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Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence.

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.

Background: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. Methods: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. Results: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. Conclusions: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss.

This study aims to investigate the wake of two tandem square cylinders based on the Proper Orthogonal Decomposition (POD) analyses of the PIV and hotwire data. The cylinder centre-to-centre spacing ratio L/w examined is from 1.2 to 4.2, covering the four flow regimes, i.e., extended body, reattachment, transition and co-shedding. The Reynolds number examined was 1.3 × 104. A novel Proper Orthogonal Decomposition (POD) technique (hereafter referred to as PODHW) is developed to analyse data from single point hotwire measurements, offering a new perspective compared to the conventional POD analysis (PODPIV) based on Particle Image Velocimetry (PIV) data. A key finding is the identification of two distinct states, reattachment and co-shedding, within the transition flow regime at L/w = 2.8, which PODPIV fails to capture due to the limited duration of the PIV data obtained. This study confirms, for the first time, the existence of these states as proposed by Zhou et al. (2024), highlighting the advantage of using PODHW for capturing intermittent flow phenomena. Furthermore, the analysis reveals how the predominant coherent structures contribute to the total fluctuating velocity energy in each individual regime. Other aspects of the flow are also discussed, including the Strouhal numbers, the contribution to the total fluctuating energy of the flow from the first four POD modes, and a comparison between different regimes.

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.

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.

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.

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.

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.

Increasingly, research indicates that steel fibers can significantly enhance the engineering properties of mortar and concrete; however, few studies have examined their impact on the reinforcement of in-service tunnel linings within sleeve arch structures. In this study, a series of 1:2 scale experiments were conducted using a specialized loading device to compare the reinforcement performance of steel fiber-reinforced concrete sleeve arches and traditional reinforced concrete sleeve arches on prefabricated cracks with depths of 1/3 and 2/3 of the lining thickness. The experimental results were validated using numerical simulations. The results indicate that under the same load, when reinforcing components with 2/3 prefabricated cracks, the maximum compressive strains for steel fiber-reinforced and reinforced concrete sleeve arches were −852 με and −985 με, respectively, and the maximum deflections were 3.57 mm and 5.48 mm. Composite sleeve arches of both materials provide a certain degree of reinforcement to linings with varying damage. The reinforcement performance of steel fiber-reinforced concrete sleeve arches is superior to that of traditional reinforced concrete sleeve arches, with particularly significant reinforcement for linings with 2/3 prefabricated cracks. Numerical simulations have shown that the stress in reinforced concrete at the concentrated stress regions is 16.15%, 6.01%, 12.68%, 36.62%, and 4.82% higher than that in steel fiber-reinforced concrete, respectively, thereby validating the reliability of the experimental results. Therefore, this study recommends the application of steel fiber materials in sleeve arches to achieve superior maintenance and reinforcement, addressing cracking issues in in-service tunnel linings and thereby improving the safety and durability of these structures.

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.

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.

Mulching cultivation with agricultural wastes is the main production pattern of coffee at present, but the effect of mulching cultivation on photosynthetic physiological processes of coffee plants is still not clear. Therefore, a randomized block design was adopted to establish a field experiment by one-year-old Coffee Canephora seedlings in this study. There were four types of mulch treatments, including non-mulch coffee waste (C), mulching coffee litter (L), mulching coffee cascara (cherry pericarp, P), and mulching coffee litter and cascara (LP) in this field experiment. Soil properties and microenvironment (e.g., moisture, temperature, pH, bulk density, organic matter content, alkali-hydrolyzed nitrogen content, available potassium content, and available potassium content), agronomic traits (e.g., specific leaf area, leaf area index, plant height, and relative chlorophyll content), and photosynthetic indices (e.g., photosynthesis, transpiration, respiration, stomatal conductance, intercellular CO2 concentration, water use efficiency, and carbon use efficiency) were investigated to determine the effects of different coffee waste mulches on the photosynthetic physiology of coffee seedlings. The results show that coffee litter and cascara mulch significantly reduced soil temperature by 0.42 or 0.33 °C, respectively, and coffee litter rather than cascara mulch significantly increased the soil’s available potassium content by 46.28%, although coffee waste mulch did not affect other soil properties or microenvironment indices; coffee cascara mulching significantly increased the specific leaf area and net and gross photosynthesis of coffee by 45.46%, 78.33%, and 91.72%, respectively, but the mulching treatments did not affect stomatal conductance, transpiration, or carbon use efficiency in this study. Additionally, coffee cascara mulching increased leaf respiration and net and gross water use efficiency by 109.34%, 80.54%, and 104.95%, respectively. The coffee cascara mulching alone had the most significant positive impact on the photosynthetic index, followed by a combination of litter and cascara, litter alone, and the control treatment. The observed variations in the coffee photosynthetic index may be attributed to the reduction of soil temperature caused by mulching treatments rather than the increase in soil nutrients content. These results indicate that coffee cascara mulching could effectively promote photosynthesis and the growth of coffee seedlings by improving the soil microenvironment.

In this study, the SiBCN-Ti series ceramics with different Ti contents were fabricated, and the oxidation resistance and microstructural evolution of the ceramics at 1500 °C for different times were explored. The results show that with the increase in oxidation time, pores and bubbles are gradually formed in the oxide layer. When the oxidation time is less than or more than 4 h, the Ti(C, N) in the ceramics will maintain its initial structure or mostly transform to TiN. The introduction of Ti content can promote the formation of rutile silicate glass, thus healing the cracks and improving the oxidation resistance of the ceramics effectively.

Graph contrastive learning has demonstrated significant superiority for collaborative filtering. These methods typically use augmentation technology to generate contrastive views, and then train graph neural networks with contrastive learning as an auxiliary task. Although these methods are very effective, they do not consider using contrastive learning from the perspective of user–item interaction. As a result, they do not fully leverage the potential of contrastive learning. It is well known that contrastive learning can maximize the consistency of positive pairs and minimize the agreement of negative pairs. Collaborative filtering expects high consistency between users and the items they like and low consistency between users and the items they dislike. If we treat the items that users like as positive examples and the items they dislike as negative examples, contrastive learning can work very well with the goal of collaborative filtering. Based on the above understanding, we propose a new objective function called DCL loss, which improves graph collaborative filtering from the perspective of user–item interaction when Directly using Contrastive Learning. Extensive experiments have shown that when a model adopts DCL loss as its objective function, both its recommendation performance and training efficiency exhibit significant improvements.

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.

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.

Lonicerae japonicae Flos (LJF) is a natural plant containing abundant antioxidant ingredients. In order to extract more antioxidants from LJF, in this study, a novel strategy was proposed for optimizing the extraction factor level by response surface methodology with a tailored deep eutectic solvent (DES) as the extraction solvent and antioxidant ability as the evaluation index. After optimizing the composition of DES and the extraction condition, the extracts obtained by our proposed method yielded better antioxidant ability (229.1–249.1 μmol TE/g DW) and higher antioxidant contents (34.2–36.5 mg GAE/g DW for total phenolics and 119.6–123.0 mg RE/g DW for total flavonoids) from LJF in 5 min without organic solvent consumption that were significantly superior to the Chinese Pharmacopoeia extraction method. The K-T solvation parameter and a scanning electron microscope were adopted to explore the extraction mechanism, and the results showed that the polarity and damage effect on plant cells of DES were crucial for the extraction of antioxidants. In addition, after combining the HPLC fingerprint and partial least squares model, chlorogenic acid, rutin, and 3,5-O-Dicaffeoylquinic acid were screened as the antioxidant Q-markers of LJF. This work demonstrates that an optimization strategy based on antioxidant ability and tailored DES has the potential to extract more antioxidants from natural plants.

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.

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.

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.

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.

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.

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.

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.

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.

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 Keff2 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.

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.

Viral co-infections, in which a host is infected with multiple viruses simultaneously, are common in the human population. Human viral co-infections can lead to complex interactions between the viruses and the host immune system, affecting the clinical outcome and posing challenges for treatment. Understanding the types, mechanisms, impacts, and identification methods of human viral co-infections is crucial for the prevention and control of viral diseases. In this review, we first introduce the significance of studying human viral co-infections and summarize the current research progress and gaps in this field. We then classify human viral co-infections into four types based on the pathogenic properties and species of the viruses involved. Next, we discuss the molecular mechanisms of viral co-infections, focusing on virus–virus interactions, host immune responses, and clinical manifestations. We also summarize the experimental and computational methods for the identification of viral co-infections, emphasizing the latest advances in high-throughput sequencing and bioinformatics approaches. Finally, we highlight the challenges and future directions in human viral co-infection research, aiming to provide new insights and strategies for the prevention, control, diagnosis, and treatment of viral diseases. This review provides a comprehensive overview of the current knowledge and future perspectives on human viral co-infections and underscores the need for interdisciplinary collaboration to address this complex and important topic.

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.

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

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.

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.

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.

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.

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.

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.

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 (

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.

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.

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.

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.

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is characterized by hepatic fat accumulation by metabolic dysfunction. The rising prevalence of MAFLD, especially among Asians, may be associated with changes in gut microbiota. We investigated gut microbiota characteristics and potential mechanisms leading to MAFLD development according to enterotypes. Case-control studies examining the gut microbiota composition between MAFLD and non-MAFLD participants were searched in public databases until July 2023. Gut microbiota was categorized into two enterotypes by principal component analysis. According to the enterotypes, LEfSe, ALDEx2, XGBoost, and DCiPatho were utilized to identify differential abundances and pathogenic microbes in the gut between the MAFLD and non-MAFLD groups. We analyzed microbial community networks with the SprCC module and predicted microbial functions. In the Prevotella enterotype (ET-P), 98.6% of Asians and 65.1% of Caucasians were associated with MAFLD (p = 0.049). MAFLD incidence was correlated with enterotype, age, obesity, and ethnicity (p < 0.05). Asian MAFLD patients exhibited decreased Firmicutes and Akkermansia muciniphila and increased Bacteroidetes and P. copri. The pathogenicity scores were 0.006 for A. muciniphila and 0.868 for P. copri. The Asian MAFLD group showed decreased stability and complexity in the gut microbiota network. Metagenome function analysis revealed higher fructose metabolism and lipopolysaccharide (LPS) biosynthesis and lower animal proteins and α-linolenic acid metabolism in Asians with MAFLD compared with the non-MAFLD group. LPS biosynthesis was positively correlated with P. copri (p < 0.05). In conclusion, P. copri emerged as a potential microbial biomarker for MAFLD. These findings enhance our understanding of the pathological mechanisms of MAFLD mediated through the gut microbiota, providing insights for future interventions.

Field-effect transistor (FET)-based biosensors are powerful analytical tools for detecting trace-specific biomolecules in diverse sample matrices, especially in the realms of pandemics and infectious diseases. The primary concern in applying these biosensors is their stability, a factor directly impacting the accuracy and reliability of sensing over extended durations. The risk of biosensor degradation is substantial, potentially jeopardizing the sensitivity and selectivity and leading to inaccurate readings, including the possibility of false positives or negatives. This paper delves into the documented degradation of silicon nanobelt FET (NBFET) biosensors induced by buffer solutions. The results highlight a positive correlation between immersion time and the threshold voltage of NBFET devices. Secondary ion mass spectrometry analysis demonstrates a gradual increase in sodium and potassium ion concentrations within the silicon as immersion days progress. This outcome is ascribed to the nanobelt’s exposure to the buffer solution during the biosensing period, enabling ion penetration from the buffer into the silicon. This study emphasizes the critical need to address buffer-solution-induced degradation to ensure the long-term stability and performance of FET-based biosensors in practical applications.

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.

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.

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.

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.

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.

An efficient and convenient method for the synthesis of phenols and aliphatic alcohols is described in this paper. The oxidative hydroxylation reaction of various organoboron compounds proceeded smoothly by employing H2O2 as the oxidant and citric acid as the catalyst in water at room temperature to produce phenols and aliphatic alcohols in satisfactory to excellent yields (up to 99% yield). Various synthetically useful functional groups, such as halogen atom, cyano, and nitro groups, remain intact during the oxidative hydroxylation. The developed catalytic system also could accommodate phenylboronic pinacol ester and potassium phenyltrifluoroborate to give the target product good yields.