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To understand the impact and mechanism of removing fat and skin tissue on the nutritional metabolism of Chinese dry cured ham, the differential metabolites (DMs) profile between lean ham (LH) and fatty ham (FH) was explored though untargeted metabolomics based on UPLC-MS/MS. The results showed significant differences of the metabolite profiles between FH and LH. A total of 450 defined metabolites were detected, and 266 metabolites among them had significantly different abundances between the two hams, mainly including organic acids and derivatives, and lipids and lipid-like molecules, as well as organoheterocyclic compounds. Furthermore, 131 metabolites were identified as DMs, among which 101 and 30 DMs showed remarkably higher contents in FH and LH, respectively. The further Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that DMs can be mostly enriched in the pathways of ABC transporters, amino acid biosynthesis, protein digestion and absorption, aminoacyl-tRNA biosynthesis, and 2-oxocarboxylic acid metabolism. Moreover, the metabolic network of DMs revealed that the prominent DMs in FH, such as 9(S)-HODE, 9,10-EpOME, 13-Oxo-ODE, L-palmitoyl carnitine, and D-fructose, were primarily involved in the endogenous oxidation and degradation of fat and glycogen. Nevertheless, the dominant DMs in LH, such as 2-isopropylmalic acid, indolelactic acid, and hydroxyisocaproic acid, were mainly the microbial metabolites of amino acids and derivates. These findings could help us understand how fat-deficiency affects the nutritional metabolism of Chinese dry-cured hams from a metabolic perspective.

The escalating discharge of textile wastewater with plenty of dye and salt has resulted in serious environmental risks. Membranes assembled from two-dimensional (2D) nanomaterials with many tunable interlayer spacings are promising materials for dye/salt separation. However, the narrow layer spacing and tortuous interlayer transport channels of 2D-material-based membranes limit the processing capacity and the permeability of small salt ions for efficient dye/salt separation. In this work, a novel sepiolite/vermiculite membrane was fabricated using Meyer rod-coating and naturally occurring clay. The intercalation of sepiolite Nanofibers between vermiculite Nanosheets provides additional transport nanochannels and forms looser permeable networks, producing composite membranes with remarkably enhanced flux. As a result, the optimized membranes with 80% sepiolite exhibit remarkable flux as high as 78.12 LMH bar−1, outstanding dye rejection (Congo Red~98.26%), and excellent selectivity of dye/salt of 10.41. In addition, this novel all-clay composite membrane demonstrates stable separation performance under acidity, alkalinity and prolonged operation conditions. The large-scale sepiolite/vermiculite membranes made by the simple proposed method using low-cost materials provide new strategies for efficient and environmentally-friendly dye/salt separation.

The scattering of reinforcement plays a crucial role in the microstructure and properties of metal matrix composites. In this study, an aluminum matrix composite (AMC) was reinforced by 10 wt% TiO2 (Al-10TiO2), with an average particle size of a submicron, combined with a different content of graphitic carbon nitride (g-C3N4), which was fabricated by shift-speed ball milling (SSBM) combined with multi-pass friction stir processing (FSP). In addition to the high hardness of TiO2, g-C3N4 has functional groups to promote in situ reactions. SSBM improves the distribution of reinforcement, refines grain size, and reduces the structural destruction of g-C3N4. The in situ reaction was achieved after multi-pass FSP at a high rotational speed and low travel speeds, which can promote uniform dispersion and grain refinement. Moreover, the g-C3N4 shows the efficient enhancement of strength while maintaining the elongation of AMC. Because the exfoliation of g-C3N4 under the effect of processing reduces the agglomeration of TiO2, boosts the flattening of Al, and enhances interface integration with the base metal. In situ phases can reduce the generation of coarse phases and improve interfacial bonding ability to enhance mechanical properties. The maximum tensile strength has been found at about 172.5 MPa in the Al-10TiO2 containing 1 wt% g-C3N4, which was enhanced by 34% compared to that of the Al-10TiO2. The tensile strength increases when the g-C3N4 content increases from 0 to 1 wt%, but then reduces with a further increase of content. The hardness was increased by 50.2%, 60.2%, and 35% with a g-C3N4 content of 0.5, 1, and 2 wt% compared to AMCs without reinforcement, respectively. According to the test, the enhancement mechanism is mainly attributed to Orowan, grain refinement strengthening, and load transfer of scattered reinforcement. In summary, the utilization of hybrid reinforcements successfully enhances the microstructure and mechanical properties.

Tuberculosis (TB) remains the major cause of mortality and morbidity, causing approximately 1.3 million deaths annually. As a highly successful pathogen, Mycobacterium tuberculosis (Mtb) has evolved numerous strategies to evade host immune responses, making it essential to understand the interactions between Mtb and host cells. G-protein-coupled receptor 84 (GPR84), a member of the G-protein-coupled receptor family, contributes to the regulation of pro-inflammatory reactions and the migration of innate immune cells, such as macrophages. Its role in mycobacterial infection, however, has not yet been explored. We found that GPR84 is induced in whole blood samples from tuberculosis patients and Mycobacterium marinum (Mm)-infected macrophage models. Using a Mm-wasabi infection model in mouse tails, we found that GPR84 is an important determinant of the extent of tissue damage. Furthermore, from our studies in an in vitro macrophage Mm infection model, it appears that GPR84 inhibits pro-inflammatory cytokines expression and increases intracellular lipid droplet (LD) accumulation, thereby promoting intracellular bacterial survival. Our findings suggest that GPR84 could be a potential therapeutic target for host-directed anti-TB therapeutics.

Proline, a critical osmoregulatory compound, is integral to various plant stress responses. The P5CS gene, which encodes the rate-limiting enzyme in proline biosynthesis, known as ∆1-pyrroline-5-carboxylate synthetase, is fundamental to these stress response pathways. While the functions of P5CS genes in plants have been extensively documented, their specific roles in cotton remain inadequately characterized. In this study, we identified 40 P5CS genes across four cotton species with diverse sequence lengths and molecular weights. Phylogenetic analysis of 100 P5CS genes from nine species revealed three subgroups, with Gossypium hirsutum closely related to Gossypium barbadense. Collinearity analysis highlighted significant differences in collinear gene pairs, indicating evolutionary divergence among P5CS genes in tetraploid and diploid cotton. Exon–intron structures and conserved motifs correlated with phylogenetic relationships, suggesting functional differentiation. Stress-responsive elements in P5CS promoters suggest involvement in abiotic stress. Expression analysis under salt stress revealed differential expressions of GhP5CS genes, with GhP5CS1 emerging as a potential key regulator. Virus-induced gene silencing confirmed the pivotal role of GhP5CS1 in cotton’s salt stress response, as evidenced by increased salt sensitivity in the silenced plants. This study enhances our understanding of the functional diversity and roles of P5CS genes in cotton under stress conditions.

Band selection is an effective way to reduce redundant information, while preserving the physical properties of hyperspectral images (HSI). However, most band selection methods merely consider the relevance and separability between pairs of bands and ignore those for different ground objects. To solve these issues, we propose a Unique Pixel extraction and Adaptive Neighbor Clustering (UPANC) band selection method in this theoretical study. First, in consideration of the characteristics of HSI data and tasks, unique pixels are obtained with a low-rank representation, where the importance of bands is analyzed from both spectral and spatial perspectives. Second, an adaptive neighbor clustering method is designed based on the unique pixels, which groups bands into several clusters through optimizing the graph structure under label smoothness. With support vector machines (SVM) as the classifier, the UPANC method achieved good performance, where the overall accuracy scores were 89.05%, 82.62%, and 92.07% on the Houston, IndianPines, and Pavia University datasets, respectively. The experimental results illustrated the advantages of the UPANC method, which could select optimal bands to enhance the performance in land cover observation.

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.

Due to the significance of carbon utilization and storage, CO2 huff and puff is increasingly receiving attention. However, the mechanisms and effects of CO2 huff and puff extraction in medium to deep saturated heavy oil reservoirs remain unclear. Therefore, in this study, by targeting the medium to deep saturated heavy oil reservoirs in the block Xia of the Xinjiang oil field, measurements of physical properties were conducted through PVT analysis and viscosity measurement to explore the dissolution and diffusion characteristics of CO2-degassed and CO2-saturated oil systems. Multiple sets of physical simulation of CO2 huff and puff in medium to deep saturated heavy oil reservoirs were conducted using a one-dimensional core holder to evaluate the EOR mechanism of CO2 huff and puff. The results demonstrate that the solubility of CO2 in degassed crude oil is linearly correlated with pressure. Higher pressure effectively increases the solubility of CO2, reaching 49.1 m3/m3 at a saturation pressure of 10.0 MPa, thus facilitating oil expansion and viscosity reduction. Meanwhile, crude oil saturated with CH4 still retains the capacity to further dissolve additional CO2, reaching 24.5 m3/m3 of incremental CO2 solubilization at 10.0 MPa, and the hybrid effect of CO2 and CH4 reduces oil viscosity to 1161 mPa·s, which is slightly lower than the pure CO2 dissolution case. Temperature increases suppress solubility but promote molecular diffusion, allowing CH4 and CO2 to maintain a certain solubility at high temperatures. In terms of dynamic dissolution and diffusion, the initial CO2 dissolution rate is high, reaching 0.009 m3/(m3·min), the mid-term dissolution rate stabilizes at approximately 0.002 m3/(m3·min), and the dissolution capability significantly decreases later on. CO2 exhibits high molecular diffusion capability in gas-saturated crude oil, with a diffusion coefficient of 8.62 × 10−7 m2/s. For CO2 huff and puff, oil production is positively correlated with the CO2 injection rate and the cycle injection volume; it initially increases with the extension of the soak time but eventually decreases. Therefore, the optimal injection speed, injection volume, and soak time should be determined in conjunction with reservoir characteristics. During the huff and puff process, the bottom hole pressure should be higher than the bubble point pressure of the crude oil to prevent gas escape. Moreover, as the huff and puff cycles increase, the content of saturates in the oil rises, while those of aromatic, resin, and asphaltene decrease, leading to a gradual deterioration of the huff and puff effect. This study provides a comprehensive reference method and conclusions for studying the fluid property changes and enhanced recovery mechanisms in medium to deep heavy oil reservoirs with CO2 huff and puff.

As a non-invasive imaging method, electrical impedance tomography (EIT) technology has become a research focus for grounding grid corrosion diagnosis. However, the existing algorithms have not produced ideal image reconstruction results. This article proposes an electrical impedance imaging method based on sparse representation, which can improve the accuracy of reconstructed images obviously. First, the basic principles of EIT are outlined, and the limitations of existing reconstruction methods are analyzed. Then, an EIT reconstruction algorithm based on sparse representation is proposed to address these limitations. It constructs constraints using the sparsity of conductivity distribution under a certain sparse basis and utilizes the accelerated Fast Iterative Shrinkage Threshold Algorithm (FISTA) for iterative solutions, aiming to improve the imaging quality and reconstruction accuracy. Finally, the grounding grid model is established by COMSOL simulation software to obtain voltage data, and the reconstruction effects of the Tikhonov regularization algorithm, the total variation regularization algorithm (TV), the one-step Newton algorithm (NOSER), and the sparse reconstruction algorithm proposed in this article are compared in MATLAB. The voltage relative error is introduced to evaluate the reconstructed image. The results show that the reconstruction algorithm based on sparse representation is superior to other methods in terms of reconstruction error and image quality. The relative error of the grounding grid reconstructed image is reduced by an average of 12.54%.

This paper investigates the design of active and passive beamforming in a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output (MU-MISO) system with the objective of maximizing the sum rate. We propose a deep evolution policy (DEP)-based algorithm to derive the optimal beamforming strategy by generating multiple agents, each utilizing distinct deep neural networks (DNNs). Additionally, a random subspace selection (RSS) strategy is incorporated to effectively balance exploitation and exploration. The proposed DEP-based algorithm operates without the need for alternating iterations, gradient descent, or backpropagation, enabling simultaneous optimization of both active and passive beamforming. Simulation results indicate that the proposed algorithm can bring significant performance enhancements.

Accurate dynamic response prediction is a challenging and crucial aspect for the fatigue or ultimate analysis of floating offshore wind turbines (FOWTs), which are increasingly recognized for their potential to harness wind energy in deep-water environments. However, traditional numerical modeling approaches like the finite element method are time-consuming, making them inefficient for generating the extensive datasets required. This paper presents an efficient deep learning-based approach, referred to as the CNN-GRU model, considering multiple external environments. This model integrates convolutional neural networks (CNNs) and gated recurrent units (GRUs), effectively extracting the coupling relationships among various input features and capturing the temporal dependencies to enhance predictive accuracy. The proposed model is applied to two distinct types of FOWTs under three sea states, and the results demonstrate its satisfactory accuracy, with an average correlation coefficient (CC) of 0.9962 and an average coefficient of determination (R²) of 0.9864. The high accuracy across all cases proves the model’s robustness and reliability. Furthermore, the model’s optimal configurations, including memory lengths, sample sizes, and optimizer, are identified through parametric studies. Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. In addition, a comparative analysis with two other ensemble models, namely random forest and gradient boosting, is conducted. The proposed approach achieves superior accuracy, with computational time approximately half that of the other two models, thereby highlighting its efficiency and effectiveness. The comprehensive framework, which encompasses feature selection, data processing, deep learning model construction, and interpretation, demonstrates significant potential for addressing a broad range of engineering problems through deep learning methodologies.

Soil compaction poses a significant challenge in modern agriculture, as it constrains root development and hinders crop growth. The increasing evidence indicated that various phytohormones collaborate in distinct root zones to regulate root growth in compacted soils. However, the study of root development in maize under such conditions has been relatively limited. Here, we identified that the ZmSPL12 gene, belonging to the SPL transcription factor family, plays a crucial and positive role in regulating root development in the compacted soil. Specifically, the overexpression of ZmSPL12 resulted in significantly less inhibition of root growth than the wild-type plants when subjected to soil compaction. Histological analysis revealed that the capacity for root growth in compacted soil is closely associated with the development of the root cap. Further exploration demonstrated that ZmSPL12 modulates root growth through regulating ethylene signaling. Our findings underscored that ZmSPL12 expression level is induced by soil compaction and then enhances root penetration by regulating root cap and development, thereby enabling roots to thrive better in the compacted soil environment.

The study of the effect of the mechanism of urea addition to sewage sludge and sawdust-composting substrates on methane production is still limited. In the present study, the systematic investigation of the effect of urea addition (0.18, 0.9 and 1.8 kg) on methane production is discussed through the dynamics of physical properties, enzymes, and the microbial community during composting. The results showed that high urea addition (1.8 kg) suppressed methane production, with a lower rate and a shorter duration of warming in the thermophilic phase, but significantly enhanced cellulase activity, urease, and peroxidase, and promoted the degradation of organic carbon, as well as the loss of nitrogen. A high addition of urea stimulated the growth and reproduction of Sinibacillus, Pseudogracilibacillus, Sporosarcina, and Oceanobacillus. The random forest model indicated that the top six independent determinants of CH4 emissions were Methanobacterium, temperature, organic matter (OM), Methanospirillum, and NH4+-N. Furthermore, structural equation modeling displayed that NH4+-N, O2, and pH were the main physicochemical properties affecting CH4 emissions. Methanobacterium, Methanosarcina, and Methanosphaera were the main archaea, and Bacillaceae were the main bacteria affecting CH4 emissions. This study provides new insights and a theoretical basis for optimizing urea addition strategies during composting.

The primary methods for hydrogen transportation include gaseous storage and transport, liquid hydrogen storage, and transport via organic liquid carriers. Among these, pipeline transportation offers the lowest cost and the greatest potential for large-scale, long-distance transport. Although the construction and operation costs of dedicated hydrogen pipelines are relatively high, blending hydrogen into existing natural gas networks presents a viable alternative. This approach allows hydrogen to be transported to the end-users, where it can be either separated for use or directly combusted, thereby reducing hydrogen transport costs. This study, based on the GERG-2008 equation of state, conducts experimental tests on the compressibility factor of hydrogen-doped natural gas mixtures across a temperature range of −10 °C to 110 °C and a pressure range of 2 to 12 MPa, with hydrogen blending ratios of 5%, 10%, 20%, 30%, and 40%. The results indicate that the hydrogen blending ratio, temperature, and pressure significantly affect the compressibility factor, particularly under low-temperature and high-pressure conditions, where an increase in the hydrogen blending ratio leads to a notable rise in the compressibility factor. These findings have substantial implications for the practical design of hydrogen-enriched natural gas pipelines, as changes in the compressibility factor directly impact pipeline operational parameters, compressor characteristics, and other system performance aspects. Specifically, the introduction of hydrogen alters the compressibility factor of the transported medium, thereby affecting the pipeline’s flowability and compressibility, which are crucial for optimizing and applying the performance of hydrogen-enriched natural gas in transportation channels. The research outcomes provide valuable insights for understanding combustion reactions, adjusting pipeline operational parameters, and compressor performance characteristics, facilitating more precise decision-making in the design and operation of hydrogen-enriched natural gas pipelines.

Improving the photogenerated carrier separation efficiency of individual semiconductor materials has always been a key challenge in photocatalysis. In this study, we synthesized a novel photocatalytic material, N-CQDs/UBWO, in situ by combining nitrogen-doped carbon quantum dots (N-CQDs) derived from discarded corn stover with ultrathin Bi2WO6 nanosheets (UBWO). Detailed characterization indicates that the random distribution of N-CQDs on the UBWO surface increases the specific surface area of UBWO, which is beneficial for the adsorption and degradation of oxytetracycline (OTC). More importantly, N-CQDs act as electron acceptors, promoting the effective separation of photogenerated charges, prolonging the lifetime of charge carriers in UBWO, and thereby enhancing the degradation efficiency of OTC. As a result, the optimized 3wt%N-CQDs/UBWO could degrade 85% of OTC within 40 min under visible light, with a removal rate four times that of pure Bi2WO6. The performance of photocatalytic degradation over OTC by 3wt%N-CQDs/UBWO exceeds that of most reported Bi2WO6-based photocatalysts. The EPR analysis confirmed that ∙O2− and ∙OH are the main active species in the photocatalytic degradation of OTC on 3wt%N-CQDs/UBWO. This study provides insight into designing green, low-cost, and efficient photocatalysts using CQDs derived from waste biomass and the degradation of emerging pollutants like antibiotics.

With the continuous advancement of electromagnetic countermeasures, new types of interference signals (e.g., multi-polarization suppression interference) pose a significant threat to conventional Global Navigation Satellite System (GNSS) services, even when the receiver employs a right-handed circularly polarized (RHCP) anti-jamming array. This paper proposes a receiving signal model for orthogonal circularly polarized (OCP) interference signals based on conventional arrays, following an analysis of the non-ideal characteristics of actual arrays. Furthermore, the mechanism by which OCP interference signals affect anti-jamming performance is examined. Power inversion (PI) and linear constrained minimum variance (LCMV) techniques, applied to both uniform linear arrays and central circular arrays, are utilized to verify the impact of these interference signals. Simulation and physical testing demonstrate that OCP interference significantly affects the interference subspace of the conventional RHCP array, potentially leading to a reduction in the anti-jamming performance of the receiver. To effectively suppress multi-polarization interference, anti-jamming GNSS receivers must either ensure the consistency of cross-polarization among the elements of the array or adopt polarization-sensitive arrays.

This study explores the drivers of geologically responsible behaviour among geotourists in three geoparks in the Greater China region: Danxiashan UNESCO Global Geopark in southern China, Hong Kong UNESCO Global Geopark, and Yehliu Geopark in northern Taiwan. On-site questionnaire surveys were conducted, collecting over 800 respondents in these geoparks, and structural equation modelling was applied for our analysis. The findings reveal that geologically responsible behaviour is positively associated with environmentally responsible attitudes, in line with some previous research. Notably, place attachment and visitor satisfaction were not directly related to geologically responsible behaviour but were positively correlated with environmentally responsible attitudes. This suggests that emotional connections to geoparks and visitor satisfaction indirectly nurture environmentally responsible attitudes, subsequently leading to geologically responsible behaviour. These results offer practical implications for geopark management practices. Providing informative guided tours and quality informational materials can enhance visitors’ geological knowledge and foster environmentally responsible attitudes. The improvement of the visitor experience, combined with the dissemination of accurate environmental knowledge and conservation messaging, can enhance visitor satisfaction, deepen attachment to geoparks, and, ultimately, encourage more geologically responsible behaviours. Understanding these relationships can assist geotourism destinations in promoting geological resources’ conservation while enhancing the visitor experience.

eBay’s feedback rating system is currently widely used. In this study, we examine if eBay’s feedback rating types (+, 0, −) are consistent with the sentiments reflected in the textual comments posted by buyers. Using the datasets collected from eBay, we test the hypotheses associated with the research questions at three levels: individual, group, and total. Overall, the types of feedback ratings are consistent with the sentiments embedded in the textual comments. However, there are some issues with eBay’s current feedback rating system: (1) at the individual level, the correlation coefficient between the ratings and the comments’ sentiments is low at 0.4311 (

Tunnel magnetoresistance (TMR) sensors, known for their high sensitivity, efficiency, and compact size, are ideal for detecting weak currents, particularly leakage currents in smart grids. However, temperature variations can negatively impact their accuracy. This work investigates the effects of temperature variations on measurement accuracy. We analyzed the operating principles and temperature characteristics of TMR sensors and proposed a high-precision, software-based temperature compensation method using cubic spline interpolation combined with polynomial regression and zero-point self-calibration. Additionally, a field-programmable gate array (FPGA)-based temperature compensation circuit was designed and implemented. An experimental platform was established to comprehensively evaluate the sensor’s performance under various temperature conditions. Experimental results demonstrate that this method significantly enhances the sensor’s temperature stability, reduces the sensitivity temperature drift coefficient, and improves zero-point drift stability, outperforming other compensation methods. After compensation, the sensor’s measurement accuracy in complex temperature environments is substantially improved, enabling effective weak current detection in smart grids across diverse environments.

With the growth of the global population and the acceleration of industrialization, the problem of water pollution has become increasingly serious, posing a major threat to the ecosystem and human health. Traditional water treatment technologies make it difficult to cope with complex pollution, so the scientific community is actively exploring new and efficient treatment methods. Biochar (BC), as a low-cost, green carbon-based material, exhibits good adsorption and catalytic properties in water treatment due to its porous structure and abundant active functional groups. However, BC’s pure adsorption or catalytic capacity is limited, and researchers have dramatically enhanced its performance through modification means, such as loading metals or heteroatoms. In this paper, we systematically review the recent applications of BC and its modified materials for water treatment in adsorption, Fenton-like, electrocatalytic, photocatalytic, and sonocatalytic systems, and discuss their adsorption/catalytic mechanisms. However, most of the research in this field is at the laboratory simulation stage and still needs much improvement before it can be applied in large-scale wastewater treatment. This review improves the understanding of the pollutant adsorption/catalytic properties and mechanisms of BC-based materials, analyzes the limitations of the current studies, and investigates future directions.

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment.

Hyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural networks (CNNs), has proven to be highly effective when run on advanced computing platforms. Nonetheless, the high degree of parameterization inherent in CNN models necessitates considerable computational and storage resources, posing challenges to their deployment in processors with limited capacity like drones and satellites. This paper focuses on advancing lightweight models for hyperspectral image classification and introduces EBCNN, a novel binary convolutional neural network. EBCNN is designed to effectively regulate backpropagation gradients and minimize gradient discrepancies to optimize BNN performance. EBCNN incorporates an adaptive gradient scaling module that utilizes a multi-scale pyramid squeeze attention (PSA) mechanism during the training phase, which can adjust training gradients flexibly and efficiently. Additionally, to address suboptimal training issues, EBCNN employs a dynamic curriculum learning strategy underpinned by a confidence-aware loss function, Superloss, enabling progressive binarization and enhancing its classification effectiveness. Extensive experimental evaluations conducted on five esteemed public datasets confirm the effectiveness of EBCNN. These analyses highlight a significant enhancement in the classification accuracy of hyperspectral images, achieved without incurring additional memory or computational overheads during the inference process.

A compact and miniaturized laser collimation system was proposed to measure the four-degrees-of-freedom of an optical payload in high-altitude space. Compared with other systems, this system has a simple structure and low cost, high measurement accuracy, and a large measurement range. The optical structure of the system was designed, the measurement principle of the four-degree-of-freedom was described in detail, the interference between the distance measurement and the angle measurement in the optical path was analyzed, and the installation error was analyzed. The error was minimized under different temperature conditions to improve the robustness of the system. An engineering prototype was built based on the system design scheme and an experiment was conducted to measure a target with a measured distance of 500 mm. The current indicators reached the requirements for the ground testing of optical payloads. The application of the system can be used to measure six degrees of freedom simultaneously by installing two systems in different coordinate systems. The system can also be used in industry; for example, by measuring the machine tool error in real time and compensating for it, the system can improve the positioning and motion accuracy. It can also be used for feedback control of the robot’s motion by measuring and controlling it.

An acoustic Doppler current profiler (ADCP) installed on a platform produces rotational tangential velocity as a result of variations in the platform’s attitude, with both the tangential velocity and radial orientation varying between each pulse’s transmission and reception by the transducer. These factors introduce errors into the measurements of vessel velocity and flow velocity. In this study, we address the errors induced by dynamic factors related to variations in attitude and propose an ADCP attitude dynamic error correction method based on angular velocity tensor and radius vector estimation. This method utilizes a low-sampling-rate inclinometer and compass data and estimates the angular velocity tensor based on a physical model of vessel motion combined with nonlinear least-squares estimation. The angular velocity tensor is then used to estimate the transducers’ radius vectors. Finally, the radius vectors are employed to correct the instantaneous tangential velocity within the measured velocities of the vessel and flow. To verify the effectiveness of the proposed method, field tests were conducted in a water pool. The results demonstrate that the proposed method surpasses the attitude static correction approach. In comparison with the ASC method, the average relative error in vessel velocity during free-swaying movement decreased by 20.94%, while the relative standard deviation of the error was reduced by 17.38%.

Berthing is one of the most dangerous phases in the process of ship navigation, and its risk assessment is crucial for both ship safety and port scheduling. To effectively enhance the safety and reliability of the berthing process, a berthing risk assessment method based on the synergy of “human-ship-environment” has been established. First, the impact of the human, ship, and environmental factors on berthing risk was analyzed, and a risk assessment index system for ship berthing was constructed. Then, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were employed for a comprehensive risk assessment. AHP was used to determine the weight of each factor reasonably, while FCE was applied for the evaluation of the berthing risk. Finally, the proposed method was applied to evaluate the berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, China. The experiment’s results indicate that the evaluation results of the method proposed here have good consistency with the expert survey method.

Bibenzyl compounds are one of the most important bioactive components of natural medicine. However, Dendrobium officinale as a traditional herbal medicine is rich in bibenzyl compounds and performs functions such as acting as an antioxidant, inhibiting cancer cell growth, and assisting in neuro-protection. The biosynthesis of bibenzyl products is regulated by bibenzyl synthase (BBS). In this study, we have cloned the cDNA gene of the bibenzyl synthase (DoBS1) from D. officinale using PCR with degenerate primers, and we have identified a novel type III polyketide synthase (PKS) gene by phylogenetic analyses. In a series of perfect experiments, DoBS1 was expressed in Escherichia coli, purified and some catalytic properties of the recombinant protein were investigated. The molecular weight of the recombinant protein was verified to be approximately 42.7 kDa. An enzyme activity analysis indicated that the recombinant DoBS1-HisTag protein was capable of using 4-coumaryol-CoA and 3 malonyl-CoA as substrates for dihydroresveratrol (DHR) in vitro. The Vmax and Km of the recombinant protein for DHR were 3.57 ± 0.23 nmol·min−1·mg−1 and 0.30 ± 0.08 mmol, respectively. The present study provides further insights into the catalytic mechanism of the active site in the biosynthetic pathway for the catalytic production of dihydroresveratrol by bibenzylase in D. officinale. The results can be used to optimize a novel biosynthetic pathway for the industrial synthesis of DHR.

The early-ripening peach industry has undergone rapid development in the Panxi region of the Sichuan Basin in recent years. However, after the introduction of some new peach varieties to the high-altitude peach-producing areas in Panxi, the titratable acid content in peach fruit has significantly increased. This study compared the fruit quality indicators of early-ripening peach varieties cultivated in Xide County (a high-altitude peach-producing area) and Longquanyi District (a low-altitude peach-producing area) in Sichuan Province and analyzed the differences in organic acid metabolism by combining primary metabolomic and transcriptomic approaches. The results showed that the ‘Zhongtaohongyu’ fruit from the high-altitude peach-producing area had a much higher accumulation of malic acid and, accordingly, a significantly higher organic acid content than the other samples. The lower annual average temperature and stronger ultraviolet radiation in high-altitude peach-producing areas may lead to the increased expression of genes (PpNAD-ME1, PpNADP-ME3, and PpPEPC1) in the organic acid synthesis pathway and the decreased expression of genes (PpACO2, PpNAD-MDH2/3/4/5, and PpPEPCK2) in the organic acid degradation pathway in peach fruit, ultimately resulting in the accumulation of more organic acids. Among them, the downregulation of the key genes PpNAD-MDH3/4/5 involved in malic acid metabolism may be the main reason for the higher malic acid accumulation in peach fruit from high-altitude peach-producing areas. Overall, this study elucidates the mechanism by which environmental factors enhance the accumulation of organic acids in peach fruit from high-altitude peach-producing areas from a multi-omics perspective, as well as providing a theoretical basis for screening key genes involved in organic acid metabolism in peach fruit.

Pitaya (Hylocereus undatus) fruit is an attractive, nutrient-rich tropical fruit with commercial value. However, low-temperature stress severely affects the yield and quality of pitaya. The relevant mechanisms involved in the response of pitaya to low-temperature stress remain unclear. To study whether the SWEET gene family mediates the response of H. undatus to low-temperature stress and the related mechanisms, we performed genome-wide identification of the SWEET gene family in pitaya, and we used ‘Baiyulong’ tissue-cultured plantlets as material in the present study. We identified 28 members of the SWEET gene family from the H. undatus genome and divided these family members into four groups. Members of this gene family presented some differences in the sequences of introns and exons, but the gene structure, especially the motifs, presented relatively conserved characteristics. The promoter regions of most HuSWEETs have multiple stress- or hormone-related cis-elements. Three duplicated gene pairs were identified, including one tandem duplication gene and two fragment duplication gene pairs. The results revealed that the SWEET genes may regulate the transport and distribution of soluble sugars in plants; indirectly regulate the enzyme activities of CAT, POD, and T-SOD through its expression products; and are involved in the response of pitaya to low-temperature stress and play vital roles in this process. After ABA and MeJA treatment, the expression of HuSWEETs changed significantly, and the cold stress was also alleviated. This study elucidated the molecular mechanism and physiological changes in the SWEET gene in sugar metabolism and distribution of pitaya when it experiences low-temperature stress and provided a theoretical basis for cold-resistant pitaya variety breeding.

Remote sensing images contain a wealth of Earth-observation information. Efficient extraction and application of hidden knowledge from these images will greatly promote the development of resource and environment monitoring, urban planning and other related fields. Remote sensing image caption (RSIC) involves obtaining textual descriptions from remote sensing images through accurately capturing and describing the semantic-level relationships between objects and attributes in the images. However, there is currently no comprehensive review summarizing the progress in RSIC based on deep learning. After defining the scope of the papers to be discussed and summarizing them all, the paper begins by providing a comprehensive review of the recent advancements in RSIC, covering six key aspects: encoder–decoder framework, attention mechanism, reinforcement learning, learning with auxiliary task, large visual language models and few-shot learning. Subsequently a brief explanation on the datasets and evaluation metrics for RSIC is given. Furthermore, we compare and analyze the results of the latest models and the pros and cons of different deep learning methods. Lastly, future directions of RSIC are suggested. The primary objective of this review is to offer researchers a more profound understanding of RSIC.

This study focuses on developing and implementing zero-carbon buildings through the integration of multiple systems to meet China’s carbon neutrality goals. It emphasizes the significant role of the building sector in carbon emissions and highlights the challenge of increasing energy consumption conflicting with China’s “dual carbon” targets. To address this, the research proposes a comprehensive framework that combines multifunctional envelope structure (MES) systems, photovoltaic power generation, energy storage, direct current (DC) systems, flexible energy management (PEDF), and regional energy stations. This framework integrates different technologies such as phase change materials, radiation cooling, and carbon mineralized cement, aiming to reduce carbon emissions throughout the building’s lifecycle. The method has been successfully applied in the Yazhou Bay Zero Carbon Post Station project in Sanya, Hainan, with precise calculations of carbon emission reductions. The carbon emission calculations revealed a reduction of 44.13 tons of CO2 annually, totaling 1103.31 tons over 25 years, primarily due to the rooftop photovoltaic systems. It demonstrates that the multi-system integration can reduce carbon emissions and contribute to China’s broader carbon neutrality goals. This approach, if widely adopted, could accelerate the transition to carbon-neutral buildings in China.

Shale gas, mainly consisting of adsorbed gas and free gas, has served a critical role of supplying the growing global natural gas demand in the past decades. Considering that the adsorbed methane has contributed up to 80% of the total gas in place (GIP), understanding the methane adsorption behaviors is imperative to an accurate estimation of total GIP. Historically, the single-site Langmuir model, with the assumption of a homogeneous surface, is commonly applied to estimate the adsorbed gas amount. However, this assumption cannot depict the methane adsorption characteristics due to various compositions and pore sizes of shales. In this work, a multi-site model integrating the energetic heterogeneity in adsorption is derived to predict methane adsorption on shale. Our results show that the multi-site model is capable of addressing the heterogeneity of shales by a wide range of adsorption energy distributions (owing to the complex compositions and different pore sizes), which is different from the single-site model only characterized by single adsorption energy. Consequently, the multi-site model results have better accuracy against the experimental data. Therefore, applying the multi-site Langmuir model for estimating GIP in shales can achieve more accurate results compared with using the traditionally single-site model.

The Kuqa Depression is rich in oil and gas resources and serves as a key production area in the Tarim Basin. However, controversy persists over the genesis of oil and gas in the various structural zones of the Kuqa Depression. This study employs natural gas composition analysis, gas carbon isotope analysis and gold pipe thermal simulation experiments, to comprehensively analyze the differences in the genesis and sources of hydrocarbon gas fluid from the eastern and western Kuqa Depression. The results show that the Kuqa Depression is dominated by alkane gas, with an average gas drying coefficient of 95.6, with nitrogen and carbon dioxide as the primary non-hydrocarbon gases. The average of δ13C1, δ13C2 and δ13C3 values in natural gas are −27.70‰, −20.43‰ and −21.75‰, respectively. Based on comprehensive natural gas geochemical maps, the CO2 in the natural gas from the Tudong and Dabei areas, as well as the KT-1 well of the Kuqa Depression, is thought to be of organic origin. Additionally, natural gas formation in the Tudong area is relatively simple, consisting entirely of thermally generated coal gas derived from the initial cracking of kerogen. The natural gas in the KT-1 well and the Dabei area are mixed gasses, formed by the initial cracking of kerogen from highly evolved lacustrine and coal-bearing source rocks, exhibiting characteristics resembling those of crude oil cracking gas. The methane (CH4) content of natural gas in the Dabei area is high and the carbon isotopes are unusually heavy. Considering the regional geological background, potential source rock characteristics and geochemical features may be related to the large-scale invasion of dry gas contributed by CH4 from highly evolved, underlying coal-bearing source rocks. Consequently, the CH4 content in the mixed gas is generally high (Ln (C1/C2) can reach up to 5.38), while the relative content of heavy components is low, though remains relatively unchanged. Thus, the map of the relative content of heavy components still reflects the characteristics of the original gas genesis (initial cracking of kerogen). Mixed-source gas was analyzed using thermal simulation experiments and natural gas composition ratio diagrams. The contributions of natural gas from deep, highly evolved coal-bearing source rocks in the KT-1 well and the Dabei area accounted for more than 90% and approximately 60%, respectively. This analysis provides theoretical guidance for natural gas exploration in the research area.

Background: The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. Methods: Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets—comprising 1091 metabolites, 309 ratios, and 179 lipids—and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation. Results: Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024–1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis. Conclusion: Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.

Hypertension has always posed a severe threat to people’s health. Food-derived angiotensin-converting enzyme (ACE)-inhibitory peptides have the potential to both prevent and treat hypertension. In the current investigation, two ACE-inhibitory peptides (SLPQ and PYVRYL) from goat milk were studied for their endothelial effects using EA.hy926 cells. PYVRYL outperformed SLPQ, yet neither impacted cell survival below 200 μg/mL. Investigation of SLPQ’s impact on EA.hy926 cell expression revealed 114 differentially expressed genes, with 65 downregulated and 49 upregulated. The genes were enriched in cytokine interactions, coagulation cascades, Hippo signaling, and ECM–receptor interaction. Decreased c-x-c motif chemokine ligand 2 (CXCL2), integrin subunit beta 2 (ITGB2), and fbj murine osteosarcoma viral oncogene homologue (FOS) expression and increased secreted phosphoprotein 1 (SPP1) expression may protect endothelial cells from inflammation. Our findings suggest that beyond ACE inhibition, SLPQ aids blood pressure control by influencing endothelial function, paving the way for its use as an antihypertensive food ingredient.

To address the common issues of lacking indicator system identification, causal relationship quantification, and path simulation analysis in the current research on safety performance in prefabricated construction, a method for improving safety performance in prefabricated construction based on the decision-making trial and evaluation laboratory (DEMATEL) and NK model is proposed. Firstly, through theoretical analysis and literature review, the indicator system for safety performance in prefabricated construction is identified using the grounded theory. Secondly, expert research and quantitative analysis are combined to analyze the causal relationship of the indicators using the DEMATEL method. Then, the DEMATEL method is integrated with the NK model to carry out a key indicator adaptability modeling analysis and three-dimensional simulation. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and method. The results show that X6 (construction and implementation of safety management system) had the highest impact on the other indicators, and X14 (quality and safety status of prefabricated components) was most influenced by other indicators. X6 (construction and implementation of safety management system), X1 (personnel safety awareness and attitude), X14 (quality and safety status of prefabricated components), and X12 (construction site working environment) were identified as key performance indicators. “X6 (construction and implementation of safety management system) → X1 (personnel safety awareness and attitude) → X14 (quality and safety status of prefabricated components) → X12 (construction site working environment)” was considered the optimal path to improve construction safety performance.

Cancer stem cells (CSCs) are most likely the main cause of lung cancer formation, metastasis, drug resistance, and genetic heterogeneity. Three-dimensional (3D) ex vivo cell culture models can facilitate stemness improvement and CSC enrichment. Considering the critical role of extracellular matrix (ECM) on CSC properties, the present study developed a thermo-responsive hydrogel using the porcine decellularized lung for 3D cell culture, and the cell-laden hydrogel culturing model was used to explore the CSC characteristics and potential utilization in CSC-specific drug evaluation. Results showed that the lung dECM hydrogel (LEH) was composed of the main ECM components and displayed excellent cellular compatibility. In addition, lung cancer cells 3D cultured in LEH displayed the overexpression of metastasis-related genes and enhanced migration properties, as compared with those in two-dimensional (2D) conditions. Notably, the CSC features, including the expression level of stemness-associated genes, colony formation capability, drug resistance, and the proportion of cancer stem-like cells (CD133+), were also enhanced in 3D cells. Furthermore, the attenuation effect of epigallocatechin gallate (EGCG) on CSC properties in the 3D model was observed, confirming the potential practicability of the 3D culture on CSC-targeted drug screening. Overall, our results suggest that the fabricated LEH is an effective and facile platform for 3D cell culture and CSC-specific drug evaluation.

Respiratory Syncytial Virus (RSV) is a significant cause of lower respiratory tract infections (LRTI) across all demographics, with increasing mortality and morbidity among high-risk groups such as infants under two years old, the elderly, and immunocompromised individuals. Although newly approved vaccines and treatments have substantially reduced RSV hospitalizations, accessibility remains limited, and response to treatment varies. This underscores the importance of comprehensive studies on host–RSV interactions. tRNA-derived RNA fragments (tRFs) are recently discovered non-coding RNAs, notable for their regulatory roles in diseases, including viral infections. Our prior work demonstrated that RSV infection induces tRFs, primarily derived from the 5′-end of a limited subset of tRNAs (tRF5), to promote RSV replication by partially targeting the mRNA of antiviral genes. This study found that tRFs could also use their bound proteins to regulate replication. Our proteomics data identified that PABPC1 (poly(A)-binding protein cytoplasmic 1) is associated with tRF5-GluCTC, an RSV-induced tRF. Western blot experimentally confirmed the presence of PABPC1 in the tRF5-GluCTC complex. In addition, tRF5-GluCTC is in the anti-PABPC1-precipitated immune complex. This study also discovered that suppressing PABPC1 with its specific siRNA increased RSV (-) genome copies without impacting viral gene transcription, but led to less infectious progeny viruses, suggesting the importance of PABPC1 in virus assembly, which was supported by its interaction with the RSV matrix protein. Additionally, PABPC1 knockdown decreased the production of the cytokines MIP-1α, MIP-1β, MCP-1, and TNF-α. This is the first observation suggesting that tRFs may regulate viral infection via their bound proteins.

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model’s feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model’s ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers’ planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of “high in winter and low in summer”; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products.

Recent inter-sample self-distillation methods that spread knowledge across samples further improve the performance of deep models on multiple tasks. However, their existing implementations introduce additional sampling and computational overhead. Therefore, in this work, we propose a simple improved algorithm, the center self-distillation, which achieves a better effect with almost no additional computational cost. The design process for it has two steps. First, we show using a simple visualization design that the inter-sample self-distillation results in a denser distribution of samples with identical labels in the feature space. And, the key to its effectiveness is that it reduces the intra-class variance of features through mutual learning between samples. This brings us to the idea of providing a soft target for each class as the center for all samples within that class to learn from. Then, we propose to learn class centers and consequently compute class predictions for constructing these soft targets. In particular, to prevent over-fitting arising from eliminating intra-class variation, the specific soft target for each sample is customized by fusing the corresponding class prediction with that sample’s prediction. This is helpful in mitigating overconfident predictions and can drive the network to produce more meaningful and consistent predictions. The experimental results of various image classification tasks show that this simple yet powerful approach can not only reduce intra-class variance but also greatly improve the generalization ability of modern convolutional neural networks.

Understanding the mechanisms of snap-off during gas–liquid immiscible displacement is of great significance in the petroleum industry to enhance oil and gas recovery. In this work, based on the original pseudo-potential lattice Boltzmann method, we improved the fluid–fluid force and fluid–solid force scheme. Additionally, we integrated the Redlich–Kwong equation of state into the lattice Boltzmann model and employed the exact difference method to incorporate external forces within the lattice Boltzmann framework. Based on this model, a pore–throat–pore system was built, enabling gas–liquid to flow through it to investigate the snap-off phenomenon. The results showed the following: (1) The snap-off phenomenon is related to three key factors: the displacement pressure, the pore–throat length ratio, and the pore–throat width ratio. (2) The snap-off phenomenon occurs only when the displacement pressure is within a certain range. When the displacement pressure is larger than the upper limit, the snap-off will be inhibited, and when the pressure is less than the lower limit, the gas–liquid interface cannot overcome the pore–throat and results in a “pinning” effect. (3) The snap-off phenomenon is controlled using the pore–throat structures: e.g., length ratio and the width ratio between pore and throat. It is found that the snap-off phenomenon could easily occur in a “long-narrow” pore–throat system, and yet hardly in a “short-wide” pore–throat system.

Extended exposure to UVB (280–315 nm) radiation results in oxidative damage and inflammation of the skin. Previous research has demonstrated that pilose antler extracts have strong anti-inflammatory properties and possess antioxidant effects. This study aimed to elucidate the mechanism of pilose antler protein in repairing photodamage caused by UVB radiation in HaCaT cells and ICR mice. Pilose antler protein (PAP) was found to increase the expression of type I collagen and hyaluronic acid in HaCaT cells under UVB irradiation while also inhibiting reactive oxygen species (ROS) production and oxidative stress in vitro. In vivo, the topical application of pilose antler protein effectively attenuated UVB-induced skin damage in ICR mice by reducing interleukin-1β (IL-β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) and inhibiting skin inflammation while alleviating UVB-induced oxidative stress. It was shown that pilose antler protein repaired UVB-induced photodamage through the MAPK and TGF-β/Smad pathways.

The airfoil is the foundation of an aircraft, and its characteristics have a significant impact on those of the aircraft. Conventional airfoil design mainly focuses on improving aerodynamic performance, while flying wing airfoil designs should also consider layout stability and stealth performance. The design requirements for an airfoil vary with its position on the flying wing layout aircraft based on corresponding spanwise flow field characteristics. By analyzing the spanwise flow characteristics of the flying wing, partition design models for flying wing airfoils were established in this study, and a series of flying wing airfoil designs that consider aerodynamics and aerodynamic/stealth were implemented. Then, the designed airfoils were configured on a three-dimensional X-47B layout for testing and verification. The results showed that the aerodynamic design and the aerodynamic/stealth design exhibited significant improvements in terms for aerodynamic and longitudinal trimming characteristics. However, the cruise drag performance of the aerodynamic/stealth design was slightly worse than that of the aerodynamic design, although the longitudinal moment trimming characteristics were basically the same. The stealth characteristics of the aerodynamic/stealth design had significant advantages, indicating that there were weak contradictions between the aerodynamic, stealth, and trimming requirements in the design of the flying wing.

The safety of airport runways is important to guarantee aircraft taking-off, landing, and taxiing, and the comparison of the mechanical response of pavement structures under dynamic and static loading by LS-DYNA has rarely been studied. The purpose of this work is to separate two analysis methods to investigate the mechanical response of rigid airport pavements. Firstly, a tire–road coupling model of an airfield was established to evaluate the suitability of dynamic and static analyses. Then, the effects of landing pitch angles, sinking speeds, and tire pressures on the effective stress, effective strain, and z-displacement of the runway were investigated for both dynamic and static analysis. Finally, the significance of influence factors was analyzed by regression analysis in Statistical Product and Service Solutions (SPSS). The results indicated that the effective stress, effective strain, and z-displacement of the runway increased with a decrease in the landing pitch angle, which also increased with an increase in the sinking speed and tire pressure. It was demonstrated that the difference in pavement mechanical response between dynamic and static analyses progressively widened at high tire pressure and sinking speed. In other words, the static analysis method can be adopted to assess the dynamic mechanical behavior when the landing pitch angle is large and the tire pressure is small. Among the various factors of mechanical response, the effect of tire pressure was the most obvious, followed by sinking speed and landing pitch angle. The work proposes a new approach to understanding the mechanical behavior of runways under complicated and varied conditions, evaluates the applicability of the dynamic and static mechanical analysis methods, identifies key factors in the dynamic and static mechanical analysis of rigid runways, and provides technical support for improving and maintaining the impact resistance of pavement facilities.

Water scarcity poses a significant threat to the sustainable production of crops in Northern China. Despite this, the effect of water management practices, such as deficit irrigation, on the yield and WUE of potatoes has been rarely explored. Based on the meta-analysis of field experiment data, this study evaluated the influence of deficit irrigation on potato yield, evapotranspiration (ET), water use efficiency (WUE) and irrigation water use efficiency (IWUE) under variable soil types, water-saving ratio, irrigation methods, soil organic carbon (SOC) content, and fertilizer rate in Northern China. Here, we determined that potato WUE and IWUE were significantly increased by 10.0 and 31.6%, respectively, under deficit irrigation, while ET was significantly decreased by 26.3% compared to full irrigation. Conclusively, deficit irrigation significantly reduced potato yields by 16.4% compared to full irrigation. Furthermore, SOC content played a vital role in improving the WUE and alleviating potato yield losses under deficit irrigation. Our study suggested that maximum WUE with lower potato yield losses under deficit irrigation can be achieved in the Central Plains region of China or in yellow loam soil, brown soil, and meadow soil under alternate root-zone irrigation when the water-saving ratio was less than 45% and fertilizer application rates were 300 kg N ha−1, >240 kg P2O5 ha−1, and 181–300 kg K2O ha−1. Overall, these findings highlight the need for a comprehensive understanding of various agricultural management practices and local environmental conditions to optimize the benefits of deficit irrigation in potato-growing regions across Northern China.

Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have been manually designed by experts with rich domain knowledge. Additionally, the optimal neural network architecture can vary depending on specific tasks and data distributions. Neural Architecture Search (NAS) is a class of techniques aimed at automatically searching for and designing neural network architectures according to the given tasks and data. Specifically, evolutionary-computation-based NAS methods are known for their strong global search capability and have aroused widespread interest in recent years. Although evolutionary-computation-based NAS has achieved success in a wide range of research and applications, it still faces bottlenecks in training and evaluating a large number of individuals during optimization. In this study, we first devise a multi-objective evolutionary NAS framework based on a weight-sharing supernet to improve the search efficiency of traditional evolutionary-computation-based NAS. This framework combines the population optimization characteristic of evolutionary algorithms with the weight-sharing ideas in one-shot models. We then design a bi-population MOEA/D algorithm based on the proposed framework to effectively solve the NAS problem. By constructing two sub-populations with different optimization objectives, the algorithm can effectively explore network architectures of various sizes in complex search spaces. An inter-population communication mechanism further enhances the algorithm’s exploratory capability, enabling it to find network architectures with uniform distribution and high diversity. Finally, we conduct performance comparison experiments on image classification datasets of different scales and complexities. Experimental results demonstrate the effectiveness of the proposed multi-objective evolutionary NAS framework and the practicality and transferability of the introduced bi-population MOEA/D-based NAS method compared to existing state-of-the-art NAS methods.

In recent years, the exercise behavior of Chinese adolescents has been on the decline, which is extremely detrimental to their physical and mental health development. However, few studies have explored the mechanisms by which exercise cognition influences Chinese adolescents’ exercise behavior. The present study aimed to investigate the relationship between exercise cognition and exercise behavior among Chinese adolescents and the mediating role of satisfying basic psychological needs for exercise. The study consisted of 996 adolescents (44.6% males, 55.4% females) between the ages of 12 and 15 (M = 13.34, SD = 1.059). Participants’ exercise behaviors and the satisfaction of basic psychological needs for exercise data were collected via surveys. Structure equation modeling (SEM) was performed to examine the direct and indirect effects. The results were as follows: (1) adolescents’ perceptions of exercise were significantly associated with exercise behavior and (2) the mediation model suggests that the satisfaction of basic psychological needs for exercise is an important mechanism by which exercise cognition influences the occurrence of exercise behavior. Therefore, it is crucial to help adolescents form good exercise cognition. Schools, families, and society should take responsibility for adolescents’ formation of good exercise cognition and satisfy adolescents’ basic psychological needs for exercise so as to enhance adolescents’ exercise behaviors and thereby develop good exercise habits.

The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model’s confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.

Recognizing the importance of incorporating different risk measures in the portfolio management model, this paper examines the dynamic mean-risk portfolio optimization problem using both variance and value at risk (VaR) as risk measures. By employing the martingale approach and integrating the quantile optimization technique, we provide a solution framework for this problem. We demonstrate that, under a general market setting, the optimal terminal wealth may exhibit different patterns. When the market parameters are deterministic, we derive the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the benefits of our dynamic portfolio model compared to its static counterpart.

Loquat leaves exhibiting obvious yellowing, blistering, mosaic, leaf upward cupping, crinkle, and leaf narrowing were identified in Panzhihua City, Sichuan Province, China. High-throughput sequencing (HTS) with the ribo-depleted cDNA library was employed to identify the virome in the loquat samples; only tomato mosaic virus (ToMV) and citrus exocortis viroid (CEVd) were identified in the transcriptome data. The complete genome sequence of ToMV and CEVd were obtained from the loquat leaves. The full-length genome of the ToMV-loquat is 6376 nt and comprises four open reading frames (ORFs) encoding 183 kDa protein, RNA-dependent RNA polymerase (RdRp), movement protein (MP), and coat protein (CP), respectively. A pairwise identity analysis showed that the complete sequence of the ToMV-loquat had a nucleotide identity between 98.5 and 99.3% with other ToMV isolates. A phylogenetic analysis indicated that ToMV-loquat was more closely related to ToMV-IFA9 (GenBank No. ON156781). A CEVd sequence with 361 nt in length was amplified based on the HTS contigs, sequence alignment indicated CEVd-loquat had the highest identity with the strain of CEVd-Balad (GenBank No. PP869624), phylogenetic analysis showed that CEVd-loquat was more closely related to CEVd-lettuce (GenBank No. ON993891). This significant discovery marks the first documentation and characterization of ToMV and CEVd infecting loquat plants, shedding light on potential threats to loquat cultivation and providing insights for disease management strategies.