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Time Domain Coding Metasurface (TDCM) is an emerging technology enabling dynamic modulation of electromagnetic waves. In response to the control characteristics of this technology, this paper presents a radar jamming method based on TDCM intrapulse and interpulse coding optimization. First, optimization models are established in fast and slow time domains. By optimizing intrapulse and interpulse phase coding, the energy redistribution of targets is achieved, thereby generating deceptive interference on the range-Doppler two-dimensional plot. Subsequently, a genetic algorithm is employed to solve this discrete optimization problem. Furthermore, this paper analyzes the effect of various modulation factors on interference effectiveness in terms of TDCM coding strategies, providing guidance for achieving optimal strategies for deceptive interference.

Due to several advantages of the Multi-Input Multi-Output (MIMO) system in terms of waveform, space diversity, and multiplexing, the MIMO Dual Function Radar and Communication (DFRC) system, which is responsible for target detection and securing the communication by sharing the software and hardware resources, has attracted great attention. This paper addresses the MIMO DFRC system based on permutation matrix modulation and proposes a DFRC signal matrix design method based on the Alternation Direction Method of Multipliers (ADMM). By maximizing the Peak Mainlobe to Sidelobe level Ratio (PMSR) of the beampattern with the constraints of the reference codebook for both users and eavesdroppers, the system guarantees excellent detection performance along with protecting the communication information from interception. Aiming at the communication demodulation of the permutation matrix, a permutation learning demodulation method based on the Alternating Direction Penalty Method (ADPM) is proposed to improve the demodulation efficiency of the co-use waveform. Numerical simulations verify the effectiveness of the proposed methods to achieve dual function, capable of realizing multiuser communication and deriving higher PMSR compared with the existing counterparts.

Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on Compress Sensing (CS) theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.

With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from −16 dB to −8 dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from −16 dB to −8 dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments.

With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional antijamming methods usually need to estimate the jamming parameters and have the risk of losing target information. For the above problems, this article proposes a deep neural network-based ISRJ recognition and antijamming target detection method which consists of four serial steps. First, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Second, a you-only-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Third, the anti-ISRJ target ranging and velocity measurement datasets are constructed according to the positioning result. Finally, an anti-ISRJ target detection model based on the convolution neural network (CNN) is designed to extract features along different dimensions and obtain the range and velocity of the real targets. Experiments on simulated and measured datasets show that the proposed method has better antijamming detection performance than the traditional method, and does not need to estimate the jamming parameters.

Abstract Here, the problem of direction detection in disturbance with unknown covariance matrix is considered. The case that the number of the training data is too small to form an effective estimate for the unknown covariance matrix is focused upon. To solve the problem, the Bayesian framework is resorted to. Precisely, the unknown covariance matrix is assumed to be ruled by an inverse Wishart distribution. Then the problem is solved by the detector design criterion of generalized likelihood ratio test. Numerical examples indicate that the proposed detector can effectively detect the target, and provide higher probability of detection than the existing detectors even when sufficient training data are available.

Abstract In this letter, the authors elaborate on a novel method to detect the number of targets in low SNR and small snapshots through using the Fuzzy C‐Means clustering based granulation‐degranulation mechanism. In the developed scheme, the eigenvalues of the array output correlation matrix are regarded as a time series, and granulated into a pair of ordered information granules. The information about the number of targets is included in the information granules. Subsequently, the degranulation mechanism is used to modify the cost function of the granulation mechanism to improve the quality of the information granules, which can increase the detection accuracy of the number of targets. Finally, the number of targets is determined by the prototype matrix and partition matrix generated by the enhanced version of the granulation. Explicit analysis and derivation of the proposed scheme are presented.

Enhanced atmospheric nitrogen deposition can greatly influence the soil nitrogen content and soil microorganisms in terrestrial ecosystems. However, there is a lack of knowledge about the response of soil microbes to changes in soil nitrogen, particularly in desert ecosystems. To address this, we used long-term artificial nitrogen addition to simulate atmospheric nitrogen deposition in a desert ecosystem in Ningxia, China. We adopted a high-throughput sequencing method (16S rRNA and ITS) to explore the effects of different amounts of atmospheric nitrogen deposition on soil microbial diversity and community structure. Six years of nitrogen addition (6 g N m−2 y−1) significantly increased the soil organic carbon content and the total soil nitrogen content (p 0.05). Further analysis revealed that nitrogen addition substantially reduced the number of positive correlations between soil microbial species and the number of core microbial species. Our results suggest that for desert ecosystems, at present, atmospheric nitrogen deposition may increase soil nitrogen use by some vegetation but it does not alleviate the competition for soil nitrogen between soil microbes and vegetation. Moreover, although enhanced atmospheric nitrogen deposition did not significantly change the soil microbial community structure, it may negatively affect the interactions among soil microbial species. This would hinder soil microbial adaptation to the changing environment and the development of desert ecosystems.

This study investigates the detection problem of multiple moving targets using a multisite radar system with widely separated stations. Spatial mapping is presented to integrate the observation data of a moving target from different angles into a spatial resolution cell (SRC). However, data association errors occur in some SRCs in this way, which causes extra false alarm, called the “ghost target”. Therefore, an interference discriminator-based detector is developed. In this way, the background interference is discriminated between “ghost target” and pure noise, and then the final decision is made based on the generalized likelihood ratio test. Statistical analyses are provided to discuss the achievable performance. Simulation results show that the proposed algorithm can accurately detect multiple moving targets while suppressing the “ghost target”.

Abstract The extensive consumption of chicken has resulted in the emergence of a significant environmental issue in the form of chicken feather waste. As such, there is an urgent need for the development of green treatment and recycling methods for chicken feathers. Chicken feathers can serve as a type of heteroatomic doping carbon source, making them an excellent candidate for the electrode materials used in electrochemical energy devices. Furthermore, their unique structures and functional groups make them highly promising for use as adsorbents, electronics, and building materials. In this paper, we provide a summary and review of recent progress made in the use of chicken feathers for energy and environmental applications. Based on the theoretical knowledge and practical applications presented in this review, promising green recycling processes of chicken feathers can be developed. These processes can help to reduce environmental pollution and promote sustainable development.

Metformin shows promise in breast cancer prevention, but its underlying mechanisms remain unclear. This study investigated the impact of metformin on the repopulation dynamics of mammary epithelial cells (MECs) and the signaling pathways in non-tumorigenic FVB/N mice. This study aimed to enhance our understanding of the role of metformin in reducing the susceptibility of MECs in premalignant tissues to oncogenic factors. In this study, female mice were administered 200 mg/kg/day of metformin via intraperitoneal (i.p.) injection from 8 to 18 weeks of age. After this treatment period, morphogenesis, flow cytometry, analyses of MEC stemness, and RNA sequencing were performed. The study findings indicated that metformin treatment in adult mice reduced mammary gland proliferation, as demonstrated by decreased Ki67+ cells and lateral bud formation. Additionally, metformin significantly reduced both basal and mammary repopulating unit subpopulations, indicating an impact on mammary epithelial cell repopulation. Mammosphere, colony-forming cell, and 3D culture assays revealed that metformin adversely affected mammary epithelial cell stemness. Furthermore, metformin downregulated signaling in key pathways including AMPK/mTOR, MAPK/Erk, PI3K/Akt, and ER, which contribute to its inhibitory effects on mammary proliferation and stemness. Transcriptome analysis with RNA sequencing indicated that metformin induced significant downregulation of genes involved in multiple critical pathways. KEGG-based pathway analysis indicated that genes in PI3K/Akt, focal adhesion, ECM-receptor, small cell lung cancer and immune-modulation pathways were among the top groups of differentially regulated genes. In summary, our research demonstrates that metformin inhibits MEC proliferation and stemness, accompanied by the downregulation of intrinsic signaling. These insights suggest that the regulatory effects of metformin on premalignant mammary tissues could potentially delay or prevent the onset of breast cancer, offering a promising avenue for developing new preventive strategies.

The corrosion behaviors of three bridge steels in a real tropical marine environment for 2 years were studied. One weathering steel (WS) was designed with higher levels of nickel, copper, and molybdenum compared to the other. These two kinds of WSs and one kind of ordinary high-strength low-alloy steel (Q345qe) were compared under two conditions (marine atmospheric zone and marine immersion zone at Sanya Marine Environmental Test Station). The morphology, corrosion rate, and corrosion product analysis of the steels were performed through SEM, XPS, FTIR and other characterization methods. The results demonstrated that weathering steels facilitate the densification of the corrosion product layer due to the addition of alloying elements Cr, Ni, and Cu, promoting rust nucleation and enhancing the compactness of the protective layer. However, in an immersion environment, the extensive erosion by chloride ions renders the benefits of WS ineffective.

This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of the Beihai Rescue Bureau. Models including the coverage quality of the jurisdictional waters, the coverage quality in high-risk areas, the maximum coverage of jurisdictional areas, and the maximum coverage of high-risk areas are constructed and solved using 0–1 integer programming. The optimal plan for eight standby points and their corresponding deployment plans for rescue vessels are obtained. A comparison with the current site selection plan of the Beihai Rescue Bureau validates the superiority of the proposed deployment plan for rescue vessel standby points in this paper.

Abstract Background The gain or loss of large chromosomal regions or even whole chromosomes is termed as genomic scarring and can be observed as copy number variations resulting from the failure of DNA damage repair. Results In this study, a new algorithm called genomic scar analysis (GSA) has developed and validated to calculate homologous recombination deficiency (HRD) score. The two critical submodules were tree recursion (TR) segmentation and filtering, and the estimation and correction of the tumor purity and ploidy. Then, this study evaluated the rationality of segmentation and genotype identification by the GSA algorithm and compared with other two algorithms, PureCN and ASCAT, found that the segmentation result of GSA algorithm was more logical. In addition, the results indicated that the GSA algorithm had an excellent predictive effect on tumor purity and ploidy, if the tumor purity was more than 20%. Furtherly, this study evaluated the HRD scores and BRCA1/2 deficiency status of 195 clinical samples, and the results indicated that the accuracy was 0.98 (comparing with Affymetrix OncoScan™ assay) and the sensitivity was 95.2% (comparing with BRCA1/2 deficiency status), both were well-behaved. Finally, HRD scores and 16 genes mutations (TP53 and 15 HRR pathway genes) were analyzed in 17 cell lines, the results showed that there was higher frequency in HRR pathway genes in high HRD score samples. Conclusions This new algorithm, named as GSA, could effectively and accurately calculate the purity and ploidy of tumor samples through NGS data, and then reflect the degree of genomic instability and large-scale copy number variations of tumor samples.

The objective of this study was to investigate the effects of rearing systems on the bone quality parameters in chickens using a metabolomics strategy. A total of 419 male one-day-old chicks were randomly allocated to two groups, a floor rearing group (FRG, n = 173) and a cage rearing group (CRG, n = 246). At 6, 8, 10, and 12 weeks of age, all chickens were radiographed by a digital X-ray machine, and body weight was recorded. At 12 weeks of age, 12 birds were selected from each group to obtain tibia and femur, and bone quality parameters of bone mineral density (BMD), mineral content (BMC), breaking strength (BBS), stiffness, Young’s modulus (YM), ash content, calcium content, and phosphorus content were determined. An untargeted metabolomics assay was performed to identify changes in the serum metabolic profile (n = 8 birds/group). The results showed that cage-reared chickens had wider tibiae and greater body weight compared with floor-reared chickens. There were no significant differences in BMC or BBS between the two groups (p > 0.05), but BMD, ash content, calcium content, and phosphorus content of the tibia and femur of FRG were significantly higher than those of CRG (p < 0.05). Greater stiffness and YM of the femur were also observed in birds raised in the FRG compared with those raised in the CRG (p < 0.05). Taken together, the results suggest that rearing systems affected bone quality parameters. Furthermore, 148 and 149 differential metabolites were identified in positive and negative ion modes by LC-MS/MS analysis, among which 257 metabolites were significantly correlated with 16 bone quality parameters, including leucine, myristoleic acid, glycocholic acid, and N-phenylacetamide. KEGG analysis indicated that 15 metabolic pathways, including six pathways of amino acid metabolism, two pathways of lipid metabolism, and two pathways of carbohydrate metabolism, were responsible for bone quality. Overall, the present study demonstrated the effect of rearing systems on bone quality parameters, and identified several metabolites and metabolic pathways associated with bone quality parameters.

The selection for improved body weight is an effective approach in animal breeding. Guangxi Partridge chickens have differentiated into two lines under selective breeding, which include line S and line D that have shown statistically significant differences in body weight. However, the meat quality analysis in our study indicated that the quality of breast and thigh muscles in line S chickens changed, which included increased values of L*, b*, and drip loss and decreased a* value, pH, and shear force in skeletal muscles. To illuminate the effect of selection on skeletal muscles, LC-MS/MS metabolomics was performed to explore differentiated metabolites in divergent tissues from the two chicken lines. The results of principal component analysis and orthogonal projection to latent structures discriminant analysis suggested that metabolites of different groups were separated, which suggested that selective breeding certainly affected metabolism of skeletal muscles. KEGG analysis identified that valine, leucine, and isoleucine biosynthesis, glycerophospholipid metabolism, and glutathione metabolism noteworthily changed in breast muscle. Amino sugars and nucleotide sugar metabolism, ascorbate and aldarate metabolism, the pentose phosphate pathway, pentose and glucuronate interconversions, fructose and mannose metabolism, and glycerophospholipid metabolism were remarkedly identified in thigh muscle. These screened pathways suggested oxidative stress in breast and thigh muscles, which corresponded with our previous results. Therefore, this study determined that glycerophospholipid metabolism conservatively functioned in muscle flavor and development but exhibited different anti-oxidative patterns in different skeletal muscles. Overall, the present study identified several differentiated metabolites and pathways for exploring differences in meat quality between different broiler populations.

Abstract ZnO@NiO core–shell heterostructures with high photocatalytic efficiency and reusability were prepared via electrochemical deposition on carbon fiber cloth substrates. Their photocatalytic properties were investigated by measuring the degradation of rhodamine B and methyl orange (MO) under ultraviolet light irradiation. The photodegradation efficiency of the ZnO@NiO heterostructures toward both dyes was better than those of the pure ZnO nanorods and NiO nanosheets. The higher performance could be attributed to the formation of p–n heterojunction between ZnO and NiO. Especially, the ZnO@NiO heterostructure formed upon deposition of NiO for 10 min degraded 95% of MO under ultraviolet light irradiation for 180 min. The high photodegradation efficiency of the ZnO@NiO heterostructures was also attributed to the high separation efficiency of photogenerated carriers, as confirmed by the higher photocurrent of the ZnO@NiO heterostructures (eightfold) when compared with that of the pure ZnO nanorods. Moreover, the high photodegradation efficiency of the ZnO@NiO heterostructures was maintained over three successive degradation experiments and decreased to 90% after the third cycle.

Abstract CdS decorated CuS structures have been controllably synthesized through a one-pot hydrothermal method. The morphologies and compositions of the as-prepared samples could be concurrently well controlled by simply tuning the amount of CdCl2 and thiourea. Using this strategy, the morphology of the products experienced from messy to flower-like morphologies with multiple porous densities, together with the phase evolution from pure CuS to the CdS/CuS composites. Serving as a photocatalyst, the samples synthesized with the addition of 1 mmol cadmium chloride and 3 mmol thiourea during synthetic process, showed the best photocatalytic activity, which could reach a maximum photocatalytic efficiency of 93% for methyl orange (MO) photodegradation after 150 min. The possible mechanism for the high photocatalytic efficiency of the sample was proposed by investigating the composition, surface area, structure, and morphology before and after photocatalytic reaction.

As one of the model organisms of Parkinson’s disease (PD) research, the zebrafish has its advantages, such as the 87% homology with human genome and transparent embryos which make it possible to observe the development of dopaminergic neurons in real time. However, there is no midbrain dopaminergic system in zebrafish when compared with mammals, and the location and projection of the dopaminergic neurons is seldom reported. In this study, Vmat2:GFP transgenic zebrafish was used to observe the development and distribution of dopaminergic neurons in real time. We found that diencephalon (DC) 2 and DC4 neuronal populations were detected at 24 hpf. All DC neuronal populations as well as those in locus coeruleus, raphe nuclei and telencephalon were detected at 48 hpf. Axons were detected at 72 hpf. At 96 hpf, all the neuronal populations were detected. For the first time we reported axons from the posterior tubercle of ventral diencephalon projected to subpallium in vivo. However, when compared with results from whole mount tyrosine hydroxylase immunofluorescence staining in wild type zebrafish, we found that DC2 and DC4 neuronal populations were mainly dopaminergic, while DC1, DC3, DC5 and DC6 might not. Neurons in pretectum and telencephalon were mainly dopaminergic, while neurons in locus coeruleus and raphe nuclei might be noradrenergic. Our study makes some corrections and modifications on the development, localization and distribution of zebrafish dopaminergic neurons, and provides some experimental evidences for the construction of the zebrafish PD model.

Supplier selection is an important decision-making link in bidding activity. When overall scores of several suppliers are similar, it is hard to obtain an accurate ranking of these suppliers. Applying the Diversity Factors Method (Diversity Factors Method, DFM) may lead to over correction of weights, which would degrade the capability of indexes to reflect the importance. A Limited Diversity Factors Method (Limited Diversity Factors Method, LDFM) based on entropy is presented in this paper in order to adjust the weights, in order to relieve the over correction in DFM and to improve the capability of identification of indexes in supplier selection. An example of salvage ship bidding demonstrates the advantages of the LDFM, in which the raking of overall scores of suppliers is more accurate.

Zearalenone (ZEA) is an estrogenic toxin produced by Fusarium species, which is widely distributed and posed a great health risk to both humans and farm animals. Reproductive disorders associated with ZEA such as premature puberty, infertility and abortion have plagued the animal husbandry, but the molecular mechanism is unclear. Because transforming growth factor-β1 (TGF-β1) signaling pathway is involved in the proliferation and apoptosis of cells, proliferating cell nuclear antigen (PCNA), B-cell lymphoma/leukemia-2 (BCL-2) and BCL-2 associated X protein (BAX) that all play indispensable roles in the normal development of the uterus, it is hypothesized that ZEA induces reproductive disorders is closely related to the expression of these genes. The objective of this study was to assess the effects of dietary ZEA at the concentrations of 0.5 to 1.5 mg/kg on the mRNA and protein expression of these genes in the uteri of post-weaning gilts and to explore the possible molecular mechanism. Forty healthy post-weaning female piglets (Duroc × Landrace × Large White) aged 38 d were randomly allocated to basal diet supplemented with 0 (Control), 0.5 (ZEA0.5), 1.0 (ZEA1.0), or 1.5 (ZEA1.5) mg/kg purified ZEA, and fed for 35 d. Piglets were euthanized at the end of the experiment and samples were taken and subjected to immunohistochemistry, qRT-PCR and Western blot analyses. The relative mRNA expressions of PCNA, BCL-2 and Smad3 in the uteri of post-weaning gilts increased linearly (p < 0.05) and quadratically (p < 0.05) as ZEA concentration increased in the diet. The relative protein expressions of PCNA, BAX, BCL-2, TGF-β1, Smad3, and phosphorylated Smad3 (p-Smad3) in the uteri of post-weaning gilts increased linearly (p < 0.05) and quadratically (p < 0.001) with an increasing level of ZEA. The results showed that uterine cells in the ZEA (0.5–1.5 mg/kg) treatments were in a high proliferation state, indicating that ZEA could accelerate the proliferation of uteri and promote the development of the uteri. At the same time, the results suggested that ZEA activates the TGF-β1/Smad3 signaling pathway, suggesting it plays an important role in accelerating the development of the uterus.