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The bridge influence line can effectively reflect its overall structural stiffness, and it has been used in the studies of safety assessment, model updating, and the dynamic weighing of bridges. To accurately obtain the influence line of a bridge, an Empirical and Variational Mixed Modal Decomposition (E-VMD) method is used to remove the dynamic component from the vehicle-induced deflection response of a bridge, which requires the preset fundamental frequency of the structure to be used as the cutoff frequency for the intrinsic modal decomposition operation. However, the true fundamental frequency is often obtained from the picker, and the testing process requires the interruption of traffic to carry out the mode decomposition. To realize the rapid testing of the influence lines of bridges, a new method of indirectly identifying the operational modal frequency and deflection influence lines of bridge structures from the axle dynamic response is proposed as an example of cable-stayed bridge structures. Based on the energy method, an analytical solution of the first-order frequency of vertical bending is obtained for a short-tower cable-stayed bridge, which can be used as the initial base frequency to roughly measure the deflection influence line of the cable-stayed bridge. The residual difference between the deflection response and the roughly measured influence line under the excitation of the vehicle is operated by Fast Fourier Transform, from which the operational fundamental frequency identification of the bridge is realized. Using the operational fundamental frequency as the cutoff frequency and comparing the influence-line identification equations, the empirical variational mixed modal decomposition, and the Tikhonov regularization to establish a more accurate identification of the deflection influence line, the deflection influence line is finally identified. The accuracy and practicality of the proposed method are verified by real cable-stayed bridge engineering cases. The results show that the relative error between the recognized bridge fundamental frequency and the measured fundamental frequency is 0.32%, and the relative error of the recognized deflection influence line is 0.83%. The identification value of the deflection influence line has a certain precision. [ABSTRACT FROM AUTHOR]

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning. [ABSTRACT FROM AUTHOR]

Precision processing of monocrystalline silicon presents significant challenges due to its unique crystal structure and chemical properties. Effective modeling and simulation are essential for advancing the understanding of the manufacturing process, optimizing design, and refining production parameters to enhance product quality and performance. This review provides a comprehensive analysis of the modeling and simulation techniques applied in the precision machining of monocrystalline silicon using diamond wire sawing. Firstly, the principles of mathematical analytical model, molecular dynamics, and finite element methods as they relate to monocrystalline silicon processing are outlined. Subsequently, the review explores how mathematical analytical models address force-related issues in this context. Molecular dynamics simulations provide valuable insights into atomic-scale processes, including subsurface damage and stress distribution. The finite element method is utilized to investigate temperature variations and abrasive wear during wire cutting. Furthermore, similarities, differences, and complementarities among these three modeling approaches are examined. Finally, future directions for applying these models to precision machining of monocrystalline silicon are discussed. [ABSTRACT FROM AUTHOR]

Pulmonary hypertension (PH) is a progressive cardiovascular disease, which may lead to severe cardiopulmonary dysfunction. As one of the main PH disease groups, pulmonary artery hypertension (PAH) is characterized by pulmonary vascular remodeling and right ventricular dysfunction. Increased pulmonary artery resistance consequently causes right heart failure, which is the major reason for morbidity and mortality in this disease. Although various treatment strategies have been available, the poor clinical prognosis of patients with PAH reminds us that further studies of the pathological mechanism of PAH are still needed. Inflammation has been elucidated as relevant to the initiation and progression of PAH, and plays a crucial and functional role in vascular remodeling. Many immune cells and cytokines have been demonstrated to be involved in the pulmonary vascular lesions in PAH patients, with the activation of downstream signaling pathways related to inflammation. Consistently, this influence has been found to correlate with the progression and clinical outcome of PAH, indicating that immunity and inflammation may have significant potential in PAH therapy. Therefore, we reviewed the pathogenesis of inflammation and immunity in PAH development, focusing on the potential targets and clinical application of anti-inflammatory and immunosuppressive therapy. [ABSTRACT FROM AUTHOR]

This article investigates heating options for poultry houses (or sheds) in order to meet their specific indoor air temperature requirements, with case studies conducted across Australia under conditions similar to those encountered worldwide. Hybrid geothermal–solar (PV)–gas heating systems with various configurations are proposed to minimise the lifecycle costs and GHG emissions of poultry shed heating, which involves six seven-week cycles per year. The baseload heating demand is satisfied using ground-source heat pumps (GSHPs), with solar photovoltaic panels generating the electricity needed. LPG burners satisfy the remaining heating demand. Integrating these systems with GSHPs aims to minimise the overall installation costs of the heating system. The primary focus is to curtail the costs and GHG emissions of poultry shed heating with these hybrid systems, considering three different electricity offsetting scenarios. It is found that a considerable reduction in the lifecycle cost (up to 55%) and GHG emissions (up to 50%) can be achieved when hybrid systems are used for heating. The Pareto front solutions for the systems are also determined. By comparing the Pareto front solutions for various scenarios, it is found that the shave factor, a measure of the GSHP proportion of the overall system, significantly influences the lifecycle cost, while the size and utilisation of the solar PV panels significantly affect the lifecycle GHG emissions. [ABSTRACT FROM AUTHOR]

Human observation of the ocean has gradually evolved from the sea surface to systematic monitoring and sampling through seafloor observation networks, and constant current power supply has become the main power supply method for seafloor observation networks due to its high reliability. There are some studies on current source to voltage source converters, but there are few studies on current source to current source (CS/CS) converters, which affects the expansion of power supply networks for seafloor observation networks. In this paper, by employing input current sharing and output voltage doubling circuits, an active clamp dual-inductor isolated CS/CS converter which uses a single-stage conversion circuit to realize constant current source conversion with a wide output voltage range is proposed. Active clamp technology at the primary side of the proposed circuit is employed to recover energy stored in leakage inductance, suppress voltage spikes of the primary side switches, and achieve zero-voltage switching of the primary side switches. The secondary side's output voltage doubling circuit resonates with transformer leakage inductance to achieve zero-current switching of the secondary side diodes, which can reduce losses and enhance efficiency. The operating principles of the proposed circuit are analyzed in detail, and the characteristic and parameter design analysis, including current conversion ratio, transformer turn ratio, power inductors, and resonant capacitors and inductor, are presented. Finally, the experimental results based on a 100 W experimental prototype validate the feasibility of the proposed converter. [ABSTRACT FROM AUTHOR]

Simple Summary: Taurine has a wide range of biological functions in vertebrates but does not participate in protein synthesis. Recent studies showed taurine exhibits an important role in the regulation of fat metabolism in fish and has the effect of reducing liver fat accumulation in high-fed fish. However, it is still unclear how taurine exerts the effect and the underlying metabolic mechanism of taurine intervention. In this study, we conducted an experiment to investigate the fat-lowering effect of taurine on orange-spotted groupers at the proteomic level. Furthermore, we performed an integrated analysis of transcriptomics and proteomics and excavated the key genes and key proteins involved in the regulation of liver fat metabolism, in an attempt to better understand the intrinsic connection between the transcriptional and translational levels and interpret the trajectories in terms of the biological phenomena in a more comprehensive way after taurine intervention on high-fat fed orange-spotted groupers. In order to understand the intervention effect of taurine on liver fat deposition induced by high fat intake in the orange-spotted grouper (Epinephelus coioides), we performed proteomic analysis and association analysis with previously obtained transcriptomic data. Three isoproteic (47% crude protein) diets were designed to contain two levels of fat and were named as the 10% fat diet (10F), 15% fat diet (15F), and 15% fat with 1% taurine (15FT). The 10F diet was used as the control diet. After 8 weeks of feeding, the 15F diet exhibited comparable weight gain, feed conversion ratio, and hepatosomatic index as the 10F diet, but the former increased liver fat content vs. the latter. Feeding with the 15FT diet resulted in an improvement in weight gain and a reduction in feed conversion ratio, hepatosomatic index, and liver fat content compared with feeding the 15F diet. When comparing liver proteomic data between the 15F and 15FT groups, a total of 133 differentially expressed proteins (DEPs) were identified, of which 51 were upregulated DEPs and 82 were downregulated DEPs. Among these DEPs, cholesterol 27-hydroxylase, phosphatidate phosphatase LPIN, phosphatidylinositol phospholipase C, and 6-phosphofructo-2-kinase were further screened out and were involved in primary bile acid biosynthesis, glycerolipid metabolism, the phosphatidylinositol signaling system, and the AMPK signaling pathway as key DEPs in terms of alleviating liver fat deposition of taurine in high-fat fed fish. With the association analysis of transcriptomic and proteomic data through KEGG, three differentially expressed genes (atp1a, arf1_2, and plcd) and four DEPs (CYP27α1, LPIN, PLCD, and PTK2B) were co-enriched into five pathways related to fat metabolism including primary bile acid synthesis, bile secretion, glycerolipid metabolism, phospholipid D signaling, or/and phosphatidylinositol signaling. The results showed that dietary taurine intervention could trigger activation of bile acid biosynthesis and inhibition of triglyceride biosynthesis, thereby mediating the liver fat-lowering effects in high-fat fed orange-spotted grouper. The present study contributes some novel insight into the liver fat-lowering effects of dietary taurine in high-fat fed groupers. [ABSTRACT FROM AUTHOR]

Protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs) regulate the level of tyrosine phosphorylation in proteins. PTKs are key enzymes that catalyze the transfer of an ATP phosphoric acid to a tyrosine residue on target protein substrates. Protein tyrosine phosphatases (PTPs) are responsible for the dephosphorylation of tyrosine residues and play a role in countering PTK overactivity. As widespread oncogenes, PTKs were once considered to be promising targets for therapy. However, tyrosine kinase inhibitors (TKIs) now face a number of challenges, including drug resistance and toxic side effects. Treatment strategies now need to be developed from a new perspective. In this review, we assess the current state of TKIs and highlight the role of PTPs in cancer and other diseases. With the advances of allosteric inhibition and the development of multiple alternative proprietary drug strategies, the reputation of PTPs as "undruggable" targets has been overturned, and they are now considered viable therapeutic targets. We also discuss the strategies and prospects of PTP-targeted therapy, as well as its future development. [ABSTRACT FROM AUTHOR]

The influence of laser-alloyed stainless steel coatings on the properties of the surfaces of cast iron discs, such as friction-induced vibration and noise, friction coefficient, residual stress, hardness, and corrosion resistance, was investigated in this study. The experimental results show that after laser alloying, the surface hardness of the cast iron discs increased significantly. The residual stresses on the surfaces of the laser-alloyed discs changed from tensile to compressive residual stresses, while any compressive residual stresses increased by more than six times. Most of the laser-alloyed discs demonstrated better performance in friction-induced vibration and noise damping and friction reduction. Metallographic observation and XRD (X-ray diffraction) analysis results show that the laser-alloyed layer is mainly a mixture of acicular martensite and dendritic material, while the phase composition of laser-treated discs is mainly martensitic, [Fe, Ni], Fe3Si, Cr23C6, and austenite, which plays a significant role in the improvement of the properties of the laser-alloyed cast iron in physics, tribology and corrosion resistance. This research has significance for the laser surface treatment of various cast irons and steels, which is an increasingly important manufacturing technology in the vehicle friction brake industry. [ABSTRACT FROM AUTHOR]

Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed. [ABSTRACT FROM AUTHOR]

X-ray images typically contain complex background information and abundant small objects, posing significant challenges for object detection in security tasks. Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar's beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches. [ABSTRACT FROM AUTHOR]

Remote sensing-based techniques have been widely used for chlorophyll content (Cab) estimations, while they are challenging when transferred across different species. Sun-induced chlorophyll fluorescence (SIF) provides a new approach to address these issues. This research explores whether SIF has transferability for Cab estimation and to enhance between-species transferability. Here, three rice datasets and a rapeseed dataset were collected. Initially, direct transfer models were constructed using partial least squares regression (PLSR) based on SIF yield (SIFY) and reflectance, respectively. Subsequently, methods were employed within the rice datasets to improve the models' transferability. Finally, the between-species transferability of two data sources was validated in the rapeseed dataset. Direct transfer models indicated that the reflectance-based model exhibited a higher accuracy in predicting Cab when the training dataset acquired sufficient features, whereas the SIFY-based model showed better performance with fewer features. Spectral preprocessing methods can enhance the transferability, especially for SIFY-based models. In addition, supplementing 10% of out-of-sample data significantly improved the transferability. The proposed methods only require a small amount of new data to extend the original model for predicting Cab in other species. Specifically, the new method reduced the average RMSE based on SIFY and reflectance models by 23.59% and 35.51%, respectively. [ABSTRACT FROM AUTHOR]

This study was to investigate the relationship between metacognition and the mathematical modeling skills of high school students, as well as the mediating role of computational thinking. A cluster sampling method was adopted to investigate 661 high school students, using the metacognition scale, computational thinking scale, and mathematical modeling skill test questions. The results showed that metacognitive knowledge and metacognitive monitoring had a direct and positive correlation with high school students' mathematical modeling skills. Additionally, the critical thinking dimension of computational thinking mediated the relationship between metacognitive knowledge, experience, monitoring, and mathematical modeling skills. These findings indicated that sufficient metacognition could improve the critical thinking of high school students' computational thinking and enhance their mathematical modeling skills. [ABSTRACT FROM AUTHOR]

Starting from February 2023, the International Laser Ranging Service (ILRS) began releasing satellite laser ranging (SLR) data for all BeiDou global navigation satellite system (BDS-3) medium earth orbit (MEO) satellites. SLR data serve as the best external reference for validating satellite orbits, providing a basis for comprehensive evaluation of the BDS-3 satellite orbit. We utilized the SLR data from February to May 2023 to comprehensively evaluate the orbits of BDS-3 MEO satellites from different analysis centers (ACs). The results show that, whether during the eclipse season or the yaw maneuver season, the accuracy was not significantly decreased in the BDS-3 MEO orbit products released from the Center for Orbit Determination in Europe (CODE), Wuhan University (WHU), and the Deutsches GeoForschungsZentrum (GFZ) ACs, and the STD (Standard Deviation) of SLR residuals of those three ACs are all less than 5 cm. Among these, CODE had the smallest SLR residuals, with 9% and 12% improvement over WHU and GFZ, respectively. Moreover, the WHU precise orbits exhibit the smallest systematic biases, whether during non-eclipse seasons, eclipse seasons, or satellite yaw maneuver seasons. Additionally, we found some BDS-3 satellites (C32, C33, C34, C35, C45, and C46) exhibit orbit errors related to the Sun elongation angle, which indicates that continued effort for the refinement of the non-conservative force model further to improve the orbit accuracy of BDS-3 MEO satellites are in need. [ABSTRACT FROM AUTHOR]

The cable-stiffened single-layer latticed shell is an innovative structural design that achieves a perfect balance between lightweight and stability by combining cables and a latticed shell. However, the study on dynamic response and failure mechanism of cable-stiffened single-layer latticed shell under seismic action is still lacking. Therefore, small-scale shaking table tests of two kinds of single-layer spherical latticed shells are carried out; the dynamic response and failure mode of the two shells under sine wave earthquake are investigated by using time history analysis. The conclusions show that the introduction of the prestressed cable plays an important role in improving the seismic performance of the single-layer latticed shell, and the cable-stiffened single-layer latticed shell has better load capacity and seismic performance under earthquake action than the ordinary single-layer latticed shell structure. [ABSTRACT FROM AUTHOR]

In nature, rock masses often exhibit fissures, and varying external forces lead to different rates of loading on fissured rock masses. By studying the influence of the loading rate on the mechanical properties of fractured rock mass and AE characteristic parameters, it can provide a theoretical basis for the safety and stability prediction of engineering rock mass. To investigate the influence of loading rates on fissured rock masses, this study utilizes surrogate rock specimens resembling actual rock bodies and prefabricates two fissures. By conducting uniaxial compression acoustic emission tests at different loading rates, the study explores changes in their mechanical properties and acoustic emission characteristic parameters. Research findings indicate the following: (1) Prefabricated fissures adversely affect the stability of specimens, resulting in lower strength compared to intact specimens. Under the same fissure inclination angle, peak strength, elastic modulus, and loading rate exhibit a positive correlation. When the fissure inclination angle varies from 0° to 60° under the same loading rate, the peak strength of specimens generally follows a "V"-shaped trend, decreasing initially and then increasing, with the minimum peak strength observed at α = 30°. (2) Prefabricated fissure specimens primarily develop tensile cracks during loading, gradually transitioning to shear cracks, ultimately leading to shear failure. (3) The variation patterns of AE (acoustic emission) characteristic parameters under the influence of loading rate differ: AE event count, AE energy, and cumulative AE energy show a positive correlation with loading rate, while cumulative AE event count gradually decreases with increasing loading rate. (4) AE characteristic parameters exhibit good correlation with the stress–strain curve and can be divided into four stages. The changes in AE characteristic parameters correspond to the changes in the stress–strain curve. With increasing loading rate, AE signals in the first three stages gradually stabilize, focusing more on the fourth stage, namely the post-peak stage, where the specimens typically experience maximum AE signals accompanying final failure. [ABSTRACT FROM AUTHOR]

Biocharcoal (BioC), a cost-effective, eco-friendly, and sustainable material can be derived from various organic sources including agricultural waste. However, to date, complex chemical treatments using harsh solvents or physical processes at elevated temperatures have been used to activate and enhance the functional groups of biochar. In this paper, we propose a novel easy and cost-effective activation method based on electrochemical cycling in buffer solutions to enhance the electrochemical performance of biocharcoal derived from almond shells (AS-BioC). The novel electrochemical activation method enhanced the functional groups and porosity on the surface of AS-BioC, as confirmed by microscopic, spectroscopic characterizations. Electrochemical characterization indicated an increase in the conductivity and surface area. A modified SPCE with activated AS-BioC (A.AS-BioC/SPCE), shows enhanced electrochemical performance towards oxidation and reduction of nitrite and paraxon ethyl pesticide, respectively. For both target analytes, the activated electrode demonstrates high electrocatalytic activity and achieves a very LOD of 0.38 µM for nitrite and 1.35 nM for ethyl paraxon with a broad linear range. The sensor was validated in real samples for both contaminants. Overall, the research demonstrates an innovative technique to improve the performance of AS-BioC to use as a modifier material for electrochemical sensors. [ABSTRACT FROM AUTHOR]

Evapotranspiration is a key link in the water cycle of terrestrial ecosystems, and the partitioning of evapotranspiration is a prerequisite for diagnosing vegetation growth and water use strategies. In this study, we used double-layer eddy covariance (DLEC) measurements within and above the canopy of poplar plantations to divide evapotranspiration into transpiration and evaporation during the growing season. We diagnosed the coupling state of airflows in the canopy vertical layer and found that the daytime coupling state at the half-hourly scale can mask nighttime decoupling. Furthermore, we investigated the daytime and nighttime vertical layer airflow coupling states separately and quantified the effects of coupling states on the DLEC of resolved transpiration. The partitioning results of the DLEC method were taken as the standard after the airflow coupling test. Then, the performance and accuracy of evapotranspiration partitioning for the modified relaxed eddy accumulation (MREA), the conditional eddy covariance (CEC), and the flux variance similarity (FVS) with DLEC were compared. Transpiration calculated from MREA showed the best agreement with DLEC, and the other methods showed different degrees of underestimation (1:1 slope = 0.64–0.83). Evaporation calculated from FVS showed the best agreement with DLEC, while CEC and FVS made an overestimation of more than 26% (1:1 slope = 1.26–1.99), but MREA made an underestimation from 5% to 35% (1:1 slope = 0.65–0.95). The correlation coefficients between DLEC and MREA for transpiration were 0.95–0.97 with RMSEs of 15.52–17.04 W m−2, and those between DLEC and FVS for transpiration were 0.73–0.78 with RMSEs of 10–21.26 W m−2 at the daily half-hourly scale. A detailed comparison of the differences between DLEC and evapotranspiration partitioning methods from high-frequency eddy covariance data under the condition of canopy vertical layer airflow mixing provides knowledge about the consistency of results for evapotranspiration partitioning in poplar plantation forests. [ABSTRACT FROM AUTHOR]

The accurate timing for collision avoidance actions is crucial for preventing maritime collisions. Traditional methods often rely on collision risk assessments, using quantitative indicators like the Distance to the Closest Point of Approach (DCPA) and the Time to the Closest Point of Approach (TCPA). Ship Officers on Watch (OOWs) are required to execute avoidance maneuvers once these indicators reach or exceed preset safety thresholds. However, the effectiveness of these indicators is limited by uncertainties in the maritime environment and the human behaviors of OOWs. To address these limitations, this study introduces a machine learning method to learn collision avoidance behavior from empirical data of ship collision avoidance, particularly in cross-encounter situations. The research utilizes Automatic Identification System (AIS) data from the open waters around Ningbo Zhoushan Port. After data preprocessing and applying spatio-temporal constraints, this study identifies ship trajectory pairs in crossing scenarios and calculates their relative motion parameters. The Douglas–Peucker algorithm is used to identify the timing of ship collision avoidance actions and a collision avoidance decision dataset is constructed. The Random Forest algorithm was then used to analyze the factors affecting the timing of collision avoidance, and six key factors were identified: the distance, relative speed, relative bearing, DCPA, TCPA, and the ratio of the lengths of the giving-way and stand-on ships. These factors serve as inputs for the XGBoost algorithm model, which is enhanced with Particle Swarm Optimization (PSO), and thus constructing a ship collision avoidance decision model. In addition, considering the inherent errors in any model and the dynamic nature of the ship collision avoidance process, an action time window for collision avoidance is introduced, which provides a more flexible time range for ships to make timely collision avoidance responses based on actual conditions and the specific encounter environment. This model provides OOWs with accurate timing for taking collision avoidance decisions. Case studies have validated the practicality and effectiveness of this model, offering new theoretical foundations and practical guidance for maritime collision avoidance. [ABSTRACT FROM AUTHOR]

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane. [ABSTRACT FROM AUTHOR]

Despite the extensively researched physical principles, numerous published simulations on SiC drift step recovery diodes (SiC DSRD) and the practical implementation of SiC DSRD-based pulses, there are few kinds of research focusing on collaborative design between a SiC DSRD and its driving circuit. In this paper, a collaborative design method of a SiC DSRD and its driving circuit are presented. In addition, a detailed simulation is conducted to verify design considerations and to analyze the impact of driving parameter changes on the output pulse waveform. A pulse generator prototype with a self-developed SiC DSRD is implemented. The experimental results show that the circuit can output a peak voltage of 790 V on a matching load of 50 Ω, with a rise time of 520 ps (20%~80%), and can work at a 1 MHz repetition frequency rate with good stability. [ABSTRACT FROM AUTHOR]

Disuse muscle atrophy (DMA) is a significant healthcare challenge characterized by progressive loss of muscle mass and function resulting from prolonged inactivity. The development of effective strategies for muscle recovery is essential. In this study, we established a DMA mouse model through hindlimb suspension to evaluate the therapeutic potential of lactate in alleviating the detrimental effects on the gastrocnemius muscle. Using NMR-based metabolomic analysis, we investigated the metabolic changes in DMA-injured gastrocnemius muscles compared to controls and evaluated the beneficial effects of lactate treatment. Our results show that lactate significantly reduced muscle mass loss and improved muscle function by downregulating Murf1 expression, decreasing protein ubiquitination and hydrolysis, and increasing myosin heavy chain levels. Crucially, lactate corrected perturbations in four key metabolic pathways in the DMA gastrocnemius: the biosynthesis of phenylalanine, tyrosine, and tryptophan; phenylalanine metabolism; histidine metabolism; and arginine and proline metabolism. In addition to phenylalanine-related pathways, lactate also plays a role in regulating branched-chain amino acid metabolism and energy metabolism. Notably, lactate treatment normalized the levels of eight essential metabolites in DMA mice, underscoring its potential as a therapeutic agent against the consequences of prolonged inactivity and muscle wasting. This study not only advances our understanding of the therapeutic benefits of lactate but also provides a foundation for novel treatment approaches aimed at metabolic restoration and muscle recovery in conditions of muscle wasting. [ABSTRACT FROM AUTHOR]

Magnesium matrix composites are essential lightweight metal matrix composites, following aluminum matrix composites, with outstanding application prospects in automotive, aerospace lightweight and biomedical materials because of their high specific strength, low density and specific stiffness, good casting performance and rich resources. However, the inherent low plasticity and poor fatigue resistance of magnesium hamper its further application to a certain extent. Many researchers have tried many strengthening methods to improve the properties of magnesium alloys, while the relationship between wear resistance and plasticity still needs to be further improved. The nanoparticles added exhibit a good strengthening effect, especially the ceramic nanoparticles. Nanoparticle-reinforced magnesium matrix composites not only exhibit a high impact toughness, but also maintain the high strength and wear resistance of ceramic materials, effectively balancing the restriction between the strength and toughness. Therefore, this work aims to provide a review of the state of the art of research on the matrix, reinforcement, design, properties and potential applications of nano-reinforced phase-reinforced magnesium matrix composites (especially ceramic nanoparticle-reinforced ones). The conventional and potential matrices for the fabrication of magnesium matrix composites are introduced. The classification and influence of ceramic reinforcements are assessed, and the factors influencing interface bonding strength between reinforcements and matrix, regulation and design, performance and application are analyzed. Finally, the scope of future research in this field is discussed. [ABSTRACT FROM AUTHOR]

With the development of wireless communication, increasing signal processing presents higher requirements for radio frequency (RF) systems. Piezoelectric acoustic filters, as important elements of an RF front-end, have been widely used in 5G-generation systems. In this work, we propose a Sc0.2Al0.8N-based film bulk acoustic wave resonator (FBAR) for use in the design of radio frequency filters for the 5G mid-band spectrum with a passband from 3.4 to 3.6 GHz. With the excellent piezoelectric properties of Sc0.2Al0.8N, FBAR shows a large K e f f 2 of 13.1%, which can meet the requirement of passband width. Based on the resonant characteristics of Sc0.2Al0.8N FBAR devices, we demonstrate and fabricate different ladder-type FBAR filters with second, third and fourth orders. The test results show that the out-of-band rejection improves and the insertion loss decreases slightly as the filter order increases, although the frequency of the passband is lower than the predicted ones due to fabrication deviation. The passband from 3.27 to 3.47 GHz is achieved with a 200 MHz bandwidth and insertion loss lower than 2 dB. This work provides a potential approach using ScAlN-based FBAR technology to meet the band-pass filter requirements of 5G mid-band frequencies. [ABSTRACT FROM AUTHOR]

A wide variety of natural bioactive compounds derived from plants have demonstrated significant clinical relevance in the treatment of various diseases such as cancer, chronic disease, and inflammation. An increasing number of studies have surfaced that give credence to the potential of plant-derived vesicle-like nanoparticles (PDVLNs) as compelling candidates for a drug delivery system (DDS). PDVLNs are cost-effective production, non-toxicity and non-immunogenicity and fascinating bi-ocompatibility. In this review, we attempt to comprehensively review and consolidate the position of PDVLNs as next-generation drug delivery nanoplatforms. We aim to give a quick glance to readers of the current developments of PDVLNs, including their biogenesis, characteristic features, composition, administration routes, advantages, and application. Further, we discuss the advantages and limitations of PDVLNs. We expect that the role of PDVLNs in drug delivery will be significantly enhanced, thus positioning them as the next generation of therapeutic modalities in the foreseeable future. [ABSTRACT FROM AUTHOR]

Recombinant adeno-associated viral (AAV) vectors have emerged as prominent gene delivery vehicles for gene therapy. AAV capsid proteins determine tissue specificity and immunogenicity and play important roles in receptor binding, the escape of the virus from the endosome, and the transport of the viral DNA to the nuclei of target cells. Therefore, the comprehensive characterization of AAV capsid proteins is necessary for a better understanding of the vector assembly, stability, and transduction efficiency of AAV gene therapies. Glycosylation is one of the most common post-translational modifications (PTMs) and may affect the tissue tropism of AAV gene therapy. However, there are few studies on the characterization of the N- and O-glycosylation of AAV capsid proteins. In this study, we identified the N- and O-glycosylation sites and forms of AAV9 capsid proteins generated from HEK293 cells using liquid chromatography–tandem mass spectrometry (LC-MS)-based glycopeptide mapping and identified free N-glycans released from AAV9 capsid proteins by PNGase F using hydrophilic interaction (HILIC) LC-MS and HILIC LC-fluorescence detection (FLD) methods. This study demonstrates that AAV9 capsids are sprinkled with sugars, including N- and O-glycans, albeit at low levels. It may provide valuable information for a better understanding of AAV capsids in supporting AAV-based gene therapy development. [ABSTRACT FROM AUTHOR]

Toona ciliata, also known as Chinese mahogany, is a high-quality and fast-growing wood species with a high economic value. The wood properties of T. ciliata of different provenances vary significantly. In this study, we conducted comprehensive transcriptome and metabolome analyses of red and non-red T. ciliata wood cores of different provenances to compare their wood properties and explore the differential metabolites and genes that govern the variation in their wood properties. Through combined analyses, three differential genes and two metabolites were identified that are possibly related to lignin synthesis. The lignin content in wood cores from T. ciliata of different provenances shows significant variation following systematic measurement and comparisons. The gene Tci09G002190, one of the three differential genes, was identified as a member of the CAD (Cinnamyl alcohol dehydrogenase) gene family of T. ciliata, which is associated with lignin synthesis. Our data provide insights into the determinants of the wood properties in T. ciliata, providing a solid foundation for research into the subsequent mechanisms of the formation of T. ciliata wood. [ABSTRACT FROM AUTHOR]

The ability to capture and record high-resolution images over long distances is essential for a wide range of applications, including connected and autonomous vehicles, defense and security operations, as well as agriculture and mining industries. Here, we demonstrate a self-assembled bistatic long-range imaging LiDAR system. Importantly, to achieve high signal-to-noise ratio (SNR) data, we employed a comprehensive suite of denoising methods including temporal, spatial, spectral, and polarization filtering. With the aid of these denoising technologies, our system has been validated to possess the capability of imaging under various complex usage conditions. In terms of distance performance, the test results achieved ranges of over 4000 m during daylight with clear weather, 19,200 m at night, 6700 m during daylight with haze, and 2000 m during daylight with rain. Additionally, it offers an angular resolution of 0.01 mrad. These findings demonstrate the potential to offer comprehensive construction strategies and operational methodologies to individuals seeking long-range LiDAR data. [ABSTRACT FROM AUTHOR]

IRAK4 is a critical mediator in NF-κB-regulated inflammatory signaling and has emerged as a promising therapeutic target for the treatment of autoimmune diseases; however, none of its inhibitors have received FDA approval. In this study, we identified a novel small-molecule IRAK4 kinase inhibitor, DW18134, with an IC50 value of 11.2 nM. DW18134 dose-dependently inhibited the phosphorylation of IRAK4 and IKK in primary peritoneal macrophages and RAW264.7 cells, inhibiting the secretion of TNF-α and IL-6 in both cell lines. The in vivo study demonstrated the efficacy of DW18134, significantly attenuating behavioral scores in an LPS-induced peritonitis model. Mechanistically, DW18134 reduced serum TNF-α and IL-6 levels and attenuated inflammatory tissue injury. By directly blocking IRAK4 activation, DW18134 diminished liver macrophage infiltration and the expression of related inflammatory cytokines in peritonitis mice. Additionally, in the DSS-induced colitis model, DW18134 significantly reduced the disease activity index (DAI) and normalized food and water intake and body weight. Furthermore, DW18134 restored intestinal damage and reduced inflammatory cytokine expression in mice by blocking the IRAK4 signaling pathway. Notably, DW18134 protected DSS-threatened intestinal barrier function by upregulating tight junction gene expression. In conclusion, our findings reported a novel IRAK4 inhibitor, DW18134, as a promising candidate for treating inflammatory diseases, including peritonitis and IBD. [ABSTRACT FROM AUTHOR]

Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate. [ABSTRACT FROM AUTHOR]

Expansive soils exhibit directionally dependent swelling that traditional isotropic models fail to capture. This study investigates the anisotropic swelling characteristics of expansive soil with a medium swelling potential through the use of modified oedometric testing. Vertical swelling strains can reach up to 1.71 times that of the horizontal movements, confirming intrinsic anisotropy. A nonlinear elastic constitutive model incorporates vertical and horizontal elastic moduli with respect to matric suction to characterize anisotropy. Three elastic parameters were determined through the experiments, and predictive equations were developed to estimate the unsaturated moduli. The constitutive model and predictive techniques provide practical tools to better assess expansive soil pressures considering anisotropy, offering guidelines for utilization and design. The outcomes advance understanding of these soils' directionally dependent behavior and stress–strain–suction response. [ABSTRACT FROM AUTHOR]

Utilizing wheat embryos as the raw material and employing Aspergillus niger as the fermentation strain, wheat embryo polypeptides were produced through microbial liquid fermentation. The protein concentration post-fermentation served as the response variable, and the preparation process underwent optimization through single-factor testing and a response surface methodology, followed by the assessment of antioxidant activity. The findings revealed that the optimal conditions for wheat embryo peptide preparation via Aspergillus niger fermentation included a fermentation duration of 24 h, an inoculum volume of 4%, an initial pH of 7, and a protein concentration of 21.47 mg/mL. Peptides with different molecular weights were then prepared by dead-end filtration. The results showed that F6 (<3 kDa) had strong scavenging ability against DPPH, ABTS, and ·OH radicals, which provided a basis for the preparation of antioxidant peptides in wheat germ and related research. [ABSTRACT FROM AUTHOR]

Increasing evidence suggests that the gut microbiota may represent potential strategies for Parkinson's disease (PD) treatment. Our previous research revealed a decreased abundance of Akkermansia muciniphila (Akk) in PD mice; however, whether Akk is beneficial to PD is unknown. To answer this question, the mice received MPTP intraperitoneally to construct a subacute model of PD and were then supplemented with Akk orally for 21 consecutive days. Motor function, dopaminergic neurons, neuroinflammation, and neurogenesis were examined. In addition, intestinal inflammation, and serum and fecal short-chain fatty acids (SCFAs) analyses, were assessed. We found that Akk treatment effectively inhibited the reduction of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and partially improved the motor function in PD mice. Additionally, Akk markedly alleviated neuroinflammation in the striatum and hippocampus and promoted hippocampal neurogenesis. It also decreased the level of colon inflammation. Furthermore, these aforementioned changes are mainly accompanied by alterations in serum and fecal isovaleric acid levels, and lower intestinal permeability. Our research strongly suggests that Akk is a potential neuroprotective agent for PD therapy. [ABSTRACT FROM AUTHOR]

Traditional bridge monitoring and damage identification techniques typically rely on full-bridge coverage of sensors, such as displacement or strain sensors. However, this approach proves economically unfeasible for the vast numbers of small- and medium-span continuous beam bridges. In response to the need for rapid damage identification and integrity assessment of continuous beam bridges, a novel bridge safety monitoring method relying solely on bearing reaction forces is proposed. Firstly, the analytical expressions for the bearing reaction influence lines of a three-span continuous beam bridge under damage conditions were derived. Secondly, a rapid structural damage localization method based on the bearing reaction influence lines was proposed. Finally, feasibility and applicability were confirmed through numerical simulations and experimental validation. Additionally, the discussion includes the implementation of the warning classification and threshold setting using data from bearing force monitoring. The research demonstrates that utilizing a limited amount of bearing reaction force information can not only identify damage areas in a "non-full-coverage" manner, but also facilitates early warning and the integrity assessment of bridges. In the future, there is potential for large-scale application in medium- and small-span continuous beam bridges. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model's exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis. [ABSTRACT FROM AUTHOR]

Trimethylamine N-oxide (TMAO) has attracted interest because of its association with cardiovascular disease and diabetes, and evidence for the beneficial effects of TMAO is accumulating. This study investigates the role of TMAO in improving exercise performance and elucidates the underlying molecular mechanisms. Using C2C12 cells, we established an oxidative stress model and administered TMAO treatment. Our results indicate that TMAO significantly protects myoblasts from oxidative stress-induced damage by increasing the expression of Nrf2, heme oxygenase-1 (HO-1), NAD(P)H dehydrogenase (NQO1), and catalase (CAT). In particular, suppression of Nrf2 resulted in a loss of the protective effects of TMAO and a significant decrease in the expression levels of Nrf2, HO-1, and NQO1. In addition, we evaluated the effects of TMAO in an exhaustive swimming test in mice. TMAO treatment significantly prolonged swimming endurance, increased glutathione and taurine levels, enhanced glutathione peroxidase activity, and increased the expression of Nrf2 and its downstream antioxidant genes, including HO-1, NQO1, and CAT, in skeletal muscle. These findings underscore the potential of TMAO to counteract exercise-induced oxidative stress. This research provides new insights into the ability of TMAO to alleviate exercise-induced oxidative stress via the Nrf2 signaling pathway, providing a valuable framework for the development of sports nutrition supplements aimed at mitigating oxidative stress. [ABSTRACT FROM AUTHOR]

CULLIN (CUL) protein is a subtype of E3 ubiquitin ligase that is involved in a variety of biological processes and responses to stress in plants. In Uncaria rhynchophylla, the CUL gene family has not been identified and its role in plant development, stress response and secondary metabolite synthesis has not been studied. In this study, 12 UrCUL gene members all contained the typical N-terminal domain and C-terminal domain identified from the U. rhynchophylla genome and were classified into four subfamilies based on the phylogenetic relationship with CULs in Arabidopsis thaliana. They were unevenly distributed on eight chromosomes but had a similar structural composition in the same subfamily, indicating that they were relatively conserved and potentially had similar gene functions. An interspecific and intraspecific collinearity analysis showed that fragment duplication played an important role in the evolution of the CUL gene family. The analysis of the cis-acting elements suggests that the UrCULs may play an important role in various biological processes, including the abscisic acid (ABA) response. To investigate this hypothesis, we treated the roots of U. rhynchophylla tissue-cultured seedlings with ABA. The expression pattern analysis showed that all the UrCUL genes were widely expressed in roots with various expression patterns. The co-expression association analysis of the UrCULs and key enzyme genes in the terpenoid indole alkaloid (TIA) synthesis pathway revealed the complex expression patterns of 12 UrCUL genes and some key TIA enzyme genes, especially UrCUL1, UrCUL1-likeA, UrCUL2-likeA and UrCUL2-likeB, which might be involved in the biosynthesis of TIAs. The results showed that the UrCULs were involved in the response to ABA hormones, providing important information for elucidating the function of UrCULs in U. rhynchophylla. The mining of UrCULs in the whole genome of U. rhynchophylla provided new information for understanding the CUL gene and its function in plant secondary metabolites, growth and development. [ABSTRACT FROM AUTHOR]

Cardiac fibrosis is a key aspect of heart failure, leading to reduced ventricular compliance and impaired electrical conduction in the myocardium. Various pathophysiologic conditions can lead to fibrosis in the left ventricle (LV) and/or right ventricle (RV). Despite growing evidence to support the transcriptomic heterogeneity of cardiac fibroblasts (CFs) in healthy and diseased states, there have been no direct comparisons of CFs in the LV and RV. Given the distinct natures of the ventricles, we hypothesized that LV- and RV-derived CFs would display baseline transcriptomic differences that influence their proliferation and differentiation following injury. Bulk RNA sequencing of CFs isolated from healthy murine left and right ventricles indicated that LV-derived CFs may be further along the myofibroblast transdifferentiation trajectory than cells isolated from the RV. Single-cell RNA-sequencing analysis of the two populations confirmed that Postn+ CFs were more enriched in the LV, whereas Igfbp3+ CFs were enriched in the RV at baseline. Notably, following pressure overload injury, the LV developed a larger subpopulation of pro-fibrotic Thbs4+/Cthrc1+ injury-induced CFs, while the RV showed a unique expansion of two less-well-characterized CF subpopulations (Igfbp3+ and Inmt+). These findings demonstrate that LV- and RV-derived CFs display baseline subpopulation differences that may dictate their diverging responses to pressure overload injury. Further study of these subpopulations will elucidate their role in the development of fibrosis and inform on whether LV and RV fibrosis require distinct treatments. [ABSTRACT FROM AUTHOR]

Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution. [ABSTRACT FROM AUTHOR]

The interaction between human serum albumin (HSA) and hispidin, a polyketide abundantly present in both edible and therapeutic mushrooms, was explored through multispectral methods, hydrophobic probe assays, location competition trials, and molecular docking simulations. The results of fluorescence quenching analysis showed that hispidin quenched the fluorescence of HSA by binding to it via a static mechanism. The binding of hispidin and HSA was validated further by synchronous fluorescence, three-dimensional fluorescence, and UV/vis spectroscopy analysis. The apparent binding constant (Ka) at different temperatures, the binding site number (n), the quenching constants (Ksv), the dimolecular quenching rate constants (Kq), and the thermodynamic parameters (∆G, ∆H, and ∆S) were calculated. Among these parameters, ∆H and ∆S were determined to be 98.75 kJ/mol and 426.29 J/(mol·K), respectively, both exhibiting positive values. This observation suggested a predominant contribution of hydrophobic forces in the interaction between hispidin and HSA. By employing detergents (SDS and urea) and hydrophobic probes (ANS), it became feasible to quantify alterations in Ka and surface hydrophobicity, respectively. These measurements confirmed the pivotal role of hydrophobic forces in steering the interaction between hispidin and HSA. Site competition experiments showed that there was an interaction between hispidin and HSA molecules at site I, which situates the IIA domains of HSA, which was further confirmed by the molecular docking simulation. [ABSTRACT FROM AUTHOR]

Head smut is a soil-borne fungal disease caused by Sporisorium reilianum that infects maize tassels and ears. This disease poses a tremendous threat to global maize production. A previous study found markedly different and stably heritable tassel symptoms in some maize inbred lines with Sipingtou blood after infection with S. reilianum. In the present study, 55 maize inbred lines with Sipingtou blood were inoculated with S. reilianum and classified into three tassel symptom types (A, B, and C). Three maize inbred lines representing these classes (Huangzao4, Jing7, and Chang7-2, respectively) were used as test materials to investigate the physiological mechanisms of tassel formation in infected plants. Changes in enzyme activity, hormone content, and protein expression were analyzed in all three lines after infection and in control plants. The activities of peroxidase (POD), superoxide dismutase (SOD), and phenylalanine-ammonia-lyase (PAL) were increased in the three typical inbred lines after inoculation. POD and SOD activities showed similar trends between lines, with the increase percentage peaking at the V12 stage (POD: 57.06%, 63.19%, and 70.28% increases in Huangzao4, Jing7, and Chang7-2, respectively; SOD: 27.01%, 29.62%, and 47.07% in Huangzao4, Jing7, and Chang7-2, respectively. These were all higher than in the disease-resistant inbred line Mo17 at the same growth stage); this stage was found to be key in tassel symptom formation. Levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) were also altered in the three typical maize inbred lines after inoculation, with changes in GA3 and IAA contents tightly correlated with tassel symptoms after S. reilianum infection. The differentially expressed proteins A5H8G4, P09233, and Q8VXG7 were associated with changes in enzyme activity, whereas P49353, P13689, and P10979 were associated with changes in hormone contents. Fungal infection caused reactive oxygen species (ROS) and nitric oxide (NO) bursts in the three typical inbred lines. This ROS accumulation caused biofilm disruption and altered host signaling pathways, whereas NO signaling triggered strong secondary metabolic responses in the host and altered the activities of defense-related enzymes. These factors together resulted in the formation of varying tassel symptoms. Thus, interactions between S. reilianum and susceptible maize materials were influenced by a variety of signals, enzymes, hormones, and metabolic cycles, encompassing a very complex regulatory network. This study preliminarily identified the physiological mechanisms leading to differences in tassel symptoms, deepening our understanding of S. reilianum-maize interactions. [ABSTRACT FROM AUTHOR]

Standard convolution sliding along a fixed direction in common convolutional neural networks (CNNs) is inconsistent with the direction of aerial targets, making it difficult to effectively extract features with high-aspect-ratio and arbitrary directional targets. To this end, We have fully considered the dynamic adaptability of remote sensing (RS) detectors in feature extraction and the balance of sample gradients during training and designed a plug-and-play dynamic rotation convolution with an adaptive alignment function. Specifically, we design dynamic convolutions in the backbone network that can be closely coupled with the spatial features of aerial targets. We design a network that can capture the rotation angle of aerial targets and dynamically adjust the spatial sampling position of the convolution to reduce the difference between the convolution and the target in directional space. In order to improve the stability of the network, a gradient adaptive equalization loss function is designed during training. The loss function we designed strengthens the gradient of high-quality samples, dynamically balancing the gradients of samples of different qualities to achieve stable training of the network. Sufficient experiments were conducted on the DOTA, HRSC-2016, and UCAS-AOD datasets to demonstrate the effectiveness of the proposed method and to achieve an effective balance between complexity and accuracy. [ABSTRACT FROM AUTHOR]

There is no analytical solution to the deflection influence line of catenary hingeless arches nor an explicit solution to the deflection influence line difference curvature of variable section hingeless arches. Based on the force method equation, a deflection influence line analytical solution at any location before and after structural damage is obtained, and then an explicit solution of the deflection influence line difference curvature of the structural damage is obtained. The indexes suitable for arch structure damage identification are presented. Based on analytical theory and a finite element model, the feasibility of identifying damage at a single location and multiple locations of an arch bridge is verified. This research shows that when a moving load acts on a damaged area of an arch structure, the curvature of the deflection influence line difference will mutate, which proves theoretically that the deflection influence line difference curvature can be used for the damage identification of hingeless arch structures. This research has provided theoretical support for hingeless arch bridge design and evaluation. Combined with existing bridge monitoring methods, the new bridge damage identification method proposed in this paper has the potential to realize normal health status assessments of existing arch bridges in the future. [ABSTRACT FROM AUTHOR]

Recently, the application of LiFePO4 (LFP) batteries in electric vehicles has attracted extensive attention from researchers. This work presents a composite of LFP particles trapped in reduced graphene oxide (rGO) nanosheets obtained through the high-temperature reduction strategy. The obtained LiFePO4/rGO composites indicate spherical morphology and uniform particles. As to the structure mode of the composite, LFP distributes in the interlayer structure of rGO, and the rGO evenly covers the surface of the particles. The LFP/rGO cathodes demonstrate a reversible specific capacity of 165 mA h g−1 and high coulombic efficiency at 0.2 C, excellent rate capacity (up to 10 C), outstanding long-term cycling stability (98%) after 1000 cycles at 5 C. The combined high electron conductivity of the layered rGO coating and uniform LFP particles contribute to the remarkable electrochemical performance of the LFP/rGO composite. The unique LFP/rGO cathode provides a potential application in high-power lithium-ion batteries. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sensorless control strategy for permanent magnet synchronous motors (PMSMs) based on an adaptive-gain sliding mode observer (ASMO) with precise compensation. Firstly, the observer adopts a saturation function with a continuous boundary layer as the switching function to avoid the delay effect of the low-pass filter. In addition, the adaptive-gain method is designed based on a conventional sliding mode observer (SMO) with constant-gain and a saturation function. The adaptive-gain mathematical model is simplified by establishing a nonlinear feedback channel of an SMO. Secondly, in order to improve the practicability and facilitate debugging, the explicit stability condition of an ASMO is deduced according to Lyapunov's second method. According to the proposed adaptive method, the chattering caused by the mismatch of the sliding-mode gain is suppressed, and the observable speed range is also improved. Thirdly, the position estimation delay problem of an SMO with a continuous switching function is analyzed in detail from the perspective of frequency characteristics. Then, a precise compensation method is proposed for the delay problem, which greatly improves the position estimation accuracy and the control performance. Finally, the correctness of the theory and the feasibility of the ASMO with precise compensation for the sensorless control of PMSMs are verified by experiments. [ABSTRACT FROM AUTHOR]

Ankle joint flexion and extension movements play an important role in the rehabilitation training of patients who have been injured or bedridden for a long time before and after surgery. Accurately guiding patients to perform ankle flexion and extension movements can significantly reduce deep vein thromboembolism. Currently, most ankle rehabilitation devices focus on assisting patients with ankle flexion and extension movements, and there is a lack of devices for effectively monitoring these movements. In this study, we designed an ankle joint flexion and extension movement-monitoring device based on a pressure sensor. It was composed of an STM32 microcontroller, a pressure sensor, an HX711A/D conversion chip, and an ESP8266 WiFi communication module. The value of the force and the effective number of ankle joint flexion and extension movements were obtained. An experimental device was designed to verify the accuracy of the system. The maximum average error was 0.068 N; the maximum average relative error was 1.7%; the maximum mean-squared error was 0.00464 N. The results indicated that the monitoring device had a high accuracy and could effectively monitor the force of ankle flexion and extension movements, ultimately ensuring that the patient could effectively monitor and grasp the active ankle pump movement. [ABSTRACT FROM AUTHOR]

Large-class few-shot learning has a wide range of applications in many fields, such as the medical, power, security, and remote sensing fields. At present, many few-shot learning methods for fewer-class scenarios have been proposed, but little research has been performed for large-class scenarios. In this paper, we propose a large-class few-shot learning method called HF-FSL, which is based on high-dimensional features. Recent theoretical research shows that if the distribution of samples in a high-dimensional feature space meets the conditions of compactness within the class and the dispersion between classes, the large-class few-shot learning method has a better generalization ability. Inspired by this theory, the basic idea is use a deep neural network to extract high-dimensional features and unitize them to project the samples onto a hypersphere. The global orthogonal regularization strategy can then be used to make samples of different classes on the hypersphere that are as orthogonal as possible, so as to achieve the goal of sample compactness within the class and the dispersion between classes in high-dimensional feature space. Experiments on Omniglot, Fungi, and ImageNet demonstrate that the proposed method can effectively improve the recognition accuracy in a large-class FSL problem. [ABSTRACT FROM AUTHOR]