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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.

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.