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This paper addresses the issues of short impulse response signal duration and susceptibility to noise interference by using the Leaky Integrate-and-Fire (LIF) neuron model to simulate the charging and discharging process for impulse signal generation. In addition, an Impulse Response Signal-Convolutional Spiking Neural Network (IRS-CSNN) is developed to exploit the time-frequency characteristics of the target impulse response signal for target identification. First, the signal sequence of pitch and azimuth angle changes is subjected to time-frequency analysis, from which the time-frequency features are extracted and fed into the IRS-CSNN with Time-Frequency Spatial Attention Mechanism (TF-SAM) to facilitate target classification and identification. The experimental results show that this approach effectively utilizes the feature information in the impulse response signal and exploits the feature extraction capabilities of the CSNN. The average recognition accuracy for targets at different angles under noise-added conditions is 89.93%, an improvement of 1.87%-4.34% over that of a similarly structured CNN and other machine learning models. These results confirm the effectiveness of the proposed method.

Abstract Organic semiconductor materials are considered to be promising photocatalysts due to their excellent light absorption by chromophores, easy molecular structure tuning, and solution‐processable properties. In particular, donor‐acceptor (D‐A) type organic photocatalytic materials synthesized by introducing D and A units intra‐ or intermolecularly, have made great progress in photocatalytic studies. More and more studies have demonstrated that the D‐A type organic photocatalytic materials combine effective carrier separation, tunable bandgap, and sensitive optoelectronic response, and are considered to be an effective strategy for enhancing light absorption, improving exciton dissociation, and optimizing carrier transport. This review provides a thorough overview of D‐A strategies aimed at optimizing the photocatalytic performance of organic semiconductors. Initially, essential methods for modifying organic photocatalytic materials, such as interface engineering, crystal engineering, and interaction modulation, are briefly discussed. Subsequently, the review delves into various organic photocatalytic materials based on intramolecular and intermolecular D‐A interactions, encompassing small molecules, conjugated polymers, crystalline polymers, supramolecules, and organic heterojunctions. Meanwhile, the energy band structures, exciton dynamics, and redox‐active sites of D‐A type organic photocatalytic materials under different bonding modes are discussed. Finally, the review highlights the advanced applications of organic photocatalystsand outlines prospective challenges and opportunities.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT’s inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition.

As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious steps associated with factorization methods. Nevertheless, ISEA’s neglect of valid information necessitates a high quantity of images and elongated operation times. This paper introduces a partitioned parallel 3D reconstruction method utilizing sorted-energy semi-accumulation with ISAR image sequences (PP-ISEA) to address these limitations. The PP-ISEA innovatively incorporates a two-step search pattern—coarse and fine—that enhances search efficiency and conserves computational resources. It introduces a novel objective function ‘sorted-energy semi-accumulation’ to discern genuine scatterers from spurious ones and establishes a redundant point exclusion module. Experiments on the scatterer model and simulated electromagnetic model demonstrate that the PP-ISEA reduces the minimum image requirement from ten to four for high-quality scatterer model reconstruction, thereby offering superior reconstruction quality in less time.

Traditional‐metal‐containing scintillators are widely used in X‐ray imaging due to their efficient X‐ray absorption and output of visible light. However, they suffer from heavy‐metal toxicity, environmental stability, harsh preparation, and afterglow. Metal‐free organic scintillators show a rising momentum, especially organic‐halogen‐containing molecules. Halogens are introduced to improve their X‐ray absorption, but the resulting increase in spin–orbit coupling leads to significant delayed fluorescence or phosphorescence, affecting the response speed to X‐rays. Moreover, there is still insufficient practice in fabricating microstructured organic scintillators for high spatial resolution of imaging. Herein, the preparation of nano organic co‐crystals (t‐Bpe‐IFB co‐crystal, abbreviated as BIC, t‐Bpe for trans‐1,2‐bis(4‐pyridyl)ethylene, and IFB for 1,3,5‐trifluoro‐2,4,6‐triiodobenzene) and its application in X‐ray imaging are explored. In contrast to previous single organic‐halogen‐containing molecules, BIC generates nanosecond‐scale fluorescence through the charge‐transfer state of the donor–acceptor. Its high iodine content ensures large X‐ray absorption, strong radioluminescence, and a low detection limit of 85 nGyair s−1. The composite film made of nano‐sized BICs and polydimethylsiloxane exhibits a high spatial resolution of 16.7 lp mm−1. Herein, the application of organic co‐crystals is expanded and ideas are provided for the development of new scintillators.

Abstract Background The Surgical Pleth Index (SPI) is an objective tool that can reflect nociception-antinociception balance and guide the use of intraoperative analgesics. Multimodal analgesia has been neglected in many previous studies. The aim of this study was to compare fentanyl consumption using SPI-guided analgesia versus conventional analgesia techniques under multimodal analgesia in laparoscopic cholecystectomy. Methods A total of 80 patients aged 18–65 years with American Society of Anaesthesiologists (ASA) grade I-II and a body mass index (BMI) of 18.5 to 30 kg/m2 who were scheduled for laparoscopic cholecystectomy under total intravenous anaesthesia from March 2020 to September 2020 were selected. Multimodal analgesia, including local infiltration of the surgical incision, nonsteroidal anti-inflammatory drugs and opioids, was adopted perioperatively. Fentanyl boluses of 1.0 µg/kg were administered to maintain the SPI value between 20 and 50 in the SPI group. By contrast, fentanyl boluses of 1.0 µg/kg were administered whenever the heart rate (HR) or mean arterial pressure (MAP) increased to 20 % above baseline or when the HR was greater than 90 beats per minute (bpm) in the control group. Preoperative and postoperative blood glucose, plasma cortisol and interleukin-6 (IL-6) levels were evaluated. Intraoperative haemodynamic events and propofol and fentanyl doses were noted. The extubation time, postoperative visual analogue scale (VAS) score, use of remedial analgesics and opioid-related adverse reactions were recorded. Results In total, 18 of 80 patients withdrew for various reasons, and data from 62 patients were finally analysed. Intraoperative fentanyl consumption was significantly lower in the SPI group than in the control group (177.1 ± 65.9 vs. 213.5 ± 47.5, P = 0.016). The postoperative extubation time was shorter in the SPI group than in the control group (16.1 ± 5.2 vs. 22.1 ± 6.3, P

Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get the accurate number of individual trees, this paper took coniferous forest and mixed coniferous and broad-leaved forest as experimental samples, as well as Digital Orthophoto Map and airborne LiDAR Point Cloud as research data. We propose a deep Learning individual tree segmentation method based on RetinaNet model and PCS algorithm by doing comparative analysis (classical Watershed Algorithm and Layer Stacking Algorithm) at the plots (with high, medium, and low densities). The experimental results show that the method proposed in this paper can solve the problem of individual tree segmentation in high density forest and improve its degree of automation. Compared with Watershed algorithm and Layer stacking algorithm, F-Measure is improved by 6%-29% and 7%-20%, respectively. In other words, the results of individual tree segmentation presented in this paper can not only improve the precision of individual tree segmentation, but also maintain a high detection rate, which can meet the accuracy and high efficiency of individual tree extraction, so as to meet the needs for modern forestry investigation.

Abstract Background Blood donor plasma samples were detected by the Ultrio Plus NAT system for HBV, HCV and HIV-1 in Shenzhen blood center, China. Reactive samples underwent further discriminatory testing of a single virus by the same methodology. A large number of cases of non-discriminated reactive (NDR) donors were found, leaving potential risk of transmitting HBV if not deferrals. This study identified those non-discriminated samples. Methods The NDR plasma samples from blood donation screening were detected and classified by additional molecular and serological tests. Molecular characterizations of DNA+ NDR were determined by sequencing analysis. Results A number of 259 (0.21%) NDR plasma samples from screening of 123,280 eligible blood donors were detected, which presented a higher rate (91.1%) of anti-HBc reactivity and nearly half (46.7%) of HBV DNA+ that classified as occult HBV infection (OBI). Most OBI strains were wild-type HBV, but some substitutions V168A, S174 N, V177A, Q129R/L/H, G145A/R in S region of genotype B (OBIB) and T47K/V/A, P49H/L, Q101R/H/K, S174 N, L175S, V177A, T118 M/R/K, G145R/A/K/E, R160K/N in S region of genotype C (OBIC) strains were identified in high frequency. Conclusion Nearly half of NDR blood samples were identified as OBI, in which a number of important mutations were detected. NDR donation might have potential risk for HBV transmission, but need to be further investigated.

Background: A recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), which began in Wuhan, China, with a high level of human-to-human transmission has been reported. There are limited data available on Coronavirus Disease 2019 (COVID-19) patients with hematological malignancies with more than 60 days of follow-up. This study describes the clinical characteristics, including multiple recurrences of COVID-19, in a patient with chronic lymphocytic leukemia (CLL) during 69 days of follow-up.Case Presentation: A 72-year-old female was admitted to hospital isolation after being infected with COVID-19 as part of a family cluster on January 30, 2020. Apart from SARS-Cov-2 virus infection, laboratory results revealed lymphocytosis of uncertain etiology and abnormal distribution of T lymphocytes. On blood smears, small blue lymphocytes with scant cytoplasm were observed, and the presence of high levels of circulating clonal B cells was also demonstrated by flow cytometry. The patient was diagnosed with COVID-19 and CLL. Among her family members, she had the highest viral loads and the fastest progression on lung injury and developed severe pneumonia. Serological results showed she had both 2019-nCoV-specific IgM and IgG antibodies; however, only IgG antibodies were detected in her husband's plasma.Results: A combination regimen of antiviral therapy and high-dose intravenous immunoglobulin (IVIG) in the early stage seemed to be effective for treating CLL and SARS-Cov-2 infection. Because of the low humoral immune response, the CLL patient could not effectively clear the SARS-Cov-2 infection and suffered from recurrence twice during the 69-day follow-up.Conclusion: In CLL, a neoplastic antigen-specific B-cell clone proliferates, and the progeny cells accumulate and outgrow other B cells, leading to immune deficiency. Considering the low humoral immune response and ineffective clearance of SARS-Cov-2 in CLL patients, the follow-up and home quarantine period should be extended. We need further studies to clarify suspending or continuing CLL therapy during COVID infection. For those patients who are prone to progression to severe disease, administering humoral immunity therapies can help to prevent disease progression and quickly meet the cure criteria.

To solve the problem of the low rate of template matching (TM) jamming decision method under the incomplete jamming rule library condition in traditional electronic warfare, a clustering and resampling – support vector machine (CRS-SVM) jamming decision method, is proposed. For the air-to-air scene of airborne multifunctional fire control radar, the inter-pulse pulse and intra-pulse features are extracted, and a jamming rule library that is divided into the source domain and target domain sample according to the missing rate is constructed in this scene. DBSCAN algorithm is used to cluster the dataset, and the structure information is found. The training samples are generated via resampling. SVM is used to transform the inner product of the target domain space into the inner product of the feature space to establish the feature space hyperplane. The target domain samples are labelled as the corresponding jamming style label to realise the fast and effective decision. The experiment results show that the proposed method can effectively improve the accuracy and robustness of jamming decision compared with the traditional TM method.

Interactions of human natural killer (NK) cell inhibitory receptors with polymorphic HLA-A, -B and -C molecules educate NK cells for immune surveillance against tumor cells. The KIR A haplotype encodes a distinctive set of HLA-specific NK cell inhibiting receptors having strong influence on immunity. We observed higher frequency of KIR A homozygosity among 745 healthy Chinese Southern Han than 836 adult patients representing three types of leukemia: ALL (OR = 0.68, 95% CI = 0.52–0.89, p = 0.004), AML (OR = 0.76, 95% CI = 0.59–0.98, p = 0.034), and CML (OR = 0.72 95% CI = 0.51–1.0, ns). We observed the same trend for NHL (OR = 0.47 95% CI = 0.26–0.88 p = 0.017). For ALL, the protective effect of the KIR AA genotype was greater in the presence of KIR ligands C1 (Pc = 0.01) and Bw4 (Pc = 0.001), which are tightly linked in East Asians. By contrast, the C2 ligand strengthened protection from CML (Pc = 0.004). NK cells isolated from KIR AA individuals were significantly more cytotoxic toward leukemic cells than those from other KIR genotypes (p < 0.0001). These data suggest KIR allotypes encoded by East Asian KIR A haplotypes are strongly inhibitory, arming NK cells to respond to leukemogenic cells having altered HLA expression. Thus, the study of populations with distinct KIR and HLA distributions enlightens understanding of immune mechanisms that significantly impact leukemia pathogenesis.

Bistatic inverse synthetic aperture radar (ISAR) can increase the probability of tracking the high-speed target and provide more angle information than monostatic ISAR. However, bistatic ISAR suffers from a serious defocusing problem, resulting from the high-speed motion. Furthermore, the inherent geometry distortion and calibration problems make bistatic ISAR (B-ISAR) imaging more complex. In response to these problems, we propose a bistatic ISAR imaging method for high-speed moving target with geometric distortion correction and calibration based on dechirping processing. At first, based on the motion decomposition idea, the B-ISAR echo model of the high-speed moving target is established. Then, by analyzing the imaging mechanism of the Range-Doppler algorithm (RDA), we eliminate the phase items influencing the imaging quality with speed compensation and Doppler compensation. After that, the analytic formula of the geometric distortion factor and calibration factor are deduced, which helps transform the geometric correction and calibration problem into a parameter estimation problem. Finally, with the sparsity of the scattering points, the required parameters are solved using the expectation maximization (EM) algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed moving target with geometric correction and calibration is obtained. The simulations show that the proposed method has a better resolution and simultaneously attains geometric correction and calibration of the image.

Flexible conductive foam-based pressure sensors, as a crucial component of wearable electronics, have garnered extensive attention in the fields of soft robotics and human-machine interaction due to their light weight, simple fabrication process and low cost. The majority of published studies, however, have focused on their foundational mechanical sensing properties including high sensitivity, wide detection range, etc., while ignoring the instability caused by low interfacial bonding strength between conductive fillers and foam skeleton, as well as the external environment in practical applications. Herein, through surface modification and interfacial microstructure design, a piezoresistive foam-type pressure sensor with high sensitivity and high stability is developed. The synergistic effect of the combination of surface modification and interfacial microstructure endows the sensor with great sensitivity (14.16 kPa-1 for the pressure range of 0-5 kPa), a low limit of detection (~6.3 x 10-6 kPa), and exceptional durability (over 13 000 compression/recovery cycles). Furthermore, a wireless pressure acquisition system based on a 5 x 5 piezoresistive foam-type sensor array is developed, which can accurately recognize the spatial pressure distribution of the placed objects. This work presents a straightforward approach for realizing the trade-off between sensitivity and stability of conductive foam-based sensors, paving the way for their practical applications in wearable electronics, bionic robots, and other fields. [ABSTRACT FROM AUTHOR]

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