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In the realm of intelligent sports, the integration of triboelectric nanogenerators (TENGs) marks a transformative approach toward energy sustainability and more advanced athletic monitoring. By leveraging the principle of triboelectricity, TENGs ingeniously convert mechanical energy from athletes’ movements into electrical energy, which offers a green and efficient power solution for wearable technology. This paper presents an innovative study on the application of TENG technology in sports science, with the results illustrating the potential utility of TENGs in revolutionizing the way we monitor, analyze, and enhance athletic performance. Through the development of self-powered wearables and equipment, TENGs facilitate real-time data collection on physiological and biomechanical parameters, ultimately enabling personalized training adjustments and injury prevention strategies. Our findings underscore the dual benefit of TENGs in promoting environmental sustainability by reducing the overall reliance on traditional energy sources and growing the capabilities of intelligent sports systems. This research contributes to the burgeoning field of nano-energy sports applications while setting the stage for future explorations into the optimization of TENG integration in athletic performance enhancement. Finally, the paper concludes by discussing remaining challenges in this area and opportunities for further research.

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

Electromagnetic exposure has become increasingly widespread, and its biological effects have received extensive attention. The purpose of this study was to explore changes in the metabolism profile of the brain and serum and to identify differentially expressed proteins in the brain after exposure to the 4.9 GHz radiofrequency (RF) field. C57BL/6 mice were randomly divided into a Sham group and an RF group, which were sham-exposed and continuously exposed to a 4.9 RF field for 35 d, 1 h/d, at an average power density (PD) of 50 W/m2. After exposure, untargeted metabolomics and Tandem Mass Tags (TMT) quantitative proteomics were performed. We found 104 and 153 up- and down-regulated differentially expressed metabolites (DEMs) in the RF_Brain group and RF_Serum group, and the DEMs were significantly enriched in glycerophospholipid metabolism. Moreover, 10 up-regulated and 51 down-regulated differentially expressed proteins (DEPs) were discovered in the RF group. Functional correlation analysis showed that most DEMs and DEPs showed a significant correlation. These results suggested that 4.9 GHz exposure induced disturbance of metabolism in the brain and serum, and caused deregulation of proteins in the brain.

Green construction is considered to be a construction model that pursues high resource efficiency and the utilization of reduced environmental impacts through technological innovation and management optimization under the realization of the project’s iron triangle. The realization of its performance relies on the level of stakeholder capability configuration. To reveal the optimal capability configuration and interaction between regulators and executors, this study constructed a utility model based on a post-positivist methodology. By analyzing the optimal capability configuration and coordination levels of regulators and executors according to the practices and constraints of green construction, this study conducted a static analysis to compare the effects of marginal value on regulators’ capability input and coordination coefficients. Finally, a sensitivity analysis uncovers the changes in capability configuration interaction and coordination coefficients at different stages of green construction. The results indicate that high levels of coordination in green construction cannot be maintained in the long term; continuous capability input from regulators is required for sustained support. Only by eliminating external uncertainties, reducing the variable costs for executors in advancing green construction, and controlling their risk aversion can executors be truly motivated to promote green construction. The capability configuration of both regulators and executors adjusts with corresponding marginal values. The capability configuration of executors shows a trend of initially increasing and then decreasing as the progressive coefficient rises. The model proposed in this study ensures that the final coordination level stabilizes at a relatively high level, which is between 0.6 and 0.7. In summary, the breakthrough findings provide critical insights into green construction management, contributing to the achievement of the anticipated green construction objectives.

The Streak-Tube Imaging Lidar (STIL) has been widely used in high-precision measurement systems due to its ability to capture detailed spatial and temporal information. In this paper, we proposed a ranging measurement method that integrates a Time-to-Digital Converter (TDC) with a streak camera in a remote STIL system. In this method, the TDC accurately measures the trigger pulse time, while the streak camera captures high time-resolution images of the laser echo, thereby enhancing both measurement accuracy and range. A corresponding ranging model is developed for this method. To validate the system’s performance, an outdoor experiment covering a distance of up to 6 km was conducted. The results demonstrate that the system achieved a distance measurement accuracy of 0.1 m, highlighting its effectiveness in long-range applications. The experiment further confirms that the combination of STIL and TDC significantly enhances accuracy and range, making it suitable for various long-range, high-precision measurement tasks.

Necroptosis is a type of regulated cell death (RCD) that is triggered by changes in the extracellular or intracellular milieu that are picked up by certain death receptors. Thanks to its potent capacity to induce immunological responses and overcome apoptotic resistance, it has garnered significant attention as a potential cancer treatment. Basic information for the creation of nano-biomedical treatments is provided by studies on the mechanisms underlying tumor necroptosis. Receptor-interacting protein kinase 1 (RIPK1)–RIPK3-mediated necroptosis, Toll-like receptor domain-containing adapter-inducing interferon (IFN)-β (TRIF)–RIPK3-mediated necroptosis, Z-DNA-binding protein 1 (ZBP1)–RIPK3-mediated necroptosis, and IFNR-mediated necroptosis are the four signaling pathways that collectively account for triggered necroptosis in this review. Necroptosis has garnered significant interest as a possible cancer treatment strategy because, in contrast to apoptosis, it elicits immunological responses that are relevant to therapy. Thus, a thorough discussion is held on the connections between tumor cell necroptosis and the immune environment, cancer immunosurveillance, and cells such as dendritic cells (DCs), cytotoxic T cells, natural killer (NK) cells, natural killer T (NKT) cells, and their respective cytokines. Lastly, a summary of the most recent nanomedicines that cause necroptosis in order to cause immunogenic cell death is provided in order to emphasize their promise for cancer immunotherapy.

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we propose a high-precision reconstruction method for point clouds that can be used without calibrating the system parameters. In essence, after each remote sensing measurement, a self-checking process is performed with experimental data to replace the fixed system parameters. In this process, the splicing error of the same region measured under different conditions is used as a criterion to optimize the reconstruction parameters via a particle swarm optimization (PSO) algorithm. For a detection distance of 3000 m, the elevation error of the point cloud reconstruction reaches more than 1 m if the placement parameters are not optimized; after optimization, the elevation error can be controlled within 0.3 m.

“The article investigates the macro-cell corrosion behavior and corrosion resistance when the alloyed steel and the carbon steel are used together because the traditional carbon steel is difficult to meet the corrosion resistance and durability of the steel structure of the transmission line in the marine environment.” In this paper, a new type of Cr-alloyed corrosion-resistant steel (00Cr10MoV) is used to partially replace carbon structural steel in order to meet the actual needs of corrosion resistance and service life improvement of steel structures for offshore transmission lines. It is important to systematically study the macro-cell corrosion behavior of combinations of the same type of steel and dissimilar steel, induced by the chloride concentration difference in simulated concrete solutions, and employ electrochemical testing methods to scientifically evaluate the corrosion resistance of steel after macro-cell corrosion. The aim is to study and evaluate the macro-cell corrosion behavior of alloyed corrosion-resistant steel and to lay a foundation for its combined use with carbon steel in a chloride corrosion environment to improve the overall corrosion resistance and service life. Under the same concentration difference, the macro-cell corrosion of the alloyed steel combination is milder compared with the carbon steel combination. The corrosion current of the alloyed steel combination at 29 times the concentration difference is only 1/10 of the carbon steel combination. Moreover, at 29 times the concentration difference, the macro-cell corrosion potential of dissimilar steel is only 1/6 of the combined potential of carbon steel combination under the same concentration difference, and the corrosion current is only 1/10 of that of the carbon steel combination.

Real-time remote sensing segmentation technology is crucial for unmanned aerial vehicles (UAVs) in battlefield surveillance, land characterization observation, earthquake disaster assessment, etc., and can significantly enhance the application value of UAVs in military and civilian fields. To realize this potential, it is essential to develop real-time semantic segmentation methods that can be applied to resource-limited platforms, such as edge devices. The majority of mainstream real-time semantic segmentation methods rely on convolutional neural networks (CNNs) and transformers. However, CNNs cannot effectively capture long-range dependencies, while transformers have high computational complexity. This paper proposes a novel remote sensing Mamba architecture for real-time segmentation tasks in remote sensing, named RTMamba. Specifically, the backbone utilizes a Visual State-Space (VSS) block to extract deep features and maintains linear computational complexity, thereby capturing long-range contextual information. Additionally, a novel Inverted Triangle Pyramid Pooling (ITP) module is incorporated into the decoder. The ITP module can effectively filter redundant feature information and enhance the perception of objects and their boundaries in remote sensing images. Extensive experiments were conducted on three challenging aerial remote sensing segmentation benchmarks, including Vaihingen, Potsdam, and LoveDA. The results show that RTMamba achieves competitive performance advantages in terms of segmentation accuracy and inference speed compared to state-of-the-art CNN and transformer methods. To further validate the deployment potential of the model on embedded devices with limited resources, such as UAVs, we conducted tests on the Jetson AGX Orin edge device. The experimental results demonstrate that RTMamba achieves impressive real-time segmentation performance.

Underwater image enhancement is critical for a variety of marine applications such as exploration, navigation, and biological research. However, underwater images often suffer from quality degradation due to factors such as light absorption, scattering, and color distortion. Although current deep learning methods have achieved better performance, it is difficult to balance the enhancement performance and computational efficiency in practical applications, and some methods tend to cause performance degradation on high-resolution large-size input images. To alleviate the above points, this paper proposes an efficient network GFRENet for underwater image enhancement utilizing gated linear units (GLUs) and fast Fourier convolution (FFC). GLUs help to selectively retain the most relevant features, thus improving the overall enhancement performance. FFC enables efficient and robust frequency domain processing to effectively address the unique challenges posed by the underwater environment. Extensive experiments on benchmark datasets show that our approach significantly outperforms existing state-of-the-art techniques in both qualitative and quantitative metrics. The proposed network provides a promising solution for real-time underwater image enhancement, making it suitable for practical deployment in various underwater applications.

This study presents the Fast Fruit 3D Detector (FF3D), a novel framework that contains a 3D neural network for fruit detection and an anisotropic Gaussian-based next-best view estimator. The proposed one-stage 3D detector, which utilizes an end-to-end 3D detection network, shows superior accuracy and robustness compared to traditional 2D methods. The core of the FF3D is a 3D object detection network based on a 3D convolutional neural network (3D CNN) followed by an anisotropic Gaussian-based next-best view estimation module. The innovative architecture combines point cloud feature extraction and object detection tasks, achieving accurate real-time fruit localization. The model is trained on a large-scale 3D fruit dataset and contains data collected from an apple orchard. Additionally, the proposed next-best view estimator improves accuracy and lowers the collision risk for grasping. Thorough assessments on the test set and in a simulated environment validate the efficacy of our FF3D. The experimental results show an AP of 76.3%, an AR of 92.3%, and an average Euclidean distance error of less than 6.2 mm, highlighting the framework’s potential to overcome challenges in orchard environments.

Accurately predicting the distance an object will travel to its destination is very important in various sports. Acceleration sensors as a means of real-time monitoring are gaining increasing attention in sports. Due to the low energy output and power density of Triboelectric Nanogenerators (TENGs), recent efforts have focused on developing various acceleration sensors. However, these sensors suffer from significant drawbacks, including large size, high complexity, high power input requirements, and high cost. Here, we described a portable and cost-effective real-time refreshable strategy design comprising a series of individually addressable and controllable units based on TENGs embedded in a flexible substrate. This results in a highly sensitive, low-cost, and self-powered acceleration sensor. Putting, which accounts for nearly half of all strokes played, is obviously an important component of the golf game. The developed acceleration sensor has an accuracy controlled within 5%. The initial velocity and acceleration of the forward movement of a rolling golf ball after it is hit by a putter can be displayed, and the stopping distance is quickly calculated and predicted in about 7 s. This research demonstrates the application of the portable TENG-based acceleration sensor while paving the way for designing portable, cost-effective, scalable, and harmless ubiquitous self-powered acceleration sensors.

The objective of this study was to identify multiple alkaloids in Coptis chinensis that demonstrate inhibitory activity against DPP-4 and systematically evaluate their activity and binding characteristics. A combined strategy that included molecular docking, a DPP-4 inhibition assay, surface plasmon resonance (SPR), and a molecular dynamics simulation technique was employed. The results showed that nine alkaloids in Coptis chinensis directly inhibited DPP-4, with IC50 values of 3.44–53.73 μM. SPR-based binding studies revealed that these alkaloids display rapid binding and dissociation characteristics when interacting with DPP-4, with KD values ranging from 8.11 to 29.97 μM. A molecular dynamics analysis revealed that equilibrium was rapidly reached by nine DPP-4–ligand systems with minimal fluctuations, while binding free energy calculations showed that the ∆Gbind values for the nine test compounds ranged from −31.84 to −16.06 kcal/mol. The most important forces for the binding of these alkaloids with DPP-4 are electrostatic interactions and van der Waals forces. Various important amino acid residues, such as Arg125, His126, Phe357, Arg358, and Tyr547, were involved in the inhibition of DPP-4 by the compounds, revealing a mechanistic basis for the further optimization of these alkaloids as DPP-4 inhibitors. This study confirmed nine alkaloids as direct inhibitors of DPP-4 and characterized their binding features, thereby providing a basis for further research and development on novel DPP-4 inhibitors.

The time–frequency-domain reflection method (TFDR) based on the Wigner–Ville distribution (WVD) is confronted with the problem of cross-term interference in existing methods to locate power cable defects. Therefore, a new method of locating cable defects based on Gaussian narrow-band envelope signals and the S-transform is proposed in this paper. In this method, the wide-band cable transfer function is obtained by adjusting the parameters of the Gaussian narrow-band envelope signal because the Gaussian narrow-band envelope signal has a good frequency-adjusting ability and time–frequency characteristics. Then, the time–frequency of the cable signal is transformed by the generalized S-transform, and the time delay of the modular matrix of the transformation matrix is estimated by the generalized cross-correlation algorithm to complete the accurate detection of the cable defect’s location. Compared with traditional methods, the proposed method can adaptively adjust the analysis time width according to the frequency change and provide intuitive time–frequency characteristics without cross-term interference. Finally, the effectiveness and practicability of the proposed method are verified in MATLAB 2017_a by simulating a 40 m/10 kV medium-voltage power cable and submarine cable with a length of 32 km.

Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet’s efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet’s performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method.

In the agroforestry system, the organic matter in the farmland and natural ecosystem enters the farmland soil in a mixed form to improve soil fertility and carbon pool quality. However, it is unclear how soil microbial carbon-degrading enzyme activity responds to carbon dynamics in this process. Therefore, we took farmland in the Loess Plateau as the research object, combining the application of corn straw and Robinia pseudoacacia leaves in a mass ratio of 4:0, 3:1, 2:2, 1:3, and 0:4 for returning to the field. We measured corn grain yield, carbon emission, organic carbon pool component content, and carbon-degrading enzyme activity of the farmland. The results showed that combining corn straw and Robinia pseudoacacia leaves had a significant impact on soil organic carbon components (readily oxidizable organic carbon and recalcitrant organic carbon), carbon-degrading enzymes (polyphenol oxidase, peroxidase, and cellobiohydrolase), and cumulative carbon emissions. The trend of different indicators in different treatments during the corn growth period was similar. We found that soil carbon emissions were closely related to ROC and soil oxidase activity, while soil carbon content was closely related to soil hydrolase activity. Compared to not returning straw to the field, the corn straw and Robinia pseudoacacia leaves returned to the field in a mass ratio of 1:3(Y1C3) can increase corn grain yield by 32.04%. The Y1C3 treatment has the highest soil carbon content and the lowest crop carbon emission efficiency. Soil water content plays a crucial role in the process of carbon pool transformation driven by soil carbon-degrading enzymes. In conclusion, soil carbon dynamics are closely related to the activity of soil carbon-degrading enzymes. Combining the application of corn straw and Robinia pseudoacacia leaves may be a more suitable farming measure for fragile habitats in the Loess Plateau than other solutions.

Background: Water intake and hydration status may potentially influence maternal and child health. However, there is little research regarding this topic. Objectives: This study aimed to investigate pregnant women’s total fluid intake (TFI) levels, hydration status, and body composition and further explore their relationship with infant birth weight. Methods: A 7-day, 24 h fluid intake recorded was applied to determine participants’ TFI levels. Morning urine samples were collected and tested to evaluate their hydration status. Maternal body compositions in their third trimester and infant birth weights were measured. Results: A total of 380 participants completed the study. The TFI was insufficient for pregnant women during their third trimester (median = 1574 mL), with only 12.1% of participants meeting the recommended adequate fluid intake level for pregnant women living in China (1.7 L per day). With the increasing TFI values, the urine osmolality decreased, which showed statistical significance among the four groups (χ2 = 22.637, p < 0.05). The participants displayed a poor hydration status. Meanwhile, the percentage of participants who were in dehydrated status decreased (χ2 = 67.618, p < 0.05), while body water content and basal metabolic rate increased with the increase in TFI levels (χ2 = 20.784, p < 0.05; χ2 = 14.026, p < 0.05). There were positive linear relationships between plain water intake, the basal metabolic rate of pregnant women and their infant birth weight (SE = 0.153, p < 0.05; SE = 0.076, p < 0.05). Conclusions: Water intake was insufficient, and poor hydration status was common among pregnant women in China. There may be potential relationships between plain water intake, basal metabolic rate, and infant birth weight.

The escalating prevalence of antibiotic-resistant bacteria has led to a serious global public health problem; therefore, there is an urgent need for the development of structurally innovative antibacterial agents. In our study, a series of biphenyl and dibenzofuran derivatives were designed and synthesized by Suzuki-coupling and demethylation reactions in moderate to excellent yields (51–94% yield). Eleven compounds exhibited potent antibacterial activities against the prevalent antibiotic-resistant Gram-positive and Gram-negative pathogens, among which compounds 4′-(trifluoromethyl)-[1,1′-biphenyl]-3,4,5-triol (6i) and 5-(9H-carbazol-2-yl) benzene-1,2,3-triol (6m) showed the most potent inhibitory activities against methicillin-resistant Staphylococcus aureus and multidrug-resistant Enterococcus faecalis with MIC (minimum inhibitory concentration) values as low as 3.13 and 6.25 μg/mL, respectively. Compounds 3′,5′-dimethyl-[1,1′-biphenyl]-3,4,4′,5-tetraol (6e), 4′-fluoro-[1,1′-biphenyl]-3,4,5-triol (6g), and 4′-(trifluoromethyl)-[1,1′-biphenyl]-3,4,5-triol (6i) showed comparable inhibitory activities with ciprofloxacin to Gram-negative bacterium carbapenems-resistant Acinetobacter baumannii. Study of the structure–activity relationship indicated that a strong electron-withdrawing group on the A ring and hydroxyl groups on the B ring of biphenyls were beneficial to their antibacterial activities, and for benzo-heterocycles, N-heterocycle exhibited optimal antibacterial activity. These results can provide novel structures of antibacterial drugs chemically different from currently known antibiotics and broaden prospects for the development of effective antibiotics against antibiotic-resistant bacteria.

This study aimed to find a unique method to assess the textural properties of Niangao (glutinous rice cakes), to determine the relationship between the textural properties of rice cakes and the indicators of glutinous rice, and to identify the key indicators that significantly affect the textural properties of Niangao. The study encompassed the analysis of the chemical composition and pasting characteristics of 22 glutinous rice varieties, revealing the substantial impact of variety on lipid content, straight-chain starch content, and pasting performance. Subsequently, the textural features of the resulting Niangao were subjected to principal component analysis (PCA) to derive a mathematical method for evaluating their textural attributes, with the obtained scores employed in hierarchical cluster analysis (HCA) to identify 12 key textural characteristics. Further analysis using stepwise linear regression (SLR) demonstrated that the regression model incorporating final and peak viscosities of the glutinous rice significantly predicted the composite score of the Niangao’s textural properties. This highlights the importance of final and peak viscosities as key indicators for assessing the textural quality of Niangao.

Electromagnetic rail launch technology has attracted increasing attention owing to its advantages in terms of range, firepower, and speed. However, due to electricity-magnetism-heat-force coupling, the surface of the armature–rail friction pair becomes severely damaged, which restricts the development of this technology. A series of studies have been conducted to reduce the damage of the armature–rail friction pair, including an analysis of the damage mechanism and protection strategies. In this study, various types of surface damage were classified into mechanical, electrical, and coupling damages according to their causes. This damage is caused by factors such as mechanical friction, mechanical impact, and electric erosion, either individually or in combination. Then, a detailed investigation of protection strategies for reducing damage is introduced, including material improvement through the use of novel combined deformation and heat treatment processes to achieve high strength and high conductivity, as well as surface treatment technologies such as structural coatings for wear resistance and functional coatings for ablation and melting resistance. Finally, future development prospects of armature–rail friction pair materials are discussed. This study provides a theoretical basis and directions for the development of high-performance materials for the armature–rail friction pair.

The phytoplankton population of Qionghai Lake was surveyed in December 2015, March 2016, June 2016, September 2016, and March 2017. A total of 196 species (including varieties) belonging to 77 genera of 7 phyla were identified. The phytoplankton communities were dominated by Chlorophyta and diatoms, and there were significant differences across the five sampling sites. The phytoplankton abundance, which ranged between 13.85 × 104 and 335.54 × 104 cells·L−1, was significantly higher in spring and summer than in autumn and winter. Chlorella sp. and Cyclotella sp. were the dominant populations, and their dominance degree reached as high as 0.54 and 0.33, respectively. The diversity of the phytoplankton populations was significantly higher in spring and summer than in autumn and winter, and the Shannon–Wiener index and Margalef index ranged from 2.49–3.65 and 2.47–3.10, respectively. The water quality of Qionghai Lake was generally good. The trophic level index was between 30 and 60, showing that the water body was overall in a mesotrophic to slightly eutrophic state. The Spearman correlation analyses revealed that ammonium nitrogen (NH4+-N), water temperature (WT), permanganate index (CODMn), and transparency (SD) were the most important environmental factors that influenced the phytoplankton communities. For example, NH4+-N was significantly correlated with Chroococcus sp. (r = 0.41, p < 0.05) and Cryptomonas ovata Ehrenberg (r = 0.45, p < 0.05), and WT was significantly correlated with Cryptomonas marssonii Skuja (r = 0.43, p < 0.05) and Cryptomonas ovata (r = 0.53, p < 0.01).

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks.

A fluorescent probe, N′-((3-methyl-5-oxo-1-phenyl-4, 5-dihydro-1H-pyrazol-4-yl) methylene)-2-oxo-2H-chromene-3-carbohydrazide (MPMC), was synthesized and characterized. Characterizations of the synthetic MPMC were conducted via proton nuclear magnetic resonance (1HNMR) spectroscopy and carbon-13 nuclear magnetic resonance spectroscopy (13C NMR). The fluorescence emission behaviors of the MPMC probe towards diverse metal ions were detected, and the probe exhibited high sensitivity and selectivity towards Cu2+ over other metal ions via the quenching of its fluorescence. Furthermore, the existence of other metal actions made no apparent difference to the fluorescence intensity of the MPMC-Cu2+ system; that is, MPMC displayed a good anti-interference ability. Job’s plot of the MPMC and copper ions indicated that the detection limit was 10.23 nM (R2 = 0.9612) for the assayed actions, with a stoichiometric ratio of 1:1 for MPMC and Cu2+. Additionally, the color of the MPMC probe solution changed from nearly colorless to yellow in the presence of Cu2+ in visible light, and the color change could be observed by the naked eye. Similarly, the color resolved from bright yellow into blue in ultraviolet light. Moreover, reusability studies indicated that the MPMC probe was reusable. The pH effect of the MPMC probe on Cu2+ had a broad range of pH detection, i.e., from 4.0 to 11.0. The response time of the MPMC probe for determining Cu2+ was within 1 min. The recognition of Cu2+ via MPMC performed on pre-treated paper under sunlight and UV light both had a distinct colour change. Thus, the solid-state method for detecting Cu2+ with the naked eye was both economical and convenient.

Fluid intake and hydration status during pregnancy may have influences on maternal and infant health. However, few studies have recorded and analyzed total fluid intake (TFI) levels during the whole pregnancy. This study mainly aimed to investigate the TFI levels of pregnant women in three trimesters, and further to assess their hydration status. The relationships of TFI and hydration status were also analyzed. A convenience sampling method was used to recruit pregnant women from the Haikou Maternity and Child Health Hospital in China in this prospective cohort study. A 7-day 24 h fluid intake questionnaire was used for recording the fluid intake of the participants in their three trimesters. Fasting blood samples and first morning urine samples were also collected and tested. Hydration status was evaluated using urine osmolality. Finally, 142 pregnant women completed the study. The median TFIs in the first, second, and third trimesters were 1336, 1477, and 1584 mL, respectively. The TFI levels increased with pregnancy progression (χ2 = 134.155, p < 0.05). Out of 142 participants, 100.0%, 97.2%, and 85.2% of participants did not reach the recommendation amount for an adequate TFI among Chinese pregnant women in the three trimesters, respectively (χ2 = 29.840, p < 0.05). Plain water was the main source of fluid intake, accounting for 92.0%, 94.2%, and 93.4% of TFI, respectively. The median values of dairy product intake were 61, 57, and 59 mL in the three trimesters. The frequency of participants without an optimal hydration status in the three trimesters was 71.8%, 76.1%, and 83.1%, respectively (χ2 = 29.909, p < 0.05). The participants of each trimester were divided into four groups according to quartiles of TFI, including participants with a lower fluid intake (LFI1 and LFI2) and higher fluid intake (HFI1 and HFI2). As the TFI values increased from the LFI1 group to the HFI2 group, the urine osmolality decreased (all p < 0.05). Moderate-intensity negative correlations were found between urine osmolality, hydration status, and TFI (all p < 0.05). It is suggested that fluid intake strategies should be promoted and health education should be conducted to improve the hydration status of pregnant women.

As a vital component of biodiversity, phyllosphere bacteria in forest canopy play a critical role in maintaining plant health and influencing the global biogeochemical cycle. There is limited research on the community structure of phyllosphere bacteria in natural forests, which creates a gap in our understanding of whether and/or how phyllosphere bacteria are connected to leaf traits of their host. In this study, we investigated the bacterial diversity and composition of the canopy leaves of six dominant tree species in deciduous broad-leaved forests in northeastern China, using high-throughput sequencing. We then compare the differences in phyllosphere bacterial community structure and functional genes of dominant tree species. Fourteen key leaf functional traits of their host trees were also measured according to standard protocols to investigate the relationships between bacterial community composition and leaf functional traits. Our result suggested that tree species with closer evolutionary distances had similar phyllosphere microbial alpha diversity. The dominant phyla of phyllosphere bacteria were Proteobacteria, Actinobacteria, and Firmicutes. For these six tree species, the functional genes of phyllosphere bacteria were mainly involved in amino acid metabolism and carbohydrate metabolism processes. The redundancy and envfit analysis results showed that the functional traits relating to plant nutrient acquisition and resistance to diseases and pests (such as leaf area, isotope carbon content, and copper content) were the main factors influencing the community structure of phyllosphere bacteria. This study highlights the key role of plant interspecific genetic relationships and plant attributes in shaping phyllosphere bacterial diversity.

Nanocarriers have been widely studied and applied in the field of cancer treatment. However, conventional nanocarriers still suffer from complicated preparation processes, low drug loading, and potential toxicity of carriers themselves. To tackle the hindrance, carrier-free nanodrugs with biological activity have received increasing attention in cancer therapy. Extensive efforts have been made to exploit new self-assembly methods and mechanisms to expand the scope of carrier-free nanodrugs with enhanced therapeutic performance. In this review, we summarize the advanced progress and applications of carrier-free nanodrugs based on different types of assembly mechanisms and strategies, which involved noncovalent interactions, a combination of covalent bonds and noncovalent interactions, and metal ions-coordinated self-assembly. These carrier-free nanodrugs are introduced in detail according to their assembly and antitumor applications. Finally, the prospects and existing challenges of carrier-free nanodrugs in future development and clinical application are discussed. We hope that this comprehensive review will provide new insights into the rational design of more effective carrier-free nanodrug systems and advancing clinical cancer and other diseases (e.g., bacterial infections) infection treatment.

The extraction of sandy roads from remote sensing images is important for field ecological patrols and path planning. Extraction studies on sandy roads face limitations because of various factors (e.g., sandy roads may have poor continuity, may be obscured by external objects, and/or have multi-scale and banding characteristics), in addition to the absence of publicly available datasets. Accordingly, in this study, we propose using the remote sensing imagery of a sandy road (RSISR) dataset and design a sandy road extraction model (Parallel Attention Mechanism-Unet, or PAM-Unet) based on Gaofen-2 (GF-2) satellite images. Firstly, the model uses a residual stacking module, which can solve the problem of poor road feature consistency and improve the extraction of fine features. Secondly, we propose a parallel attention module (PAM), which can reduce the occlusion effect of foreign objects on roads during the extraction process and improve feature map reduction. Finally, with this model, the SASPP (Strip Atrous Spatial Pyramid Pooling) structure, which enhances the model’s ability to perceive contextual information and capture banding features, is introduced at the end of the encoder. For this study, we conducted experiments on road extraction using the RSISR dataset and the DeepGlobe dataset. The final results show the following: (a) On the RSISR dataset, PAM-Unet achieves an IoU value of 0.762, and its F1 and IoU values are improved by 2.7% and 4.1%, respectively, compared to U-Net. In addition, compared to the models Unet++ and DeepLabv3+, PAM-Unet improves IoU metrics by 3.6% and 5.3%, respectively. (b) On the DeepGlobe dataset, the IoU value of PAM-Unet is 0.658; compared with the original U-Net, the F1 and IoU values are improved by 2.5% and 3.1%, respectively. The experimental results show that PAM-Unet has a positive impact by way of improving the continuity of sandy road extraction and reducing the occlusion of irrelevant features, and it is an accurate, reliable, and effective road extraction method.

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2’s AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality.

During the Early-Middle Ordovician, the Upper Yangtze Platform experienced extensive development of a distinctive set of mixed siliciclastic-carbonate deposits known as the Meitan Formation. To conduct a comprehensive study of the relationship between mixed sedimentation and sea-level changes, the Hailong section, situated at the southwest margin of the Upper Yangtze Platform in SW China, was selected as the study area due to its typical mixed sedimentary sequence. This section was effectively compared with sections in other regions. Clustering analysis of the point-count groups from the Honghuayuan and Meitan Formations revealed nine microfacies that developed during this period. Such a diverse range of microfacies provides the evidence of frequent sea-level changes in the Yangtze Platform throughout this period. Specifically, two sea-level rises were identified during the early TS.2b and early TS.3b, with the early TS.2b event occurring globally. Furthermore, four sea-level falls were observed in the late TS.2b, TS.2c, TS.3a, and late TS.3b periods. The late TS.2b sea-level fall was globally significant. From TS.2c onwards, distinct variations in sea-level changes among the Yangtze Platform, North China, Baltoscandia, Australia, and North America emerged due to alterations in the paleogeographic pattern. By comparing the sea-level curves in the Upper Yangtze Platform with the diversity curves of chitinozoans, acritarchs, and brachiopods, it became evident that environmental changes played a crucial role in the Great Ordovician Biodiversification Event (GOBE), especially during the Floian. The rising sea level and increased detrital materials fostered the development of diverse habitats, prompting organisms to adapt to varying environments. In general, rising sea levels favored increased brachiopod diversity, while falling sea levels favored enhanced planktonic diversity among chitinous and acritarch species. By shedding new light on the relationship between Ordovician sea-level changes and biodiversification in the Yangtze region, this study offers a fresh perspective on the subject from the microfacies analysis.

Since July 2022, the Yangtze River basin has experienced the most severe hydro-meteorological drought since record collection started in 1961, which has greatly affected the ecological environment of the Dongting Lake (DTL) basin. To investigate the effects of drought events on the eutrophication and phytoplankton community structure of DTL, the lake was sampled twice in August and September 2022 based on the water level fluctuations resulting in 47 samples. Furthermore, we combined the comprehensive trophic level index (TLI) and phytoplankton Shannon–Wiener diversity index (H) to characterize and evaluate the eutrophication status. The key influencing factors of the phytoplankton community were identified using redundancy analysis (RDA), hierarchical partitioning, and the Jaccard similarity index (J). Our results showed that the TLI of DTL changed from light–moderate eutrophication status (August) to mesotrophic status (September), whereas the H changed from light or no pollution to medium pollution. The phytoplankton abundance in August (122.06 × 104 cells/L) was less than that in September (351.18 × 104 cells/L) in DTL. A trend in phytoplankton community succession from Bacillariophyta to Chlorophyta and Cyanophyta was shown. The combination of physiochemical and ecological assessment more accurately characterized the true eutrophic status of the aquatic ecosystem. The RDA showed that the key influencing factors in the phytoplankton community were water temperature (WT), pH, nitrogen and phosphorus nutrients, and the permanganate index (CODMn) in August, while dissolved oxygen (DO) and redox potential (ORP) were the key factors in September. Hierarchical partitioning further indicated that temporal and spatial variations had a greater impact on the phytoplankton community. And the J of each region was slightly similar and very dissimilar, from August to September, which indicated a decreased hydrological connectivity of DTL during drought. These analyses indicated that the risk to the water ecology of DTL intensified during the summer–autumn drought in 2022. Safeguarding hydrological connectivity in the DTL region is a prerequisite for promoting energy flow, material cycle, and water ecosystem health.

The focus of the current research is how to effectively identify aircraft targets in SAR images. There are only 2000 SAR images and 6556 aircraft instances in our dataset. SAR images have complex backgrounds, and the sizes of aircraft targets are multi-scale. How to improve the detection accuracy of aircraft targets is the research topic of this paper, especially for small target detection. We proposed four improved methods based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion. It is applied at the shallow layer of the backbone of YOLOv5s. It can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four decoupled detection heads to replace the original part in YOLOv5s. It can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multiple data-augmentation methods, which can enhance the diversity and generalization of the network. Finally, this paper proposed the K-means++ to replace the original K-means to improve the network convergence speed and detection accuracy. Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

Owing to the special working systems of streak tube imaging lidar (STIL), the time and space dimensions are coupled together on the streak images. This coupling can cause measurement errors in 3D point clouds and can make measurement results more complicated to calibrate than other kinds of lidars. This paper presents a method to generate a time calibration array and an angle calibration array to separate the offset of the streak into time dimension and space dimension. The time and space information of the signal at any position on the streak image can be indexed through these two arrays. A validation experiment on aircraft was carried out, and the range error of the 3D point cloud was improved from 0.41 m to 0.27 m using the proposed calibration method. Thus, using the proposed calibration method can improve the accuracy of the point cloud produced by STIL.

The Loess Plateau region is characterized by fragmented habitats and ecological vulnerability. Analyzing the changes in land use and ecological risk within the region is of great significance for promoting high-quality development of the Loess Plateau. The study utilized land use data from 2000, 2010, and 2020 in the Loess Plateau region to assess the spatio-temporal variation in land use patterns and landscape ecological risks, aiming to provide valuable references and decision support for ecological risk management and sustainable development in the area. The results indicated that the main land use types in the region are grassland and cropland. From 2000 to 2020, forest, grassland, and water areas increased by 1.39 × 106, 6.25 × 105, and 7.09 × 104 ha, respectively. The impervious area increased rapidly, growing from 9.77 × 104 ha in 2000 to 1.85 × 106 ha in 2020. The cropland decreased by 1.82 × 106 ha from 2000 to 2020, with 4.61 × 105, 4.95 × 106, and 8.91 × 105 ha of cropland converted to forest, grassland, and impervious area, respectively. The fragmentation of the ecological landscape in the region has decreased, and the diversity and richness of landscape types have increased. The fragmentation of cropland, forest, and grassland has decreased, and landscape patches have become more concentrated. High-value areas of landscape ecological risk in the region show a trend of continuous aggregation, altering the dispersion pattern of high-risk areas. Currently, high-risk areas of landscape ecology in the Loess Plateau region are mainly concentrated in northern Shaanxi and some areas along the Yellow River, such as Yulin, Yan’an, Ordos, and others. Currently, the ecological environment remains a bottleneck constraining the high-quality development of the Loess Plateau. It is necessary to persist in coordinated governance and ecological engineering construction, and improving the quality of ecological environment is a prerequisite for consolidating the social foundation and leading the high-quality development of the ecological industry on the Loess Plateau.

This work studied the phase constitution, bond characteristics, and microwave dielectric performances of Sr2TiO4 ceramics. Based on XRD and Rietveld refinement analysis, pure tetragonal Ruddlesden–Popper type Sr2TiO4 ceramic is synthesized at 1425~1525 °C. Meanwhile, the microstructure is dense and without porosity, indicating its high sinterability and densification. Great microwave dielectric performances can be obtained, namely an εr value of 39.41, and a Q × f value of 93,120 GHz, when sintered at 1475 °C. Under ideal sintering conditions, the extrinsic factors are minimized and can be ignored. Thus, the intrinsic factors are considered crucial in determining microwave dielectric performances. Based on the P–V–L complex chemical bond theory calculation, the largest bond ionicity, and proportions to the bond susceptibility from Sr–O bonds suggest that Sr–O bonds mainly determine the dielectric polarizability. However, the Ti–O bonds show lattice energy about three times larger than Sr–O bonds, emphasizing that the structural stability of Sr2TiO4 ceramics is dominated by Ti–O bonds, and the Ti–O bonds are vital in determining the intrinsic dielectric loss. The thermal expansion coefficient value of the Sr2TiO4 structure is also mainly decided by Ti–O bonds.

Cartilage tissue engineering has attracted great attention in defect repair and regeneration. The utilization of bioactive scaffolds to effectively regulate the phenotype and proliferation of chondrocytes has become an elemental means for cartilage tissue regeneration. On account of the simultaneous requirement of mechanical and biological performances for tissue-engineered scaffolds, in this work we prepared a naturally derived hydrogel composed of a bioactive kartogenin (KGN)-linked chitosan (CS-KGN) and an aldehyde-modified oxidized alginate (OSA) via the highly efficient Schiff base reaction and multifarious physical interactions in mild conditions. On the basis of the rigid backbones and excellent biocompatibility of these two natural polysaccharides, the composite hydrogel demonstrated favorable morphology, easy injectability, good mechanical strength and tissue adhesiveness, low swelling ratio, long-term sustainable KGN release, and facilitated bone marrow mesenchymal stem cell activity, which could simultaneously provide the mechanical and biological supports to promote chondrogenic differentiation and repair the articular cartilage defects. Therefore, we believe this work can offer a designable consideration and potential alternative candidate for cartilage and other soft tissue implants.

Soil erosion and extensive management, as key factors limiting the sustainability of the agroecosystem in the Loess Plateau, severely hamper the high-quality development of regional agriculture. Soil fertility and element synergy can be enhanced by applying mulching measures properly. However, there is a lack of systematic research into how soil stoichiometric characteristics under mulching affect crop productivity. This study focused on the agroecosystem of the Loess Plateau. Based on the 11-year field positioning experiment, the management measures of straw mulching (SM), plastic mulching (PM) and ridge-film mulching (RM) were selected to investigate the effects of long-term mulching measures on the stoichiometric characteristics of topsoil and the impact of crop productivity under the ecological stoichiometry theory. The findings revealed a significant increase in soil carbon, nitrogen and phosphorus contents and microbial biomass under mulching. SM and RM significantly decreased the stoichiometric ratio of soil available nutrients, whereas PM and RM had effective alleviating effects on C:N and C:P imbalance. The yield components of long-term SM and RM greatly increased and responded favorably to the synergy of soil carbon and phosphorus. This study provides theoretical guidance and technical support for the assessment of the effective and sustainable use of agricultural resources on the Loess Plateau.

In order to improve and alleviate environmental pollution caused by the disposal of seedling pots, a rice straw fiber-based headed vegetable seedling pot material, which is suitable for mechanical transplantation and biodegradable, was studied. Used rice straw as the main raw material, a five-factor and five-level (1/2 full implementation) quadratic regression orthogonal rotation central of rotation combination test method. The experimental factors included the beating degree of rice straw fiber, quantity, the proportion of rice fiber, neutral sizing agents, and wet strength agent mass fraction. The performance evaluation index included dry and wet tensile strength, burst strength, tear strength, air permeance, and degradation period. The results showed that when the parameter combination of the beating degree of rice straw fiber was 50 ± 1°SR, the quantity was 87.5 ± 4 g/m2, the proportion of fiber in rice was 70%, the neutral sizing agents mass fraction was 1 ± 0.25%, and the wet strength agent mass fraction was 1.5 ± 0.1%. The dry tensile strength was ≥ 1.8 kN·m−1, the wet tensile strength was ≥ 0.7 kN·m−1, the burst strength was ≥ 140 kPa, the tear strength was ≥ 350 m·N, the air permeance was ≤ 1.33 μm/Pa·s, and the degradation period was ≤ 80 d. The dry tensile strength reduction rate was 0.0274 kN/(m·d) and the wet tensile strength reduction rate was 0.0113 kN/(m·d), during the nursery period [30, 40], while the dry tensile strength was ≥ 1 kN·m−1 and the wet tensile strength was ≥ 0.4 kN·m−1 during the transplanting period.

The aim of the present study was to investigate the effects of sodium butyrate (SB) supplementation on the growth and intestinal microbiota of preweaning dairy calves. Eighty newborn Holstein calves (56 female and 24 male) were randomly allocated to four treatment groups with 20 calves each (14 female and 6 male). The suckling milk for the four treatments was supplemented with 0, 4.4, 8.8, or 17.6 g/d SB. During the 6-week experiment, dry matter intake was recorded daily, body weight was measured weekly, and rectal fecal samples were collected in the 2nd week. The V3–V4 hypervariable regions of the microbial 16S rRNA were amplified and then sequenced. SB supplementation elevated average daily gains (ADGs) in the first and second weeks. The optimal SB supplementation level for the whole preweaning period was 8.78 g/d, as revealed by analyzing the whole preweaning period ADG using second-order polynomial regression (quadratic) equations. The alpha diversity (Shannon diversity index), beta diversity, core phyla and genera, and function of the intestinal microbiota were affected by SB supplementation. In addition, the Shannon diversity index and core phyla and genera of the intestinal microbiota were correlated with calf growth-related indices. Overall, SB supplementation in suckling milk improved the growth performance and intestinal microbiota development of dairy calves in a quadratic manner, and regression analysis indicated an optimal supplementation level of 8.78 g/d.

With a series of Cenozoic climate fluctuations, the global paleoclimate shifted from a warm climate to a cold climate, causing Arctic ice caps to be formed. The Late Miocene is a critical time in this transition period, in which the climate was rapidly cooling. Plant fossils from this epoch could be used as ideal indicators for reconstructing climate change throughout this time interval. In this study, plant fossils were collected from the Shengxian Formation in Ninghai and Tiantai of eastern Zhejiang. We divided the fossiliferous strata of the Shengxian Formation into five layers according to different lithology and chronological order, which were named: JHU0, DLX, JHU1, JHUW, and JHU3 from old to new geological times, respectively. We used Leaf Margin Analysis and Climate Leaf Analysis Multivariate Program to reconstruct paleoclimatic changes in eastern Zhejiang during the Late Miocene. The paleoclimatic information of the five stages from old to new times was obtained based on the plant fossils of each layer. The mean annual temperature values in eastern Zhejiang were reconstructed using the Climate Leaf Analysis Multivariate Program and Leaf Margin Analysis at the same time. However, the former mean annual temperature values are lower than the latter values. After comparing the two sets of mean annual temperature data with previously reported values, it is found that the results obtained by Climate Leaf Analysis Multivariate Program are more reliable, whose values are 18.05 °C, 16.03 °C, 17.96 °C, 16.57 °C, and 15.52 °C from old to new times, respectively. Moreover, 11 climatic parameters were reconstructed using the Climate Leaf Analysis Multivariate Program PhysgAsia2 calibration, among which the growing season precipitation was found to be 195.54 cm, 181.25 cm, 207.99 cm, 180.7 cm, and 165.07 cm; while the difference between the coldest and warmest months was found to be 22.14 °C, 23.4 °C, 22.07 °C, 21.36 °C, and 23.37 °C. The relatively low difference between the coldest and warmest months values and the growing season precipitation values during the Late Miocene might be due to a weaker East Asian monsoon system in the Late Miocene than in modern times.

The issue of freezing often occurs when using all-glass vacuum tube solar water heaters during cold winter seasons, leading to problems such as pipe ruptures and tank leakage. In order to further study the nocturnal heat dissipation and freezing characteristics of these heaters, a three-dimensional transient numerical model of their nocturnal heat dissipation was established. The model simulated the nocturnal heat dissipation process, and experimental validations were conducted through nocturnal temperature drops of the collector and temperature drops of individual tubes without a storage tank. Experimental and simulation results revealed that in clear weather conditions during cold winters in Luoyang, the all-glass vacuum tube solar water heaters experienced freezing issues during the night, with freezing predominantly starting from the bottom surface of the vacuum tubes. The frozen length along the tube wall and the thickness of ice at the bottom section reached up to 1180 mm and 5 mm, respectively. In the absence of a storage tank, the freezing situation was severe, with approximately 4/5 of the individual tubes completely frozen. Under specified operating conditions, different storage tank volumes exhibited varying degrees of freezing in the all-glass vacuum tube solar water heaters. When the volume was increased to 15 L, the temperature drop in the storage tank and the vacuum tubes decreased by 12.1% and 7.6%, respectively. Larger storage tank volumes resulted in reduced freezing risks in all-glass vacuum tube solar collectors. This study provides valuable guidance for the design and application of solar collectors and serves as a reference for the development and application of solar energy utilization technologies.

Global climate change and rapid urbanization have placed enormous pressure on the urban ecological environment worldwide. Urban green spaces, which are an important component of urban ecosystems, can maintain ecological and environmental sustainability and benefits, including biodiversity conservation and carbon sequestration. However, land use changes across urban landscapes, especially in plain urban areas with high development pressure, have significantly impacted the carbon sequestration efficiency of urban green spaces. Nevertheless, research examining the impact of land use change and development pressure on urban green spaces and carbon sequestration is relatively scarce. Understanding the carbon sequestration efficiency of urban green spaces and its determining factors will help predict future carbon capture trends within urban ecosystems and formulate more targeted sustainable urban planning and management strategies to improve urban carbon sink efficiency and achieve the goal of carbon neutrality. Therefore, to understand the factors affecting the carbon sequestration efficiency of urban green spaces, this paper used an integrated framework that combined the Carnegie–Ames–Stanford approach (CASA) model, landscape pattern index, multiple linear regression, and Markov–FLUS model. The study explored the impact of urban land use and land cover changes on carbon sequestration within the plain urban areas of Beijing at street scale. The results showed that, at street scale, there was a significant positive and negative correlation between the landscape pattern index and net primary productivity (NPP). In addition, the green spaces located in areas with more complex landscape structures had better carbon sequestration benefits. In addition, multiscenario carbon sequestration efficiency prediction suggested that the sustainable development (SD) scenario could achieve a positive increment of overall NPP. In contrast, the business-as-usual development (BD), the fast development (FD), and the low development (LD) scenarios showed a downward trend in NPP. This paper also proposed strategies for optimizing and enhancing green spaces within urban plain areas. Based on the strategies, the results guide decision making for sustainable urban green space planning that maintains the ecological, economic, and social integrity of urban landscapes during urbanization.

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.

In the process of modernization and urbanization, some government projects or facilities with negative externalities have caused the psychology of residents nearby to “Not in My Backyard” (NIMBY). That is, adopting strong and resolute, sometimes highly emotional collective opposition or even resistance behavior. This triggered a NIMBY conflict. From the perspective of Complexity, this study re-examines the characteristics and evolution mechanism of NIMBY conflict and draws the following conclusions: (1) NIMBY conflict is a complex system that interacts between multiple subjects and the environment; (2) Adaptability is the driving force for the evolution of NIMBY conflict. Through detectors, regularizers, and effectors, NIMBY subjects can be encouraged to gradually adapt to changes in the external environment and maximize their own interests; (3) In NIMBY conflict, the government conflict response method is more important than the intervention time. Residents’ communication efficiency and connection probability will affect residents’ behavior choices. The lower the residents’ communication efficiency, the less likely it is to form a NIMBY conflict.

Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.

The isoselective ring-opening polymerization of rac-LA is a challenging goal. In this work, a series of potassium amidate complexes (K1–K10) were easily prepared and characterized using the 1H/13C NMR spectrum. The molecular structures of potassium complexes K2 and K10 were determined by X-ray diffraction, which showed that both were two-dimensional coordination polymers due to the adjacent π interactions of the aryl. In the presence of benzyl alcohol (BnOH), all of the potassium complexes exhibited a high catalytic activity toward the ring-opening polymerization of L-lactide and rac-LA, yielding linear polylactides capped with BnO or CH3O end groups. A significant solvent effect on the ROP of the L-LA was observed, with a superior efficiency in toluene than in THF and CH2Cl2. These complexes are iso-selective and act as active catalysts for the controlled ring-opening polymerization of rac-lactide, with a Pm from 0.54 to 0.76. This is a rare example of simple alkali metal complexes for the isoselective ROP of rac-lactide. The substituent greatly affected the monomer conversion and isoselectivities.

The fluid intake and hydration status during pregnancy may influence the health outcomes of both the mother and the fetus. However, there are few studies related to this. The aim of the present study was to investigate fluid intake behaviors among pregnant women in their second trimester, to evaluate their hydration status and pregnancy complications, and to further explore the association of fluid intake and the amniotic fluid index (AFI). Participants’ total fluid intake (TFI) levels were determined using a 7-day 24 h fluid intake questionnaire. The levels of water intake from food were not recorded or measured. Morning urine samples were collected, and both urine osmolality levels and urine specific gravity (USG) were tested to evaluate their hydration status. Fasting blood samples were also collected and measured for osmolality and complete blood count (CBC). A total of 324 participants completed the study. They were divided into four groups based on quartiles of TFI, including participants with lower (LFI1 and LFI2) and higher (HFI1 and HFI2) fluid intake levels. The median TFI was 1485 mL, and the median values of the four groups with different TFI levels were 1348, 1449, 1530, and 1609 mL, respectively. Only 3.4% of the participants attained the recommended value following an adequate water intake (1.7 L) level for pregnant women in China. Plain water was the main TFI resource (78.8~100.00%), and differences in the plain water intake levels among the four groups were evident (χ2 = 222.027, p < 0.05). The urine osmolality decreased sequentially with increasing TFI values from the LFI1 to HFI2 group, and significant differences in the urine osmolality levels among the four groups were evident (p < 0.05). Meanwhile, the percentage of dehydrated participants decreased from 26.8% in the LFI1 group to 0.0% in the HFI2 group (χ2 = 131.241, p < 0.05). Participants with higher TFI values had higher AFI values (χ2 = 58.386, all p < 0.05), and moderate-intensity correlations were found between TFI and urine osmolality, hydration status, and AFI (all p < 0.05). A large proportion of the participants had insufficient TFIs during the second trimester of pregnancy, and a proportion of the participants were dehydrated. The preliminary analysis showed that the AFI was correlated with the TFI during the second trimester of pregnancy. A sufficient TFI is necessary for pregnant women to improve their hydration status and may have effects on their health. The results can provide appropriate scientific references for the development of beneficial recommendations concerning adequate water intake levels for pregnant women in China.

Deep-learning-based registration methods can not only save time but also automatically extract deep features from images. In order to obtain better registration performance, many scholars use cascade networks to realize a coarse-to-fine registration progress. However, such cascade networks will increase network parameters by an n-times multiplication factor and entail long training and testing stages. In this paper, we only use a cascade network in the training stage. Unlike others, the role of the second network is to improve the registration performance of the first network and function as an augmented regularization term in the whole process. In the training stage, the mean squared error loss function between the dense deformation field (DDF) with which the second network has been trained and the zero field is added to constrain the learned DDF such that it tends to 0 at each position and to compel the first network to conceive of a better deformation field and improve the network’s registration performance. In the testing stage, only the first network is used to estimate a better DDF; the second network is not used again. The advantages of this kind of design are reflected in two aspects: (1) it retains the good registration performance of the cascade network; (2) it retains the time efficiency of the single network in the testing stage. The experimental results show that the proposed method effectively improves the network’s registration performance compared to other state-of-the-art methods.

A force-feeding device with a double-roller anisotropic was designed for the D200 single screw straw fiber extruder to keep the performance continuity of the system, which could improve the productivity of straw fiber. Four factors (the diameter of the auxiliary roll, the difference in linear speed of the two rolls, the gap between two rollers and the spindle speed as the test factors) were investigated to establish regression model to analyze the influence of the coupling of multiple factors on the test indexes. It was demonstrated that significant effects (p < 0.05) on the feeding rate and material loss were produced by four factors according to the experimental results. The sequence of influence in descending order on feeding rate was spindle speed, linear speed difference, gap and auxiliary roller diameter. The effects on materials loss in descending order were auxiliary roller diameter, line speed difference, spindle speed and gap. The optimal combination of parameters was obtained by the response surface, which were an auxiliary roller diameter of 230 mm, a spindle speed of 104.49 rpm, a line speed difference of 2840 mm/s and a gap between the two rolls of 14 mm. The average feeding rate was 2.3798 t/h, and the loss was 1.908 kg/h, and the errors were within 3.28%, which satisfied the feeding requirements for rice straw fiber production with high feeding efficiency and low raw material losses. This study provided a reference for the forced compression feeding and fibrillation process of rice straw.

In this study, a 16 mm thick A710 low-alloy high-strength steel was welded by using flux-cored wire gas-shielded welding with an E81T1-Ni1M flux-cored wire. The microstructure characteristics and mechanical properties of the joints were systematically studied. The results showed that the joint was well formed without obvious welding defects. The center of the weld was mainly needle-like ferrite, the coarse grain area was mainly slat-like and granular bainite, and the fine grain area was mainly ferrite and pearlite. The lowest hardness in the weld area was the weakest area of the joint. The average tensile strength of the joint was 650 MPa, reaching 95% of the base metal; the samples were all fractured in the weld area, and the fracture morphology showed typical plastic fracture characteristics. The low-temperature (−40 °C) impact energy of the joint weld area and the heat-affected zone were 71 J and 253 J; the fracture morphology was characterized by a ductile–brittle mixed fracture, and the ductile area of the specimen fracture in the heat-affected zone was larger. The bending performance was good. Under the specified life of 2 × 106 cycles; the median fatigue limit and the safety fatigue limit were 520 MPa and 492 MPa, and the fatigue cracks germinated on the surface of the priming weld.