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Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed. [ABSTRACT FROM AUTHOR]

Lactation initiation refers to a functional change in the mammary organ from a non-lactating state to a lactating state, and a series of cytological changes in the mammary epithelium from a non-secreting state to a secreting state. Like the development of the mammary gland, it is regulated by many factors (including hormones, cytokines, signaling molecules, and proteases). In most non-pregnant animals, a certain degree of lactation also occurs after exposure to specific stimuli, promoting the development of their mammary glands. These specific stimuli can be divided into two categories: before and after parturition. The former inhibits lactation and decreases activity, and the latter promotes lactation and increases activity. Here we present a review of recent progress in research on the key factors of lactation initiation to provide a powerful rationale for the study of the lactation initiation process and mammary gland development. [ABSTRACT FROM AUTHOR]

The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by computer vision techniques, and deep learning semantic segmentation methods have higher accuracy and robustness than traditional image methods. However, the lack of data images and insufficient detection performance remain challenges in concrete dam surface crack detection scenarios. Therefore, this paper proposes an intelligent detection method for concrete dam surface cracks based on two-stage transfer learning. First, relevant domain knowledge is transferred to the target domain using two-stage transfer learning, cross-domain and intradomain learning, allowing the model to be fully trained with a small dataset. Second, the segmentation capability is enhanced by using residual network 50 (ResNet50) as a UNet model feature extraction network to enhance crack feature information extraction. Finally, multilayer parallel residual attention (MPR) is integrated into its jump connection path to improve the focus on critical information for clearer fracture edge segmentation. The results show that the proposed method achieves optimal mIoU and mPA of 88.3% and 92.7%, respectively, among many advanced semantic segmentation models. Compared with the benchmark UNet model, the proposed method improves mIoU and mPA by 4.6% and 3.2%, respectively, reduces FLOPs by 36.7%, improves inference speed by 48.9%, verifies its better segmentation performance on dam face crack images with a low fine crack miss detection rate and clear crack edge segmentation, and achieves an accuracy of over 85.7% in crack area prediction. In summary, the proposed method has higher efficiency and accuracy in concrete dam face crack detection, with greater robustness, and can provide a better alternative or complementary approach to dam safety inspections than the benchmark UNet model. [ABSTRACT FROM AUTHOR]

The optimization of grain boundary character distribution (GBCD) is of great significance to improve the GB-related properties for heavy-gauge austenitic stainless steels worked in harsh environments such as reactors of nuclear power, which can usually be realized by regulating the thermomechanical process. In this paper, special solution annealing processes for a hot-rolled nuclear grade 316H plate were designed to introduce different character distribution of Σ3n boundaries (1 ≤ n ≤ 3) and random high-angle GBs (RHAGBs), and the regulation principle among them were clarified. It was worked out that the optimized GBCD by characterization of large twin related domains, abundant interconnected Σ3n boundaries and interrupted topology network of RHAGBs could be effectively facilitated through solution annealing with a long time period at lower temperature or short time period at higher temperature, in which the recrystallization, grain growth and GB migration during heat treatment process played key roles. Moreover, the length fraction of Σ3n boundaries were found to be hardly changed when they reached about 77%, but their character distribution could be continuously optimized. [ABSTRACT FROM AUTHOR]

Fish are indicative species with a relatively balanced ecosystem. Underwater target fish detection is of great significance to fishery resource investigations. Traditional investigation methods cannot meet the increasing requirements of environmental protection and investigation, and the existing target detection technology has few studies on the dynamic identification of underwater fish and small targets. To reduce environmental disturbances and solve the problems of many fish, dense, mutual occlusion and difficult detection of small targets, an improved CME-YOLOv5 network is proposed to detect fish in dense groups and small targets. First, the coordinate attention (CA) mechanism and cross-stage partial networks with 3 convolutions (C3) structure are fused into the C3CA module to replace the C3 module of the backbone in you only look once (YOLOv5) to improve the extraction of target feature information and detection accuracy. Second, the three detection layers are expanded to four, which enhances the model's ability to capture information in different dimensions and improves detection performance. Finally, the efficient intersection over union (EIOU) loss function is used instead of the generalized intersection over union (GIOU) loss function to optimize the convergence rate and location accuracy. Based on the actual image data and a small number of datasets obtained online, the experimental results showed that the mean average precision (mAP@0.50) of the proposed algorithm reached 94.9%, which is 4.4 percentage points higher than that of the YOLOv5 algorithm, and the number of fish and small target detection performances was 24.6% higher. The results show that our proposed algorithm exhibits good detection performance when applied to densely spaced fish and small targets and can be used as an alternative or supplemental method for fishery resource investigation. [ABSTRACT FROM AUTHOR]

Although corporate capability has been recognized as a key factor affecting corporate acquisition performance, the role of R&D capability in acquisition performance has not been fully explained. The aim of this paper was to research the impact of internal R&D on acquisition performance according to a sample of 215 acquisitions of Chinese listed pharmaceutical companies from 2012 to 2016. First, it was found that R&D has a significant negative effect on acquisition performance. Furthermore, it was confirmed that the acquisition motive and the ownership of the acquiring firm have a moderating effect on the relationship between R&D and acquisition performance. Compared to non-technical acquisitions, the negative effect of internal R&D on acquisition performance was reduced for technical acquisitions. Compared with non-state-owned-enterprise acquisition, the negative effect of internal R&D on the acquisition performance of state-owned enterprises was weakened. Our study enriches the research of the path dependence theory on the acquisition performance of enterprises and also the interpretation of acquisition performance on the basis of internal and external innovation and the institutional theory. [ABSTRACT FROM AUTHOR]