existence of coupling relationships, quality characteristics form a variation propagation process, which increases the risk possibility of product quality. In order to improve the robustness of quality characteristics, this paper proposes a fuzzy clustering-based key quality characteristics decoupling planning considering risk criticality. Firstly, based on the design structure matrix, a modular correlation matrix of key quality characteristics was established to quantify the coupling relationships among them. Secondly, according to the variation characteristics of quality characteristics, the variation propagation model is constructed to identify the potential quality risk. Thirdly, the fuzzy clustering algorithm is used to obtain the optimal control sequence of key quality characteristics. Finally, the computerized numerical control machine tool is taken as an actual case, the effectiveness and superiority of this method are verified by the comparison of the numerical result and method. This method provides a new indicator system solution for the coupling analysis of product design.

Bolt empowers significant efficacy in reinforcing jointed rock mass, which is mainly embodied in the enhancement of shear performance and the restraint to the dislocation between rock blocks. However, the prevailing assumption that the influencing area of bolt covers the whole joint surface contradicts to the experimental evidence of direct shear tests. In this case, by adopting saw-tooth filling joint samples made of rock-like materials, the direct shear tests were conducted under different normal stresses, and the influencing mechanism of bolt on the shear performance was investigated. Then, the similarities and differences in shear strength, deformation, and failure patterns of unbolted and bolted joints with different filling ratios were analyzed. Besides, based on the assumption of elliptical influencing area of bolt, a new shear strength calculation model of bolted joints was established, and theoretical calculation results show an excellent agreement with test results, verifying the rationality and validity of the proposed model.

This article presents the design of a novel low-profile ultrawideband textile antenna for wearable microwave medical imaging systems. The proposed antenna is based on a monopole structure, where two triangles and a few parallel slots are cut at the bottom corners and top edge of the radiation patch, respectively, to achieve an optimized ultrawide bandwidth and a reduced size of the antenna. Polyester fabrics and conductive copper taffeta, which are universally available on the market, are selected to construct the antenna. Thus, the fabricated prototypes well suit the needs of wearable applications, where flexible components are required to conform to the curved human bodies. The measured results show that the antenna prototype can realize a bandwidth of 109% from 1.198 to 4.055 GHz, with a realized gain of 2.9 dBi. Furthermore, the on-phantom measurements show that there is limited effect on the operating bandwidth and realized gain of the antenna when it is working in the proximity to human bodies. The antenna is used to monitor the recovery process of a bone fracture that is emulated by a body-mimicking phantom with a size-varying blood strip. The time-domain reflection coefficient of the antenna varies significantly with the size of the fracture introduced, which demonstrates the applicability of the antenna for such use scenarios in microwave medical imaging.

Objective: The performance of targeted immuno-chemotherapy of tumor is highly exposed to drug absorption in systemic circulation, which reduces its efficiency and increases side-effects. Direct drug targeting (DDT) combined with immuno-chemotherapy has the potential to mitigate the undesired systemic exposure, by using drug-loaded nanorobots to target cancer cells with the shortest possible physiological routes. This process can be modeled by the “touchable” (i.e., externally controllable and trackable) molecular communication system. However, such a complex process still suffers from various pharmacokinetic uncertainties caused by diffusion, degeneration, and branching of nanorobots (DDT pharmacokinetic uncertainties), as well as tumor/immune system modeling errors. The current work aims at identifying optimal drug administration plans by overcoming such challenges. Methods: A revisited tumor-immune interaction model is proposed to incorporate randomness of the drug concentration in the tumor site. Then, a robust multiple model predictive control (MMPC) scheme for the proposed tumor-immune interaction model is designed that uses multiple system models and an adaptive switch to identify the optimal plans for mixed drug administration via drug-loaded nanorobots. Furthermore, a wide range of prediction horizons under different loss scenarios of drug-loaded nanorobots and system model mismatches have been investigated in order to identify safe operating regions. From the molecular communications paradigm, this can be considered as a more robust information transmission system with feedback of channel state information to the transmitter implemented in the control unit. Results: The efficacy of the proposed MMPC is illustrated through identification of globally optimized drug administration schedules subject to various controller operation imperfections, which lead to successful cancer treatment as demonstrated through computational experiments. Conclusion: By combining DDT with conventional mixed immunotherapy and chemotherapy, the proposed robust MMPC offers promising performance in controlling tumor growth while keeping the immune cell density higher than a specific level in the presence of both DDT pharmacokinetic uncertainties and system model mismatches. Significance: We believe that the proposed design framework represents an important first step towards clinically relevant DDT in the combined immunotherapy and chemotherapy of tumor given its robust performance.

A glide-symmetry Goubau line leaky-wave antenna for achieving both high scanning rate and wide scanning angle range is introduced. The novelty of the design is its combination of glide symmetry and a single-layer Goubau line. This combination leads to performance enhancement in both scanning rate and scanning angle range. Utilizing a corrugated Goubau line in a glide-symmetry fashion closes its stopband at the cutoff frequency while slowing down its group velocity. As the dispersion around the closed stopband is shifted to the fast-wave region by periodic profile modulation, the space harmonics exhibit high-scanning-rate radiation. In conjunction with this, the single-layer nature of the Goubau line allows the beam to scan from nearly endfire to backfire directions, leading to a wide scanning angle range. Numerical examples and experimental validation are provided to validate the proposed design. These show that the proposed antenna exhibits dual-band operation, where one band scans 152° within 2.2 GHz (corresponding to scanning rate of 69°/GHz) with 67% efficiency, while the second band scans 145° within 0.7 GHz (corresponding to scanning rate of 207°/GHz) with 26% efficiency. A tradeoff between scanning rate and efficiency always exists in high-scanning-rate leaky-wave antennas. One is able to freely choose the preferred scanning rate in this single antenna. A comparison with other related works shows that the proposed antenna exhibits the best performance.

A 5 × 100-Gb/s discrete-Fourier-transform spread (DFT-spread) discrete multi-tone wavelength division multiplexing passive optical network (DMT-WDM-PON) employing low-density parity-check (LDPC) coded probabilistic shaping 16-ary quadrature amplitude modulation (PS-16 QAM) is proposed and experimentally demonstrated. We first introduce PS-16 QAM mapping scheme with 16-point constellation reshaped from 32 symbols into splitter-based DMT-WDM-PON. The DMT-WDM-PON employing PS-16 QAM can achieve superior receiver power sensitivity (RPS) and enhanced spectral efficiency (SE) compared with the unshaped one. Compatible with the forward error correction (FEC) implemented in most of the optical transponders today, an optimized bit-interleaved coded modulation iterative decoding (BICM-ID) system based on LDPC coding is proposed to encode the PS-16 QAM DMT signal without any extra shaping codes or overheads. Then the labeling optimization with the assistance of extrinsic information transfer (EXIT) chart is investigated, in which the extrinsic messages are transferred between the PS demapper and the LDPC decoder for retrieving the PS-16 QAM DMT signal. Furthermore, the experimental results show that 1.5-dB RPS improvement and 2-dB fiber nonlinear effect tolerance enhancement are respectively realized over a 20-km standard single-mode fiber (SSMF) link by using joint applications of DFT-spread scheme and PS-16 QAM mapping in proposed DMT-WDM-PON system, which can thus provide abundant system loss budget for the legacy optical distribution networks (ODNs).

Deep learning has achieved impressive performance across a variety of tasks, including medical image processing. However, recent research has shown that deep neural networks are susceptible to small adversarial perturbations in the image. We study the impact of such adversarial perturbations in medical image processing where the goal is to predict an individual’s age based on a 3D MRI brain image. We consider two models: a conventional deep neural network, and a hybrid deep learning model which additionally uses features informed by anatomical context. We find that we can introduce significant errors in predicted age by adding imperceptible noise to an image, can accomplish this even for large batches of images using a single perturbation, and that the hybrid model is much more robust to adversarial perturbations than the conventional deep neural network. Our work highlights limitations of current deep learning techniques in clinical applications, and suggests a path forward.

In vivo evolutionary computation is a novel knowledge-aided, microrobots-oriented tumor targeting framework, where externally manipulable microrobots are employed to detect the cancer in the human vascular network similar to the procedure of solving an optimization problem by swarm intelligence algorithms. The microrobots play the role of computational agents in the optimization procedure, the vascular network is the search space, and the tumor represents the maximum or minimum to be found by agents. Previous work on this topic provided basic computational models and search strategies, which, however, were solely verified in silico. In this letter, we use Janus microparticles as magnetic microrobots, a two-dimensional microchannel network as the human vasculature, and two representative test functions as the exemplar tumor-triggered biological gradient fields to validate in vitro the orthokinetic gravitational search algorithm proposed. The results herein demonstrate the advantages of the algorithm by presenting experimental observations on the targeting performance.

The determination of channel characteristics has always been a research hotspot of wireless communication systems. It directly affects the correctness of channel state analysis. Channel feature extraction methods are endless, but most of the existing extraction methods are limited by specific environments. The methods are complex, the system analysis is not comprehensive enough, the applicability is not extensive, and it is affected by external interference. This paper avoids the disadvantages of direct extraction and innovatively introduces an image construction method that can analyze the channel state in real time. In addition, an improved image feature extraction algorithm is proposed for the state images proposed in this paper, which verifies high robust performance of the algorithm. In the end, a model capable of efficiently and accurately predicting real-time conditions of short-wave fading channel states is constructed. First, channel state images are constructed, including time domain image, frequency domain image, and related domain image. The three state images reflect the channel state in all aspects. Then, the multi-texton image feature extraction method is optimized and improved, and the multi-structure color difference (MSC) extraction method is constructed to extract the channel state image information. Then, through the experimental simulation, the method of channel state division is specified, and the performance advantage of the improved algorithm MSC relative to the original algorithm is verified. Finally, discriminating channel state based on SVM algorithm. A series of experiments on rotation invariance test, immunity from interference test and illumination variation test verify that the proposed method has high robustness and the recognition accuracy is between 80% and 100%, which can effectively identify Instantaneous channel advantages and disadvantages.

A $5\times60$ Gb/s low-density parity-check (LDPC) coded WDM-PON using 8-level pulse amplitude modulation (PAM8) signals with probabilistic shaping (PS) over 20-km standard single-mode fiber (SSMF) with direct detection is proposed and experimentally demonstrated. Each optical carrier at the optical line terminal (OLT) carries a 20-Gbaud probabilistically-shaped LDPC-coded PS-PAM8 signal from a conventional uniform PAM16 signal following a designed approximate Gaussian distribution (AGD). A LDPC-based bit-interleaved coded modulation scheme with iterative decoding (BICM-ID) is utilized to recover the inherently overlapped symbols due to the proposed PS mapping. Combining decision-directed least-mean-squares (DD-LMS) and constant modulus algorithm (CMA), a blind feedback pre-equalizer is adopted to reduce the frequency loss of the signal owing to imperfect electro-optical components in experiment. As for experimental verification and application, 1.48-dB superior receiver power sensitivity and 1.7-dB launch fiber power optimization are gained respectively by using LDPC-coded PS-PAM8 signals in WDM-PON, which can bring higher spectral efficiency and power loss budget for the optical distribution networks.

Developing a highly efficient and low-cost oxygen-evolution-reaction (OER) electrocatalyst is an important route to overcome the rate-determining step of water splitting. Here, we directly grew a CoS2 nanoarray core with an N-doped graphitic carbon (NGC) shell on Ni foam (NF) to construct a core–shell nanorod array electrode, CoS2@NGC@NF. The CoS2 core with a high work function (∼5.5 eV) tends to capture electrons from the NGC layer (4.5–5.2 eV) to induce an electron-poor NGC shell with a significant downshift of the carbon Fermi level, facilitating the adsorption of O-containing intermediates and affording decreased activation energy during the OER process. As a result, the three-dimensional NGC encapsulated CoS2 array assembly exhibited excellent electrocatalytic OER activity with a low overpotential of 243 mV at a current density of 10 mA cm−2 and a small Tafel slope of 71 mV dec−1 in 1.0 M KOH. Our findings may suggest that manipulating the interface energetics by the work function difference is a potential route to the design of high-performance OER electrocatalysts.

In this study, red, green, cyan, warm white and cool white (RGCWW) LEDs are individually controlled to simulate dynamic daylight. The spectral power distributions (SPDs) are measured for both the daylight and the mixed white light. The correlated color temperature (CCT), circadian action factor (CAF) curves in the daytime of the mixed white LED almost overlap to those of daylight with a small error of about 1%. The CIE chromaticity coordinates of the mixed white light are distributed along the Planckian locus with a little deviation below 0.003. Also, its color rendering index (CRI) is above 90. The RGCWW five-chip LEDs of high circadian and visual performances will benefit to synchronize circadian rhythm in closed environment during long time without daylight, combined with better mood and alertness. The simulation errors and development trend of the mixed lighting are discussed.

Large pore size of fibrous membrane (FM) deteriorates the cycle lifetime and rate capabilities, and the way of obtaining FM with improved electrolyte wettability and decreased pore size is still challenging. Herein, we report a facile method for the fabrication of the polydopamine modification polyacrylonitrile (PDA@PAN) FM via the combination of the spin coating and electrospinning techniques. The PDA coating increases the fiber diameter and the surface roughness of the PAN fibers, thus decreasing the pore size and mitigating the lithium dendrite formation by uniformly distributing Li-ion flux. Meanwhile, PDA modification not only improves the affinity for electrolyte and enhances the ion conductivity (1.39 mS cm−1), but also doubles the stress strength (13.92 MPa) because of the formation of more adhesion structure. Consequently, both the Li/LiFePO4 and Li/Li4Ti5O12 cells involving PDA@PAN FM demonstrate more stable cycle life and better rate capability compared with those of cells based on PAN FM and Celgard membrane. The work also provides a promising separator fabrication strategy for other energy storage systems.

An efficient and simple strategy is designed for the synthesis of Cu2−xSe with two different morphology, including networks nanostructure and nanosheets structure, based on Ti substrate using a sacrificial template method (CuTi2S4 and Cu). The corresponding Cu2−xSe/CuTi CE and Cu2−xSe/Cu CE could be applied as counter electrodes (CE) for quantum dot sensitized solar cells (QDSCs). As a result, the cell fabricated by Cu2−xSe/CuTi-4 CE exhibits a photoelectric conversion efficiency (PCE) of 6.25% under 1 sun (100 mW/cm2) illumination as compared to that of Cu2−xSe/Cu CE (4.83%). According to transient photocurrent (TPC) result, the improving photovoltaic performance could be attributed to the improving electrocatalytic activity towards polysulfide electrolyte as compared to Cu2−xSe/Cu CE. These results are in accordance with the EIS analysis and Tafel polarization curve results for symmetrical cell. In addition, the outstanding stability of Cu2−xSe/CuTi CE could be further proved by cyclic voltammetry (CV) test. In conclusion, the Cu2−xSe/CuTi CE could be a promising material and the applications in solar cells.

The performance of the Photo-Fenton process is still limited by the low conversion rate of Fe3+/Fe2+ under near neutral conditions. We report a α-Fe2O3/BiOI catalyst that enhanced the efficiency of Fe2+ generation. The structure, morphology, chemical composition and chemical states of the catalyst were characterized. The α-Fe2O3/BiOI catalyst exhibited remarkable degradation performance for methyl orange (MO), phenol and tetracycline hydrochloride (TCH). The degradation efficiency of α-Fe2O3/BiOI with an optimum α-Fe2O3 loading was 3 and 10 times higher than those of pristine BiOI and α-Fe2O3, respectively. The excellent degradation performance arose from the synergistic effect of the efficient separation and transfer of photogenerated charge at the α-Fe2O3/BiOI solid-solid interface and the optimized Fenton reaction at the solid-liquid interface. The effects of operating parameters including the H2O2 concentration, solution pH, catalyst concentration and MO concentration on the degradation efficiency were investigated. Electron spin resonance results and reactive oxidation species scavenger experiments indicated that superoxide radicals could be transferred into hydroxide radicals via the activation of H2O2. A possible degradation pathway of TCH is proposed. This strategy provides a new perspective for improving the cyclic ability of Fe3+/Fe2+ over heterogeneous catalysts.

Almost all surfaces sensitive to the ambient environment are covered by water, whereas the impacts of water on surface-dominated colloidal quantum dot (CQD) semiconductor electronics have rarely been explored. Here, strongly hydrogen-bonded water on hydroxylated lead sulfide (PbS) CQD is identified. The water could pilot the thermally induced evolution of surface chemical environment, which significantly influences the nanostructures, carrier dynamics, and trap behaviors in CQD solar cells. The aggravation of surface hydroxylation and water adsorption triggers epitaxial CQD fusion during device fabrication under humid ambient, giving rise to the inter-band traps and deficiency in solar cells. To address this problem, meniscus-guided-coating technique is introduced to achieve dense-packed CQD solids and extrude ambient water, improving device performance and thermal stability. Our works not only elucidate the water involved PbS CQD surface chemistry, but may also achieve a comprehensive understanding of the impact of ambient water on CQD based electronics., Surface of colloidal quantum dot is sensitive to water, and the interaction could potentially alter its chemical environments. Here, Shi et al. investigate how the interaction effects the nanostructures and carrier dynamic in CQDs, and subsequently introduce meniscus-guided coating technique to mitigate CQD fusion triggered by water adsorption.

When young children create, they are exploring their emerging skills. And when young children reflect, they are transforming their learning experiences. Yet early childhood play environments often lack toys and tools to scaffold reflection. In this work, we design a stuffed animal robot to converse with young children and prompt creative reflection through open-ended storytelling. We also contribute six design goals for child-robot interaction design. In a hybrid Wizard of Oz study, 33 children ages 4-5 years old across 10 U.S. states engaged in creative play then conversed with a stuffed animal robot to tell a story about their creation. By analyzing children’s story transcripts, we discover four approaches that young children use when responding to the robot’s reflective prompting: Imaginative, Narrative Recall, Process-Oriented, and Descriptive Labeling. Across these approaches, we find that open-ended child-robot interaction can integrate personally meaningful reflective storytelling into diverse creative play practices.

To solve the problem of low equalization or discharge efficiency, and improve the poor equalization effect due to large dispersion of individual batteries in the discharge process of redundancy equalization, a pre-equalization strategy of redundancy equalization based on K-means clustering analysis was proposed in this paper. According to this strategy, the battery pack can be divided into three classes, that is, high power type, medium power type, and low power type, which are then used as the foundation to achieve the charge and discharge between implementation classes based on Buck - Boost circuit. Compared with the traditional maximum-power battery which discharges to the minimum-power one, this strategy uses a type of battery as the balance standard, improves the balance of the efficiency of the pre-balanced adjustment battery discrete degree. At the same time, appropriate limitation is applied to the number of access to the battery to reduce the size of the equilibrium current, thereby reducing the power consumption. The simulation results of MATLAB/Simulink show that, under the condition of small battery quantity and large discrete degree of individual battery, the pre-equalization speed following clustering strategy is 4 times higher than that of traditional pre-equalization, which greatly improves the efficiency of pre-equalization, and shows certain practical value.

The redundant equalization system of lithium battery pack is generally implemented based on metal-oxide-semiconductor field-effect transistors (MOSFET). During the working process, the temperature of the MOSFET will be very high, resulting in energy waste. In order to reduce the waste, this paper proposes a power supply system for electric bicycles taillights similar to uninterruptible power supply based on thermoelectric power generation. Using thermoelectric power generation technology and referring to the design concept of uninterruptible power supply system, the power supply system called SUPS is designed. It is based on LTC3108 and STM32F407 chips. The experimental results show that SUPS can ensure the normal use of taillights and effectively use the MOSFET heating energy. The design idea of SUPS can also be used in other occasions with temperature difference energy but no suitable load to directly use it. It has reference value for the design of the system in similar scenarios.

This paper reports a one-step hydrothermal method of in-situ growing copper selenide (CuSe) nanoparticles on the 2D materials-MXene (Ti3C2) nanosheets to form CuSe/Ti3C2 composite with three-dimensional structure. The CuSe/Ti3C2 slurry prepared via a solvothermal method was screen-printied onto graphite sheet to form a counter electrode (CE) in quantum dot-sensitized solar cells (QDSCs). The CuSe/Ti3C2 CE shows the better electrical conductivity for electron transfer, and a larger specific surface area to provide more active sites for polysulfide electrolyte reduction, as compared to the pristine CuSe CE and Ti3C2 CE. Hence, the device with CuSe/Ti3C2-30 mg CE achieves an efficiencies of 5.12%, which much better than that of CuSe CE (3.47%) and Ti3C2 CE (2.04%). The symmetrical dummy cells consisting of two CEs are further designed to confirm the best electrocatalytic activity and the excellent stability of composite CE towards polysulfide electrolyte. Therefore, CuSe/Ti3C2 is very promising as CE for QDSCs and this strategy provides a facile route for the design of the composite with MXene as a CE for QDSCs.

The scale effect of rock joint shearing is of great significance in rock engineering. Most existing shear constitutive models could describe the pre- and post-peak deformation of rock joints, but only in one particular scale, that is, those existing models will fail to depict the rock joint shearing in different length scales. Therefore, this study aims to establish a constitutive relationship for rock joints with considering the scale effect. Based on the assumption of a random statistical distribution of rock material strength and statistical mesoscopic damage theory, damage variables are defined as the ratio of the number of damaged elements to the total number in the shear process. Together with the nonlinear relationship between the microelement failure and the joint scale, an empirical statistical constitutive relationship for joint is established. And then, the determination method of constitutive relationship parameters and the variation laws with the scale are discussed. Results show that the predicted results of the proposed empirical relationship not only agree well with the experimental results but also fully describe nonlinear deformation, pre-peak softening, post-peak softening, residual stage, and other mechanical properties of the shear deformation of joint with different dimensions, thereby demonstrating the rationality of the constitutive relationship. The physical meaning of the constitutive relationship parameters is clear, and the expressions of the constitutive relationship parameters can be deduced from the experimental results. In addition, the influence of scale effect on the shear deformation of rock joints can be quantified using parameters, which help accurately describe the action form of scale effect.

Silicon is regarded as the next-generation alternative anode material of lithium–ion battery due to the highest theoretical specific capacity of 4200 mAh g−1. Nevertheless, the drastic volume expansion/shrink (~ 300%) during the lithiation/delithiation process and the poor electrical conductivity obstruct its commercial application. Herein, we report a novel design of litchi-structural Si@C nanoparticles (L-S Si@C) as long cycle life anode material for lithium–ion battery. The L-S Si@C with nanosized (~ 50 nm) SiC dispersed in the inner surface and ~ 6 wt% carbon layer covering the outer surface delivers a specific capacity of 1145 mAh g−1 in the initial cycle and maintains a specific capacity of 570 mAh g−1 after 300th cycling times, which can be attributed to the specially structurally stable L-S Si@C nanoparticles.

We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted “activator” nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the “brute-force” search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

Porous Si (PoSi) is regarded as one of the most promising Si-based anodes for next-generation lithium-ion batteries. Herein, we demonstrate the novel structure of Cu3Si-embedded three-dimensional porous Si synthesized by one-pot solid-state reaction between Mg2Si and CuO. The porous structure buffers the volume change and keeps structural stability. The embedded Cu3Si nanoparticles improve the electrical conductivity, reduce polarity and boost charges’ transfer. When used as anodes of lithium-ion batteries, the Cu3Si-embedded 3D PoSi expressed high Coulombic efficiency, good rate capability and enhanced cycling stability with a reversible charge capacity of ∼700 mAhg−1 after 200 cycles.

Visual inspection of texture surface defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture defects based on a small number of defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background images. The residual images are obtained by subtracting these texture backgrounds from the input image individually; then, they are fused into one defect image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no defect samples. This is extremely important for industrial applications because identifying and labeling defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.

Although graphitic carbon nitride (g-C3N4) is an attractive photocatalyst for solar H2 generation, the preparation of g-C3N4 nanosheets via a “green” and simple method as well as the construction of highly-efficient g-C3N4-based photocatalysts are still challenges. In this study, g-C3N4 nanosheets prepared by a simple probe sonication assisted liquid exfoliation method were used to construct 2D-2D MoS2/g-C3N4 photocatalyst for photocatalytic H2 production. The 2D-2D MoS2/g-C3N4 photocatalyst containing 0.75% MoS2 showed the highest H2 evolution rate of 1155 μmol·h−1·g−1 with an apparent quantum yield of 6.8% at 420 nm monochromatic light, which is much higher than that of the optimized 0D-2D Pt/g-C3N4 photocatalyst. The high photocatalytic H2 production activity of 2D-2D MoS2/g-C3N4 photocatalyst can be attributed to the large surface area and the formed 2D interfaces between MoS2 and g-C3N4 nanosheets. As demonstrated by photoluminescence quenching and time-resolved fluorescence decay studies, the 2D interfaces can accelerate the photoinduced charge transfer, resulting in the high photocatalytic H2 production performance. This study provides a new strategy in developing highly-efficient g-C3N4-based photocatalysts for H2 production via using 2D nanojunction as a bridge to promote the photoinduced charge separation and transfer.

A leaky-wave antenna capable of frequency-dependent beam steering and featuring a high scanning rate is introduced. The potential usefulness of the antenna resides in its ability to act as a time-space transformer in which temporal frequency-domain information can be transformed into the spatial frequency domain. Possible applications for the antenna include analog spectrum analyzers based on time-to-space transformation and also microwave imaging systems based on space-to-time conversion. One desirable property for the antenna in these types of applications is a high scanning rate where a small change in frequency can lead to significant changes in beam angle. To meet this requirement, we propose a novel high scanning-rate leaky-wave antenna using complementary microstrip-slot stubs. The design features a simple implementation and easy fabrication. It is verified by numerical examples and an experimental example. The resultant antenna design can provide a scanning rate of 135°/GHz.

The selection of appropriate monitoring methods is necessary to realize the early warning of slope stability. In this study, a new monitoring method was proposed on the basis of bolt stress variation with reduction factors. Numerical simulation was conducted to construct a rock slope model with joint, and the monitoring bolt was placed at the joint. The relationship between bolt axial force and slope stability was investigated. Moreover, the position and inclination of the bolt were investigated thoroughly. A comprehensive comparison was conducted using an existing displacement monitoring method. Results show that (1) a single peak distribution of the bolt axial force occurs along the length, and the peak appears at the location of the joint and bolt intersection with bolt axial force saltation, which is considered the optimal axial force monitoring position. (2) Regardless of the bolt inclination, the axial force saltation constantly exists, and the internal slope stress can be monitored, except when the angle is 90°. (3) The bolt position does not influence the monitoring effects provided that the bolt penetrates the joint. (4) The axial force saltation of the bolt is earlier than the displacement saltation of the slope; this phenomenon can achieve remarkable monitoring effects appropriately for slope stability early warning.

Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs, and investigates two different - probabilistic and expected - semantics in terms of support definitions. First, we present an enumeration-evaluation algorithm to solve the problem under probabilistic semantics. By showing the support computation under probabilistic semantics is #P-complete, we develop an approximation algorithm with accuracy guarantee for efficient problem-solving. To enhance the solution, we devise computation sharing techniques to achieve better mining performance. Afterwards, the algorithm is extended in a similar flavor to handle the problem under expected semantics, where checkpoint-based pruning and validation techniques are integrated. Experiment results on real-life datasets confirm the practical usability of the mining algorithms.