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CeO2-ZrO2-La2O3 supported Pt-Pd bimetallic three-way catalysts (0.6Pt-0.4Pd/CZL) were synthesized through the conventional impregnation method and then subjected to severe thermal aging. Reactivating treatments under different temperatures were then applied to the aged catalysts above. Three-way catalytic performance evaluations and dynamic operation window tests along with detailed physio-chemical characterizations were carried out to explore possible structure–activity evolutions during the reactivating process. Results show that the reactivating process conducted at proper temperatures (500~550 °C) could effectively restore the TWC catalytic performance and widen the operation window width. The suitable reactivating temperature ranges are mainly determined by the decomposing temperature of PMOx species, the thermal stability of PM-O-Ce species, and the encapsulation temperature of precious metals by CZL support. Reactivating under appropriate temperature helps to restore the interaction between Pt and CZL support to a certain extent and to re-expose part of the encapsulated precious metals. Therefore, the dynamic oxygen storage/release capacity, redox ability, as well as thermal stability of PtOx species, can be improved, thus benefiting the TWC catalytic performances. However, the excessively high reactivating temperature would cause further embedment of Pd by CZL support, thus leading to a further decrease in both dynamic oxygen storage/release capacity and the TWC catalytic performance after reactivating treatment.

Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness.

5Cu-USY and Ce-doped 5Cu8Ce-USY zeolite catalysts were prepared by the conventional impregnation method. The obtained catalysts were subjected to the hydrothermal ageing process. The catalytic performance of the selective catalytic reduction of NOx with NH3 (NH3-SCR) was evaluated on both fresh and aged catalysts. Physical/chemical characterizations such as X-ray diffraction (XRD), H2 temperature-programmed reduction (H2-TPR), and X-ray photoelectron spectroscopy (XPS) were performed, along with detailed in situ diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) experiments including CO adsorption, NH3 adsorption, and NO + O2 reactions. Results showed that, for the 5Cu-USY catalyst, hydrothermal ageing treatment could somehow improve the low-temperature SCR activity, but it also led to significant formation of unfavorable byproducts NO2 and N2O. Such an activity change can be attributed to hydrothermal ageing inducing the migration of isolated Cu+ species in the sodalite cavities towards the super cages of the USY zeolites. The increased content of Cu+ species in the super cages was beneficial for the low-temperature activity improvement, but, at the same time, it also facilitated ammonia oxidation at high temperatures. Ce doping after hydrothermal ageing has a “double-edged sword” effect on the catalytic performance. First of all, Ce doping can inhibit Cu species migration by self-occupying the internal cage sites; thus, the catalytic performance of 5Cu8Ce-USY-700H remains stable after ageing. Secondly, Ce doping introduces a CuOx–CeO2 strong interaction, which facilitates lattice oxygen mobility by forming more oxygen vacancies so as to increase the concentration of surface active oxygen. These changes, on the one hand, could help to promote further oxidative decomposition of nitrate/nitrite intermediates and improve the catalytic performance. But, on the other hand, it also causes the byproduct generation to become more severe.

A series of supported MnOx/MgAl-layered double hydroxide (LDH) catalysts were prepared by hydrothermal co-precipitation to investigate their catalytic performances for low-temperature formaldehyde oxidation reactions. Activity tests show that the 10Mn/Mg3Al1-LDH catalyst exhibits higher efficiency for low-temperature formaldehyde oxidation with a high CO2 yield. It also shows remarkable long-term operational stability as well as good adaptability to different velocities and humidities. Various characterizations were carried out to establish the possible structure–activity correlations. The results show that there were a large number of hydroxyl groups in the 10Mn/MgAl-LDH catalysts, and the hydroxyl groups were positively correlated with Mg2+ content. The outstanding catalytic performance of 10Mn/Mg3Al1-LDH can be attributed to abundant surface hydroxyl groups, surface adsorbed oxygen and higher Mn4+/Mn3+ ratios. Through in situ Fourier-transform infrared spectroscopy (in situ FTIR), it was revealed that formaldehyde was gradually converted into CO2 and water with dioxymethylene (DOM), formate and carbonate as the major intermediates under the action of both active oxygen and active hydroxyl groups. The active oxygen and active hydroxyl groups consumed in the process are continuously replenished by the effective reaction between the oxygen molecules in the air and the active site of the catalyst. The low-temperature asynchronous conversion of formaldehyde results in the accumulation of some intermediates on the catalyst surface covering the active center, which induces catalyst deactivation.

Polyimide (PI) films are widely used in electronic devices owing to their excellent mechanical and electrical properties and high thermal and chemical stabilities. In particular, PI films play an important role in flexible printed circuit boards (FPCBs). However, one challenge currently faced with their use is that the adhesives used in FPCBs cause a high dielectric loss in high-frequency applications. Therefore, it is envisioned that PI films with a low dielectric loss and Cu films can be used to prepare two-layer flexible copper-clad laminates (FCCLs) without any adhesive. However, the preparation of ultra-thin FCCLs with no adhesives is difficult owing to the low peel strength between PI films and Cu films. To address this technical challenge, an FCCL with no adhesive was prepared via high-power helicon wave plasma (HWP) treatment. Field-emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) were tested. Also, the surface roughness of the PI film and the peel strength between the PI film and Cu film were measured. The experimental results show that the surface roughness of the PI film increased by 40–65% and the PI film demonstrated improved adhesion (the peel strength was >8.0 N/cm) with the Cu film following plasma treatment and Cu plating.

Improving the dynamic regulation ability of thermal power units is effective for realizing flexible scheduling in modern power systems. At present, the unit regulation capacity is usually reflected by the load adjustment of the main steam pressure and flow tracking ability, through the calculation of the given and real-time deviation to complete the load, and by pressure adjustment. However, although the calculation involved in this method is easy and the results are intuitive, overshoot and lag can easily occur. The main reason for this is that the process from boiler combustion to turbine works has strong hysteresis and inertia, and the feedback signal of the pressure and flow rate cannot dynamically reflect the change in boiler combustion and steam energy. According to the heat transfer process of the unit, the main steam temperature can directly reflect the energy transfer in the furnace combustion process and then reflect the changing trend of steam energy. Analyzing the changing characteristics of the temperature, pressure, and flow of superheated steam under rapid load regulations makes it possible to calculate the instantaneous energy storage value of the main steam before the regulating valve, and this value was inserted into the coordinate system as a new feedforward signal. Finally, a simulation model was established by using the actual running data of the unit. A simulation experiment under variable working conditions demonstrated that this method could improve the dynamic adjustment of the unit to load and pressure and help the power grid absorb renewable energy.

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.

Because the generator power-measuring equipment is often accompanied by electrical interference in a complex electromagnetic environment in an actual thermal power plant, the output signal will change or even distort while it passes through the devices of acquisition and conversion. Several practical cases have found that the abnormal generation change phenomenon, impulse or oscillation caused by electrical interference, has different effects on the load regulation of steam turbines. These faults also exist in combined-cycle power plants (CCPPs). However, the insufficient installed capacity and operating life of CCPPs domestically cause similar load mutation failures that are scarcely found. We had to acknowledge that CCPPs and steam turbine regulation characteristics differ. It is of great value to study the influence of differences in load mutation on the load regulation of single-shaft CCPPs. We extracted the fault characteristics of two sudden load change phenomena using the operation data of an actual steam turbine and analyzed them through simulation. Furthermore, a fault detection and isolation method for sudden load changes in a single-shaft CCPP was proposed and the simulation results verified the method’s effectiveness.

Ti-4Al-5Mo-5V-5Cr-1Nb (wt.%) is a new type of high-strength (~1300 MPa) titanium (Ti) alloy developed for aerospace applications. Until now, the research on its welding and subsequent heat treatment is barren. Herein, we employed electron beam welding (EBW) to a solutionized Ti-4Al-5Mo-5V-5Cr-1Nb with a phase constituent of α + β and investigated its microstructure and mechanical properties in both as-welded (AW) and post-weld aging treated (PWAT) conditions. Results showed that due to the thermal input of the welding process, the α phase in the original microstructure of base material (BM) transformed into the β phase in the fusion zone (FZ). Similar microstructural evolution was observed for the heat-affected zone (HAZ) near the FZ (Near-HAZ), whereas the HAZ far away from FZ (Far-HAZ) contained a small amount of round α phase (ghost α) due to its slower cooling rate. Such a microstructural change resulted in poor tensile strength (~780 Mpa) for the as-welded joint. After PWAT, a large number of acicular α precipitated in the FZ and HAZ and its size (S) in different zones followed the order of SFar-HAZ < SFZ ≈ SNear-HAZ < SBM. The presence of αs precipitates remedied the tensile strength of the weld joint almost to the same as that of the BM. The present findings established the foundation of the application of this high-strength Ti alloy.

Point cloud semantic segmentation, a challenging task in 3D data processing, is popular in many realistic applications. Currently, deep learning methods are gradually being applied to point cloud semantic segmentation. However, as it is difficult to manually label point clouds in 3D scenes, it remains difficult to obtain sufficient training samples for the supervised deep learning network. Although an increasing number of excellent methods have been proposed in recent years, few of these have focused on the problem of semantic segmentation with insufficient samples. To address this problem, this paper proposes a random graph based on graph convolution network, referred to as RG-GCN. The proposed network consists of two key components: (1) a random graph module is proposed to perform data augmentation by changing the topology of the built graphs; and (2) a feature extraction module is proposed to obtain local significant features by aggregating point spatial information and multidimensional features. To validate the performance of the RG-GCN, the indoor dataset S3DIS and outdoor dataset Toronto3D are used to validate the proposed network via a series of experiments. The results show that the proposed network achieves excellent performance for point cloud semantic segmentation of the two different datasets.

Credit default prediction for the energy industry is essential to promoting the healthy development of the energy industry in China. While previous studies have constructed various credit default prediction models with brilliant performance, the class-imbalance problem in the credit default dataset cannot be ignored, where the numbers of credit default cases are usually much smaller than the number of non-default ones. To address the class-imbalance problem, we proposed a novel CT-XGBoost model, which adds to XGBoost with two algorithm-level methods for class imbalance, including the cost-sensitive strategy and threshold method. Based on the credit default dataset consisting of energy corporates in western China, which suffers from the class-imbalance problem, the CT-XGBoost model achieves better performance than the conventional models. The results indicate that the proposed model can efficiently alleviate the inherent class-imbalance problem in the credit default dataset. Moreover, we analyze how the prediction performance is influenced by different parameter settings in the cost-sensitive strategy and threshold method. This study can help market investors and regulators precisely assess the credit risk in the energy industry and provides theoretical guidance to solving the class-imbalance problem in credit default prediction.

Wetness loss of a steam turbine seriously affects the security of the unit when operating in deep peak regulation. To obtain the distribution characteristics of the high wetness loss area under different working conditions, especially low-load conditions, the last two stages of the low-pressure cylinder (LPC) of a 600 MW steam turbine were simulated using the non-equilibrium condensation model proposed in this study. The nucleation rate distribution, supercooling degree, and steam velocity droplet were analyzed. Consequently, the diameter distribution of coarse water droplets under 100%, 50%, 40%, 30%, and 20% THA conditions and the distribution of the thermodynamic loss and water droplet resistance loss were obtained. Thermodynamic loss mainly occurred at the front end of second-stage stator blades and trailing end of the last-stage stator blades. The water droplet resistance loss mainly occurred at 40% of the blade height and at the tip of the last-stage stator blades. Moreover, with a reduction in the unit load, the thermodynamic loss continued to decrease, but the water droplet resistance loss continued to increase.

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data.

Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and unordered structure in a 3D space. To alleviate this problem, a Dilated Graph Attention-based Network (DGANet) with a certain feature for learning ability is proposed. Specifically, we first build a local dilated graph-like region for each input point to establish the long-range spatial correlation towards its corresponding neighbors, which allows the proposed network to access a wider range of geometric information of local points with their long-range dependencies. Moreover, by integrating the dilated graph attention module (DGAM) implemented by a novel offset–attention mechanism, the proposed network promises to highlight the differing importance on each edge of the constructed local graph to uniquely learn the discrepancy feature of geometric attributes between the connected point pairs. Finally, all the learned edge attention features are further aggregated, allowing the most significant geometric feature representation of local regions by the graph–attention pooling to fully extract local detailed features for each point. The validation experiments using two challenging benchmark datasets demonstrate the effectiveness and powerful generation ability of our proposed DGANet in both 3D object classification and segmentation tasks.

The key point of forest height and underlying topography inversion using synthetic aperture radar tomography (TomoSAR) depends on the accurate positioning of the phase centers of different scattering mechanisms. The traditional nonparametric spectrum analysis methods (such as beamforming and Capon) have limited vertical resolution and cannot accurately distinguish closely spaced scatterers. In addition, it is very difficult to accurately estimate the ground or canopy heights with single polarimetric SAR images because there is no guarantee that the vertical profile will generate two clear and separate peaks for all resolution cells. A polarimetric TomoSAR method based on SKP (sum of Kronecker products) decomposition and iterative maximum likelihood estimation is proposed in this paper. On the one hand, the iterative maximum likelihood TomoSAR method has a higher vertical resolution than that of the traditional methods. On the other hand, the separation of the canopy scattering mechanism and the ground scattering mechanism is conducive to the positioning of the phase centers. This method was applied to the inversion of forest height and underlying topography in a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana with six passes of polarimetric SAR images. The inversion accuracy of underlying topography of the proposed method was up to 1.489 m and the inversion accuracy of forest height was up to 1.765 m. Compared with the traditional polarimetric beamforming and polarimetric capon methods, the proposed method greatly improved the inversion accuracy of forest height and underlying topography.

The wind power ramp event includes large fluctuations in wind power within a short period of time. To maintain grid stability, defining, identifying, and predicting the wind power ramp event is inevitable. Therefore, a comprehensive assessment method of wind power ramp events that combines the generalized information of the source, grid, and load sides is proposed. In this method, we put forward a channel self-selected multi-layer coefficient correction model (CSMCC) and wind power ramp threshold, according to the allowable value of a grid frequency change. Additionally, the availability of data-driven modeling methods is verified by performing autocorrelation analysis. Finally, the comprehensive evaluation method, which combines the back propagation (BP) neural network, supports the vector machine and CSMCC model is proved to be effective. This paper has a certain reference significance for basic research on large-scale wind power safety and efficient utilization.

Ethanol (EtOH) metabolism itself can be a predisposing factor for initiation of alcoholic liver disease (ALD). Therefore, a dose dependent study to evaluate liver injury was conducted in hepatic alcohol dehydrogenase (ADH) deficient (ADH−) and ADH normal (ADH+) deer mice fed 1%, 2% or 3.5% EtOH in the liquid diet daily for 2 months. Blood alcohol concentration (BAC), liver injury marker (alanine amino transferase (ALT)), hepatic lipids and cytochrome P450 2E1 (CYP2E1) activity were measured. Liver histology, endoplasmic reticulum (ER) stress, AMP-activated protein kinase (AMPK) signaling and cell death proteins were evaluated. Significantly increased BAC, plasma ALT, hepatic lipids and steatosis were found only in ADH− deer mice fed 3.5% EtOH. Further, a significant ER stress and increased un-spliced X-box binding protein 1 were evident only in ADH− deer mice fed 3.5% EtOH. Both strains fed 3.5% EtOH showed deactivation of AMPK, but increased acetyl Co-A carboxylase 1 and decreased carnitine palmitoyltransferase 1A favoring lipogenesis were found only in ADH− deer mice fed 3.5% EtOH. Therefore, irrespective of CYP2E1 overexpression; EtOH dose and hepatic ADH deficiency contribute to EtOH-induced steatosis and liver injury, suggesting a linkage between ER stress, dysregulated hepatic lipid metabolism and AMPK signaling.

To enhance the synchronous detection of the horizontal and vertical positions of the torch in swing arc narrow gap welding, a torch pose detection (TPD) method is proposed. This approach utilizes passive visual sensing to capture images of the arc on the groove sidewall, using advanced image processing methods to extract and fit the arc contour. The coordinates of the arc contour center point and the highest point are determined through the arc contour fitting line. The torch center position is calculated from the average horizontal coordinates of the arc contour centers in adjacent welding images, while the height position is determined from the vertical coordinate of the arc’s highest point. Experimental validation in both variable and constant groove welding conditions demonstrated the TPD method’s accuracy within 0.32 mm for detecting the torch center position. This method eliminates the need to construct the wire centerline, which was a requirement in previous approaches, thereby reducing the impact of wire straightness on detection accuracy. The proposed TPD method successfully achieves simultaneous detection of the torch center and height positions, laying the foundation for intelligent detection and adaptive control in swing arc narrow gap welding.

To address the issues of decreased detection accuracy, false detections, and missed detections caused by scale differences between near and distant targets and environmental factors (such as lighting and water waves) in surface target detection tasks for uncrewed vessels, the YOLOv8-MSS algorithm is proposed to be used to optimize the detection of water surface targets. By adding a small target detection head, the model becomes more sensitive and accurate in recognizing small targets. To reduce noise interference caused by complex water surface environments during the downsampling process in the backbone network, C2f_MLCA is used to enhance the robustness and stability of the model. The lightweight model SENetV2 is employed in the neck component to improve the model’s performance in detecting small targets and its anti-interference capability. The SIoU loss function enhances detection accuracy and bounding box regression precision through shape awareness and geometric information integration. Experiments on the publicly available dataset FloW-Img show that the improved algorithm achieves an mAP@0.5 of 87.9% and an mAP@0.5:0.95 of 47.6%, which are improvements of 5% and 2.6%, respectively, compared to the original model.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Although a combination of immunoprophylaxis and antiviral therapy can effectively prevent mother-to-child transmission (MTCT) of hepatitis B virus (HBV), a considerable number of infants born to highly viremic mothers still develop occult HBV infection (OBI). To uncover the virological factor and risk predictor for OBI in infants, we found that the diversity and complexity of maternal HBV quasispecies in the case group were lower than those in the control group. Mutations with significant differences between the two groups were most enriched in the NTCPbd and PreC regions. Genetic distance at the amino-acid level of the PreC region, especially the combination of three amino-acid mutations in the PreC region, could strongly predict the risk of OBI in infants. HBV quasispecies in OBI infants were highly complex, and the non-synonymous substitutions were mainly found in the RT and HBsAg regions. The sK47E (rtQ55R) and sP49L mutations in OBI infants might contribute to OBI through inhibiting the production of HBV DNA and HBsAg, respectively. This study found the potential virological factors and risk predictors for OBI in infants born to highly viremic mothers, which might be helpful for controlling OBI in infants.

The application of heterosis can not only effectively improve the disease resistance and meat quality of livestock, but also significantly enhance the reproduction and growth of livestock and poultry. We conducted genome-wide association studies using data from F2 crossbred meat rabbits to screen out candidate genes with significant dominant effects associated with economic trait variation. High-throughput sequencing technology was used to obtain SNPs covering the whole genome to evaluate the homozygosity of the population genome, and analyze the number, length, frequency, and distribution of ROHs in the population. Candidate genes related to economic traits of meat rabbits were searched based on high-frequency ROH regions. After quality control filtering of genotype data, 380 F2 crossbred rabbits were identified with 78,579 SNPs and 42,018 ROHs on the autosomes. The fitting of the Logistic growth curve model showed that 49-day-old rabbits were a growth inflection point. Then, through genome-wide association studies, 10 SNP loci and seven growth trait candidate genes were found to be significantly related to body weight in meat rabbits at 84 days of age. In addition, we revealed the functional roles and locations of 20 candidate genes in the high-frequency ROH region associated with economic traits in meat rabbits. This study identified potential genes associated with growth and development in the high-frequency ROH region of meat rabbits. In this study, the identified candidate genes can be used as molecular markers for assisted selection in meat rabbits. At the same time, the inbreeding situation based on ROH assessment can provide reference for breeding and breeding preservation of meat rabbits.

Strawberry plants have shallow roots and large leaves, which are highly sensitive to variations in water levels. To explore the physicochemical and molecular mechanisms of strawberry response to water stress, and provide new ideas for strawberry scientific irrigation, we measured the transpiration rate, fresh weight, biomass gain, and other indicators of potted “Zhangji” strawberry plants under drought and waterlogging treatments using a Plantarray system. Transcriptomic and metabolomic analyses of strawberry leaves following mild drought, moderate drought, severe drought, and rehydration treatments were performed to identify key genes and metabolites involved in the response to drought stress. Below a certain threshold, the transpiration rate of strawberry plants was significantly lower after the deficit irrigation treatment than the conventional water treatment. Transcriptome analysis revealed that genes involved in oxidoreductase activity and in sulfur and nitrogen metabolism were up-regulated, as well as starch and sucrose. Strawberry plants secrete various endogenous growth hormones to maintain their normal growth under drought stress. The syntheses of salicylic acid (SA) and abscisic acid (ABA) were up-regulated in the mild and moderate drought treatments. However, the syntheses of 1-aminocyclopropanecarboxylic acid (ACC) and indole-3-acetic acid (IAA) were down-regulated in severe drought treatment and up-regulated in rehydration after severe drought treatment.

Gas control in the upper corner of the natural coal mining face with high gas is always a difficult problem that troubles the safe production of the working face. Among them, a high gas-prone natural coal mining face with ground air leakage is more likely to cause gas and CO to exceed limits in the corner of the working surface and is difficult to control. The traditional treatment methods often have some problems; for example, it is easy to increase air leakage in the gob with the method of gas extraction in the gob, which is not conducive to the prevention and control of spontaneous combustion of coal in the gob. At present, the more effective method is the pressure-equalization method. However, the pressure-equalization measures need to establish a complex pressure-equalization system, and close cooperation between the systems is required; once the system power fails or equipment failure occurs, the pressure-equalization state changes randomly, and it is easy to cause gas over-limits and other faults. Therefore, this paper presents a new method to control gas in the gob of a coal seam by pumping the gob of the upper-adjacent layer, using the negative pressure of pumping, and balancing the negative pressure of the upper-adjacent layer and the gob of the coal seam to form a new pressure-equalization relationship. This method can prevent the toxic and harmful gases in the goaf of the upper-adjacent layer from escaping into the passageway of the gob of the local coal seam, reduce the air leakage in the goaf, and benefit the gas control and spontaneous coal combustion prevention in the goaf.

The construction of a thin, uniform, and robust solid electrolyte interphase (SEI) film on the surface of active materials is pivotal for enhancing the overall performance of lithium-ion batteries (LiBs). However, conventional electrolytes often fail to achieve the desired SEI characteristics. In this work, we introduced 1,3,6-hexanetrinitrile (HTCN) in the baseline electrolyte (BE) of 1.0 M LiPF6 in Ethylene Carbonate/Dimethyl Carbonate (EC/DMC) (3:7 by volume) with 5 wt.% fluoroethylene carbonate (FEC), denoted as BE-FH. By systematically investigating the influence of FEC: HTCN weight ratios on the electrochemical performance of graphite anodes, we identified an optimal composition (FEC:HTCN = 5:4 by weight, denoted as BE-FH54) that demonstrated greatly improved initial Coulombic efficiency, rate capability, and cycling stability compared with the baseline electrolyte. Deviations from the optimal FEC:HTCN ratio resulted in the formation of either small cracks or excessively thick SEI layers. The enhanced performance of BE-FH54-based LiB is mainly ascribed to the synergistic effect of FEC and HTCN in forming a robust, thin, homogeneous, and ion-conducting SEI. This research highlights the importance of rational electrolyte design in enhancing the electrochemical performance of graphite anodes in LiBs and provides insights into the role of nitrile-based additives in modulating the SEI properties.

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios.

A multi-strain yeast-based paraprobiotic (MsYbP) comprising inactive cells and polysaccharides (β-glucan, mannan oligosaccharides, and oligosaccharides) derived from Saccharomyces cerevisiae and Cyberlindnera jadinii could ensure optimal growth and health in farmed fish. This study assessed the impact of an MsYbP on the growth, immune responses, antioxidant capacities, and liver health of largemouth bass (Micropterus salmoides) through lab-scale (65 days) and pilot-scale (15 weeks) experiments. Two groups of fish were monitored: one fed a control diet without the MsYbP and another fed 0.08% and 0.1% MsYbP in the lab-scale and pilot-scale studies, respectively (referred to as YANG). In the lab-scale study, four replicates were conducted, with 20 fish per replicate (average initial body weight = 31.0 ± 0.8 g), while the pilot-scale study involved three replicates with approximately 1500 fish per replicate (average initial body weight = 80.0 ± 2.2 g). The results indicate that the MsYbP-fed fish exhibited a significant increase in growth in both studies (p < 0.05). Additionally, the dietary MsYbP led to a noteworthy reduction in the liver function parameters (p < 0.05), such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (AKP), and hepatic nuclear density, indicating improved liver health. Furthermore, the dietary MsYbP elevated the antioxidative capacity of the fish by reducing their malondialdehyde levels and increasing their levels and gene expressions related to antioxidative markers, such as total antioxidant ca-pacity (T-AOC), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), catalase (CAT), nuclear factor erythroid 2-related factor 2 (nrf2) and kelch-1ike ech-associated protein (keap1) in both studies (p < 0.05). In terms of hepatic immune responses, the lab-scale study showed an increase in inflammation-related gene expressions, such as interleukin-1β (il-1β) and transforming growth factor β1 (tgf-β1), while the pilot-scale study significantly suppressed the expressions of genes related to inflammatory responses, such as tumor necrosis factor α (tnfα) and interleukin-10 (il-10) (p < 0.05). In summary, our findings underscore the role of dietary multi-strain yeast-based paraprobiotics in enhancing the growth and liver health of largemouth bass, potentially through increased antioxidative capacity and the modulation of immune responses, emphasizing the significance of employing yeast-based paraprobiotics in commercial conditions.

Electrochemical water-splitting is widely acknowledged as a renewable strategy for hydrogen production, but it is primarily constrained by the sluggish reaction kinetics of the anode oxygen evolution reaction (OER). In our study, we employ a fast room-temperature corrosion engineering strategy for the construction of a sulfur-doped Ni-Fe layered dihydroxide catalyst (S-NiFe LDH). With the assistance of a sulfur source, microsphere morphology with an ultra-thin lamellar surface cross-arrangement can be rapidly grown on the surface of an iron foam substrate, ensuring a substantial electrochemical interface. The composition of Ni species in the catalysts can be regulated by simply adjusting the amount of Ni2+ and reaction time. Functioning as an OER catalyst, the S-NiFe LDH demonstrates high activity and reaction kinetics, featuring a minimal overpotential of 120.0 mV to deliver a current density of 10 mA cm−2, a small Tafel slope of 39.5 mV dec−1 and a notable electrical double-layer capacitance (Cdl) of 31.3 mF cm−2. The remarkable electrocatalytic performance can be attributed to its distinctive three-dimensional (3D) structure and sulfur dopants, which effectively regulate the electrochemical interface and electronic structure of NiFe LDH. This work provides valuable insights for expeditious materials design.

An improved understanding of the mechanisms underlying plant adaptation to habitat heterogeneity can be achieved by clarifying the climate-driving factors of the hydraulic and photosynthetic traits of different populations. With a focus on Populus pruinosa Schrenk, which is the predominant tree species in the desert riparian forests of the Tarim Basin, Xinjiang, this study investigated the hydraulic and photosynthetic trait relationships and their interactions with environmental factors in 11 P. pruinosa populations using a Pearson correlation analysis, plant trait networks, a redundancy analysis, and a least squares linear regression analysis. The results showed that the degree of variation in the hydraulic traits was higher than that in the photosynthetic traits. The net photosynthetic rate (Pn) showed a significantly positive correlation with leaf-specific conductivity (Kl) and the Huber value (Hv). The Hv exhibited a significantly positive correlation with the water-use efficiency and Kl, and the branch–leaf mass ratio significantly affected the hydraulic traits. The groundwater depth (GD) in natural P. pruinosa forest habitats ranged from 3.4 to 7.9 m. With an increase in the annual average temperature, the hydraulic conductivity of the xylem significantly increased; with an increase in GD, Pn and Kl significantly decreased. The temperature annual range, temperature seasonality (standard deviation), min temperature of the coldest month, and GD were significantly correlated with the diameter and average path length of the overall trait network parameters, and these environmental factors affected the coordination of the functional traits of P. pruinosa.

In seasonally frozen soil areas, high-steep rocky slopes resulting from open-pit mining and slope cutting during road construction undergo slow natural restoration, making ecological restoration generally challenging. In order to improve the problems of external soil attachment and long-term vegetation growth in the ecological restoration of high-steep rocky slopes in seasonally frozen areas, this study conducted a series of experiments through the combined application of polyacrylamide (PAM) and carboxymethyl cellulose (CMC) to assess the effects of soil amendments on soil shear strength, water stability, freeze–thaw resistance, erosion resistance, and vegetation growth. This study showed that the addition of PAM-CMC significantly increased the shear resistance and cohesion of the soil, as well as improving the water stability, freeze–thaw resistance, and erosion resistance, but the internal friction angle of the soil was not significantly increased after reaching a certain content. Moderate amounts of PAM-CMC can extend the survival of vegetation, but overuse may cause soil hardening and inhibit vegetation growth by limiting air permeability. It was observed by a scanning electron microscope (SEM) that the gel membrane formed by PAM-CMC helped to “bridge” and bind the soil particles. After discussion and analysis, the optimum application rate of PAM-CMC was 3%, which not only improved the soil structure but also ensured the growth of vegetation in the later stage under the optimum application rate. Field application studies have shown that 3% PAM-CMC-amended soil stably attaches to high-steep rocky slopes, with stable vegetation growth, and continues to grow after five months of freeze–thaw action, with no need for manual maintenance after one year.

Suspended sediment plays a major role in estuary morphological change and shoal erosion and deposition. The impact of storm waves on sediment transport and resuspension in the Yangtze River Estuary (YRE) was investigated using a 3D coupling hydrodynamic-wave model with a sediment transport model during Typhoon Ampil. This model has been validated in field observations of water level, current, wave, and sediment concentration. The model was run for tide only, tide + wind, tide + wind and wave forcing conditions. It was found that: (1) typhoons can increase the suspended sediment concentration (SSC) by enhancing bed shear stress (BSS), especially in the offshore area of the YRE, and there is hysteresis between SSC and BSS variation; (2) exponential and vertical-line types are the main vertical profile of the SSC in the YRE and typhoons can strengthen vertical mixing and reconstruct the vertical distribution; and (3) waves are the dominating forcing factor for the SSC in the majority of the YRE through wave-induced BSS which releases sediment from the seabed. This study comprehensively investigates the spatio-temporal variation in SSC induced by Typhoon Ampil in the main branch of the YRE, which provides insights into sediment transport and resuspension during severe storms for estuaries around the world.

Amphibians serve as reliable indicators of ecosystem health and are the most threatened group of vertebrates. Studies on their spatial distribution pattern and threats are crucial to formulate conservation strategies. Gongga Mountains, with a peak at 7509 m a.s.l. and running latitudinally, are in the center of the Hengduan Mountains Range and at the eastern steep edge of the Qinghai–Xizang Plateau, providing heterogeneous habitats and varied niches for amphibians. In this study, we combined 83 days of field work with information from 3894 museum specimens that were collected over the past 80 years, and identified twenty amphibian species belonging to seven families and twelve genera by morphology. Of these species, seven were listed in the threatened categories of the Red List of China’s Biodiversity and thirteen were endemic to China. Ten species were found on the plateau side (western slope) and eleven species were found on the other side close to the Sichuan Basin (eastern slope). Only one species was found on both sides, indicating different community structures horizontally. The species richness was unimodal vertically and peaking at mid elevation on both sides, with the maximum number (ten vs. nine) of species occurring at 3300–3700 vs. 1700–1900 m a.s.l. and in different types of vegetation. The elevation span and body length of species distributed on both slopes did not show significant differences. These findings help to understand the horizontal and vertical distribution pattern of amphibian diversity, laying a foundation for future biogeographical and conservation research in this area.

This study used feces from 0-day-old (36 rabbits), 10-day-old (119 rabbits), and 60-day-old (119 rabbits) offspring rabbits and their corresponding female rabbits (36 rabbits) as experimental materials. Using 16s rRNA sequencing, the study analyzed the types and changes of gut microbiota in rabbits at different growth and development stages, as well as the correlation between gut microbiota composition and the weight of 60-day-old rabbits. All experimental rabbits were placed in the same rabbit shed. Juvenile rabbits were fed solid feed at 18 days of age and weaned at 35 days of age. In addition to identifying the dominant bacterial phyla of gut microbiota in rabbits at different age stages, it was found that the abundance of Clostridium tertium and Clostridium paraputrificum in all suckling rabbits (10-day-old) was significantly higher than that in rabbits fed with whole feed (60-day-old) (p < 0.05), while the abundance of Gram-negative bacterium cTPY13 was significantly lower (p < 0.05). In addition, Fast Expected Maximum Microbial Source Tracing (FEAST) analysis showed that the contribution of female rabbits’ gut microbiota to the colonization of offspring rabbits’ gut microbiota was significantly higher than that of unrelated rabbits’ gut microbiota (p < 0.05). The contribution of female rabbits’ gut microbiota to the colonization of gut microbiota in 0-day-old rabbits was significantly higher than that to the colonization of gut microbiota in the 10- and 60-day-old rabbits (p < 0.05). Finally, the correlation between gut microbiota composition and body weight of 60-day-old rabbits was analyzed based on a mixed linear model, and six ASVs significantly affecting body weight were screened. The above results provide important theoretical and practical guidance for maintaining gut health, improving growth and development performance, and feeding formulation in rabbits.

Refined separation not only controls the variability of reclaimed asphalt pavement (RAP), but also improves the mixing ratio of RAP and the quality of recycled asphalt mixtures. This study examines RAP treated with various refined separation frequency parameters, analyzes the variation rules and the variability of RAP aggregate gradation, asphalt content, asphalt properties, and aggregate properties, and calculates the maximum mixing percentage of coarse RAP material by using the gradation variability control method and the asphalt content variability control method. The results show that the variability of gradation and asphalt content of coarsely separated RAP is considerable, and a refined separation process significantly reduces the variability of gradation and asphalt content of RAP; the agglomeration of RAP decreases with an increase in the refined separation frequency; and the RAP agglomeration of three kinds of RAPs (E1, E2, and E3) under a refined separation frequency of 55 Hz reduces by 6.40%, 4.30%, and 4.30%, respectively, as compared with that of coarsely separated RAPs. The asphalt content of the refined separation RAP gradually decreases with an increase in frequency, and the asphalt content of E1 and E2 (55 Hz) was only 0.95% and 1.10%, respectively. The maximum percentage of RAP in recycled asphalt mixtures was calculated using the gradation variability control method and the asphalt content variability control method, respectively. The maximum proportions of RAP were 45% and 33% for A1 (0 Hz), respectively, and the maximum proportions of RAP for E1 (55 Hz) were all 100%. The results of the two methods show that the process of refined separation can increase the maximum proportion of blended RAP materials. They also demonstrate that the refined separation process can increase the maximum blending ratio of coarse RAP materials, thereby improving the quality of the RAP, increasing the proportion of RAP blending, and ensuring the quality of the recycled asphalt mixture. In conclusion, the refined separation process holds promise for maximizing the potential value of RAP and optimizing its recycling, environmental, and economic benefits.

Large-area oriented ZnO nanoarrays (including nanowire, nanorod, and nanotube) on ITO glass substrates are synthesized via the simple hydrothermal, electrodeposition, and electrochemical etching approach. The morphology of ZnO nanoarrays is controlled by adjusting the reaction temperature, reaction time, and current density. The scanning and transmission electron microscopy (SEM and TEM) results indicate the successful preparation of large-area oriented ZnO nanoarrays with different types, and the energy-dispersive X-microanalysis spectrum (EDS) and X-ray diffraction (XRD) results confirm that the composition of the obtained nanoarrays is ZnO. More importantly, the as-prepared ZnO nanotube arrays are observed with about a 40% increase in ultraviolet absorption intensity compared to the ZnO nanowire/nanorod arrays, due to having larger specific surface areas. The as-prepared different types of ZnO nanoarrays have great potential for applications in low-cost and high-performance optoelectronic devices.

The interaction between soil moisture (SM) and vegetation dynamics has been proven in previous studies. In situ measurements have provided reliable data to investigate and validate the time effect in different zones, which is important in the hydrology and agriculture fields. There were 845 SM in situ monitoring measurements utilized with the correlation between SM and vegetation across various soil depths and climate zones in China. The impact of climate and teleconnection factors on SM and the leaf area index (LAI) are also discussed. The results indicate that SM increases from northwest to southeast in China. The time lag responses of SM to temperature, precipitation, relative humidity, and sunshine duration are 0–3 days, 3–7 days, 1–3 days, and 3–15 days, respectively. The LAI is most strongly correlated with the climate of the current month. When the LAI leads SM, a negative correlation is observed, whereas a positive correlation is observed when SM leads the LAI. This proves that vegetation growth restricts the increase in SM, and soil drying further restricts the growth of vegetation. There was a response time of 2–4 months between the LAI and SM. The effect of vegetation and deeper SM was significant in the arid zone, while they were coupled with shallow SM in the humid zone. Additionally, the El Niño–Southern Oscillation (ENSO) showed a significant positive correlation with SM in 2015–2016 with signals of 9–14 months. The results provide support for balancing the contradiction between future vegetation restoration and water resource scarcity.

When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical transmission (AMT) clutches and proposes an optimal control of the clutch torque for launching heavy-duty vehicles. Firstly, a fuzzy neural network (FNN)-based vehicle launching states recognition (LSR) system is designed for distinguishing the driver’s launching intention and the vehicle’s launching equivalent moment of resistance. Secondly, jerk, friction work, and launching reserve power are taken as the performance indexes for clutch torque optimization, the weight coefficients of each performance index are adjusted according to the LSR results, and the optimal clutch torque is solved by using the minimum value principle based on the shooting method. Finally, simulations and tests are conducted to validate the strategy of optimizing clutch torque, and the impact of torque optimization on the position change during the engagement process is analyzed. The results indicate that under different driver’s intentions, vehicle masses, and road gradient conditions, the jerk, friction work, and slipping time of heavy vehicles during the launching process are improved by applying the optimization strategy.

Personalized laser powder bed fusion (PBF-LB/M) Ti-6Al-4V (TC4) has a broader application prospect than that of traditional casting. In this paper, the composition and corrosion resistance of the passive film formation mechanism of TC4 prepared by optimization of PBF-LB/M techniques and traditional casting were systematically studied in 0.9 wt.% NaCl at 37 °C by electrochemical technique and surface analysis. The rates of the passive film formation process, corrosion resistance and composition of TC4 show different characteristics for the different preparation processes. Although the rate of passive film formation of cast-TC4 was higher at the initial immersion, the open circuit potential was more positive, and the film thickness was larger after stabilization, those facts show no positive correlation with corrosion resistance. On the contrary, with no obvious defects on the optimized PBF-LB/M-TC4, the passive film resistance is 2.5 times more, the defect concentration is reduced by 30%, and the TiO2 content is higher than that of the cast-TC4, making the martensitic-based PBF-LB/M-TC4 exhibit excellent corrosion resistance. This also provides good technical support for the further clinical application of PBF-LB/M-TC4.

Barley (Hordeum vulgare L.), a diverse cereal crop, exhibits remarkable versatility in its applications, ranging from food and fodder to industrial uses. The content of cellulose in barley is significantly influenced by the COBRA genes, which encode the plant glycosylphosphatidylinositol (GPI)-anchored protein (GAP) that plays a pivotal role in the deposition of cellulose within the cell wall. The COBL (COBRA-Like) gene family has been discovered across numerous species, yet the specific members of this family in barley remain undetermined. In this study, we discovered 13 COBL genes within the barley genome using bioinformatics methods, subcellular localization, and protein structure analysis, finding that most of the barley COBL proteins have a signal peptide structure and are localized on the plasma membrane. Simultaneously, we constructed a phylogenetic tree and undertook a comprehensive analysis of the evolutionary relationships. Other characteristics of HvCOBL family members, including intraspecific collinearity, gene structure, conserved motifs, and cis-acting elements, were thoroughly characterized in detail. The assessment of HvCOBL gene expression in barley under various hormone treatments was conducted through qRT-PCR analysis, revealing jasmonic acid (JA) as the predominant hormonal regulator of HvCOBL gene expression. In summary, this study comprehensively identified and analyzed the barley COBL gene family, aiming to provide basic information for exploring the members of the HvCOBL gene family and to propose directions for further research.

The flowability and mechanical properties are increasingly crucial in the filling process of deep metal mines with mining depths exceeding 1000 m. The rheological properties of filling slurry in the pipeline were analyzed through rheological tests, L-tube self-flow tests, and semi-industrial loop tests. The results revealed that with an increase in the cement-to-tailings mass ratio (c/t ratio) and mass concentration, the slurry exhibited a higher flow resistance and decreased stowing gradient. During slurry transportation, the pressure loss in the straight pipe was positively correlated with the slurry flow rate, c/t ratio, and mass concentration. A uniaxial compressive strength (UCS) test was conducted to analyze the mechanical properties of the cemented paste backfill containing BMC (CCPB) in both standard and deep-underground curing environments. The UCS of the CCPB showed an increasing trend with the rise in curing age, mass concentration, and the c/t ratio. The comprehensive analysis concluded that when the c/t ratio is 1:4, and the mass concentration is approximately 74%, and parameters such as the slump, bleeding rate, and flowability of the filling slurry meet the criteria for conveying and goaf filling, resulting in a high-strength filling body.

High-strength steel (HSS) members with welded sections exhibit a notably lower residual compressive stress ratio compared with common mild steel (CMS) members. Despite this difference, current codes often generalize the findings from CMS members to HSS members, and the previous unified residual stress models are generally conservative. This study focuses on the membrane residual stress distribution in Q690 steel welded box sections. By leveraging experimental results, the influence of section sizes and welding parameters on membrane residual stress was delved into. A larger plate size correlates with a decrease in the residual compressive stress across the section, with a more pronounced reduction observed in adjacent plates. Additionally, augmenting the number of welding passes tends to diminish residual stresses across the section. Results showed that membrane residual stress adhered to the section’s self-equilibrium, while the self-equilibrium in the plates was not a uniform pattern. A reliable residual stress simulation method for Q690 steel welded box sections was established using a three-dimensional thermal–elastic–plastic finite element model (3DTEFEM) grounded in experimental data. This method served as the cornerstone for parameter analysis in this study and set the stage for subsequent research. As a result, an accurate unified residual stress model for Q690 steel welded box sections was derived.

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress.

The utilization of natural products in food preservation represents a promising strategy for the dual benefits of controlling foodborne pathogens and enhancing the nutritional properties of foods. Among the phytonutrients, flavonoids have been shown to exert antibacterial effects by disrupting bacterial cell membrane functionality; however, the underlying molecular mechanisms remain elusive. In this study, we investigated the effect of quercetin on the cell membrane permeability of Staphylococcus aureus ATCC 27217. A combined metabolomic and transcriptomic approach was adopted to examine the regulatory mechanism of quercetin with respect to the fatty acid composition and associated genes. Kinetic analysis and molecular docking simulations were conducted to assess quercetin’s inhibition of β-ketoacyl-acyl carrier protein reductase (FabG), a potential target in the bacterial fatty acid biosynthesis pathway. Metabolomic and transcriptomic results showed that quercetin increased the ratio of unsaturated to saturated fatty acids and the levels of membrane phospholipids. The bacteria reacted to quercetin-induced stress by attempting to enhance fatty acid biosynthesis; however, quercetin directly inhibited FabG activity, thereby disrupting bacterial fatty acid biosynthesis. These findings provide new insights into the mechanism of quercetin’s effects on bacterial cell membranes and suggest potential applications for quercetin in bacterial inhibition.

Soybean vegetable oil is an important source of the human diet. However, the analysis of the genetic mechanism leading to changes in soybean oil content is still incomplete. In this study, a total of 227 soybean materials were applied and analyzed by a genome-wide association study (GWAS). There are 44 quantitative trait nucleotides (QTNs) that were identified as associated with oil content. A total of six, four, and 34 significant QTN loci were identified in Xiangyang, Hulan, and Acheng, respectively. Of those, 26 QTNs overlapped with or were near the known oil content quantitative trait locus (QTL), and 18 new QTNs related to oil content were identified. A total of 594 genes were located near the peak single nucleotide polymorphism (SNP) from three tested environments. These candidate genes exhibited significant enrichment in tropane, piperidine, and pyridine alkaloid biosynthesiss (ko00960), ABC transporters (ko02010), photosynthesis-antenna proteins (ko00196), and betalain biosynthesis (ko00965). Combined with the GWAS and weighted gene co-expression network analysis (WGCNA), four candidate genes (Glyma.18G300100, Glyma.11G221100, Glyma.13G343300, and Glyma.02G166100) that may regulate oil content were identified. In addition, Glyma.18G300100 was divided into two main haplotypes in the studied accessions. The oil content of haplotype 1 is significantly lower than that of haplotype 2. Our research findings provide a theoretical basis for improving the regulatory mechanism of soybean oil content.