The result of the present electricity price prediction as a key signal in the power market,for the normal operation of the power system,plays an important role.In this paper,a prediction model of the present electricity price is proposed based on the self-attention mechanism with the long-nosed raccoon optimization algorithm of the convolutional neural network and the bi-directional gated recurrent unit network.The model fully considers many factors such as the boundary conditions of the power market and the external environment that affect the price of electricity,and firstly uses the Pearson correlation coefficient method to correlate the disclosure data of the power market in Shandong Province,and comes up with the key factors that affect the price of electricity.Then the data are input into a CNN-BiGRU model based on self-attention mechanism and long-nosed raccoon optimization algorithm for training.The experimental results show that the three evaluation indexes of the model,Mean Absolute Error (δ_MAE),Mean Absolute Percentage Error (δ_MAPE),and Coefficient of Determination (R-square,R²),are 10.481, 3.23%, respectively, 0.954,the three indicators are obviously better than other models,with higher prediction accuracy and stability,fully verifying the feasibility of the model in the prediction of the present electricity price.

To address the low detection accuracy caused by occluded pest targets and camouflage effects (where pest body colors blend with the environment) in crop pest detection,this study proposes an RT-DETR-based algorithm named RT-DETR-SDIC.First,the original backbone network’s early stages (S2,S3) are augmented with DBRB.By integrating multi-branch topological structures and heterogeneous paths of varying scales and complexities,the DBRB enriches the feature space.The later stages (S4,S5) of the backbone network are enhanced with IRMB_CGA.This module mitigates the lack of direct long-range semantic interaction in the original architecture while improving discrimination capability for environmental features.Second,in the feature fusion network,a parameter-free attention mechanism— SPA—is introduced to capture fine-grained spatial information.Additionally,CGFM is proposed for the feature fusion layer,orchestrating multi-scale feature integration.Experimental results demonstrate that RT-DETR-SDIC achieves a 19.6% reduction in parameters,a 9.9% decrease in computational load,a 6.2% improvement in P_mA,0.5 (average precision at IoU=50%),and a 2.6% improvement in P_mA,50:95 (mean average precision across IoU thresholds from 50% to 95%).

Tomato fruit is rich in lycopene,vitamin C,polyphenols and other active ingredients,which have both nutritional and health values.In this paper,the content,physiological function,synergistic effect and lycopene extraction technology of these components were reviewed.Lycopene can resist oxidation through conjugated double bond structure,reducing the risk of cardiovascular and cerebrovascular diseases and cancer;Vitamin C participates in immune regulation and collagen synthesis;Polyphenols regulate inflammatory pathways and induce cancer cell apoptosis.These components can synergistically enhance the physiological functions of tomato,such as antioxidation and anti-inflammation.For the extraction technology of lycopene,the traditional organic solvent extraction method is mature,but the solvent residue is obvious.Enzymatic hydrolysis is suitable for high value-added products,but the cost is high.Modern technologies,such as supercritical CO₂ extraction and membrane separation,are efficient and environmentally friendly.Emerging microbial fermentation and nano-embedding technologies have potential applications,but the problems of safety and scale need to be solved.At present,the optimization of energy consumption and cost of collaborative mechanism analysis and extraction technology still needs to be broken through,and future research can focus on collaborative target mining,industrial adaptation of new technologies and functional product development.This study provides theoretical support for efficient utilization of tomato resources.

To enhance vehicle ride comfort,an active suspension system based on the inverse piezoelectric effect with piezoelectric stacks is proposed.According to Lagrange’s theorem,a six-degree-of-freedom active suspension dynamics model is established.Six performance metrics including vertical cabin acceleration are selected to formulate a fitness function,and an LQR (Linear Quadratic Regulator) controller is designed by integrating a genetic algorithm.Numerical simulations demonstrate that the piezoelectric effect-based active suspension outperforms the passive suspension in performance.
Key words:piezoelectric actuators;vehicle-road coupling;genetic algorithms;ride comfort

Waste denim fabric,due to its resistance to degradation and low recycling efficiency,presents significant obstacles to effective resource reutilization,particularly as traditional recycling methods are often technologically complex and prone to causing deterioration in fiber performance,thereby limiting their reuse potential.In this study,waste denim was subjected to various pretreatment strategies and utilized as a reinforcing phase within a polylactic acid (PLA) matrix to prepare fabric-reinforced composites through a composite processing technique.The effects of different pretreatments,including alkali treatment and cyclic tensile conditioning,on the composites’ microstructure,mechanical behavior,and thermal properties were comprehensively investigated.The findings revealed that the combination of alkali treatment and cyclic tensile conditioning led to a marked enhancement in the overall mechanical performance of the composites.This research not only offers a novel route for the high-value reutilization of waste textiles but also contributes to the development of sustainable composite materials with promising application prospects in construction,automotive interiors,and aerospace engineering.

To address the issues of weakened target information and loss of fine details in fused images,this paper proposes a progressive multi-scale feature extraction and fusion method for infrared and visible image fusion.A structurally symmetric,parameter-independent dual-branch generation network is constructed.The original images and their enhanced versions are first fed into dilated convolution modules to extract contextual features at multiple scales,effectively capturing multi-scale information.Then,a multi-attention complementary residual aggregation module is introduced to enhance feature selectivity by emphasizing salient features and suppressing redundant ones,enabling progressive interaction and complementary fusion across scales.In the discriminator design,a dual-discriminator architecture is adopted to separately model the distributions of infrared and visible images,mitigating contrast shift and detail attenuation problems commonly encountered in multi-modal adversarial learning.Experimental results demonstrate that the proposed method outperforms most state-of-the-art algorithms in both objective metrics and subjective visual quality,retaining more texture details and achieving superior fusion performance.

In order to optimize the preparation process of porous noodles fermented by yeast.Through a single-factor experimental design,the influence of multiple process parameters such as raw material ratio and processing conditions on the mature and broken strip rate and sensory score of porous hanging noodles was systematically studied.Then,a significance analysis of four factors and three levels was conducted through the response surface optimization test,with the response values being the sensory score and the rate of cooked and broken noodles.The optimal combination of process parameters was obtained:Taking 100 g of flour as the benchmark,3 g of yeast,34 g of water,1.5 g of edible salt were added,and the fermentation time was 60 min.The sensory score of the porous noodles prepared by this process formula was the highest at 93.2,and the rate of cooked and broken strips was the lowest at 2%.Finally,the quality characteristics of the porous noodles were analyzed.The optimal cooking time was 260 seconds,the rate of cooked broken strips was 3%,the cooking loss rate was 6.52%,the mass fraction of protein was 8.61%,the mass fraction of reducing sugar was 8.75%,the mass fraction of ash was 2.13%,the mass fraction of fat was 0.58%,and the pH was 6.51,which significantly improved the quality characteristics of the porous noodles.The research provides a theoretical basis for the technological production of porous noodles.

Oxidative stress is the root cause of chronic diseases.Soybean protein can be hydrolyzed or fermented to obtain antioxidant peptides (SAPs),which can scavenge free radicals and hold great potential in the functional food industry.Research has elucidated their structure-activity relationships and some in vivo and in vitro mechanisms,but large-scale production and safety evaluation still require further exploration.This study aimed to utilize probiotic liquid fermentation of soybean prote in to prepare antioxidant peptides.Two stepwise fermentation methods were adopted:(1)inoculation with Bacillus subtilis natto(37 ℃,24 h),followed by inoculation with Bacillus coagulans (47 ℃,42 h);(2)on the basis of method (1),sequential inoculation with Lactobacillus rhamnosus (37 ℃,36 h).The two fermentation broths were ultrafiltered to obtain fractions with molecular weight <1 kDa (SPAP1,SPAP2),and their in vitro antioxidant activities and in vivo efficacy in Caenorhabditis elegans were evaluated.Results showed that the IC50 values for ABTS radical scavenging activity were (0.509±0.016) and (0.591±0.028) mg/mL,respectively,and the FRAP values were (0.427±0.02) and (0.314±0.019)mmol/L,respectively.The C.elegans assayindicated that SPAP2 was superior to SPAP1 in delaying aging,reducing reactive oxygen species (ROS) levels,improving swallowing ability,and prolonging boths tress resistance and natural lifespan.RP-HPLC-MS/MS analysis identified a total of 175 potential antioxidant peptides in SPAP2.In conclusion,this study explored a probiotic combination and its stepwise fermentation conditions for the preparation of soybean antioxidant peptides,verified their efficacy through in vitro and in vivo experiments,and identified their peptide composition by mass spectrometry,providing technical support for the development and application of soybean bioactive peptides.

Fire monitoring is crucial for reducing the loss of life and property.However,traditional methods suffer from insufficient real-time performance and accuracy in complex environments.This paper proposes a lightweight fire image detection algorithm based on improved YOLOv5s,which optimizes the monitoring system by integrating edge computing technology.By introducing the Convolutional Block Attention Module (CBAM) to enhance feature learning capabilities,employing Atrous Spatial Pyramid Pooling (ASPP) to expand the model’s receptive field,and utilizing the EIoU Loss function to accelerate convergence and improve regression accuracy,the improved model’s fire recognition rate is increased to 94%,with precision and recall rates reaching 94.2% and 92.4% respectively.By deploying the system on a modular AI module to directly process video data,cloud transmission latency is avoided,and detection real-time performance is significantly enhanced.This method provides an efficient solution for fire monitoring in complex scenarios and is of great significance for improving emergency response capabilities.

To address the issues of high computational cost in the backbone network,insufficient feature extraction capability,and limited accuracy in small target detection in the YOLOv11 model for printed circuit board(PCB) surface defect detection,an improved YOLOv11 model is proposed.The model introduces the C3k2_iRMB_Cascaded module,which integrates Cascaded Group Attention(CGA) and Inverted Residual Mobile Block(iRMB) to enhance multi-scale feature extraction while reducing computational complexity.A Selective Boundary Aggregation(SBA) module is adopted to construct a Re-Calibration Feature Pyramid Network(Re-Calibration FPN),addressing the semantic loss issue in traditional FPN through bidirectional feature fusion and an adaptive attention mechanism,thereby improving small target detection accuracy.A lightweight Detect_LSCD detection head is designed,incorporating shared convolution and a scale-adaptive scaling mechanism to enhance the robustness of multi-scale object detection.Experimental results demonstrate that the improved model achieves a 3.4% increase in PmA 0.5 and a 1.5% increase in PmA 0.5:0.95 compared with the original YOLOv11n model,effectively balancing detection accuracy and real-time performance,making it suitable for quality inspection requirements in modern electronic manufacturing systems.

This study investigates the influence of nickel content on the microstructure evolution and mechanical properties of low-temperature ductile iron (QT400-18L) through the synergistic control of nickel microalloying and annealing heat treatment processes.Based on the research path of composition design-process optimization-performance characterization,a composition range with a nickel gradient of 0~1% was designed,combined with heat treatment processes.The performance of the samples was tested through tensile and impact tests,and the microstructure before and after heat treatment was characterized to analyze the effects of nickel on the matrix structure and properties.The experimental results show that the as-cast structure of the samples obtained by adding different amounts of nickel to ductile iron is mainly composed of spheroidal graphite, ferrite, and a small amount of pearlite. Nickel has a slight graphite-forming effect and promotes pearlite formation.To improve the impact toughness of the samples,heat treatment was used to regulate the microstructure, significantly reducing pearlite content.The microstructure of the heat-treated samples consists of spheroidal graphite and ferrite. When the nickel mass fraction is 0.4%,the heat-treated sample exhibits optimal strength and plasticity,with a tensile strength of 395 MPa, an elongation of 17.5%, a hardness of 139 HBW,and a -40 ℃low-temperature impact absorption work of 14.7 J, which represents an approximately 22.5% improvement in impact toughness compared to the QT400-18L standard.

This study utilizes data from Chinese A-share listed companies from 2007 to 2019 to empirically analyze the impact of urban agglomeration population concentration on firms’ new quality productivity and its heterogeneous characteristics.It also examines the mediating roles of knowledge exchange and industrial structure optimization.The findings are as follows:(1) Urban agglomeration population concentration has a significant impact on the level of firms’ new quality productivity;(2) Knowledge exchange and industrial structure optimization play mediating roles in the relationship between urban agglomeration population concentration and firms’ new quality productivity;(3) Population concentration in cross-regional urban agglomerations has a positive impact on firms’ new quality productivity,with this promoting effect being more remarkable in central and western regions and areas with lower marketization levels.

The correct disposal of monitoring and alarm information in substation is very important to ensure the safe operation of substation and equipment maintenance.The premise of correct disposal is to extract the power entities in the monitoring alarm information quickly and accurately.Because of the existing power entity extraction methods,it is difficult to meet the dual requirements of accuracy and speed in practical application.In order to solve the above problems,a semi-supervised-ERNIE-GP power entity extraction method based on enhanced representation through knowledge integration (ERNIE) and Global Pointer (GP) and using semi-supervised learning strategy is proposed.This method is based on ERNIE-GP to improve the accuracy and speed of power entity extraction,and introduces semi-supervised learning idea to mine entity extraction knowledge from unlabeled data.In order to verify the effectiveness of the proposed method,the monitoring and alarm information of substation is used to construct a data set and conduct a series of experiments.Comparative experiments show that,compared with the better baseline model BERT-Bi-LSTM-CRF,the Semi-Supervised-ERNIE-GP adopted in this thesis improves the accuracy,recall and F1 score by 4.90%,2.50% and 3.71% respectively.Through curve analysis,the superiority of the extraction speed of this method in large-scale data application scenarios is further confirmed.

In this research,a strain was screened from the rotten kelp and was identified and named as Vibrio furnissii C1.Two alginate lyase genes alg792 and alg796 from V.furnissii C1 were heterologously expressed in Escherichia coli.The recombinant alginate lyases Alg792 and Alg796 were purified,and their properties were analyzed.The specific activities of Alg792 and Alg796 were 950 and 116 U/mg,respectively.The optimal temperature for Alg792 and Alg796 were 40 ℃ and 25 ℃,respectively.When incubated at 30 ℃,Alg792 possessed better enzyme viability stability.When the temperature was above 40 ℃,more than 80% of both enzymes activities were lost in 30 min.Both Alg792 and Alg796 possessed an optimum pH of 7.0.Alg796 possessed a wider pH applicable range,while Alg792 had better pH stability.Cu²⁺,Zn²⁺ and EDTA completely inhibited the activities of the two recombinases.Mg²⁺ and Na⁺ promoted the enzyme activities observably,while Ca²⁺,Mn²⁺ and K⁺ had opposite effects on the two recombinases.Both recombinant enzymes were bifunctional,Alg792 was endonuclease and Alg796 was exonuclease.These two alginate lyases have good application potential in the development of high value-added products of alginate.

With the continuous development of medical imaging technology,chest CT images play a crucial role in the early diagnosis and treatment of lung diseases.Computer-aided detection systems can provide valuable references for clinicians,thereby reducing diagnostic errors caused by human factors.To address the challenge of varying feature channel importance in lung cancer segmentation from chest CT images,TransUnet-SE is proposed,this net is an enhanced Transformer-based U-Net architecture incorporating residual-aware mechanisms for pulmonary lesion segmentation.The SENet attention mechanism is embedded into the decoder’s upsampling process to accurately mitigate multi-channel feature differences through a three-step process of ‘squeeze,excite,and scale’.To validate the model’s generalizability,EMPIRICAL TEST was firstly conducted on the public Synapse multi-organ CT dataset,followed by fine-tuning and evaluation on lung cancer-specific CT images from the Lung-PET-CT-Dx dataset.Comparative experiments with state-of-the-art models demonstrate that our method achieves a Dice Similarity Coefficient of 86.05%.Furthermore,a user-friendly lung cancer segmentation assistant system was developed using PyQt5 for clinical implementation.This system invokes the weight parameters of the TransUnet-SE model to implement the segmentation function,thereby providing support for clinical diagnosis.

Ceramic coatings were synthesized on the surface of 2195 Al-Li alloy by micro arc oxidation(MAO),and the structure and high temperature friction wear performance of the coatings were studied.The phase and elemental composition were investigated by XRD and SEM.The surface,cross-section and abrasion morphology and elemental composition of the coatings were observed by SEM and EDS.The 3D morphology of abrasion tracks were observed by laser confocal microscopy(CLSM).The frictional wear performance of the coating was investigated at 200 ℃using an HT-1000 high-temperature friction and wear tester.The results show that the coatings consist of α-Al2O3 phase.The surfaces of the coatings are rough and porous,showing a "lotus leaf" like pile-up shape,with a surface roughness of about 14.3 μm.The thickness of the coating is about 7.1 μm.The average friction coefficient of the coating is about 0.68.The volume wear rate and mass loss of the coating are much lower than that of the Al-Li alloy substrate,with the mass loss being about one-ninth of that of the substrate and the wear rate being about one-tenth of that of the substrate.This indicates that the micro arc oxide coating has desirable wear resistance under high temperature conditions.Analysis of the wear track morphology and composition reveals that the wear mechanism of the Al-Li alloy substrate at 200 ℃ is abrasive wear,fatigue wear and oxidation wear,and the abrasive wear is the main one,while the MAO coating is mainly adhesive wear and also accompanied by oxidation wear.The researches have shown that micro-arc oxidation coatings exhibit good wear resistance under high-temperature conditions.

In this study,CuS/BiOI heterostructures were successfully synthesized via a hydrothermal method.The materials were characterized using Zeta potential analysis,XRD,SEM,TEM,XPS,UV-DRS,which collectively confirmed the successful construction of the CuS/BiOI heterojunction.Under visible-light irradiation,the photocatalytic degradation performance of the CuS/BiOI heterojunction toward tetracycline (TC) was studied.The optimized CuS/BiOI-2 sample exhibited a degradation efficiency of 81.46% and a degradation rate constant of 0.014 3 min^-1 representing a 68.55% improvement and a 2.64-fold enhancement compared to pristine BiOI,respectively.By analyzing the band structures of CuS and BiOI,combined with photoelectrochemical analyses and radical scavenging experiments,it was demonstrated that the S-scheme heterojunction formed between p-type CuS (with localized surface plasmon resonance,LSPR properties) and n-type BiOI significantly enhanced the light absorption capacity of BiOI and promoted the efficient separation of photogenerated charge carriers.These synergistic effects ultimately endowed the CuS/BiOI-2 composite with exceptional photocatalytic performance.

To further reveal the influence of grain size and morphology on the mechanical properties of metal additive manufacturing materials,this paper adopts the crystal plasticity finite element method(CPFEM) and takes the AlSi10Mg alloy formed by selective laser melting(SLM) as the research object to establish representative volume element(RVE) models with different average grain sizes.It explores the impact of grain size on its mesoscopic tensile mechanical behavior.The results show that as the grain size decreases,the distribution of stress and strain becomes more uniform,and the yield strength and tensile strength of the AlSi10Mg alloy increase.When the average grain size decreases from 20 μm to 6 μm,the yield strength and tensile strength of the material increase by approximately 37.89% and 17.91% respectively,which is consistent with the Hall-Petch relationship.In addition,stress and strain are concentrated at the grain boundaries,indicating that grain boundaries can hinder the movement of dislocations.The distribution of strain bands is affected by crystal orientation and forms a 45° angle with the loading direction,which is in line with Schmid's slip law.

To investigate the immunomodulatory effects of porcine skin collagen peptides on RAW264.7 cells,this study prepared Porcine skin collagen peptides via enzymatic hydrolysis and analyzed their amino acid composition and molecular weight distribution.The effects of porcine collagen peptides on RAW264.7 cells were systematically evaluated using a range of methods,including the MTT assay,neutral red phagocytosis assay,cell morphological observation,cytokine content measurement,and real-time quantitative PCR.These analyses assessed cell proliferation activity,phagocytic function,morphological changes,the production levels of NO,TNF-α,IL-2,and IL-12,as well as the mRNA expression of related immune factors(iNOS,TNF-α,IL-2,and IL-12).The results demonstrated that Porcine skin collagen peptides are primarily composed of glycine,proline,alanine,glutamic acid,aspartic acid,and arginine,with molecular weights mainly distributed between 500 and 5 000 Da.Porcine skin collagen peptides significantly promoted RAW264.7 cell proliferation and phagocytosis,induced morphological changes,enhanced NO,TNF-α,IL-2,and IL-12 production,and upregulated the gene expression of iNOS,TNF-α,IL-2,and IL-12.In conclusion,Porcine skin collagen peptides exhibit potential in activating macrophages and enhancing non-specific immune functions.

To address the safety hazards caused by histamine contamination in aquatic product processing,this study used rosemary leaves as raw material and extracted rosemary essential oil by atmospheric steam distillation,optimizing the extraction process through single-factor experiments and response surface methodology.The results showed that the optimal process conditions were a solid-to-liquid ratio of 1∶10(mg∶L),distillation time of 3.8 h,soaking time of 1.9 h,and raw material particle size of 19 mesh,under which the essential oil yield reached 2.18 %.The extracted essential oil was applied to the inhibition study of Morganella morganii,a high histamine-producing microorganism,and its antibacterial activity was systematically evaluated.Antibacterial experiments showed that 30 and 40 mg/mL concentrations of rosemary essential oil had inhibition zone diameters of (21.90±0.20) and (24.73±0.45) mm,respectively,and the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were 0.4 and 1.0 mg/mL,respectively.Further mechanism studies showed that rosemary essential oil destroyed the cell membrane structure,changed the osmotic pressure inside and outside the membrane,and caused the leakage of intracellular macromolecules such as RNA and proteins,thus significantly inhibiting the growth of Morganella morganii.This study provides theoretical basis and methodological support for the application of rosemary essential oil as a natural antibacterial agent in the control of histamine in aquatic products,and has positive significance for improving the safety of skipjack tuna products.

This study developed a whisky-making process using tartary buckwheat as the raw material and optimized the process.Optimization of tartary buckwheat gelatinization and liquefaction process:Through single-factor and orthogonal experimental designs,the optimal conditions for gelatinization and liquefaction were determined:thermostable amylase 16 U/g,thermostable cellulase 4 U/g,solid-to-water ratio 1∶6(g/mL),temperature 96 ℃,time 30 min.Under these conditions,the dextrose equivalent(ED) value of the liquefied mash reached 18.3%.The optimized saccharification conditions were:α-amylase 75 U/g,pullulanase 60 U/g,protease 50 U/g,tartary buckwheat addition ratio 30%,saccharification time 60 min.The resulting wort contained 11.85% reducing sugars and 203.5 mg/L α-amino nitrogen. Using Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry(HS-SPME-GC-MS),46 volatile compounds were detected in the heart cut of the tartary buckwheat whisky.Among these,12 compounds had odor activity values(VOA)>1,such as ethyl decanoate(VOA=251),isoamyl octanoate(VOA=36),and ethyl hexanoate(VOA=34).After aging for 2 months in pottery jars with oak chips,the spirit acquired a light caramel hue,retained the aroma of tartary buckwheat ,and its aroma exhibited increased complexity and a more layered profile.

In practical engineering applications,factors such as modeling errors and parameter perturbations can lead to model uncertainties,while external disturbances may significantly degrade the tracking accuracy and dynamic performance of the controlled system,even causing instability.To address these critical challenges,this paper proposes a control strategy based on disturbance estimation for linear systems subject to both model uncertainties and external disturbances,achieving system stabilization.First,a stabilizing controller is designed for model uncertainties using Lyapunov stability theory.Subsequently,for external disturbances,a disturbance observer with asymptotic estimation capability is constructed by selecting an appropriate filter,enabling asymptotic rejection of the disturbances.Theoretical analysis demonstrates that the proposed control method not only effectively mitigates the impact of parameter uncertainties but also achieves complete cancellation of external disturbances,ensuring global asymptotic stability of the closed-loop system.Numerical simulations further validate the superior performance of the control scheme in terms of dynamic response and robustness.

Rosa roxburghii pomace is an abundant yet underutilized byproduct of the fruit juice industry,which has attracted extensive research interest due to its rich nutritional composition.In this study,a combined milling and acidic pretreatment was applied to enhance its glucan conversion potential.The effects of pretreatment were systematically investigated using SEM,FTIR,TGA,and XRD.Results demonstrated that the pretreatment significantly improved enzymatic hydrolysis efficiency,increasing cellulose conversion by 30.39%.Compositional analysis revealed an elevated relative cellulose content after pretreatment.SEM and swelling capacity analysis indicated that the pretreatment disrupted the compact structure of the pomace,creating visible pores and a looser,more fragmented morphology,thereby increasing the specific surface area.FTIR,XRD,and TGA analyses confirmed the effective removal of non-cellulosic components,leading to higher cellulose exposure,enrichment,and crystallinity.These structural modifications enhanced cellulose accessibility to enzymes,ultimately improving hydrolysis efficiency.

G-quadruplex (G4) is a non-classical secondary structure formed by guanine-rich nucleic acid sequences,which is widely distributed in the promoter region of oncogenes and telomeres and other key regions of the genome,and is involved in a variety of disease processes.Histone H1,as an important regulator of chromatin high level structure,forms dynamic condensates with G4 by liquid-liquid phase separation (LLPS).Among them,the H1 carboxy-terminal structural domain (CTD) plays a key role in driving LLPS due to its long sequence and high charge density properties.However,there is still a research gap on the minimum length threshold required for CTD-triggered phase separation and its role in regulating the topological conformation of G4.In this study,we systematically investigated the effect of different lengths of histone H1 truncated peptide chains on the LLPS of G4-short peptide system by comprehensively utilizing various experimental techniques.The experimental results showed that LLPS formation was significantly inhibited when the peptide chain length was below a critical threshold;further studies revealed that the LLPS behavior of G4-short peptides was related to the short peptide-induced enhancement of folding and compression of the G4 structure.These results support that multivalent interactions are one of the important driving forces mediating phase separation.

This paper is concerned with social optimality for a class of mean-field linear quadratic (Linear Quadratic,LQ) control systems with Markov jump parameters,where the individual diffusion coefficient can depend on both the state and the control of the agent.All agents cooperate with each other to minimize the social objective.First,with the aid of person-by-person optimality principle,one arrives at an auxiliary LQ control problem.A decentralized strategy is then obtained by a mean-field forward-backward stochastic differential equation (Forward-backward Stochastic Differential Equation,FBSDE) consistency condition.Finally,by some estimates of FBSDEs,the obtained decentralized strategy is proved to be asymptotic social optimality.

To explore the quality differences among various commercially available rose tea products,this study selected eight representative rose tea samples for experimentation.By testing indicators such as color,moisture content,total polyphenols,total flavonoids,and DPPH radical scavenging capacity,a quality evaluation system for different rose teas was established.Pearson correlation analysis was also conducted to compare the mass fraction of active substances and antioxidant capacity in rose tea samples.The results showed that the moisture content of Kushui rose was the lowest,while the brightness value(L*) and redness value(a*) of double-petal rose were the highest.The total polyphenol mass fraction was highest in Kushui rose,and the difference was significant compared with other samples(p<0.05).The total flavonoid mass fraction in Muhong rose was 14.03 mg/g.The total anthocyanin mass fraction in Damask rose and French rose was relatively low,and the difference among samples was not significant(p>0.05).Additionally,all rose tea samples demonstrated good antioxidant capacity.Muhong rose received a higher overall sensory evaluation score and was more acceptable to consumers.Pearson correlation analysis revealed a highly significant positive correlation between total polyphenols and total flavonoids in the tested samples(p<0.01).Overall,the variety of rose has a significant impact on the quality of rose tea.This study provides a theoretical basis for consumers to choose rose tea and promotes the upgrading and optimization of edible rose processing products.

Plant immune elicitors are a class of substances capable of activating a plants innate immune system and enhancing its resistance to pathogenic microorganisms.They hold significant potential in green agriculture and sustainable crop protection.Compared to traditional chemical pesticides,immune elicitors offer advantages such as being non-toxic,environmentally friendly,and less likely to induce resistance,thereby helping to reduce pesticide usage,ensure food safety,and protect the ecological environment.In recent years,oligosaccharides have attracted increasing research interest due to their promising biological activity;however,challenges such as limited variety and inconsistent efficacy still hinder their practical application.This study aims to develop a plant immune elicitor that is abundant in source,cost-effective,and stable in quality.Using hyaluronic acid as the raw material,hyaluronic acid oligosaccharides were prepared through a low-cost process,and their eliciting effects were evaluated in an Arabidopsis thaliana-Pseudomonas syringae pv.tomato DC3000(Pst DC3000) infection system.The experimental results showed that the optimal pretreatment concentration of hyaluronic acid oligosaccharides was 25 mg/L.Pretreating Arabidopsis seedlings with this concentration prior to inoculation with Pst DC3000 significantly reduced the disease index,showing a 35.13% decrease compared to the untreated control group.These findings indicate that hyaluronic acid oligosaccharides can partially suppress pathogen infection and enhance plant resistance.

China is a major producer and consumer of antibiotics,and the extensive use of these drugs has caused significant environmental pollution and health risks.To address these issues,this study developed innovative Ti/PbO2-La composite electrodes via electrochemical deposition,significantly enhancing their catalytic activity and stability compared to conventional electrodes.Through optimization of chloramphenicol's electrochemical degradation parameters,we obtained the reaction kinetic equation y=0.055x-0.288 (R²=0.981) under the following conditions:current density of 1.00 A,electrolyte concentration of 0.1 mol/L, pH 7,and initial chloramphenicol concentration of 10 mg/L.LC-MS analysis revealed that chloramphenicol degradation primarily occurs through ·OH-mediated pathways.Furthermore,this electrode overcomes the limitation of incomplete mineralization characteristic of conventional methods,achieving efficient pollutant mineralization. This provides an effective and economical strategy for removing trace antibiotics from aquatic environments,with significant practical applications.

This paper focuses on the study of semi-varying coefficient nonlinear models and applies empirical likelihood estimation based on modal regression with orthogonality for statistical inference of its parameters.By combining modal regression methods with orthogonal projection techniques,an estimation algorithm based on modal empirical likelihood is proposed.Finally,the parameters estimated using the proposed orthogonal modal empirical likelihood method are subjected to data simulation.The absolute deviation values of the parameters are calculated and compared to verify the effectiveness of the proposed method.

CD44 is a type I transmembrane glycoprotein whose extracellular domain binds specifically to hyaluronic acid (HA) of the extracellular matrix.In the presence of fluid shear,this binding promotes the rolling behavior of cells and provides the basis for cell-extracellular matrix (ECM) interactions,and the binding is dependent on conformational changes in the N-and C-termini of the extracellular domain of CD44.Under physiological conditions,the interaction between HA and CD44 exhibits a typical Catch-bond effect.In this study,single-molecule magnetic tweezers were used to perform stretching experiments on CD44 extracellular structural domains expressed in Escherichia coli (E.coli) and human embryonic kidney epithelial cells (HEK293F).At a constant tensile loading rate (1 pN/s),four stepwise unfolding events were observed in the defolding process of the E.coli-expressed CD44 protein,with corresponding elongation changes indicating that all 159 amino acids were defolded.In contrast,HEK293F-expressed CD44 showed only one unfolding signal,corresponding to the unfolding of 8 amino acid residues at the N-terminal and 49 amino acid residues at the C-terminal,suggesting that all three pairs of disulfide bonds are formed in this protein.Despite the lack of disulfide bonds,E.coli expressed CD44 maintained a certain degree of structural stability,and HEK293F expressed CD44 was closer to the physiological state in terms of structural integrity and kinetic properties.

Urate oxidase,a key enzyme in the biological purine metabolism pathway,reduces the level of uric acid in the body and alleviates and treats a range of diseases caused by high uric acid.In this paper,the literature related to urate oxidase was systematically analyzed and summarized using bibliometric methods.A systematic analysis of 2 240 urate oxidase-related papers published from 1980 to 2024 in the Web of Science Core Collection database revealed that researchers have been paying more attention to urate oxidase since 2000 and have conducted a large number of studies on it.The United States ranks as the country with the highest number of published articles,while Chongqing Medical University stands out as the institution with the most significant output.Sensors and Actuators B-Chemical and Biosensors & Bioelectronic were the journals with the highest number of articles(36) and citations(2 488),respectively.Analysis of the highly cited literature shows that the topics of highest interest related to urate oxidase are “hyperuricemia” “rasburicase” and “Structure and evolutionary significance of urate oxidase”.Keyword clustering(uric acid,urate oxidase,hyperuricemia,gout,electrochemical biosensor) reveals the complete chain of urate oxidase from the underlying metabolic mechanism(oxidation of uric acid into allantoin,hydrogen peroxide,and carbon dioxide),to pathological phenotypes(hyperuricemia,gout,etc.),and to technological innovations(biologic sensor applications),providing a systematic perspective for disease research,diagnostic tool development and precision therapy.The present study provides a systematic description of urate oxidase from a macroscopic point of view,which will provide a valuable guideline for subsequent research.

Aiming at the problems of significant noise interference,blurred edge information and difficult spatial positioning in liver ultrasound images,a REC-UNet model is proposed to verify its segmentation accuracy in extracting liver parenchyma from liver ultrasound images.The REC-UNet model was constructed by integrating the dual attention mechanism with the ResNet50 residual network.587 liver ultrasound images were trained using a mixed loss function.Based on ablation experiments,the evaluation indicators of the improved model were compared with those of the mainstream network models.The mean intersection over union(MiOU),precision,recall rate and Dice coefficient of the improved model reached 91.67%,91.58%,93.23% and 91.58% respectively.The global Dice coefficient of this model was 5.10%,4.60%,4.44% and 6.13% higher than that of the U-Net,UNet++,Attention-UNet and FCN models,respectively.The REC-UNet model can precisely segment the liver parenchyma in ultrasound images of the liver,and its various segmentation indicators have significantly improved compared with other mainstream neural network models.

Fumonisins(FBs) have stable properties and are difficult to break down in their structure through conventional production and processing,resulting in severe residues in agricultural products.Among various detoxification methods,enzymatic detoxification is highly favored due to its safety,efficiency,and minimal environmental impact.In order to explore the detoxification effect of lipase products on fumonisin B1(FB1),this study evaluated the detoxification performance of three kinds of lipase products.Under the optimal conditions,the detoxification rate of the three kinds of lipase products on 1 000 ng/mL FB1 was close to 100%.Further,the highest detoxification rate was (88.00±0.19)% and (90.42±0.07)% respectively in the scenario application of three kinds of feed raw materials,namely,jade Rice noodles,peanut meal,and distillers dried grains with solids(DDGS),as well as gastrointestinal juice.Through liquid chromatography-mass spectrometry(LC-MS),it was found that the action mode of lipase product is a dual mechanism of "degradation adsorption",and the degradation product is C22H47NO5.The study on the detoxification effect of FB1 provides important theoretical support for the large-scale application of FB1 removal and has potential application prospects.

Consider the estimation problem for a class of single-index Autoregressive Conditional Heteroscedasticity-in-mean (ARCH-M) models.Based on the empirical likelihood method and the theory of local least squares estimation,we propose an empirical likelihood-based estimation approach for the parametric components of the model.Under certain regularity conditions,it is demonstrated that the constructed log empirical likelihood ratio statistic asymptotically follows a standard chi-square distribution.Consequently,this result enables the construction of confidence regions for the parametric components.The theoretical framework establishes the asymptotic properties of the proposed estimator and provides rigorous statistical inference tools for the parameters in this semi-parametric ARCH-M specification.Some simulation studies are carried out to compare finite sample performances of the proposed empirical likelihood estimation method with some existing estimation methods under different estimation method settings.

Short-term power load forecasting is a critical task for ensuring optimal scheduling and secure operation of power systems.Given the prevalent nonlinear,periodic,and fluctuating characteristics in load data,traditional single-kernel Support Vector Machine models exhibit limitations in both representational capacity and prediction accuracy.To address this issue,this paper proposes an Adaptive Multi-Kernel Support Vector Machine model that integrates Chaotic Particle Swarm Optimization and an adaptive kernel parameter adjustment mechanism,aiming to improve forecasting accuracy and robustness.Specifically,a weighted composite kernel is constructed by combining a linear kernel,a periodic kernel,and a radial basis function(RBF) kernel,wherein the kernel weights are globally optimized using CPSO to enhance multi-scale feature modeling capabilities.Meanwhile,an adaptive mechanism is introduced to dynamically update kernel parameters based on validation error feedback,enabling the model to better respond to nonstationary inputs.The prediction results show that the proposed CPSO-AMK-SVM model significantly outperforms traditional models in terms of prediction accuracy,stability,and generalization ability,indicating strong potential for practical applications.

A high-quality agricultural product supply chain can effectively synchronize supply and demand across urban and rural markets,while rural e-commerce generates new impetus for rural revitalization.Using provincial panel data from China for 2011 to 2022,this paper applies fixed-effects estimations and heterogeneity analysis to empirically assess the influence of rural e-commerce on the development level of agricultural product supply chains.Findings indicate that the expansion of rural e-commerce substantially enhances the development level of these supply chains,a result that withstands robustness checks.Heterogeneity analysis further shows that the positive impact of rural e-commerce is more pronounced in eastern regions and in provinces where agriculture is not the dominant sector.Consequently,all provinces should capitalize on the opportunities afforded by rural e-commerce,leverage supply-chain mechanisms to meet agricultural product demand,and devise differentiated development strategies.

Anxiety disorder is a psychiatric condition characterized by excessive fear and anxiety as its core features,often accompanied by behavioral abnormalities.Severe anxiety and depression can trigger suicidal attempts or behaviors,which has attracted extensive public attention.As a safe and effective complementary therapy,aromatherapy demonstrates clear potential in alleviating mild to moderate anxiety.To investigate the role of aromatherapy in emotional regulation,this study examined the effects and underlying mechanisms of vetiver volatile extracts on anxiety-like behaviors induced by chronic unpredictable mild stress (CUMS) in mice.Twenty-eight male C57BL/6J mice were randomly assigned to four groups (n=7 per group):control group,model group,vetiver volatile extract group,and positive control group (fluoxetine hydrochloride,3 mg/kg).An anxiety mouse model was established using CUMS.After 18 days of modeling,mice in the vetiver volatile extract group were exposed to vetiver volatile extracts for 14 days before the experiment concluded.Behavioral changes were evaluated using the sucrose preference test,open field test,and forced swimming test.Hippocampal levels of tryptophan (TRP) and serotonin (5-HT),as well as serum concentrations of TNF-α,IL-6 and IL-1β,were measured by enzyme-linked immunosorbent assay (ELISA).The results revealed that in behavioral tests,compared with the model group,both the vetiver volatile extract group and the positive control group showed significantly increased sucrose preference rates,greater distance traveled and time spent in the central area,and a notable rise in vertical rearing episodes.Regarding physiological indicators,the vetiver volatile extract group reduced serum levels of IL-6,IL-1β and TNF-α in model mice and upregulated the mass concentration of TRP and 5-HT in the hippocampus.In summary,vetiver volatile extracts effectively alleviated anxiety-like behaviors in mice by modulating neurotransmitter balance and inhibiting inflammatory responses.

The current new energy vehicle industry is developing rapidly.Driven by policies and the economy,the recycling and utilization of lithium iron phosphate batteries have been moving towards a large-scale direction.According to statistics,currently,lithium iron phosphate batteries account for over 60% of the total retired batteries in China,but the recycling rate is less than 50%,and the poor economic performance is a bottleneck that needs to be urgently broken through.Therefore,analyzing the economic critical point of lithium iron phosphate battery recycling is of great significance for improving the recycling rate of lithium iron phosphate batteries and building a standardized,safe and efficient recycling and utilization system.This study integrates multi-dimensional data including policy texts and market prices from 2018 to 2025,and constructs a dynamic analysis framework.It adopts an integrated analytical framework of game theory-system dynamics-Bayesian hierarchical hybrid model.First,it defines the game subjects and objectives of the government and recycling enterprises (represented by GEM and CATL),designs strategy sets for subsidy intensity and production capacity strategies,and solves the subgame perfect Nash equilibrium in different metal price ranges through payoff functions.Based on this,a hybrid model is constructed to quantify the dynamic interaction between policy subsidies and metal prices,and describe the extreme scenarios through Monte Carlo simulation.After empirical calibration and convergence validation,it is clarified that the economic threshold fluctuates in the range of 79 000 to 115 000 yuan/ton.For every 100 yuan/ton increase in subsidy intensity,the threshold decreases by 5 000 to 8 000 yuan/ton;while for every 10% increase in lithium price volatility,the threshold rises by 3 000 to 5 000 yuan/ton.The research results can provide a quantitative basis for industrial policy optimization and enterprise operations,play an important role in improving the recycling rate of lithium iron phosphate batteries,and help achieve the dual goals of resource security and economic sustainable development.

Kluyveromyces marxianus is a thermotolerant,non-conventional yeast.Pyruvate decarboxylase(Pdc1) catalyzes the conversion of pyruvate to acetaldehyde and is a key factor for ethanol fermentation in K.marxianus.In this study,a K.marxianus strain,KM-502,was isolated from kefir grains.Genomic sequencing predicted 6 801 genes.Growth trait analysis revealed that this strain grew fastest at 37 ℃,had an optimal growth pH from 3 to 5,a NaCl tolerance threshold of 8%,and could utilize carbon sources including glucose,fructose,sucrose,lactose,galactose,xylose,and xylitol.Subsequently,a genetic manipulation system for K.marxianus strains was established based on CRISPR-Cas9 and Cre-loxP technologies.This system was used to construct the Pdc1 gene deletion strain KMΔPdc1 and a uracil auxotrophic strain KMΔUra3.The KMΔPdc1strain lost its ethanol production capability and exhibited significant growth lag.After continuous subculturing of KMΔPdc1,an adapted strain,KMΔPdc1D,was obtained.The growth lag in KMΔPdc1D was alleviated,recovering to a level close to that of the wild-type strain.Compared to KMΔPdc1,KMΔPdc1D showed increased production of acetate and glycerol.This study serves as a reference for the utilization and development of Kluyveromyces marxianus.

In semi-parametric models,estimation robustness is often compromised by nonlinear trends or model misspecification.Traditional doubly robust estimators,which rely heavily on mean-oriented assumptions and propensity score models,frequently fail to deliver reliable inference.This paper develops an enhanced semi-parametric quantile doubly robust estimation model that integrates local linear quantile regression to simultaneously optimize both the nonparametric component g(X) and the parametric component β(τ),thereby obtaining efficient estimators.Numerical simulations—conducted under scenarios where at least one of g(X) or β(τ) is misspecified—show that the proposed model achieves significantly lower bias and root mean square error compared to conventional linear quantile regression.Specifically,bias is reduced by approximately 45%,highlighting the models superior robustness.By adopting a joint optimization strategy for both components,the proposed framework exhibits stronger robustness than existing mean-based models,making it particularly suitable for applications involving model misspecification or nonlinear trends