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Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys.

The genus Gymnopus plays a significant role in ecological systems, with certain species holding potential as food or medicinal resources. However, the species diversity of Gymnopus in China remains unclear. In recent years, more than one thousand Gymnopus specimens have been collected across China. Thus, through the integration of ecological evidence, detailed morphological studies, and phylogenetic analysis using a multiloci dataset of ITS + nLSU + tef1-ɑ, four new species—Gymnopus longistipes, Gymnopus striatipileatus, Gymnopus viridiscus, and Gymnopus spadiceus—have been differentiated from known species. Gymnopus similis has been newly documented from Jiangxi Province, China. Detailed descriptions and vivid illustrations have been provided based on the newly collected specimens, along with comparisons to closely related species. Additionally, a key to the reported species of Gymnopus s.l. from East China has been included.

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method.

Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach.

In the context of efforts aimed at reducing carbon emissions, the utilization of recycled aggregate soil mixes for soil stabilization has garnered considerable interest. This study examines the mechanical properties of mixed soil samples, varying by dosage of a soft soil curing agent C, recycled aggregate R content, and curing duration. Mechanical evaluations were conducted using unconfined compressive strength tests (UCS), field emission scanning electron microscopy (FESEM), and laser diffraction particle size meter tests (PSD). The results indicate that the strength of the mixed soil samples first increases and then decreases with higher dosages of recycled aggregate, reaching optimal strength at a 20% dosage. Similarly, an increase in curing agent dosage enhances the strength, peaking at 20%. The maximum strength of the mixed soils is achieved at 28 days under various proportions. The introduction of the curing agent leads to the formation of a flocculent structure, as observed in FESEM, which contributes to the enhanced strength of the soil mixes. Specimens prepared with a combination of 20% R and 20% C, maintained at a constant moisture content of 20%, and cured for 28 days exhibit a balance between economic, environmental, and engineering performance.

The impact of coronavirus disease 2019 (COVID-19) history on Crohn’s disease (CD) is unknown. This investigation aimed to examine the effect of COVID-19 history on the disease course, oral-gut microbiota, and serum metabolomics in patients with CD. In this study, oral-gut microbiota and serum metabolomic profiles in 30 patients with CD and a history of mild COVID-19 (positive group, PG), 30 patients with CD without COVID-19 history (negative group, NG), and 60 healthy controls (HC) were assessed using 16S rDNA sequencing and targeted metabolomics. During follow-up, the CD activity index showed a stronger decrease in the PG than in the NG (p = 0.0496). PG patients demonstrated higher α-diversity and distinct β-diversity clustering in both salivary and fecal microbiota compared to NG and HC individuals. Notably, the gut microbiota composition in the PG patients showed a significantly greater similarity to that of HC than NG individuals. The interaction between oral and intestinal microbiota in the PG was reduced. Moreover, serum metabolome analysis revealed significantly increased anti-inflammatory metabolites, including short-chain fatty acids and N-Acetylserotonin, among PG patients; meanwhile, inflammation-related metabolites such as arachidonic acid were significantly reduced in this group. Our data suggest that the gut microbiota mediates a potential beneficial effect of a mild COVID-19 history in CD patients.

In this paper, the freezing and strengthening project of the Sanya estuary tunnel is analyzed, which is facilitated by the use of the partial differential equation (PDE) module in COMSOL Multiphysics software. The solid–liquid ratio is utilized as the water–heat coupling term, and the solid mechanics module is introduced to achieve three-field coupling. Numerical simulations are conducted to study changes in the temperature field, moisture field, and vertical displacement due to freezing and expansion in the most unfavorable soil layer during the freezing process. The results indicate that a complete freezing curtain forms around the 30th day. The distribution of freezing pipes significantly influences the freezing effect. The strong freezing zone is characterized by a high cooling rate and rapid water content reduction with the opposite trends being observed in the weak freezing zone. Upon completion of the freezing process, a large uplift of the ground surface is observed with more pronounced vertical displacement changes in areas affected by temperature and phase changes. The maximum vertical displacement of the ground surface deviates from the center position. While the frozen soil curtain meets the design requirements for freezing, the effects of freezing and expansion should be taken into account. These findings could be instrumental in elaborating the most effective freezing and expansion control measures for areas with powdery clay-based layers in AGF-based projects.

The gastrointestinal (GI) tract is an organ actively involved in mechanical processes, where it detects forces via a mechanosensation mechanism. Mechanosensation relies on specialized cells termed mechanoreceptors, which convert mechanical forces into electrochemical signals via mechanosensors. The mechanosensitive Piezo1 and Piezo2 are widely expressed in various mechanosensitive cells that respond to GI mechanical forces by altering transmembrane ionic currents, such as epithelial cells, enterochromaffin cells, and intrinsic and extrinsic enteric neurons. This review highlights recent research advances on mechanosensitive Piezo channels in GI physiology and pathology. Specifically, the latest insights on the role of Piezo channels in the intestinal barrier, GI motility, and intestinal mechanosensation are summarized. Additionally, an overview of Piezo channels in the pathogenesis of GI disorders, including irritable bowel syndrome, inflammatory bowel disease, and GI cancers, is provided. Overall, the presence of mechanosensitive Piezo channels offers a promising new perspective for the treatment of various GI disorders.

This paper takes the freezing project of the North Arch Tunnel of the Hong Kong–Zhuhai–Macao Bridge as an example. Based on Darcy’s law and the theory of heat transfer in porous media, using the coupled module of the temperature field and seepage field in the COMSOL Multiphysics software, numerical simulations of the freezing reinforcement of the new pipe curtain freezing method are conducted to study the influence of different factors on this method under seepage conditions. The research shows that an increase in the groundwater flow velocity will affect the development of frozen soil curtains, prolonging the formation time of frozen soil curtains. A rise in the initial ground temperature will increase the time required for the formation of frozen soil curtains during the freezing process, resulting in a slight increase in the temperature of the final frozen soil curtains. With an increase in the salinity of the groundwater, the temperature at the temperature measurement point upstream of the freezing pipe increases, while the temperature at the temperature measurement point downstream of the freezing pipe decreases. The average temperature of the frozen soil curtain also increases with an increase in the salinity of the groundwater. This study is expected to provide a valuable reference for similar projects in the future.

Fibroblasts, among the most prevalent and widely distributed cell types in the human body, play a crucial role in defining tissue structure. They do this by depositing and remodeling extracellular matrixes and organizing functional tissue networks, which are essential for tissue homeostasis and various human diseases. Pulmonary hypertension (PH) is a devastating syndrome with high mortality, characterized by remodeling of the pulmonary vasculature and significant cellular and structural changes within the intima, media, and adventitia layers. Most research on PH has focused on alterations in the intima (endothelial cells) and media (smooth muscle cells). However, research over the past decade has provided strong evidence of the critical role played by pulmonary artery adventitial fibroblasts in PH. These fibroblasts exhibit the earliest, most dramatic, and most sustained proliferative, apoptosis-resistant, and inflammatory responses to vascular stress. This review examines the aberrant phenotypes of PH fibroblasts and their role in the pathogenesis of PH, discusses potential molecular signaling pathways underlying these activated phenotypes, and highlights areas of research that merit further study to identify promising targets for the prevention and treatment of PH.

Currently, coastal sandy soils face issues such as insufficient foundation strength, which has become one of the crucial factors constraining urban development. Geotechnical engineering, as a traditional discipline, breaks down disciplinary barriers, promotes interdisciplinary integration, and realizes the green ecological and low-carbon development of geotechnical engineering, which is highly important. Based on the “dual carbon” concept advocating a green and environmentally friendly lifestyle, Bacillus spores were utilized to induce calcium carbonate precipitation technology (MICP) to solidify coastal sandy soils, leveraging the rough-surface and low-permeability characteristics of silty soil. The mechanical-strength variations in the samples were explored through experiments, such as calcium carbonate generation rate tests, non-consolidated undrained triaxial shear tests, and scanning electron microscopy (SEM) experiments, to investigate the MICP solidification mechanism. The results indicate that by incorporating silty soil into sandy soil for MICP solidification, the calcium carbonate generation rates of the samples were significantly increased. With the increase in the silty-soil content, the enhancement range was 0.58–3.62%, with the maximum calcium carbonate generation rate occurring at a 5% content level. As the silty-soil content gradually increased from 1% to 5%, the peak deviator stress increased by 4.2–43.2%, enhancing the sample shear strength. Furthermore, the relationship between the internal-friction angle, cohesion, and shear strength further validates the enhancement of the shear strength. Silty soil plays roles in adsorption and physical filling during the MICP solidification process, reducing the inter-particle pores in sandy soil, increasing the compactness, providing adsorption sites, and enhancing the calcium carbonate generation rate, thereby improving the shear strength. The research findings can provide guidance for reinforcing poor coastal sandy-soil foundations in various regions.

Groundwater resources are crucial to socio-economic development and the ecosystem, and over-extraction can cause the groundwater level to drop, deplete reserves, and trigger geological hazards like land subsidence. The North China Plain (NCP) has experienced both subsidence and groundwater depletion due to over-extraction in the past 70 years. In this study, we used MT-InSAR technology and ascending C-band Sentinel-1 SAR data from 2017 to 2023 to study land deformation in the junction area of Shijiazhuang–Baoding–Cangzhou–Hengshui. We identified multiple subsidence funnels with a maximum rate exceeding −150 mm/year and a total deformation surpassing 600 mm. Seasonal decomposition methods accurately separated seasonal signals in the time-series deformation and groundwater level data. An exponential function model applied to long-term deformation showed no significant decrease in subsidence in severely affected areas. By modeling seasonal deformation and seasonal groundwater levels, we determined the elastic skeletal storage coefficients (Ske) to be in the range of 1.02 × 10−3~6.53 × 10−3 in subsidence areas. We obtained the spatiotemporal evolution of the total groundwater storage (TGWS), irreversible ground storage (IGWS), and recoverable ground storage (RGWS). The TGWS and IGWS decreased annually while the RGWS increased, which is attributable to the implementation of the South-to-North Water Diversion Project (SNWDP) and the issuance of groundwater withdrawal policies in the NCP.

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

This paper proposes a short-term forecasting method for high-frequency broadcast Maximum Usable Frequency (MUF) based on Long Short-Term Memory (LSTM) to meet the demand for refined and precise high-frequency broadcast coverage. Based on the existing infrastructure of broadcast and television stations, we established an experimental verification system to collect data for approximately three years. Two links were selected based on data quality: Urumqi to Lhasa and Lanzhou to Lhasa. A short-term forecast of MUF was conducted using the data from these two links. Comparison and analysis were conducted between the forecasting results of our model and those of the REC533 model. Our proposed method outperforms the REC533 forecasting results overall, with a reduction in root mean square error (RMSE) of 0.66 MHz and an improvement in forecast accuracy of 14.77%. The comparison result demonstrates the feasibility and accuracy of our model.

The ozone (O3) variations in southeast China are largely different between mountainous forest areas located inland, and lowland urban areas located near the coast. Here, we selected these two kinds of areas to compare their similarities and differences in surface O3 variability from diurnal to seasonal scales. Our results show that in comparison with the lowland urban areas (coastal areas), the mountainous forest areas (inland areas) are characterized with less human activates, lower precursor emissions, wetter and colder meteorological conditions, and denser vegetation covers. This can lead to lower chemical O3 production and higher O3 deposition rates in the inland areas. The annual mean of 8-h O3 maximum concentrations (MDA8 O3) in the inland areas are ~15 μg·m−3 (i.e. ~15%) lower than that in the coastal areas. The day-to-day variation in surface O3 in the two types of the areas is rather similar, with a correlation coefficient of 0.75 between them, suggesting similar influences on large scales, such as weather patterns, regional O3 transport, and background O3. Over 2016–2020, O3 concentrations in all the areas shows a trend of “rising and then falling”, with a peak in 2017 and 2018. Daily MDA8 O3 correlates with solar radiation most in the coastal areas, while in the inland areas, it is correlated with relative humidity most. Diurnally, during the morning, O3 concentrations in the inland areas increase faster than in the coastal areas in most seasons, mainly due to a faster increase in temperature and decrease in humidity. While in the evening, O3 concentrations decrease faster in the inland areas than in the coastal areas, mostly attributable to a higher titration effect in the inland areas. Seasonally, both areas share a double-peak variation in O3 concentrations, with two peaks in spring and autumn and two valleys in summer and winter. We found that the valley in summer is related to the summer Asian monsoon that induces large-scale convections bringing local O3 upward but blocking inflow of O3 downward, while the one in winter is due to low O3 production. The coastal areas experienced more exceedance days (~30 days per year) than inland areas (~5-10 days per year), with O3 sources largely from the northeast. Overall, the similarities and differences in O3 concentrations between inland and coastal areas in southeastern China are rather unique, reflecting the collective impact of geographic-related meteorology, O3 precursor emissions, and vegetation on surface O3 concentrations.

In this study, molecules of propyl gallate (PG) and polyethylene glycol methacrylate (PEGMA) were covalently bonded via a transesterification reaction and subsequently grafted onto polyvinylidene fluoride substrates using a homogeneous radiation grafting technique. The enhancement of the membranes’ hydrophilicity with the increment of the grafting rate was corroborated by scanning electron microscopy imaging and measurements of the water contact angle. At a grafting degree of 10.1% and after a duration of 4 min, the water contact angle could decrease to as low as 40.1°. Cyclic flux testing demonstrated that the membranes modified in this manner consistently achieved a flux recovery rate exceeding 90% across varying degrees of grafting, indicating robust anti-fouling capabilities. Furthermore, these modified membranes exhibited significant antioxidant ability while maintaining antifouling performance over 30 days. The ability of the modified membranes to scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS+) free radicals remained nearly unchanged after being stored in pure water for 30 days, and the flux recovery rate remained above 95% after immersion in sodium hypochlorite solution for 30 days. Among the tested membranes, the PVDF-g-PEGMAG modified membrane with a grafting degree of 7.2% showed the best antioxidant effect.

Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks.

The postharvest softening and corresponding quality deterioration of blueberry fruits are crucial factors that hinder long-distance sales and long-term storage. Cold plasma (CP) is an effective technology to solve this, but the specific mechanism of delaying fruit softening remains to be revealed. Here, this study found that CP significantly improved blueberry hardness. Physiological analysis showed that CP regulated the dynamic balance of reactive oxygen species (ROS) to maintain hardness by increasing antioxidant content and antioxidant enzyme activity, resulting in a 12.1% decrease in the H2O2 content. Transcriptome analysis revealed that CP inhibited the expression of cell wall degradation-related genes such as the pectin hydrolase gene and cellulase gene, but up-regulated the genes of the ROS-scavenging system. In addition, the resistance genes in the MAPK signaling pathway were also activated by CP in response to fruit ripening and softening and exhibited positive response characteristics. These results indicate that CP can effectively regulate the physiological characteristics of blueberries at a genetic level and delay the softening process, which is of great significance to the storage of blueberries.

APSES (Asm1p, Phd1p, Sok2p, Efg1p, and StuAp) family transcription factors play crucial roles in various biological processes of fungi, however, their functional characterization in phytopathogenic fungi is limited. In this study, we explored the role of SsStuA, a typical APSES transcription factor, in the regulation of cell wall integrity (CWI), sclerotia formation and pathogenicity of Sclerotinia sclerotiorum, which is a globally important plant pathogenic fungus. A deficiency of SsStuA led to abnormal phosphorylation level of SsSmk3, the key gene SsAGM1 for UDP-GlcNAc synthesis was unable to respond to cell wall stress, and decreased tolerance to tebuconazole. In addition, ΔSsStuA was unable to form sclerotia but produced more compound appressoria. Nevertheless, the virulence of ΔSsStuA was significantly reduced due to the deficiency of the invasive hyphal growth and increased susceptibility to hydrogen peroxide. We also revealed that SsStuA could bind to the promoter of catalase family genes which regulate the expression of catalase genes. Furthermore, the level of reactive oxygen species (ROS) accumulation was found to be increased in ΔSsStuA. In summary, SsStuA, as a core transcription factor involved in the CWI pathway and ROS response, is required for vegetative growth, sclerotia formation, fungicide tolerance and the full virulence of S. sclerotiorum.

In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation.

Given the importance of net primary productivity (NPP) as an indicator of ecosystem productivity and its role in the global carbon cycle, understanding the factors that influence its stability can greatly improve our understanding of shrubland ecosystems and their response to global change. Between 2015 and 2022, we visited most of the primary shrubland in China and surveyed 751 shrubland patches. We collected data on community structure (canopy cover), shrub and herb species diversity, and soil physicochemical properties (soil nitrogen content and soil phosphorus content). We also used remote sensing data to obtain NPP from 2001 to 2022. We quantified temporal trends in shrubland stability in China and used structural equation modeling to disentangle the direct and indirect effects of climate, soil, and shrub community structural properties. We found that 85.3% of China’s shrublands remained stable in terms of productivity from 2001 to 2022, 10.6% of the shrublands increased in stability, and 4.1% of the shrublands need to be alerted to a significant decrease in stability. These changes in stability were mainly related to precipitation changes. We found that changes in precipitation directly affected the stability of net productivity and also indirectly through changes in shrub cover and species richness. Some aspects of community structure moderated these effects. We further found that the portion of shrubland with an upward trend in stability did not have a significant correlation between the stability of net primary productivity and community structure and soil N. Instead, soil phosphorus was the largest direct influence.

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R2, respectively, which is better than all the baseline deep learning models.

In this study, methoxypolyethylene glycol acrylate (mPEGA) served as a PEGylated monomer and was grafted onto polyvinylidene fluoride (PVDF) through homogeneous solution gamma irradiation. The grafting process was confirmed using several techniques, including infrared spectroscopy (FTIR), thermodynamic stability assessments, and rotational viscosity measurements. The degree of grafting (DG) was determined via the gravimetric method. By varying the monomer concentration, a range of DGs was achieved in the PVDF-g-mPEGA copolymers. Investigations into water contact angles and scanning electron microscopy (SEM) images indicated a direct correlation between increased hydrophilicity, membrane porosity, and higher DG levels in the PVDF-g-mPEGA membrane. Filtration tests demonstrated that enhanced DGs resulted in more permeable PVDF-g-mPEGA membranes, eliminating the need for pore-forming agents. Antifouling tests revealed that membranes with a lower DG maintained a high flux recovery rate, indicating that the innate properties of PVDF could be largely preserved.

A fast spraying speed, wide working area, and easy operation are the operational advantages of high-clearance boom sprayers. To address the issue of spray boom mechanical vibration affecting the spraying effect, a double-link trapezoidal boom suspension is designed for the 3WPYZ sprayer. This suspension can achieve passive vibration reduction, active balance, and ground profiling. The kinematic model of the boom suspension is established based on D’Alembert’s principle and the principle of multi-body dynamics, and the design factors affecting the stability of the boom are determined. Through orthogonal experimental design and virtual kinematics simulation, the influence of the boom length and orifice diameter of each part on the swing angle and the natural frequency of the boom suspension is investigated. Design-Expert 8.0.6 software is used to analyze and optimize the test results. The optimization results show that, when the connecting boom length LAB is 265 mm, the inner boom suspension boom length LAD is 840 mm, the outer boom suspension boom length LBC is 1250 mm, and the throttle hole diameter d is 4 mm; the maximum swing angle of the boom suspension is reduced by 53.02%. In addition, the natural frequency of the boom is reduced from 1.3143 rad/s to 1.1826 rad/s, and the dynamic characteristic optimization effect is remarkable. The modal analysis results show that the first sixth-order vibration test frequency of the boom sprayer suspension designed in this paper meets the requirements and avoids the influence of external factors. Field tests show that, when the sprayer is excited by the environment at 3.5° to 4°, the boom suspension can reduce the vibration transmitted by the body to a reasonable range. The static analysis shows that the structural design of this study reduces the stress at the connection of the end boom suspension, the maximum displacement, and the maximum stress of the inner boom suspension. The test results of the dynamic characteristics of the implement are basically consistent with the virtual model simulation test results, thus achieving the optimization objectives.

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved.

To counter the harmful impacts of agricultural chemicals on the environment and human health, there is an increasing demand for safe, eco-friendly, and potent plant-based biopesticides. In this study, we aimed to investigate the antimicrobial effects of ginger essential oil and selected volatile compounds (linalool, eugenol, citral, and cinnamaldehyde [CA]) against Fusarium oxysporum FOX-1. Minimum inhibitory concentrations (MICs) were determined using the mycelium growth inhibition method. The compound CA exhibited the most potent antifungal effect against F. oxysporum FOX-1 and was selected for further investigation. After treatment with CA at 1/2 MIC or MIC, the spore germination of F. oxysporum FOX-1 was significantly inhibited at 12 h. Furthermore, microscopic observation revealed that CA treatment resulted in the morphological degradation of F. oxysporum FOX-1. CA destroyed the cell membrane integrity of F. oxysporum FOX-1, increasing the relative conductivity and the leakage of intracellular protein, nucleic acids, and malondialdehyde, affecting the integrity and metabolism of the cell membrane. The effects were positively related to CA concentration. Additionally, in vivo experiments with rhizome sections showed that CA significantly reduced the pathogenicity of F. oxysporum FOX-1. Overall, these findings provide evidence for the potential of using ginger essential oil components as fungicides, offering a basis for future research to develop robust and eco-friendly plant-derived fungicides that serve as a sustainable means to reduce fungus-driven agricultural losses.

Sucrose synthase (SUS) and sucrose phosphate synthase (SPS) are essential in plant sucrose metabolism. The potato is an important crop worldwide, but systematic analyses of the StSUS and StSPS gene families in potatoes are still lacking. Ten sucrose metabolism-related genes were identified in this study. The SUSs and SPSs could each be split into three subgroups through phylogenetic analysis. StSUSIc was the most highly expressed gene in different developmental tissues. Ka/Ks analysis showed that StSUSIb and StSUSIc were subjected to more-significant homozygous selection pressure. Our cis-acting element analysis of the StSUS and StSPS promoter sequences showed four elements: defense- and stress-responsive, hormone-responsive, light-responsive, and transcription factor elements. The expression of StSUS and StSPS genes was found to be regulated by circadian rhythm. In the treatments of 1% to 5% sucrose, glucose, and fructose, the expression of StSUS and StSPS family genes was enhanced by sucrose, but inhibited at high-glucose and fructose concentrations. This study identified six StSUS and four StSPS genes and analyzed their gene structure, conserved motifs, chromosome position, promoter elements, phylogenetic tree, and tissue-specific expression patterns. Our results will motivate more research into the biological process underlying the genes of sucrose metabolism in potatoes.

To make measurement of end-wall flow between blade rows in a compact multistage configuration possible, a miniature L-shaped five-hole probe was employed in this paper. This compact tip structure, realized by laser-printing instead of the conventional machining technique, reduces the blockage effect of this intrusive measurement on the flow and ensures high spatial resolution. The zonal method is introduced to extend the usable flow angle range up to 60 degrees. A local least-squares interpolation technique is utilized to acquire flow angle and static/total pressure. In order to improve accuracy for the points located at the sector boundary, the overlap region method is included in the interpolation. Additional test data indicate that the maximum error in flow angle is nearly within 1 degree, and the maximum errors of total pressure and static pressure are 0.56% and 1.9% respectively. The application in a low-speed multistage axial compressor indicates that the zonal method can decrease the number of points exceeding the measurable flow range and is of great significance for the end-wall flow measurement, especially for the near-stall condition. Compared with the traditional method, the proportion of available data for the near-stall state measurement was increased by 18% by using the zonal method.

Based on the transient-performance calculation model of a dual-spool mixed-flow turbofan engine, this article improves the dynamic algorithm of geometric adjustment mechanisms and establishes a transient-performance calculation model suitable for a three-stream adaptive cycle engine (three-stream ACE). Using this model, the transient characteristics of a three-stream ACE were analyzed. The results indicate that the delay in the area of the fan nozzle significantly reduces the surge margin of the front fan during deceleration, while the delay in the angle of the front-fan and aft-fan guide vanes significantly reduces the surge margin of the front fan during acceleration, therefore becoming a limitation of the transient performance of the engine. At the same time, to meet the demand for equal-thrust mode switching, this article also proposes a mode-switching control scheme that solves the problem of engine state oscillation during the mode-conversion process and achieves a smooth conversion with thrust fluctuations within 1%. The research results of this article can guide the optimization design of three-stream ACE transition-state control laws and the design of control system architecture, which has important engineering significance.

Soil quality is a very important indicator used to assess ecosystem restoration states in terms of vegetation recovery and establishment. Evaluating the soil quality of different vegetation restoration types in mountainous areas of Beijing and identifying their influencing factors would provide a scientific basis and be helpful for vegetation restoration in the future. Six vegetation types (or communities), including Platycladus orientalis (L.) Franco pure forest (POP), Pinus tabulaeformis Carr. pure forest (PTP), Platycladus orientalis–Pinus tabulaeformis mixed forest (PPM), Platycladus orientalis coniferous and broadleaved mixed forest (POCB), Pinus tabulaeformis coniferous and broadleaved mixed forest (PTCB), deciduous broadleaved mixed forest (DBMF), and one area of non-afforested land (NF), with similar stand conditions were selected and fourteen factors of soil physical and chemical characteristics were measured and used to establish a total data set (TDS), while a minimum data set (MDS) was obtained by using the principal component analysis (PCA) and Pearson correlation analysis methods. Two scoring methods, linear (L) and non-linear (NL), were used to calculate the soil quality index (SQI), and the key factors influencing soil quality by vegetation were identified by a general linear model (GLM), PCA, and correlation analysis. The results showed that: (1) The screened MDS indicators which showed good relationships with the SQIs in the study areas were total nitrogen (TN), sand content, total potassium (TK), pH, and available water capacity (AWC). The SQI–NLM method has better applicability. (2) The contribution rates of vegetation to different soil factors accounted for 28.644% (TN), 21.398% (sand content), 24.551% (TK), 16.075% (pH), and 9.332% (AWC). (3) TN showed a positive relationship with all vegetation types; the content of TN in PTCB and DBMF was obviously larger than in the other types in the 0–10 cm layer; PPM, PTCB, and POCB affected the sand content, which showed negative correlativity; and DBMF showed positive correlativity with AWC. The mechanism of how different species affect TN, sand content, and AWC should be focused on and taken into consideration in further studies.

Microwave medical sensing and imaging (MMSI) is a highly active research field. In MMSI, electromagnetic inverse scattering (EIS) is a commonly used technique that infers the internal characteristics of the diseased area by measuring the scattered field. It is worth noting that the image formed by EIS often exhibits the super-resolution phenomenon, which has attracted much research interest over the past decade. A classical perspective is that multiple scattering leads to super-resolution, but this is subject to debate. This paper aims to analyze the super-resolution behavior for Born-iterative-type algorithms for the following three aspects. Firstly, the resolution defined by the traditional Rayleigh criterion can only be applied to point scatterers. It does not suit general scatterers. By using the Sparrow criterion and the generalized spread function, the super-resolution condition can be derived for general scatterers even under the Born approximation (BA) condition. Secondly, an iterative algorithm results in larger coefficients in the high-frequency regime of the optical transfer function compared to non-iterative BA. Due to the anti-apodization effect, the spread function of the iterative method becomes steeper, which leads to a better resolution following the definition of the Sparrow criterion mentioned above. Thirdly, the solution from the previous iteration, as the prior knowledge for the next iteration, will cause changes in the total field, which provides additional information outside the Ewald sphere and thereby gives rise to super-resolution. Comprehensive numerical examples are used to verify these viewpoints.

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.

In 2013, an epidemic of falciparum malaria involving over 820 persons unexpectedly broke out in Shanglin County, Guangxi Zhuang Autonomous Region, China, after a large number of migrant workers returned from Ghana, where they worked as gold miners. Herein, we selected 146 isolates randomly collected from these patients to investigate the resistance characteristics of the parasite to sulfadoxine–pyrimethamine (SP) by screening mutations in the dhfr and dhps genes. All 146 isolates were successfully genotyped for dhps, and only 137 samples were successfully genotyped for dhfr. In the dhfr gene, point mutations occurred at three codons: 51 (83.2%, 114/137), 59 (94.9%, 130/137), and 108 (96.4%, 132/137). In the dhps gene, mutations occurred at four codons: 436 (36.3%, 53/146 for S436A, 0.7%, 1/146 for S436Y), 437 (95.2%, 139/146), 540 (3.4%, 5/146), and 613 (2.7%, 4/146). All 146 isolates had mutations in at least one codon, either within dhfr or dhps. Quadruple mutation I51R59N108/G437 (41.1%, 60/146) of partial or low resistance level was the most prevalent haplotype combination. Quintuple I51R59N108/G437E540 accounted for 2.1% (3/146). Sextuple I51R59N108/A436G437S613 was also found and accounted for 1.4% (2/146). A chronological assay incorporating two sets of resistance data from the studies of Duah and Amenga-Etego provided an overview of the resistance trend from 2003 to 2018. During this period, the results we obtained generally coincided with the total development tendency of SP resistance. It can be concluded that Plasmodium falciparum samples collected from Chinese migrant workers from Ghana presented prevalent but relatively partial or low resistance to SP. A chronological assay incorporating two sets of data around 2013 indicates that our results possibly reflect the SP resistance level of Ghana in 2013 and that the possibility of increased resistance exists. Therefore, reasonable drug use and management should be strengthened while also maintaining a continuous screening of resistance to SP. These findings also underscore the need to strengthen the prevention of malaria importation from overseas and focus on preventing its reintroduction and transmission in China.

Ship classification using the synthetic aperture radar (SAR) images has a significant role in remote sensing applications. Aiming at the problems of excessive model parameters numbers and high energy consumption in the traditional deep learning methods for the SAR ship classification, this paper provides an energy-efficient SAR ship classification paradigm that combines spiking neural networks (SNNs) with Siamese network architecture, for the first time in the field of SAR ship classification, which is called the Siam-SpikingShipCLSNet. It combines the advantage of SNNs in energy consumption and the advantage of the idea in performances that use the Siamese neuron network to fuse the features from dual-polarized SAR images. Additionally, we migrated the feature fusion strategy from CNN-based Siamese neural networks to the SNN domain and analyzed the effects of various spiking feature fusion methods on the Siamese SNN. Finally, an end-to-end error backpropagation optimization method based on the surrogate gradient has been adopted to train this model. Experimental results tested on the OpenSARShip2.0 dataset have demonstrated the correctness and effectiveness of the proposed SAR ship classification strategy, which has the advantages of the higher accuracy, fewer parameters and lower energy consumption compared with the mainstream deep learning method of the SAR ship classification.

Using the temperature and seepage field-coupling module within COMSOL Multiphysics software, we examined freezing behavior and its evolving patterns in curved underground freezing pipes. This study employed transient states, with the Darcy’s law and porous-media heat-transfer options activated in the Physical Field Interface of the Physical Field and Variable Selection column. The models were created to establish numerical models of freezing reinforcement for both single and multiple pipes with various curvatures. These models were designed to simulate the evolving temperature and seepage fields of soil under diverse freezing conditions. Subsequently, this research utilized the models to simulate the freezing and consolidation conditions of a shallowly buried tunnel within the context of shallow tunnel conditions. The study reveals that after freezing a single pipe using water flow, the change in thickness of the frozen wall in curved pipes is notably smaller than that in straight pipes. This difference is particularly pronounced in the upstream section. Specifically, at a distance of −2000 mm from the main surface, the change in thickness of the frozen wall in straight pipes exceeds that in s = 7 curved pipes by approximately 350 mm. The smaller the long arc ratio s, the greater the arc of the freezing tube and the better the water-blocking effect. In the multi-pipe freezing model, the s = 7 curved pipes exhibit a frozen-wall thickness approximately 120 mm greater than that of straight pipes at a distance of −2000 mm from the main surface. Under the condition of a shallow buried concealed excavation with surging water, a pipe with a long arc ratio s = 7 arc freezing at 46 d attains a permafrost curtain thickness that is equivalent to that achieved by the straight pipe freezing at 58 d. This reduction in thickness shortens the working period by 12 days, resulting in a more efficient process. The successful application of the freezing method in the water-rich aquifer is expected to be a valuable reference for similar projects in the future.

Bridges have a special place in transportation infrastructures and road networks due to their direct relationship with other places. These structures have the purpose of maintaining the traffic loads of the highway, crossing any obstacle, and performing effective communication between two destinations. Costs associated with bridge maintenance continue to be expensive due to their widespread use and stringent inspection requirements. Many researchers have been working on methods to use machine-learning (ML) techniques to forecast specific situations rather than physically checking bridges as part of the maintenance process in recent years. The practical value of the models has, however, been severely constrained by issues such relatively poor model evaluation results, unstable model performances, and the ambiguous application of established models in real-world scenarios. This work showed a thorough method of bridge condition prediction model building from feature engineering to model evaluation, along with a clear procedure of applying the produced model to actual usage, using data from the United States National Bridge Inventory (NBI) and the Adaboost algorithm. Multiple ML model assessment metrics’ findings revealed that the given model outperformed the majority of earlier studies in terms of values and stability. The case study demonstrated that there is a 30% reduction in the number of bridges that need to be inspected. This study serves as a crucial resource for the practical application of ML approaches in the forecast of the status of civil infrastructure. Additionally, it shows that boosted ML models may be a superior option as modeling algorithms advance. To explore the main influencing aspects of bridge conditions, a predictor importance analysis is also offered.

The prevalence of schistosomiasis japonica in China is now characterized by a low epidemic rate and low-intensity infections. Some diagnostic methods with high sensitivity and specificity are urgently needed to better monitor this disease in the current situation. In this study, the detection efficacy of a real-time fluorescent quantitative PCR (qPCR) assay was assessed for schistosomiasis japonica in mice, and before and after treatment with praziquantel (PZQ). Our results showed that the sensitivity of the qPCR was 99.3% (152/153, 95% CI: 96.41–99.98%) and its specificity was 100% (77/77, 95% CI: 95.32–100%) in mice infected with different numbers of Schistosoma japonicum. After the oral administration of PZQ, mice infected with 10 cercariae or 40 cercariae were all Schistosoma japonicum-negative 6 weeks after treatment. However, the negativity rates on a soluble egg antigen (SEA)-based enzyme-linked immunosorbent assay (ELISA) were only 34.8% (8/23, 10 cercariae group) and 6.7% (1/15, 40 cercariae group) at the sixth week after PZQ treatment. These results demonstrated that the qPCR method had good sensitivity and specificity, and suggested that its sensitivity correlated with the infection intensity in mice. Moreover, this method had better potential utility for evaluating the treatment efficacy of PZQ in schistosome-infected mice than SEA-based ELISA.

In the field of shield tunnels, the occurrence of uneven longitudinal settlement in segment linings has presented persistent challenges, including heightened risks of localized damage and water leakage. While the adoption of a secondary lining has been proposed as a viable solution to these issues, the question of how to select an appropriate construction time for the secondary lining, one that enables it to fully harness its load-bearing capacity while optimizing the tunnel’s overall stress and deformation characteristics, continues to be a pressing concern. To address this issue, this study established a three-dimensional longitudinal refined numerical model of double-layer-lined shield tunnel. In addition, the deformation degree of the segment lining was used as a time indicator to define the construction time for the secondary lining. Subsequently, an analysis of the impact of the construction time of the secondary lining on the longitudinal mechanical properties of the double-layer-lined shield tunnel is conducted through an assessment of tunnel longitudinal deformation and structural stress. The research findings indicated that the construction of the secondary lining improved the longitudinal deformation resistance of shield tunnels. Simultaneously, it led to a significant increase in the longitudinal shear forces within the segment lining and a notable reduction in longitudinal bending moments. Moreover, the construction time of the secondary lining played a pivotal role in these alterations. Considering the longitudinal force situations and load-bearing characteristics of the double-layer lining structure, it was determined that the optimal construction time for the secondary lining fell within the range of 20% to 40% of the total construction duration. In this scenario, the deformation and internal forces within the segment lining remained within permissible limits. Additionally, both the segment lining and the secondary lining were able to fully utilize their load-bearing capacities, ensuring the economic and safety aspects of the tunnel.

Glioblastoma multiforme (GBM) is a highly aggressive malignancy and represents the most common brain tumor in adults. To better understand its biology for new and effective therapies, we examined the role of GDP-mannose pyrophosphorylase B (GMPPB), a key unit of the GDP-mannose pyrophosphorylase (GDP-MP) that catalyzes the formation of GDP-mannose. Impaired GMPPB function will reduce the amount of GDP-mannose available for O-mannosylation. Abnormal O-mannosylation of alpha dystroglycan (α-DG) has been reported to be involved in cancer metastasis and arenavirus entry. Here, we found that GMPPB is highly expressed in a panel of GBM cell lines and clinical samples and that expression of GMPPB is positively correlated with the WHO grade of gliomas. Additionally, expression of GMPPB was negatively correlated with the prognosis of GBM patients. We demonstrate that silencing GMPPB inhibits the proliferation, migration, and invasion of GBM cells both in vitro and in vivo and that overexpression of GMPPB exhibits the opposite effects. Consequently, targeting GMPPB in GBM cells results in impaired GBM tumor growth and invasion. Finally, we identify that the Hippo/MMP3 axis is essential for GMPPB-promoted GBM aggressiveness. These findings indicate that GMPPB represents a potential novel target for GBM treatment.

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range–time–Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

Background and Objectives: Non-alcoholic steatohepatitis (NASH) is a significant risk factor for hepatocellular carcinoma (HCC) development. Timely treatment during the NASH stage is essential to minimize the possibility of disease progression to HCC. Cuproptosis is a newly identified form of cellular death that could impact the progression of various diseases and cancers. Materials and Methods: Transcriptome and single-cell sequencing datasets were utilized to investigate the role of cuproptosis-related genes (CRGs) in NASH progression to HCC. FDX1, LIPT1, and PDHP were identified as CRGs in NASH patients, and FDX1, DBT, GCSH, SLC31A1, and DLAT were identified as CRGs in patients with NASH progressing to HCC. FDX1 was found to play a significant role in both NASH patients and patients with NASH progressing to HCC. This study constructed cuproptosis-related clusters (CRCs) using the Nonnegative Matrix Factorization algorithm, and they were linked to fatty acid metabolism and the PPAR signaling pathway in both NASH CRCs and HCC CRCs. The Weighted Correlation Network Analysis algorithm identified CRP, CRC, TAT, CXCL10, and ACTA1 as highly relevant genes in NASH CRCs and HCC CRCs. The expression of FDX1 was validated in both mouse models and human NASH samples. Results: The investigation highlights FDX1 as a pivotal CRG in both NASH and NASH progression to HCC. The comprehensive characterization of CRGs sheds light on their potential biofunctional importance in the context of NASH and HCC. Our experimental results show that FDX1 expression was significantly increased in NASH patients. Conclusions: The present study identified key CRGs, revealing their potential impact on NASH and HCC. Meanwhile, targeting FDX1 may prevent the progression of NASH to HCC.

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

With the rapid development of autonomous driving and robotics applications in recent years, visual Simultaneous Localization and Mapping (SLAM) has become a hot research topic. The majority of visual SLAM systems relies on the assumption of scene rigidity, which may not always hold true in real applications. In dynamic environments, SLAM systems, without accounting for dynamic objects, will easily fail to estimate the camera pose. Some existing methods attempt to address this issue by simply excluding the dynamic features lying in moving objects. But this may lead to a shortage of features for tracking. To tackle this problem, we propose OTE-SLAM, an object tracking enhanced visual SLAM system, which not only tracks the camera motion, but also tracks the movement of dynamic objects. Furthermore, we perform joint optimization of both the camera pose and object 3D position, enabling a mutual benefit between visual SLAM and object tracking. The results of experiences demonstrate that the proposed approach improves the accuracy of the SLAM system in challenging dynamic environments. The improvements include a maximum reduction in both absolute trajectory error and relative trajectory error by 22% and 33%, respectively.

An auroral substorm is an important physical process of energy accumulation and explosive release in the Earth’s magnetosphere, and is an important research object of space environment monitoring and space weather warnings. A westward traveling surge (WTS) is a typical auroral physical process of an auroral substorm. Its static characteristic is auroral folding at the polar boundary of an auroral oval and its dynamic characteristic is the westward motion of auroral folding. According to the static characteristic of a WTS, we defined a set of feature parameters based on the morphology and designed a set of automatic detection and discrimination methods; that is, the WTS was identified by using the extracted features and pattern recognition approaches. This approach was tested by using the aurora data of the ultraviolet auroral spectral imager of the Defense Meteorological Satellite Program (DMSP) satellite. The results showed that the accuracy rate of automatic recognition was 61.39%~63.61% and the precision rate was 55.52%~57.92%. The experimental results showed that the approach was effective at detecting the typical characteristics of an auroral substorm (WTS).

With the wide application of microwave technology, concerns about its health impact have arisen. The signal transmission mode of the central nervous system and neurons make it particularly sensitive to electromagnetic exposure. It has been reported that abnormal release of amino acid neurotransmitters is mediated by alteration of p-SYN1 after microwave exposure, which results in cognitive dysfunction. As the phosphorylation of SYN1 is regulated by different kinases, in this study we explored the regulatory mechanisms of SYN1 fluctuations following microwave exposure and its subsequent effect on GABA release, aiming to provide clues on the mechanism of cognitive impairment caused by microwave exposure. In vivo studies with Timm and H&E staining were adopted and the results showed abnormality in synapse formation and neuronal structure, explaining the previously-described deficiency in cognitive ability caused by microwave exposure. The observed alterations in SYN1 level, combined with the results of earlier studies, indicate that SYN1 and its phosphorylation status (ser-553 and ser62/67) may play a role in the abnormal release of neurotransmitters. Thus, the role of Cdk5, the upstream kinase regulating the formation of p-SYN1 (ser-553), as well as that of MEK, the regulator of p-SYN1 (ser-62/67), were investigated both in vivo and in vitro. The results showed that Cdk5 was a negative regulator of p-SYN1 (ser-553) and that its up-regulation caused a decrease in GABA release by reducing p-SYN1 (ser-553). While further exploration still needed to elaborate the role of p-SYN1 (ser-62/67) for neurotransmitter release, MEK inhibition had was no impact on p-Erk or p-SYN1 (ser-62/67) after microwave exposure. In conclusion, the decrease of p-SYN1 (ser-553) may result in abnormalities in vesicular anchoring and GABA release, which is caused by increased Cdk5 regulated through Calpain-p25 pathway after 30 mW/cm2 microwave exposure. This study provided a potential new strategy for the prevention and treatment of microwave-induced cognitive dysfunction.

Microwave medical sensing and imaging (MMSI) has been a research hotspot in the past years. Imaging algorithms based on electromagnetic inverse scattering (EIS) play a key role in MMSI due to the super-resolution phenomenon. EIS problems generally employ far-field scattered data to reconstruct images. However, the far-field data do not include information outside the Ewald’s sphere, so theoretically it is impossible to achieve super resolution. The reason for super resolution has not been clarified. The majority of the current research focuses on how nonlinearity affects the super-resolution phenomena in EIS. However, the mechanism of super-resolution in the absence of nonlinearity is routinely ignored. In this research, we address a prevalent yet overlooked problem where the image resolution due to scatterers of extended structures is incorrectly analyzed using the model of point scatterers. Specifically, the classical resolution of EIS is defined by the Rayleigh criterion which is only suitable for point-like scatterers. However, the super-resolution in EIS is often observed for general scatterers like cylinders, squares or Austria shapes. Subsequently, we provide theoretical results for the Born approximation framework in EIS, and employ the Sparrow criteria to quantify the resolution for symmetric objects of extended structures. Furthermore, the modified Sparrow criterion is proposed to calculate the resolution of asymmetric scatterers. Numerical examples show that the proposed approach can better explain the super-resolution phenomenon in EIS.

The Tuwaishan gold deposit is located at the northeastern end of the Gezhen shear zone in the western part of Hainan Island, South China. It is one of a series of similar gold deposits hosted in the Mesoproterozoic basement rocks and structurally controlled by the Gezhen shear zone. The hydrothermal ore-forming period can be divided into quartz-pyrite-arsenopyrite stage, quartz-pyrite-base metal sulfides stage and quartz-carbonate stage. Eleven gold-bearing quartz vein samples yield δDV-SMOW and δ18OV-SMOW values of −75.9‰ to −54.4‰ and +8.1‰ to +13.7‰, respectively, and the corresponding δ18Owater values range from +3.1‰ to +8.7‰. In addition, the pyrite separates from 14 ore samples yield δ34S values of +4.5‰ to +7.9‰. The H-O-S isotopic data, along with fluid properties of the Tuwaishan and other gold deposits along the Gezhen shear zone, suggest that the ore-forming fluid and materials are of metamorphic rather than magmatic origin. Hence, we propose that the Tuwaishan gold deposit is best classified as orogenic gold deposit that resulted from regional metamorphism. Considering that the Mesoproterozoic basement rocks have experienced amphibolite facies metamorphism prior to the gold mineralization, the metamorphic devolatilization of the Ordovician-Silurian rocks at depth would provide a realistic source of fluid, gold and sulfur for the Tuwaishan and other gold deposits of the Gezhen gold belt.

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Image registration is the basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, the significant nonlinear radiation difference (NRD) and the geometric imaging model difference render the registration quite challenging. To solve this problem, both traditional and deep learning methods are used to extract structural information with dense descriptions of the images, but they ignore that structural information of the image pair is coupled and often process images separately. In this paper, a deep learning-based registration method with a co-attention matching module (CAMM) for SAR and optical images is proposed, which integrates structural feature maps of the image pair to extract keypoints of a single image. First, joint feature detection and description are carried out densely in both images, for which the features are robust to radiation and geometric variation. Then, a CAMM is used to integrate both images’ structural features and generate the final keypoint feature maps so that the extracted keypoints are more distinctive and repeatable, which is beneficial to global registration. Finally, considering the difference in the imaging mechanism between SAR and optical images, this paper proposes a new sampling strategy that selects positive samples from the ground-truth position’s neighborhood and augments negative samples by randomly sampling distractors in the corresponding image, which makes positive samples more accurate and negative samples more abundant. The experimental results show that the proposed method can significantly improve the accuracy of SAR–optical image registration. Compared to the existing conventional and deep learning methods, the proposed method yields a detector with better repeatability and a descriptor with stronger modality-invariant feature representation.

This study assessed the modification effects of zirconia-based nanopowder and cement contents and curing age on the mechanical properties of silty soil. The orthogonal test design was applied to derive the best combination of each influencing factor using the lateral unconfined compressive test. Two-dimensional particle flow code (PFC2D) distinct-element modeling software was also used to fit and analyze the test curves, as well as simulate the triaxial test with the derived parameters. The test results reveal the optimal combination of 20% cement, 2% zirconia-based nanopowder, and 28 d curing age. The extreme difference table was used to plot the orthogonal trend diagram, and cement content was found to be the most significant factor controlling the silty soil strength. The maximum peak stress was 2196.33 kPa under the optimum combination of factors, which could be obtained through the index estimation, and these results were experimentally verified. According to the predicted strength envelope, the cohesive force of nanopowder-cement-modified silty soil in the optimal proportion was 717.11 kPa, and the internal friction angle was 21.05°. Nano zirconium dioxide will accelerate the hydration reaction of cement, the flocculent structure produced by the hydration of cement and soil particles connected to each other, play the role of filling and anchoring, and thus increase the strength of the nano-zirconium dioxide, and the optimal dosage of nano-zirconium dioxide is 2%.