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The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn’t require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4N+2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption.

Plants constantly suffer from simultaneous infection by multiple pathogens, which can be divided into biotrophic, hemibiotrophic, and necrotrophic pathogens, according to their lifestyles. Many studies have contributed to improving our knowledge of how plants can defend against pathogens, involving different layers of defense mechanisms. In this sense, the review discusses: (1) the functions of PAMP (pathogen-associated molecular pattern)-triggered immunity (PTI) and effector-triggered immunity (ETI), (2) evidence highlighting the functions of salicylic acid (SA) and jasmonic acid (JA)/ethylene (ET)-mediated signaling pathways downstream of PTI and ETI, and (3) other defense aspects, including many novel small molecules that are involved in defense and phenomena, including systemic acquired resistance (SAR) and priming. In particular, we mainly focus on SA and (JA)/ET-mediated signaling pathways. Interactions among them, including synergistic effects and antagonistic effects, are intensively explored. This might be critical to understanding dynamic disease regulation.

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities.

Small bowel volvulus secondary to Meckel’s diverticulum is rare, and a delayed diagnosis results in disastrous outcomes. Computed tomography is conducive to early differential diagnosis. In particular, a blind-ending pouch structure on CT always indicates Meckel’s diverticulum. Diverticulectomy with or without adjacent partial small intestinal resection is the standard treatment for symptomatic Meckel’s diverticulum. However, the therapy for asymptomatic Meckel’s diverticulum is controversial. Here, we report the case of a 20-year-old man who suffered intestinal obstruction secondary to small bowel volvulus caused by an axially torsional, gangrenous, and giant Meckel’s diverticulum. Diverticulectomy with partial intestinal resection was performed.

Organic light-emitting diodes (OLEDs) suffer from notorious light trapping, resulting in only moderate external quantum efficiencies. Here, we report a facile, scalable, lithography-free method to generate controllable nanostructures with directional randomness and dimensional order, significantly boosting the efficiency of white OLEDs. Mechanical deformations form on the surface of poly(dimethylsiloxane) in response to compressive stress release, initialized by reactive ions etching with periodicity and depth distribution ranging from dozens of nanometers to micrometers. We demonstrate the possibility of independently tuning the average depth and the dominant periodicity. Integrating these nanostructures into a two-unit tandem white organic light-emitting diode, a maximum external quantum efficiency of 76.3% and a luminous efficacy of 95.7 lm W −1 are achieved with extracted substrate modes. The enhancement factor of 1.53 ± 0.12 at 10,000 cd m −2 is obtained. An optical model is built by considering the dipole orientation, emitting wavelength, and the dipole position on the sinusoidal nanotexture. For organic light-emitting diodes (OLEDs) to reach their potential for lighting applications, improved light out-coupling using industry-compatible methods are required. Here, the authors report reactive ion etching-induced quasi-periodic nanostructures for improved light extraction in white OLEDs.

Phytoalexin sakuranetin functions in resistance against rice blast. However, the mechanisms underlying the effects of sakuranetin remains elusive. Here, we report that rice lines expressing resistance (R) genes were found to contain high levels of sakuranetin, which correlates with attenuated endocytic trafficking of plasma membrane (PM) proteins. Exogenous and endogenous sakuranetin attenuates the endocytosis of various PM proteins and the fungal effector PWL2. Moreover, accumulation of the avirulence protein AvrCO39, resulting from uptake into rice cells by Magnaporthe oryzae , was reduced following treatment with sakuranetin. Pharmacological manipulation of clathrin-mediated endocytic (CME) suggests that this pathway is targeted by sakuranetin. Indeed, attenuation of CME by sakuranetin is sufficient to convey resistance against rice blast. Our data reveals a mechanism of rice against M. oryzae by increasing sakuranetin levels and repressing the CME of pathogen effectors, which is distinct from the action of many R genes that mainly function by modulating transcription. Sakuranetin is an important phytoalexin. The authors find that sakuranetin in rice attenuates endocytosis of effectors from the fungus Magnaporthe oryzae and enhances resistance against rice blast.

With the continuous advancement and upgrading of human space exploration, the complexity and difficulty of space missions are increasing. Space manipulation, rendezvous and docking, and situation awareness of non cooperative targets have become important components of space missions. Relative position and attitude navigation system is an important part of space rendezvous and docking and space manipulation system. At present, the method of matching with cooperative target is mainly used for high-precision relative position and attitude measurement. This paper introduces a method of matching solid-state lidar with high-precision cooperative target for attitude determination. First, a design method of three-dimensional cooperative target is carried out, and then a mathematical model of target recognition is constructed. Three criteria for recognition and matching are proposed. In the third part of the article, the specific process of this method is introduced, and experimental verification is carried out. The experiment shows that the method can achieve the position measurement accuracy of 0.006m and the attitude measurement accuracy of 0.15 ° , and has important application value in various high-precision space missions.

This work is concerned with the issue of stability analysis and controller synthesis for a class of Fornasini–Marchesini second-type systems with polytopic uncertainty, state delays and saturation nonlinearity. Firstly, by utilizing the co-positive Lyapunov function method, sufficient conditions in the linear programming setting are presented assuring the positivity and robust asymptotic stability of the proposed systems. Then, both the state feedback controller and the observer-based state feedback controller are considered based on the obtained results. In addition, the design schemes of the related controller gain matrices are also explicitly provided. Finally, two illustrative examples are given to demonstrate the effectiveness of the method outlined in this paper.

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

In real life, multiple network public opinion emergencies may break out in a certain place at the same time. So, it is necessary to invite emergency decision experts in multiple fields for timely evaluating the comprehensive crisis of the online public opinion, and then limited emergency resources can be utilized to give priority to respond to the one with the highest crisis. Due to the complexity of network public opinion emergencies and the limited cognition of experts, most of the decision problems for evaluating the network public opinion emergencies are highly uncertain. Also, prior to the selection of the highest crisis, it is preferable that experts reach a high degree of consensus among their assessments or opinions. To address such problems, this paper presents a novel adaptive consensus reaching model for multiattribute group decision making (MAGDM) with probabilistic linguistic decision matrices (PLDMs). First, to quantify the difference between any two probabilistic linguistic term sets (PLTSs) accurately and efficiently, we define a novel distance measure between PLTSs based on the Wasserstein metric. Then, by integrating the defined PLTSs-based Wasserstein (PL-Wasserstein) distance measure into the classical CCSD method, we construct an optimization model for objectively determining attribute weights. Subsequently, we develop the individual cumulative consensus contribution (ICCC) measure and the group consensus measure, respectively, following which is to present an integrated consensus improving strategy that considers both weight-updating (i.e., dynamic weights of experts and attributes) and assessment-adjusting. Finally, the feasibility and the applicability of the proposed approach are illustrated via a real evaluation of network public opinion emergencies. Through comparing with existing probabilistic linguistic MAGDM approaches, the proposed approach offers the advantages in terms of the accurate measurement of information difference and the integrated improvement of consensus efficiency.