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To alleviate the operation pressure of charging electric vehicles (EVs) in the smart grid, mobile charging technologies have emerged in recent years. This article presents a new charging scheme for EVs based on the mobile charging robots (MCRs) cluster. First, the MCR operator manages the mobile charging service in a uniform manner, while the interaction between the MCR operator and the EV owners is modeled as a game, this game aims to satisfy the total revenue of the MCR operator and the charging demand of the EV owners by dividing the initial optimization problem into two subproblems. Second, the article addresses the voltage issue in active distribution networks (ADN) through the proposed distributed finite-time voltage restoration, which utilizes the reactive power of the MCRs and renewable energy source (RES)-based distributed generators (DGs) to regulate the system voltage without disrupting the MCR operator's charging service. Finally, simulation results are conducted to prove the effectiveness and superiority of the proposed new charging scheme.

Low-foaming nonionic gemini surfactants have a wide range of applications in industrial cleaning and photoresist development. In this study, three low-foaming nonionic gemini surfactants (S1, S2, and S3) with different poly(oxyethylene) chain lengths have been synthesized by using methoxy poly(ethylene glycol) and 2,5-di-tert-pentylhydroquinone. The chemical structures of the novel surfactants are confirmed by 1H NMR, Fourier transform infrared, and gel permeation chromatography (GPC), and their properties such as surface tension, wetting properties, emulsifying properties, and foaming properties are investigated. The surface tension values of S1, S2, and S3 at the critical micelle concentration are 40.29, 37.14, and 41.64 mN/m, exhibiting good surface activity. When the surfactant concentration is higher than 2 mM, the contact angles of S1 and S2 no longer change and can maintain 62 and 64°, showing good wetting properties. In addition, S3 can keep an emulsion state for 2 months at high concentrations, exhibiting good emulsion stability. Furthermore, all the prepared surfactants show good low-foaming properties. The initial foaming volumes of S1 are very low, less than 0.1 mL at various concentrations, less than 2% of the conventional surfactant sodium dodecyl sulfate. For S2, the initial foaming volumes at the concentration of 0.1, 1, 2, and 3 g/L are 1, 0.5, 0.55, and 0.45 mL, respectively. For S3, the initial foaming volumes show a general trend of increasing with concentration. The surfactants with longer poly(oxyethylene) chains possess more initial foaming volumes at the same concentration, which is because the greater cohesion between the surfactant molecules can increase the elasticity of the bubble film. Our study enriches the design rationales for low-foaming surfactants and motivates researchers to develop more advanced surfactants for industrial cleaning and photoresist development.

Time-varying quadratic programming (TV-QP) is widely used in artificial intelligence, robotics, and many other fields. To solve this important problem, a novel discrete error redefinition neural network (D-ERNN) is proposed. By redefining the error monitoring function and discretization, the proposed neural network is superior to some traditional neural networks in terms of convergence speed, robustness, and overshoot. Compared with the continuous ERNN, the proposed discrete neural network is more suitable for computer implementation. Unlike continuous neural networks, this article also analyzes and proves how to select the parameters and step size of the proposed neural networks to ensure the reliability of the network. Moreover, how to achieve the discretization of the ERNN is presented and discussed. The convergence of the proposed neural network without disturbance is proven, and bounded time-varying disturbances can be resisted in theory. Furthermore, the comparison results with other related neural networks show that the proposed D-ERNN has a faster convergence speed, better antidisturbance ability, and lower overshoot.

The top metal-frame antenna (TMFA) of a smartphone independently offers limited capacity for satellite communication. This communication proposes a novel antenna booster (AB) technical architecture to enhance channel capacity and communication quality. The AB suppresses the original radiation and redirects energy to a circularly polarized (CP) radiator, significantly enhancing the gain. The AB comprising a monopole coupler (MC) and a CP patch is designed and measured for feasibility verification. Testing on a real-world smartphone shows the proposed AB achieving an impedance bandwidth (IBW) of 18.7% (1.91–2.32 GHz) and an axial ratio bandwidth (ARBW) of 17.6% (1.87–2.32 GHz). In the Tiantong satellite’s Tx and Rx bands, it attains a peak gain of 6.1 dBic and an efficiency over 52.9%. The smartphone’s CP gain increases by about 6 dB after installing the AB, indicating the feasibility of the architecture.

In this letter, a deployable coplanar waveguide (CPW) coaxial colinear (CoCo) antenna is proposed with full coverage in the H-plane. The antenna features an ultra-thin profile of only 0.1 mm with excellent deployability. The proposed antenna consists of eight units, which are connected through metal crossover lines. The top and bottom metal layers alternate as the inner and outer conductors of the CPW. Metal bridges are introduced to mitigate the open stopband (OSB) phenomenon, which also eliminates the undesired CPW slot mode. A planar choke is designed to eliminate the effects of unbalanced coaxial feeding, ensuring consistency between the measured and simulated results. The antenna was fabricated using flexible printed circuit (FPC) technology. Within the target 4.8 GHz to 4.9 GHz band, the measured S11 is below −10 dB, and the efficiency is about 80%. The average gain in the H-plane is 5.8 dBi. The antenna shows great potential for applications in unmanned aerial vehicles (UAVs) and other mobile communication devices.

A swarm-exploring neurodynamic network (SENN) based on a two-timescale model is proposed in this study for solving nonconvex nonlinear programming problems. First, by using a convergent-differential neural network (CDNN) as a local quadratic programming (QP) solver and combining it with a two-timescale model design method, a two-timescale convergent-differential (TTCD) model is exploited, and its stability is analyzed and described in detail. Second, swarm exploration neurodynamics are incorporated into the TTCD model to obtain an SENN with global search capabilities. Finally, the feasibility of the proposed SENN is demonstrated via simulation, and the superiority of the SENN is exhibited through a comparison with existing collaborative neurodynamics methods. The advantage of the SENN is that it only needs a single recurrent neural network (RNN) interact, while the compared collaborative neurodynamic approach (CNA) involves multiple RNN runs.

To enable distributed multiple mobile manipulator systems to complete collaborative tasks safely and stably, this article investigates and presents a motion generation scheme that considers both orientation and position coordination based on a distributed recurrent neural network. Moreover, physical limits are also considered. Specifically, the orientation and position coordination constraints and physical limits are modeled separately as equality and inequality constraints with coupled variables. Subsequently, a motion generation scheme for multiple mobile manipulators based on quadratic programming is established. Finally, a distributed linear variational inequality-based primal-dual neural network is constructed to solve the motion generation scheme and obtain the motion trajectories of all the mobile manipulators. The simulation results demonstrate that the hybrid orientation and position collaboration motion generation scheme effectively addresses the position and orientation coordination problem for multiple mobile manipulator systems. Compared to other schemes, the proposed scheme based on a distributed computing structure greatly enhances the stability of the system. Additionally, the proposed approach introduces orientation coordination and physical limits, which increases the practicality of the system.

Bacterial infections have emerged as a significant contributor to global mortality and morbidity rates. Herein, we introduce a dual fluorescence “turn-on” supramolecular sensor array composed of three assembled complexes (C1–C3), formed from three positively charged fluorophores (A1–A3) and one cucurbit[7]uril (CB[7]). The ability of this three-element array to simultaneously recognize 10 bacterial species within just 30 s was remarkable, boasting an impressive 100% accuracy. Additionally, the array excelled at distinguishing among various bacterial mixtures and enabled the quantitative detection of common bacterial strains. Notably, it has been skillfully applied to differentiate 10 bacterial samples in urine, achieving excellent differentiation and showcasing promising potential for medical diagnostic applications.

In this article, a novel decoupling scheme based on active impedance matching is proposed for the decoupling of axial symmetrical three antennas. Three simultaneous excitation modes with the same amplitude and different phase relationships are utilized to analyze the decoupling problem. In the perspective of simultaneous excitations, the decoupling of the three antenans can be transformed into active impedance matching. Theoretical demonstration reveals that achieving decoupling for axial symmetrical three antennas necessitates matching the active impedances under the three excitations. The active impedance matching can be solved intuitively in a clear step-by-step process by exploiting the impedance characteristics of the coupled three-line and the unique field distribution under different excitation modes. To validate the effectiveness of the proposed method, the decoupling of axial symmetric three inverted-F antennas (IFAs) is presented as an example. The achieved isolation exceeds 17 dB in the desired band with a peak isolation of 31 dB. With good isolation and a straightforward design process, the approach holds promise for future multi-input-multi-output (MIMO) antenna systems. Furthermore, the proposed method provides a new physical intuition for solving the decoupling problem of multiple antennas without the need for numerous optimizations.

This communication proposes an ultrawideband (UWB) H-plane monopulse log-periodic antenna designed for UAV applications in jamming signal detection. The main radiators are two log-periodic dipole antennas (LPDAs). Good isolation between the two LPDA elements is achieved through the common mode (CM) and differential mode (DM) cancellation methods. The spacing factor of the LPDAs, and the distance between the elements are adjusted to improve DM matching and CM matching, with the radiation patterns also considered to ensure the detection distance, correctness, and accuracy of the direction-finding system. The balanced feeding network consists of a UWB monopulse comparator, parallel lines, and tapered microstrip lines. The proposed antenna operates in a band exceeding four octaves (1.45–5.95 GHz) for both the sum and differential ports, covering the GPS band (1.575 GHz) and Wi-Fi bands (2.4, 5 GHz). It exhibits low-ripple radiation patterns, a sum beam gain above 10.5 dBi, a null depth below –20 dB, and a front-to-back ratio exceeding 14 dB.

When a robot completes end-effector tasks, internal error noises always exist. To resist internal error noises of robots, a novel fuzzy recurrent neural network (FRNN) is proposed, designed, and implemented on field-programmable gated array (FPGA). The implementation is pipeline-based, which guarantees the order of overall operations. The data processing is based on across-clock domain, which is beneficial for computing units’ acceleration. Compared with traditional gradient-based neural networks (NNs) and zeroing neural networks (ZNNs), the proposed FRNN has faster convergence rate and higher correctness. Practical experiments on a 3 degree-of-freedom (DOs) planar robot manipulator show that the proposed fuzzy RNN coprocessor needs 496 lookup table random access memories (LUTRAMs), 205.5 block random access memories (BRAMs), 41384 lookup tables (LUTs), and 16743 flip-flops (FFs) of the Xilinx XCZU9EG chip.

Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.

We consider the singular Dirichlet problem for the Monge-Ampère type equation detD2u=b(x)g(−u)(1+|∇u|2)q/2,u<0,x∈Ω,u|∂Ω=0, where Ω is a strictly convex and bounded smooth domain in ℝn, q∈ [0, n+1), g∈ C∞(0, ∞) is positive and strictly decreasing in (0, ∞) with lims→0+g(s)=∞, and b∈ C∞(Ω) is positive in Ω. We obtain the existence, nonexistence and global asymptotic behavior of the convex solution to such a problem for more general band g. Our approach is based on the Karamata regular variation theory and the construction of suitable sub-and super-solutions.

The crosstalk between immune cells and the neurovascular unit plays a pivotal role in neural regeneration following central nervous system (CNS) injury. Maintaining brain immune homeostasis is crucial for restoring neurovascular function. In this study, an interactive bridge was developed via an immunomodulatory hydrogel microsphere to link the interaction network between microglia and the neurovascular unit, thereby precisely regulating immune-neurovascular crosstalk and achieving neural function recovery. This immunomodulatory crosstalk microsphere (MP/RIL4) was composed of microglia-targeted RAP12 peptide-modified interleukin-4 (IL-4) nanoparticles and boronic ester-functionalized hydrogel using biotin-avidin reaction and air-microfluidic techniques. We confirmed that the immunomodulatory microspheres reduced the expression of pro-inflammatory factors including IL-1β, iNOS, and CD86, while upregulating levels of anti-inflammatory factors such as IL-10, Arg-1, and CD206 in microglia. In addition, injection of the MP/RIL4 significantly mitigated brain atrophy volume in a mouse model of ischemic stroke, promoted neurobehavioral recovery, and enhanced the crosstalk between immune cells and the neurovascular unit, thus increasing angiogenesis and neurogenesis of stroke mice. In summary, the immunomodulatory microspheres, capable of orchestrating the interaction between immune cells and neurovascular unit, hold considerable therapeutic potential for ischemic stroke and other CNS diseases.

Aim:Animal models of fatal pneumonia caused by Streptococcus pneumoniae(Spn) have not been reliably generated using many strains of less virulent serotypes.Materials & methods:Pulmonary infection of a less virulent Spnserotype1 strain in the immunocompetent mice was established via the intratracheal aerosolization (ITA) route. The survival, local and systemic bacterial spread, pathological changes and inflammatory responses of this model were compared with those of mice challenged via the intratracheal instillation, intranasal instillation and intraperitoneal injection routes.Results:ITA and intratracheal instillation both induced fatal pneumonia; however, ITA resulted in better lung bacterial deposition and distribution, pathological homogeneity and delivery efficiency.Conclusion:ITA is an optimal route for developing animal models of severe pulmonary infections.

In this letter, a CP antenna for satellite communication on a smartphone is proposed. The antenna is designed based on the proposed narrowed crossed dipole, which has a significantly smaller size compared to the conventional one. Only one of the dipoles is excited directly; the other works as a parasitic element. By adjusting the lengths of the dipoles, CP radiation is realized. Finally, a matching circuit based on a coupled microstrip line and a balun chip for balanced feeding is introduced. A prototype of the proposed antenna was fabricated and tested to validate the concept. The measured bandwidth with S11 &lt; −10 dB for the antenna is (2.47 to 2.51) GHz [covering the S-band of the BeiDou Navigation Satellite System of (2483.5 to 2500) MHz]. Besides, the measured 3 dB axial ratio bandwidth is about 1.4%. The maximum gain is 1.9 dBic, and the efficiency is higher than 78% over the operating band. The proposed antenna shows great potential in the satellite communication application for the smartphone.

Detecting oriented objects along with estimating their rotation information is one crucial step for image analysis, especially for remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g., the rotation angle) or a few (e.g., several coordinates) groundtruth (GT) values individually. Oriented object detection would be more accurate and robust if extra constraints, with respect to proposal and rotation information regression, are adopted for joint supervision during training. To this end, we propose a mechanism that simultaneously learns the regression of horizontal proposals, oriented proposals, and rotation angles of objects in a consistent manner, via naive geometric computing, as one additional steady constraint. An oriented center prior guided label assignment strategy is proposed for further enhancing the quality of proposals, yielding better performance. Extensive experiments on six datasets demonstrate the model equipped with our idea significantly outperforms the baseline by a large margin and several new state-of-the-art results are achieved without any extra computational burden during inference. Our proposed idea is simple and intuitive that can be readily implemented. Source codes are publicly available at: https://github.com/wangWilson/CGCDet.git.

In high sea conditions and complex environments, for the safe landing of unmanned aerial vehicles (UAVs) on unmanned surface vehicles (USVs), it is necessary to accurately measure the distance and speed between UAV and USV simultaneously. In response to this requirement, this article proposes a frequency modulated continuous wave (FMCW) lidar system that uses a visible light wavelength (632 nm), which is low absorption in humid environments, enabling simultaneous measurement of speed and distance. The system directly modulates a 632 nm semiconductor distributed Bragg reflector (DBR) laser with a triangular wave current modulation signal. The modulated laser output features continuous frequency tuning and is divided into two beams. One beam serves as the reference light, while the other beam is used as the detection light and incident on the measurement target. The detection light reflected (scattered) by the target is collected by a photodetector (PD) and mixed with the reference light for coherent heterodyne detection. The beating signal carries information about the distance and velocity of the target. The system has been experimentally verified to simultaneously measure distance within a detection range of 5–350 cm and a velocity range of 0.5–850 cm/s. Moreover, the system’s capability for simultaneous distance and velocity measurement under humid conditions has also been demonstrated. This lidar system would have broad application in the precise measurement of distance and velocity simultaneously in humid environment.

To address the real-time motion generation problem of a multimanipulator collaborative system, a novel distributed varying-parameter recurrent neural network (DVP-RNN) is proposed in this article. First, an undirected graph is used to simplify the communication topology of the multimanipulator collaborative system. Then, the communication and coupled constraints of the multimanipulator collaborative system are expressed as equality constraints with coupled variables. The physical limits (i.e., angular limits and angular velocity limits) of the multimanipulator collaborative system are expressed as inequality constraints. Based on the minimum velocity norm optimization criterion, a quadratic programming problem with coupled variables and constraints is employed to formulate the motion generation problem of a multimanipulator collaborative system. Finally, a DVP-RNN is designed to solve the quadratic programming problem with coupled variables and constraints to obtain the joint trajectory of the multimanipulator collaborative system. Simulations show that the proposed DVP-RNN can effectively solve the motion generation problem of a multimanipulator collaborative system. Comparisons confirm the superiority of the DVP-RNN in terms of applicability and precision.

Photosynthesis is a potential strategy to enable endergonic process that usually needs high-temperature in thermochemistry to supply the energy for inert-bond activation and/or strong endothermic reaction. The conversion of CO2into value-added C2-oxygenates is a promising process to realize artificial photosynthesis, but suffers from relatively lower efficiency due to complex multi-electron (≥ 10) transfer processes and sluggish kinetics of C–C coupling. This work proposes an all-new H2O-promoted strategy for efficient production of C2oxygenates from the concurrent activation and subsequent co-conversion of CO2with CH4under photo-thermal cooperation, in which photocatalytic H2O-splitting derived active hydrogen species for CO2activation, and concomitant active oxygen species for CH4activation. A formation rate of as high as 2.05 mmol g−1h−1for C2-oxygenates (CH3CHO and CH3CH2OH) in a selectivity of > 86% has been afforded over SrTiOxsupported CuCo under 200 °C and ultraviolet-visible illumination. It has been revealed that SrTiOxdrives photocatalytic H2O-splitting under the excitation primary from ultraviolet light, paired CuI/Cu0sites promote the formation of *CHxO intermediate from CO2, Co sites conduct CH4-to-*CH3, and C–C coupling of *CHxO and *CH3on adjacent Cu-Co facilitates the generation of C2-oxygenates.

In this work, a numerical simulation model of an industrial-scale magnesite flash calciner (MFC) was established based on computational fluid dynamics (CFD) method. The discrete phase model (DPM) was employed, and the results of kinetic analysis for magnesite decomposition were taken into consideration. Then, the influence of swirling gas inlet design on particle motion and decomposition in MFC was analyzed, thereby providing suggestions for production. The results indicate that incorporating a swirling design can attenuate the particle deposition at the feeding port and increase particle residence time in furnace. However, the conversion degree of magnesite is decreased. This phenomenon can be attributed to the particle accumulation near the wall, resulting in a localized lower gas temperature and subsequently leading to a gas-solid heat transfer degradation. By elevating the gas temperature and reducing its flow rate, the enhancement of magnesite decomposition and reduction in energy consumption are achieved. The gas-solid water equivalent ratio, determined by heat of reaction and sensible heat of flue gas, needs to be higher than 1.8 to maintain a mass fraction of MgO above 0.9.

The South American tomato pinworm, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), is considered one of the most economically important invasive pests that causes severe damage to tomato crops. The biopesticide spinosad is widely used for controlling T. absoluta. This study aimed to investigate the spinosad-induced intergenerational sublethal effects on T. absoluta. Results showed that spinosad exhibited high toxicity against 3rd instar T. absoluta with the LC50 of 0.04 mg L-1 after 72 h exposure. The LC10 and LC30 of spinosad significantly decreased the durations of various developmental stages and some reproductive parameters of parental (F0) T. absoluta compared to control. The developmental durations, survival rate, reproduction and life table parameters (intrinsic rate of increase, r; finite rate of increase, λ; net reproductive rate, R 0; and oviposition days, Od ) were significantly decreased in the progeny generations (F1 and F2) following F0 exposure to the LC10 and LC30 of spinosad as compared to the control group. Additionally, the mRNA expression levels of development and reproduction related genes such as Vg, VgR, and JHBP were down-regulated in parental and progeny generations at both spinosad treatments as compared to control treatment. The resistance related cytochrome P450 genes CYP321C40 and CYP6AW1, followed by CYP4M116, CYP9A307v2, CYP15C1, CYP339A1, and CYP6AB327 were up-regulated at spinosad-treated groups in the parental and progeny generations of T. absoluta as compared to control. Overall, these results showed that although spinosad exhibited high toxicity and induces intergenerational sublethal effects on the parental and progeny generations, the indiscriminate and long-term application might causes resistance development in of T. absoluta.

Tuta absoluta (Meyrick) is a destructive pest of tomato crops. The cryptic nature of larvae often leads to low control efficacy. Sterile insect technique (SIT) has the potential to be an alternative management option for T. absoluta. In this study, the optimal dose for T. absoluta sterility was determined by estimating the effect of gamma radiation on parental generation (F0) and their descendants (first and second generation, F1 and F2). Adult emergence was not affected when T. absoluta pupae were exposed to gamma radiation at doses of 100~400 Gy. However, increasing doses of radiation increased deformed individuals and shortened their longevity. Furthermore, as the dose increased, F0 females showed decreased fecundity and fertility, and they were completely sterile when exposed to 300 Gy. The survival and sex ratio of females declined significantly in F1 of irradiated males with increasing doses. Moreover, we found that 300 Gy of gamma radiation did not affect the mating competitiveness of F0 males. Interestingly, this treatment caused a considerable reduction in the number of descendants and caused inherited sterility in F1 by lowering their fecundity and fertility. Therefore, the optimal radiation dose was 300 Gy, and the release of sterile adults may suppress T. absoluta population growth in the field. Our findings provide basis for controlling T. absoluta through SIT programs.

Soft wires of robot operating tools often interfere with end-effector tasks in practical automated production scenarios. In order to avoid soft wires of industrial robot manipulators executing end-effector tasks, a real-time 3-D visual detection-based soft wire avoidance (3D-VDWA) scheme is proposed, which considers the soft wire detection and location, the soft wire avoidance, and the motion planning at the same time. The proposed 3D-VDWA includes three modules: 1) perception module; 2) motion planning module; and 3) soft wire avoidance module. The perception module is based on hue–saturation–value (HSV) range and depth information to detect and locate soft wires. The motion planning module is based on the method of the pseudo-inverse matrix to plan the path of the manipulator to the target position and orientation. The soft wire avoidance module is based on the Jacobian transpose method to dynamically avoid soft wire obstacles in the process of movement. Experiments demonstrate the effectiveness and the feasibility of the proposed scheme to solve the motion planning problem with soft wire avoidance of industrial manipulators for end-effector tasks.

ABSTRACT To prospective research the efficacy of dual-energy computed tomography (DECT) in predicting contrast medium extravasation and secondary cerebral hemorrhage after stent thrombectomy in acute ischemic cerebral infarction. Ninety-two patients with acute ischemic stroke who underwent intra-arterial thrombolysis in our hospital from December 2019 to January 2022 have opted as the study subjects. DECT was performed immediately after stent thrombectomy. Images were generated through the image workstation and routine diagnosis was performed 24 hours after the operation. To analyze the diagnostic value of To analyze the diagnostic value of DECT, and to explore the diagnostic status of lesions with hemorrhagic transformation or increased hemorrhage and their correlation with iodine concentration. (1) 68 situations were confirmed, 56 positive and 12 negative with detection rates of 10.71% for hemorrhage, 75.00% for contrast agent extravasation, and 14.29% for extravasation combined with hemorrhage; (2) DECT diagnosed 8 cases of postoperative bleeding and 44 cases of extravasation of contrast media and 4 cases of extravasation of contrast media with hemorrhage ; The accuracy of DECT in diagnosing postoperative hemorrhage was 96.43%. The accuracy of diagnosis of extravasation was 96.43%. (3) The mean iodine concentration of lesions with increased hemorrhage or hemorrhagic transformation was higher compared to those without; (4) There was a correlation between hemorrhagic transformation or increased hemorrhage and iodine concentration. Dual-energy CT (DECT) can accurately distinguish the extravasation of contrast agent and secondary cerebral hemorrhage, and can predict the increased bleeding and bleeding transformation, with good diagnostic value and good predictive efficacy.

The rapid development of electric vehicles (EVs) brings great challenges to the charging infrastructure construction and the smooth operation of power systems, which facilitates emerging of a new charging scheme based on mobile charging robots (MCRs) cluster. In this article, a novel framework with three-level optimization and control on multiple time scales is proposed, and the interactions among the MCR operator, power systems and EVs are realized by means of the cloud-edge-terminal coordination-based architecture. In the first level, the operation dispatch scheduling of the MCRs is formulated as a multiobjective optimization problem considering the voltage security of power grids, which improves the charging service efficiency of MCRs on a slow time scale. In the second level, a new solution algorithm based on the proposed resource competition and occupation model is used to handle the charging decision for the MCR operator, which greatly reduces the calculation time and ensures the solution accuracy simultaneously. In the third level, a local coordinated control is proposed to enable cooperation among the MCRs on a fast time scale, which provides charging services for EVs within the optimization interval of the second level and achieves the fairness of the residual state of charge of the MCRs. Finally, simulation results validate the effectiveness and superiority of the proposed three-level coordination with comparisons of existing methods.

Lossy image compression is a fundamental technology in media transmission and storage. Variable-rate approaches have recently gained much attention to avoid the usage of a set of different models for compressing images at different rates. During the media sharing, multiple re-encodings with different rates would be inevitably executed. However, existing Variational Autoencoder (VAE)-based approaches would be readily corrupted in such circumstances, resulting in the occurrence of strong artifacts and the destruction of image fidelity. Based on the theoretical findings of preserving image fidelity via invertible transformation, we aim to tackle the issue of high-fidelity fine variable-rate image compression and thus propose the Invertible Continuous Codec (I2C). We implement the I2C in a mathematical invertible manner with the core Invertible Activation Transformation (IAT) module. I2C is constructed upon a single-rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors. Extensive experiments demonstrate that the proposed I2C method outperforms state-of-the-art variable-rate image compression methods by a large margin, especially after multiple continuous re-encodings with different rates, while having the ability to obtain a very fine variable-rate control without any performance compromise.

Silicon (Si) is typically considered a nonessential but beneficial element for most plants, and it can alleviate nutrient imbalance stress in crops. However, few studies have investigated the impact of Si application on the growth and fruit quality of cherry tomatoes, leading uncertainty regarding whether Si application can improve cherry tomato yields and quality under N imbalance. In this study, we performed a pot experiment with cherry tomato plants, and used two N fertilizer inputs (high N level, 0.40 g N kg− 1soil; low N level, 0.20 g N kg− 1soil) with or without Si application, to investigate the influence of Si application on plant growth, photosynthesis, leaf N metabolic enzyme activities, and fruit quality in cherry tomatoes under N imbalance. This study aimed to assess the promotive effects of Si application on the growth and fruit quality of cherry tomatoes under nitrogen (N) imbalance. The results showed that Si application enhanced dry matter accumulation and photosynthesis, regardless of the N conditions. Compared with treatment without Si application, the total dry matter accumulation, net photosynthetic rate, stomatal conductance, and transpiration rate were enhanced by 2.59%, 3.76%, 23.9%, and 17.1% under low N conditions, and by 7.50%, 26.2%, 49.1%, and 26.3% under high N conditions, respectively. Furthermore, Si regulated the activities of leaf N metabolic enzymes, increasing the N content of the plant under low N conditions and decreasing it under high N conditions. Si application improved fruit quality, as the vitamin C content and firmness were increased by 13.2% and 3.57% under low N conditions and by 7.09% and 17.4% under high N conditions, respectively. This study provides evidence regarding Si application as a beneficial strategy for cherry tomato production, highlighting its potential role in optimizing plant responses to varying N levels.

Nonlinear inequalities are widely used in science and engineering areas, attracting the attention of many researchers. In this article, a novel jump-gain integral recurrent (JGIR) neural network is proposed to solve noise-disturbed time-variant nonlinear inequality problems. To do so, an integral error function is first designed. Then, a neural dynamic method is adopted and the corresponding dynamic differential equation is obtained. Third, a jump gain is exploited and applied to the dynamic differential equation. Fourth, the derivatives of errors are substituted into the jump-gain dynamic differential equation, and the corresponding JGIR neural network is set up. Global convergence and robustness theorems are proposed and proved theoretically. Computer simulations verify that the proposed JGIR neural network can solve noise-disturbed time-variant nonlinear inequality problems effectively. Compared with some advanced methods, such as modified zeroing neural network (ZNN), noise-tolerant ZNN, and varying-parameter convergent-differential neural network, the proposed JGIR method has smaller computational errors, faster convergence speed, and no overshoot when disturbance exists. In addition, physical experiments on manipulator control have verified the effectiveness and superiority of the proposed JGIR neural network.

This article explores the interaction between active distribution networks (ADN) and mobile energy, specifically mobile charging robots (MCRs) and electric vehicles (EVs). We introduce the concept of MCR operator, who acts as a mediator between EVs and the ADN. To realize the interaction among the three parties, a cloud-edge-terminal control framework is formulated considering the coupling factors with the transportation systems. On this basis, the interaction between MCRs and EVs is achieved based on the game theory, which is implemented in a fully distributed manner to protect the data privacy. To reduce the computation complexity, the game-based interactive problem is further decomposed into a charging price bargaining problem and a charging decision and power regulation problem. Furthermore, the power quality of the ADN is guaranteed through incentivizing the charging/discharging behaviors of MCRs, which is formulated as a coordination between the ADN operator and the MCR operator, and the topology switching is also taken into consideration, thus the voltage regulation ability of the ADN and the economics of the MCR operator can be enhanced at the same time. Simulation results confirm the effectiveness and superiority of the proposed approach, which also indicate that mobile energy and the ADN can achieve a win-win situation by benefiting all parties: Improving the utility for the EV aggregator by 35.48% (reducing the charging cost for EV owners), increasing the charging revenue for the MCR operator by 12%, and addressing the voltage issue for the ADN with a 100% voltage security rate.

This study presents a two-beam interferometric velocimeter based on the measurement of scattered light from particles in the air and in the natural seawater. A 638 nm narrow-linewidth diode laser was deployed as the light source. By optimal design of spatial components in the light receiver, the receiving efficiency was enhanced significantly that enabled the measurement of the weaker scattering signal from the particles in seawater. Such a velocimetry was calibrated by both the rotating disk and the towing sliding rail on the water tank. It has demonstrated experimentally that the velocimetry has a range of 0.00565–31.29 $\text{m}\cdot \text{s}^{-{1}}$ in the air, and a range of 0.01–2.50 $\text{m}\cdot \text{s}^{-{1}}$ in the water with particles size as small as $50 \mu \text{m}$ in diameter. The applicability of the system in natural seawater was verified in spring seawater of Aoshan Bay, Qingdao, Shandong, China, with a turbidity range of 0.5–51.8 NTU, particle size range of 66.9– $352 \mu \text{m}$ , and minimum velocity of 2 mm $\cdot \text{s}^{-{1}}$ .

Skin aging is a systematic and complex process, mainly including intrinsic aging caused by time change and extrinsic aging caused by environmental factors. Photoaging caused by ultraviolet (UV) irradiation is the principle extrinsic factor of skin aging, which can directly or indirectly cause damage to DNA and other biomolecules, generate reactive oxygen species (ROS), and induce a decrease in collagen synthesis and other adverse effect. Herein, to address the causative factors and multiple side effects of skin photoaging, we constructed multifunctional peptide-melanin hybrid nanodots (PMHDs) to achieve multifaceted synergistic anti-skin aging by using melanin-like nanodots (MDs) and functional peptides as active motifs. Among them, the peptide has the activity of promoting collagen synthesis, while the MDs have the function of absorbing UV light and scavenging free radicals. Additionally, the MDs also act as a carrier to increase the cellular uptake of the peptide. The anti-photoaging effects of the PMHDs were systematically verified in a multidimensional manner at the biochemical, cellular and small animal levels, respectively.

Exploiting the potential for aggregation regulation of virtual power plants (VPPs) on the distribution network side to support the frequency regulation (FR) of the main grid is an essential research topic. However, in the process of implementing FR through communication networks, the imperfect communication may affect the rapid power support performance of the VPP. As such, we propose a cyber-physical collaboration based control method. In the physical layer, in order to effectively respond to power regulation instructions, a dispatch-based control strategy is presented according to the adjustment capacity of the VPP. In the cyber layer, a joint spectrum scheduling and relay power allocation method is developed to mitigate the impact of network uncertainty and meet communication demands. Finally, extensive simulation results verify the effectiveness of the proposed method.

Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this article, a deep ensemble dynamic learning network is proposed. After a chain of image preprocessing steps and the division of image dataset, convolution blocks and the final average pooling layer are pretrained as a feature extractor. For classifying the extracted feature samples, two-stage bagging dynamic learning network is trained based on neural dynamic learning and bagging algorithms, which diagnoses the presence and types of pneumonia successively. Experimental results manifest that using the proposed deep ensemble dynamic learning network obtains 98.7179% diagnosis accuracy, which indicates more excellent diagnosis effect than existing state-of-the-art models on the open image dataset. Such accurate diagnosis effects provide convincing evidences for further detections and treatments.

In this article, a multiple time-scale voltage regulation is proposed based on the three-level coordinated control approach. This approach aims at solving the voltage issues in different scopes of space-time for active distribution networks. First, to address the global voltage issue of the entire network on a relatively slow-time scale, a multimode switching control is designed based on the Petri-net, which can effectively switch the on-load tap changer to extend its service life. Second, a multiobjective optimization considering the voltage differences with the security boundary and the transmission loss of the entire network is proposed. As such the global voltage issue can be handled by cooperating with the first-level switching control. Third, to solve the local voltage issue on a fast-time scale, a fully distributed optimal control integrating the active/reactive power sharing of all the distributed units is proposed. This control facilitates the control efficiency and active power consumption for renewable energy sources. Finally, the efficiency and effectiveness of the proposed method are validated under different scenarios in case studies.

The long‐term development of fossil energy has led to the destruction of carbon balance. Carbon capture technology needs to be used to reduce carbon emissions before clean energy completely replaces fossil energy. Metal‐organic frameworks (MOFs), a porous crystalline material, show great potential in gas adsorption and has attracted great attention. The predictability of MOFs' structure and function also make it possible to use computational methods to advance and accelerate research. This review gives a brief overview of carbon dioxide capture and separation by MOFs, including adsorption and membrane separation. In the future, membrane separation technology is expected to be a crucial area of research for carbon capture applications due to its favorable characteristics such as high treatment efficiency and low carbon footprint, while mixed matrix membranes (MMMs) have been given more attention by scholars due to their lower cost and better separation performance. In summary, developing high‐performance MOFs or MOF derivatives and researching more efficient separation methods, such as the application of MOF‐based MMMs, should be the focus of future research by scholars in this field. This paper has reviewed the use of metal–organic frameworks to achieve efficient carbon capture and summarizes the basic theory of computational research. Research on MOFs for carbon capture based on the adsorption principle is discussed. The combination of membrane separation technology with MOFs is discussed. The research direction of carbon capture with rich research prospects is pointed out.

A polarization reconfigurable antenna based on dipoles is proposed for foldable smartphones in this communication. Two dipoles, a driven dipole and a parasitic dipole, are placed on the top of the two parts of the foldable phone. The induced current on the parasitic dipole is similar in amplitude to the current on the driven dipole, with a phase difference of 90°. When the two parts of the phone are at 90°, the two dipoles are L-arranged orthogonally, realizing circularly polarized (CP) radiation. When the phone is in the fully unfolded or fully folded state, the proposed antenna can still work with linearly polarized (LP) radiation. Users can freely choose the folded state for satellite communication. A prototype for three different states was fabricated and tested to validate the concept. The measured bandwidth with $S_{11} &lt; -10$ dB for the antenna in each state can cover the target band 2.483–2.5 GHz. Besides, the measured 3-dB axial ratio (AR) bandwidth of the antenna in the CP state is 2.464–2.516 GHz. The maximum gains of the three states are 3.9, 5.9, and 4.1 dBi over the operating band. The efficiencies are higher than 70%. The proposed antenna shows great potential in the satellite communication application for foldable smartphone.

In this communication, a compact endfire circularly polarized (CP) antenna designed for satellite communication on mobile terminals is proposed. In our design, a rectangular loop antenna is proposed with a compressed vertical dimension which is suitable to be integrated into the plastic bezel of the mobile terminals. The folded dipole mode and loop mode of the rectangular loop antenna provide orthogonal radiation components. An L-shaped stub is introduced to excite the two modes by capacitive coupling. The directivity of the horizontally polarized (HP) radiation is improved to be comparable to that of the vertically polarized (VP) radiation when integrating with the chassis ground. Independent mode tuning can be achieved with capacitive loading on the antenna. A prototype was fabricated to validate the design. The measured results indicate that a −6 dB impedance bandwidth of 10.0%, axial ratio (AR) bandwidth of 0.76% (covering the $S$ band of the BeiDou Navigation Satellite System (BDS) of 2483.5–2500 MHz), and a peak gain of 4.80 dBic are achieved with a compact size of $0.47\lambda _{0} \times 0.06\lambda _{0} \times 0.004\lambda _{0}$ . The proposed CP antenna has the merits of compact size, improved gain, and quasi-planar structure which is a good candidate for mobile terminals supporting satellite communication.

In this article, a novel approach of utilizing an integral dynamic learning network (IDLN) is presented for addressing a general time-varying Lyapunov matrix equation (TVLME). First, a cost function is defined by designing a variable unbounded vector/matrix-type error function. The goal is to make the cost function approximate to zero. Second, an integral neural dynamic equation with an odd activation function that is monotonically increasing is designed and applied to guarantee that the error function can converge to zero. Third, a novel IDLN with a recurrent topological structure is exploited to find the time-varying theoretical solution. The proposed IDLN with strong robustness to bounded noise with unknown amplitude regardless of the value of hyperparameters, can be implemented through electronic circuits as a method of parallel computing and achieve global convergence from any initial state. In addition, for better convergence rates, the novel linear-arcsine-type and softsign-arcsine-type activation functions are designed and utilized to the proposed IDLN. The effectiveness, stability, and practicability of the proposed IDLN are verified by comparative computer simulations and the application to the analysis of voltage stability in a single-machine infinite bus system.

The manipulation of electron donor/acceptor (D/A) shows an endless impetus for innovating optical materials. Currently, there is booming development in electron donor design, while research on electron acceptor engineering has received limited attention. Inspired by the philosophical idea of “more is different”, two systems with D′–D–A–D–D′ (1A system) and D′–D–A–A–D–D′ (2A system) structures based on acceptor engineering were designed and studied. It was demonstrated that the 1A system presented a weak aggregation-induced emission (AIE) to aggregation-caused quenching (ACQ) phenomenon, along with the increased acceptor electrophilicity and planarity. In sharp contrast, the 2A system with one more acceptor exhibited an opposite ACQ-to-AIE transformation. Interestingly, the fluorophore with a more electron-deficient A–A moiety in the 2A system displayed superior AIE activity. More importantly, all compounds in the 2A system showed significantly higher molar absorptivity (ε) in comparison to their counterparts in the 1A system. Thanks to the highest ε, near-infrared-II (NIR-II, 1000–1700 nm) emission, desirable AIE property, favorable reactive oxygen species (ROS) generation, and high photothermal conversion efficiency, a representative member of the 2A system handily performed in fluorescence-photoacoustic-photothermal multimodal imaging-guided photodynamic-photothermal collaborative therapy for efficient tumor elimination. Meanwhile, the NIR-II fluorescence imaging of blood vessels and lymph nodes in living mice was also accomplished. This study provides the first evidence that the dual-connected acceptor tactic could be a new molecular design direction for the AIE effect, resulting in high ε, aggregation-intensified NIR-II fluorescence emission, and improved ROS and heat generation capacities of phototheranostic agents.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

Whole-cell catalysis, which utilizes enzymes expressed in whole organism (e.g. bacteria and fungi) as the catalyst, is a specific mode of biocatalysis. Compared with pure enzyme catalysis, the catalysis with whole-cell catalysts is more cost-effective. However, in the process of whole-cell catalysis, heat treatment is often necessary due to the high optimum temperature of the enzyme. To enable efficient industrial application of whole-cell catalysis, an environmental friendly heating approach is highly desired. Inspired by the light harvest by blackbody materials, in this paper, we introduced a photothermal approach for harnessing the photon energy for enhanced whole-cell catalysis. A blackbody porous sponge (BPS) with excellent photothermal conversion efficiency was prepared as a bioreactor. Escherichia coliexpressed with a thermophilic enzyme (β-glucosidase) was utilized as a model whole-cell catalyst. Moreover, the photothermal properties of the BPS and light-assisted whole-cell catalysis were systematically investigated, demonstrating promising application prospects.

Building self-assembly nanostructures is an important way to overcome the limitations of paclitaxel in tumor therapy. However, this strategy is also faced with challenges, such as difficulties in efficient release and the potential for drug resistance. Herein, we developed a near-infrared light-activatable melanized paclitaxel self-assembly nanoparticles for synergistic anti-tumor therapy. In this strategy, paclitaxel dimer prodrugs were synthesized and paclitaxel nanoparticles were obtained through self-assembly. Finally, the paclitaxel dimer nanoparticles were capped with polydopamine (PDA, melanoidin) and human serum albumin (HSA). The disulfide bonds in paclitaxel dimeric prodrug specifically respond to high concentrations of glutathione (GSH) and reactive oxygen species (ROS) in tumor cells. PDA enhances the biocompatibility of the drug molecules and imparts near-infrared photothermal conversion capability to the nano–self-assemblies. Both the in vitro and in vivo experiments demonstrated that this paclitaxel nanoprodrug exhibited enhanced tumor therapeutic efficacy under near-infrared light irradiation.

A compact dual-band multibeam antenna system with high gain is presented for multiple-input-multiple-output (MIMO) wireless fidelity (Wi-Fi) application. The antenna system is composed of four rotationally symmetric antenna panels occupying a compact size of $60\times 200\times7.2$ mm3. Two series-fed microstrip patch antenna (MPA) arrays are proposed to operate at the 5-GHz band. They are fed with ±90° phase difference signals for two directional radiation patterns with about 45° horizontal 3-dB beamwidth and distinct radiation directions. Two dual-function metasurface based on periodically loaded parallel lines is designed and compactly placed between the two MPA arrays. The metasurface works as a 2.4-GHz antenna with about 90° 3-dB beamwidth in the azimuth. Besides, the application of metasurface improves the gains at the 5-GHz band. Then, four antenna panels are placed in a rotating arrangement with an electric size of $0.59\lambda _{\mathrm {L}} \times 0.59\lambda _{\mathrm {L}} \times 1.57\lambda _{\mathrm {L}}$ ( $\lambda _{\mathrm {L}}$ is the free-space wavelength at the lowest operating frequency). The antenna system was manufactured and demonstrated the characteristics of 360° pattern coverage in azimuth and high gain. The measured average gains are, respectively, higher than 8 and 12 dB at the 2.4- and 5-GHz bands.

A simple approach for radiation pattern reshaping of the third mode of dipole and slot antennas is proposed. Short parasitic strips or slots are added to both sides of the dipole or slot antenna. In the third mode, the reversed current or E-field will destroy the omnidirectivity or bidirectivity of these two antennas. But, the induced current or E-field on the parasitic elements is opposite to that on the original antenna canceling the effect of the reversed current or E-field. As a result, the radiation patterns of the dipole and slot in the third mode are reshaped to be omnidirectional or bidirectional, respectively. The effects of parasitic element length, spacing, and position on radiation patterns have also been studied. Besides, it is proved that parasitic loops have the same effect. For simplifying impedance matching, a novel feeding method for slot antenna is designed especially without using lumped elements. Two prototypes, dipole and slot antennas with parasitic elements, were fabricated and tested to validate the concept. The measured results are in good agreement with the simulated ones. The measured patterns of the third mode are shaped with lower sidelobes and high realized gains. In both the first and third modes, the dipole and slot antennas realize omnidirectional and bidirectional radiations, respectively.