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With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Soil phosphorus (P) can be divided into inorganic P (Pi) and organic P (Po). Microorganisms play essential roles in soil P transformation. However, there are many ways to detect P transformation, and the relationship between P forms and microorganisms under long-term fertilization is largely unclear. In this study, soil P forms were analyzed by a chemical sequential fractionation method and solution 31P nuclear magnetic resonance (31P-NMR) technique. Phospholipid fatty acid (PLFA) contents were measured by gas chromatography as the characterization of soil microbial community structures. The objective was to determine the changes of soil P forms and associated microbial community composition in mollisol with long-term fertilization. We sampled soil from a field experiment with 26-year-old continuous maize (Zea mays L.) cropping in Northeastern China. Three fertilization treatments were selected as chemical fertilization (NPK), NPK with crop straw (NPKS), and NPK with manure (NPKM). As shown in 31P-NMR spectra, orthophosphate accounted for 62.8–85.8% of total extract P. Comparison to NPK and NPKS treatments, NPKM application notably increased the concentrations of Po, Olsen-P, orthophosphate, orthophosphate monoester, and total P. Soil P fractions including resin-Pi, NaHCO3-P, NaOH-P, and HCl-P, especially Pi fractions, were enhanced by NPKM. The amounts of total PLFAs and PLFAs in bacteria, Gram-positive (G+) and Gram-negative (G−) bacteria, actinomycetes, and fungi were high in NPKM-treated soil. The percentages of PLFAs in bacteria and fungi in total soil PLFAs were 56.8% and 9.7%, respectively, which did not show any significant difference among the treatments. NPKM increased the proportions (%) of PLFAs in G+ bacteria, and NPKS increased the proportions (%) of G− bacteria in total PLFAs. The composition of soil microbial community was found to be significantly affected by soil total carbon and pH. There was a close relationship between HCl-Pi, NaHCO3-Po, orthophosphate, and pyrophosphate with anaerobe, aerobes, and G+. Manure addition directly increased soil available P concentrations, and indirectly acted through the alterations of anaerobe, aerobes, and G+. It is concluded that long-term NPKM application would lead to the accumulation of labile P and moderately labile P in mollisol through the activity of soil microbes.

In this paper, the mineralogical composition, concentrations, distribution, and modes of occurrence of the trace elements in coal from the Anjialing coal seam 9 in the Pingshuo mining district, Ningwu coalfield, were studied using optical microscopy, X-ray powder diffraction (XRD), inductively coupled plasma optical emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and sequential chemical extraction procedures (SCEPs). The identified minerals included mainly kaolinite, boehmite, pyrite, calcite, quartz, and muscovite. Compared to other hard coal from around the world, the coal from seam 9 was enriched with lithium (Li); slightly enriched with gallium (Ga), hafnium (Hf), zirconium (Zr), and mercury (Hg); typically enriched with lead (Pb), and depleted in arsenic (As). The results of the SCEPs analysis showed that Li, Ga, Zr, and Hf were mainly associated with clay minerals. Arsenic mainly occurred in its silicate and sulfide forms in pyrite and Pb was mainly associated with aluminosilicate, sulfide, and carbonate minerals.

The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.

In the field of industrial wastewater treatment, membrane separation technology, as an emerging separation technology, compared with traditional separation technology such as precipitation, adsorption, and ion exchange, has advantages in separation efficiency, low energy consumption, low cost, simple operation, and no secondary pollution. The application has been expanding in recent years, but membrane fouling and other problems have seriously restricted the development of membrane technology. Natural cellulose is one of the most abundant resources in nature. In addition, nanocellulose has characteristics of high strength and specific surface area, surface activity groups, as well as being pollution-free and renewable, giving it a very wide development prospect in many fields, including membrane separation technology. This paper reviews the current status of nanocellulose filtration membrane, combs the widespread types of nanocellulose and its derivatives, and summarizes the current application of cellulose in membrane separation. In addition, for the purpose of nanocellulose filtration membrane in wastewater treatment, nanocellulose membranes are divided into two categories according to the role in filtration membrane: the application of nanocellulose as membrane matrix material and as a modified additive in composite membrane in wastewater treatment. Finally, the advantages and disadvantages of inorganic ceramic filtrations and nanocellulose filtrations are compared, and the application trend of nanocellulose in the filtration membrane direction is summarized and discussed.

Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.

Buthus martensii Karsch (BmK), is a kind of traditional Chinese medicine, which has been used for a long history for the treatment of many diseases, such as inflammation, pain and cancer. In this study, DKK-SP1/2/3 genes were screened and extracted from the cDNA library of BmK. The DKK-SP1/2/3 were expressed by using plasmid pSYPU-1b in E. coli BL21, and recombinant proteins were obtained by column chromatography. In the xylene-induced mouse ear swelling and carrageenan-induced rat paw swelling model, DKK-SP1 exerted a significant anti-inflammatory effect by inhibiting the expression of Nav1.8 channel. Meanwhile, the release of pro-inflammatory cytokines (COX-2, IL-6) was decreased significantly and the release of anti-inflammatory cytokines (IL-10) were elevated significantly. Moreover, DKK-SP1 could significantly decrease the Nav1.8 current in acutely isolated rat DRG neurons. In the acetic acid-writhing and ION-CCI model, DKK-SP2 displayed significant analgesic activity by inhibiting the expression of the Nav1.7 channel. Moreover, DKK-SP2 could significantly inhibit the Nav1.7 current in the hNav1.7-CHO cells.

In order to determine the structural characterization of aluminosilicates in two types of fly ashes, two samples from Shanxi Province, China were selected for study. One was from a pulverized coal boiler (FA-1), and the other from a circulating fluidized bed boiler (FA-2). FA-1 had a much higher content of silicon dioxide (SiO2) (70.30%) than FA-2(42.19%), but aluminum oxide (Al2O3) was higher in FA-2 (25.41%) than in FA-1 (17.04%). The characterizations were investigated using various methods including X-ray diffraction (XRD), Fourier transform infrared spectrometry (FTIR), magic angle spinning nuclear magnetic resonance (MAS−NMR) spectrometry, and X-ray photoelectron spectroscopy (XPS). The XRD analysis showed that FA-1 contained aluminosilicate glass, quartz and mullite, while FA-2 contained significant amounts of amorphous aluminosilicate, quartz and gypsum. The FTIR results showed an increased substitution of Al3+ for Si4+ as the band of asymmetric stretching vibrations Si−O(Si) (1100 cm−1) moved to 1090 cm−1 for FA-2, much lower than for FA-1(1097 cm−1). Moreover, the sharpness of the bands in the 1250−1000 cm−1 region for FA-2 indicates that the silicate structure of FA-2 was more ordered than for FA-1. It can be understood from the 29Si MAS−NMR results that Q4(mAl) (Q4 are connected via 4 bridging O atoms to mAl) is the main structural type in FA-1 and FA-2, and that FA-2 contains more Al, which substitutes for Si in the Q4 structure. 27Al MAS−NMR demonstrated that a combination of tetrahedral, pentahedral, and octahedral Al existed in FA-1 and FA-2. The Si 2p XPS spectra suggested that there were three forms of Si, including bridging Si (Si−O2), non−bridging Si (Si−O), and SiO2 gel. The content of Si−O2 for FA-1 was 37.48% higher than Si−O (28.57%), while the content of Si−O2 was 30.21% lower than Si−O (40.15%) for FA-2. The Al 2p XPS spectra showed that octahedral Al was the dominant form for FA-1 with a content of 40.25%, while the main phase was tetrahedral Al for FA-2 with a proportion of 37.36%, which corresponds well with the 27Al MAS−NMR results.

Deciphering the code of cis-regulatory element (CRE) is one of the core issues of current biology. As an important category of CRE, enhancers play crucial roles in gene transcriptional regulations in a distant manner. Further, the disruption of an enhancer can cause abnormal transcription and, thus, trigger human diseases, which means that its accurate identification is currently of broad interest. Here, we introduce an innovative concept, i.e., abelian complexity function (ACF), which is a more complex extension of the classic subword complexity function, for a new coding of DNA sequences. After feature selection by an upper bound estimation and integration with DNA composition features, we developed an enhancer prediction model with hybrid abelian complexity features (HACF). Compared with existing methods, HACF shows consistently superior performance on three sources of enhancer datasets. We tested the generalization ability of HACF by scanning human chromosome 22 to validate previously reported super-enhancers. Meanwhile, we identified novel candidate enhancers which have supports from enhancer-related ENCODE ChIP-seq signals. In summary, HACF improves current enhancer prediction and may be beneficial for further prioritization of functional noncoding variants.

A series of novel spirooxindole-O-naphthoquinone-tetrazolo[1,5-a]pyrimidine hybrids were designed, synthesized and evaluated as potent antitumor agents. These hybrids exhibited relatively high cytotoxic activity against cancer cell line HepG2 (IC50 = 2.86–36.34 μM), while normal cell line LO2 was less sensitive to these hybrids (IC50 = 36.37–248.39 μM). On the whole, among all the compounds tested, compound 4e, with a mean IC50 value of 2.86 μM, was the most active. The novel hybrids may find their pharmaceutical applications after further investigations.