Recent advancements in spatial transcriptomics(ST)technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues,p...Recent advancements in spatial transcriptomics(ST)technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues,providing critical insights into spatial gene expression patterns,tissue organization,and gene functionality.However,existing methods for clustering spatially variable genes(SVGs)into co-expression modules often fail to detect rare or unique spatial expression patterns.To address this,we present spatial transcriptomics iterative hierarchical clustering(stIHC),a novel method for clustering SVGs into co-expression modules,representing groups of genes with shared spatial expression patterns.Through three simulations and applications to ST datasets from technologies such as 10x Visium,10x Xenium,and Spatial Transcriptomics,stIHC outperforms clustering approaches used by popular SVG detection methods,including SPARK,SPARK-X,MERINGUE,and SpatialDE.Gene ontology enrichment analysis confirms that genes within each module share consistent biological functions,supporting the functional relevance of spatial co-expression.Robust across technologies with varying gene numbers and spatial resolution,stIHC provides a powerful tool for decoding the spatial organization of gene expression and the functional structure of complex tissues.展开更多
BACKGROUND Gastric cancer(GC)is one of the most common malignant tumors of the digestive system worldwide,the prognosis of patients with advanced GC remains poor.AIM To evaluate the combined expression characteristics...BACKGROUND Gastric cancer(GC)is one of the most common malignant tumors of the digestive system worldwide,the prognosis of patients with advanced GC remains poor.AIM To evaluate the combined expression characteristics of cancer stem cell markers CD24 and CD133 in GC pathological tissues,and to explore their association with patients’clinicopathological parameters and postoperative survival outcomes.METHODS A total of 304 GC patients who underwent surgical treatment in our hospital from January 2018 to January 2020 were retrospectively included.Immunohistochemistry was used to detect the protein expression of CD24 and CD133 in tumor tissues,adjacent tissues,and normal gastric mucosa tissues.Based on staining intensity and the proportion of positive cells,expression levels were classified into low and high expression,while clinicopathological parameters were recorded.χ2 test was used to evaluate the correlation between expression and categorical variables,Spearman rank correlation analysis was performed to assess the correlation between the expression intensities of the two markers,and multivariate regression models were applied to identify independent risk factors influencing co-expression.Kaplan-Meier survival curves and Log-rank test were used to compare survival differences among groups with different expression patterns.RESULTS Among the 304 patients,155 cases(50.99%)were CD24 positive,including 91 low-expression and 64 highexpression;133 cases(43.75%)were CD133 positive,including 81 low-expression and 52 high-expression.There were 74 cases(24.34%)with double positivity and 81 cases(26.64%)with double negativity.Compared with tumor tissues,the positive rates of CD24 and CD133 in normal gastric tissues and adjacent tissues were significantly lower(P<0.05).Univariate analysis showed that co-expression of CD24 and CD133 in GC tissues was significantly correlated with tumor size,Lauren classification,T stage,N stage,and vascular invasion(P<0.05),but not with patient age,gender,tumor site,World Health Organization histological classification,or M stage(P>0.05).Further multivariate regression analysis suggested that tumor size,T stage,N stage,and vascular invasion were independent risk factors promoting CD24 and CD133 double positivity.Spearman rank correlation analysis indicated a moderate positive correlation between their expression intensities(r=0.420,P<0.001).During follow-up,29 of 304 patients were lost(loss rate 9.54%);146 deaths occurred.According to expression combination,there were 89 cases of CD24 single positivity(39 deaths),68 cases of CD133 single positivity(31 deaths),81 cases of double negativity(25 deaths),and 66 cases of double positivity(51 deaths).Log-rank test showed significant differences in overall survival among the four groups(χ2=20.89,P<0.001),with CD24+/CD133+group showing the worst prognosis.CONCLUSION CD24 and CD133 exhibit high positive detection rates in GC tissues,and their co-positivity is closely associated with tumor stage progression and significantly indicates unfavorable survival outcomes.The co-expression of CD24/CD133 may reflect higher aggressiveness and metastatic potential of GC,serving as a potential prognostic marker and a direction for targeted therapeutic strategies.However,as this is a single-center retrospective study with limitations such as patient loss to follow-up and sample size,further prospective,multicenter,and mechanistic studies are required to validate its clinical applicability and biological role.展开更多
Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict t...Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.展开更多
Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary ac...Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.展开更多
Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： ...Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address ...Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.展开更多
Starch serves as a critical storage component,significantly influencing the grain yield and quality of maize(Zea mays L.).Understanding the genetic basis of natural variation in kernel starch content(SC)is essential f...Starch serves as a critical storage component,significantly influencing the grain yield and quality of maize(Zea mays L.).Understanding the genetic basis of natural variation in kernel starch content(SC)is essential for maize breeding to meet future demands.A genome-wide association study(GWAS)identified 84 and 96 loci associated with kernel SC across two years,overlapping with 185 candidate genes.The candidate gene Zm MYB71,encoding a MYB-related transcription factor,demonstrated the highest co-expression frequency with starch synthesis genes.Analysis revealed that Zm MYB71 functions as a nuclear located transcription repressor,and mutants exhibited increased kernel SC by over 2.32%,with minimal impact on amylose content or 100-grain weight.Sh1,Sh2,and GBSSI exhibited up-regulation in mutants by 1.56-,1.45-and 1.32-fold,respectively,aligning with RNA sequencing results;their promoter activities appear directly repressed by Zm MYB71 through the GATATC and TTAGGG motifs.Additionally,the Zm MYB71 elite haplotype Hap1 occurred in over 55%of the high-starch maize sub-populations Iowa Stiff Stalk Synthetic(BSSS)and Partner B(PB),but only in 7.14%of the low-starch sub-population Partner A(PA).Analysis of Hap1 haplotype frequencies across breeding stages revealed a significant increase to 40.28%in inbred groups released after 2010,compared to 28.57 and 27.94%in 1980 and 1990,and 2000,respectively.These findings enhance understanding of natural variation in maize kernel SC and establish Zm MYB71 as a negative regulator with potential applications in SC improvement.展开更多
The fabrication of efficient and stable flexible perovskite solar modules(F-PSMs)using poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine](PTAA)remains a significant challenge due to its hydrophobic properties and the mis...The fabrication of efficient and stable flexible perovskite solar modules(F-PSMs)using poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine](PTAA)remains a significant challenge due to its hydrophobic properties and the mismatch in interface energy-level alignment.Here,we introduced[2-(3,6-dimethoxy-9H-carba zol-9-yl)ethyl]phosphonic acid(MeO-2PACz)to modify the PTAA layer,which effectively suppressed surface potential fluctuations and aligned energy levels at the interface of PTAA/perovskite.Additionally,MeO-2PACz enhanced the hydrophilicity of PTAA,facilitating the fabrication of dense,uniform,and pinhole-free perovskite films on large-area flexible substrates.As a result,we achieved an F-PSM with a power conversion efficiency(PCE)of 16.6% and an aperture area of 64 cm^(2),which is the highest reported value among F-PSMs with an active area exceeding 35 cm^(2)based on PTAA.Moreover,the encapsulated module demonstrated outstanding long-term operational stability,retaining 90.2% of its initial efficiency after 1000 bending cycles(5 mm radius),87.2% after 1000 h of continuous illumination,and 80.3% under combined thermal and humid conditions(85℃ and 85% relative humidity),representing one of the most stable F-PSMs reported to date.展开更多
Let D(n)be the finite dimensional non-pointed and non-semisimple Hopf algebra,which is a quotient of a prime Hopf algebras of GK-dimension one for an odd number n>1.In this paper,we investigate the structure of Yet...Let D(n)be the finite dimensional non-pointed and non-semisimple Hopf algebra,which is a quotient of a prime Hopf algebras of GK-dimension one for an odd number n>1.In this paper,we investigate the structure of Yetter-Drinfeld simple modules over D(n)and give iso-classes of them.展开更多
Self-assembled prodrug nanomedicine has emerged as an advanced platform for antitumor therapy,mainly comprise drug modules,response modules and modification modules.However,existing studies usually compare the differe...Self-assembled prodrug nanomedicine has emerged as an advanced platform for antitumor therapy,mainly comprise drug modules,response modules and modification modules.However,existing studies usually compare the differences between single types of modification modules,neglecting the impact of steric-hindrance effect caused by chemical structure.Herein,single-tailed modification module with low-steric-hindrance effect and two-tailed modification module with high-steric-hindrance effect were selected to construct paclitaxel prodrugs(P-LA_(C18)and P-BAC18),and the in-depth insights of the sterichindrance effect on prodrug nanoassemblies were explored.Notably,the size stability of the two-tailed prodrugs was enhanced due to improved intermolecular interactions and steric hindrance.Single-tailed prodrug nanoassemblies were more susceptible to attack by redox agents,showing faster drug release and stronger antitumor efficacy,but with poorer safety.In contrast,two-tailed prodrug nanoassemblies exhibited significant advantages in terms of pharmacokinetics,tumor accumulation and safety due to the good size stability,thus ensuring equivalent antitumor efficacy at tolerance dose.These findings highlighted the critical role of steric-hindrance effect of the modification module in regulating the structureactivity relationship of prodrug nanoassemblies and proposed new perspectives into the precise design of self-assembled prodrugs for high-performance cancer therapeutics.展开更多
Most carbon-based catalysts utilized in Fenton-like systems face challenges such as structural instability,susceptibility to deactivation,and a tendency to disperse during operation.Wood-derived catalysts have garnere...Most carbon-based catalysts utilized in Fenton-like systems face challenges such as structural instability,susceptibility to deactivation,and a tendency to disperse during operation.Wood-derived catalysts have garnered considerable attention due to their well-defined structures,extensive pipeline networks,superior mechanical strength,and adaptability for device customization.However,there remains a paucity of research that systematically summarizes Fenton-like systems based on wood-derived catalysts.In this review,we first summarize the structural designs of wood-derived catalysts based on nano-metal sites and single-atom sites,while also outlining their advantages and limitations applied in Fenton-like systems.Furthermore,we evaluate catalytic modules of wood-derived catalysts for scale-up and continuous Fenton-like systems.Additionally,wood-inspired catalytic materials utilizing commercial textures and their applications in Fenton-like processes are also discussed.This paper aims to comprehensively explore the fundamental mechanisms(e.g.,characteristics of catalytic sites,catalytic performance,and mechanisms)of wood-based catalysts in Fenton-like chemistry,as well as their equipment designs and application scenarios,as well as providing the insights into future developments.展开更多
This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in ...This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.展开更多
Currently,perovskite solar cells have achieved commendable progresses in power conversion efficiency(PCE)and operational stability.However,some conventional laboratory-scale fabrication methods become challenging when...Currently,perovskite solar cells have achieved commendable progresses in power conversion efficiency(PCE)and operational stability.However,some conventional laboratory-scale fabrication methods become challenging when scaling up material syntheses or device production.Particularly,the prolonged high-temperature annealing process for the crystallization of perovskites requires a substantial amount of energy consumption and impact the modules’throughput.Here,we report a modified near-infrared annealing(NIRA)process,which involves the excess PbI_(2)engineered crystallization,efficiently reduces the preparation time for perovskite active layer to within 20 s compared to dozens of min in conventional hot plate annealing(HPA)process.The study showed that the incorporated PbI_(2)promoted the consistent nucleation of the perovskite film,leading to the subsequent rapid and homogeneous crystallization at the NIRA stage.Thus,highly crystalized perovskite film was realized with even better crystallization performance than conventional HPA-based film.Ultimately,efficient perovskite solar modules of 36 and 100 cm^(2)were readily fabricated with the optimal PCEs of 22.03%and 20.18%,respectively.This study demonstrates,for the first time,the successful achievement of homogeneous and high-quality crystallization in large-area perovskite films through rapid NIRA processing.This approach not only significantly reduces energy consumption during production,but also substantially shortens the manufacturing cycle,paving a new path toward the commercial-scale application of perovskite solar modules.展开更多
Modules enable students to engage with content at their own pace,fostering autonomy and deeper understanding.The modular approach ensures clarity in presenting objectives,instructions,and concepts,while having illustr...Modules enable students to engage with content at their own pace,fostering autonomy and deeper understanding.The modular approach ensures clarity in presenting objectives,instructions,and concepts,while having illustrations,activities,and assessments could enhance comprehension and retention.This paper was a developmental study on STS module for college students using the ADDIE Model(Analysis,Design,Development,Implementation,and Evaluation).Sampled 673 first-year students from Northwest Samar State University participated in the study,with 299 participating in a test try-out and 374 in the students’performance evaluation.Three expert evaluators with backgrounds in science,English,and psychology,each with over four years of experience,assessed the modules to ensure alignment with the study’s constructivist learning goals and instructional integrity.The findings revealed that both students and experts had rated the instructional module positively,indicating its effectiveness in facilitating learning and completing lessons.Key aspects such as the style of illustrations and written expressions,the usefulness of learning activities,and the guidance provided by illustrations and captions were especially well-received.The module was praised for its clear objectives,understandable instructions,and engaging tasks like trivia and puzzles.Expert evaluations highlighted relevance,simplicity,and balanced emphasis on topics in the module content.Furthermore,students in test group demonstrated significant improvement in performance,with post-test scores notably higher than pre-test scores,confirming the module’s effectiveness in enhancing learning outcomes.Consequently,this paper provides an opportunity to integrate science learning with initiatives aimed at promoting environmental preservation and driving social change.展开更多
Transcription factors(TFs)play key roles in the regulatory network of leaf senescence.However,many nodes in this network remain unclear.To elucidate the mechanism of leaf senescence mediated by a rice TF,WRKY10,the ex...Transcription factors(TFs)play key roles in the regulatory network of leaf senescence.However,many nodes in this network remain unclear.To elucidate the mechanism of leaf senescence mediated by a rice TF,WRKY10,the expression of multiple senescence-related genes and physiological phenotypes were monitored in WRKY10-and VQ MOTIF-CONTAINING PROTEIN8(VQ8)-overexpressing plants and the wrky10 and vq8 mutants.Our results showed that WRKY10 positively regulates abscisic acid(ABA)-and dark-induced senescence(DIS)by directly regulating the expression of multiple senescence-related genes.The VQ8 protein,a repressor of WRKY10,negatively regulates WRKY10-mediated DIS.The WRKY10-VQ8 module fine-tunes the progression of DIS.ABA,methyl jasmonate,and H_(2)O_(2) accelerate WRKY10-mediated DIS,whereas ammonium nitrate and dithiothreitol delay WRKY10-mediated DIS.Further analysis revealed that WRKY10 and VQ8 interact with ABA RESPONSIVE ELEMENT BINDING FACTOR1(ABF1)or ABF2.VQ8 represses the transcriptional activity of ABF1 and ABF2.Overexpression of ABF1 or ABF2 accelerates ABA-and dark-induced senescence and H_(2)O_(2) accumulation in N.benthamiana leaves,and WRKY10 and VQ8 can inhibit either ABF1-or ABF2-induced cell necrosis.Taken together,WRKY10 integrates multiple senescence signals to establish an orderly progression of leaf senescence.The VQ8 protein acts as a brake on WRKY10-induced senescence and ABF1/2-induced cell death,preventing uncontrolled cell death.展开更多
The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(...The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.展开更多
Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally effi...Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself.Some existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless networks.Additionally,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing attacks.To overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely SE-ConvNet.To be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep learning.Moreover,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication accuracy.Compared with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.展开更多
With the rapid development of the new energy automotive industry,the enhancement of lithium battery performance and production efficiency has become critical.This article explores the application of artificial intelli...With the rapid development of the new energy automotive industry,the enhancement of lithium battery performance and production efficiency has become critical.This article explores the application of artificial intelligence technology in the lithium battery module PACK line,analyzing how it optimizes the production process and improves production efficiency,and predicts future development trends.The PACK line is an important link in battery manufacturing,involving complex processes such as cell sorting,welding,assembly and testing.The application of AI technology in image recognition,data analysis and predictive maintenance provides new solutions for the intelligent upgrading of the PACK line.This article describes the process of the PACK line in detail,analyzes the challenges under current technological levels,and reviews the application cases of AI technology in the manufacturing industry.The study aims to provide theoretical and practical guidance for the intelligent development of lithium battery module PACK lines,discussing the integration of AI technology,its actual performance,technical challenges,and solutions.It is expected that AI technology will play a greater role in the PACK line,and future research will focus on improving the adaptability of models,developing efficient algorithms,and further integrating into the production line.展开更多
The intelligent vehicle corner module system,which integrates four-wheel independent drive,independent steering,independent braking and active suspension,can accurately and efficiently perform vehicle driving tasks an...The intelligent vehicle corner module system,which integrates four-wheel independent drive,independent steering,independent braking and active suspension,can accurately and efficiently perform vehicle driving tasks and is the best carrier of intelligent vehicles.Nevertheless,too many angle/torque control inputs make control difficult and non-real-time.In this paper,a hierarchical real-time motion control framework for corner module configuration intelligent electric vehicles is proposed.In the trajectory planning module,an improved driving risk field is designed to describe the surrounding environment’s driving risk.Combined with the kinematic vehicle-road model,model predictive control(MPC)method,spline curve method,the local reference trajectory of safety,comfort and smoothness is planned in real time.The optimal steering angle is determined using MPC method in path tracking module.In the motion control module,a feedforward-feedback controller assigns the optimal steering angle to the front/rear axles,and an angle allocation controller distributes the target angles of the front/rear axles to four steered wheels.Finally,the PreScan-Simulink-CarSim joint simulation environment is established for conducting the human-in-the-loop emergency obstacle avoidance experiment.It took only 0.005 s for the hierarchical motion control system to determine its average solution time.This proves the effectiveness of the hierarchical motion control system.展开更多
基金Science Foundation Ireland,Grant/Award Numbers:18/CRT/6214,18/CRT/6214(S4)。
文摘Recent advancements in spatial transcriptomics(ST)technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues,providing critical insights into spatial gene expression patterns,tissue organization,and gene functionality.However,existing methods for clustering spatially variable genes(SVGs)into co-expression modules often fail to detect rare or unique spatial expression patterns.To address this,we present spatial transcriptomics iterative hierarchical clustering(stIHC),a novel method for clustering SVGs into co-expression modules,representing groups of genes with shared spatial expression patterns.Through three simulations and applications to ST datasets from technologies such as 10x Visium,10x Xenium,and Spatial Transcriptomics,stIHC outperforms clustering approaches used by popular SVG detection methods,including SPARK,SPARK-X,MERINGUE,and SpatialDE.Gene ontology enrichment analysis confirms that genes within each module share consistent biological functions,supporting the functional relevance of spatial co-expression.Robust across technologies with varying gene numbers and spatial resolution,stIHC provides a powerful tool for decoding the spatial organization of gene expression and the functional structure of complex tissues.
基金National Natural Science Foundation of China,No.82003223and China Postdoctoral Science Foundation,No.2020M671398.
文摘BACKGROUND Gastric cancer(GC)is one of the most common malignant tumors of the digestive system worldwide,the prognosis of patients with advanced GC remains poor.AIM To evaluate the combined expression characteristics of cancer stem cell markers CD24 and CD133 in GC pathological tissues,and to explore their association with patients’clinicopathological parameters and postoperative survival outcomes.METHODS A total of 304 GC patients who underwent surgical treatment in our hospital from January 2018 to January 2020 were retrospectively included.Immunohistochemistry was used to detect the protein expression of CD24 and CD133 in tumor tissues,adjacent tissues,and normal gastric mucosa tissues.Based on staining intensity and the proportion of positive cells,expression levels were classified into low and high expression,while clinicopathological parameters were recorded.χ2 test was used to evaluate the correlation between expression and categorical variables,Spearman rank correlation analysis was performed to assess the correlation between the expression intensities of the two markers,and multivariate regression models were applied to identify independent risk factors influencing co-expression.Kaplan-Meier survival curves and Log-rank test were used to compare survival differences among groups with different expression patterns.RESULTS Among the 304 patients,155 cases(50.99%)were CD24 positive,including 91 low-expression and 64 highexpression;133 cases(43.75%)were CD133 positive,including 81 low-expression and 52 high-expression.There were 74 cases(24.34%)with double positivity and 81 cases(26.64%)with double negativity.Compared with tumor tissues,the positive rates of CD24 and CD133 in normal gastric tissues and adjacent tissues were significantly lower(P<0.05).Univariate analysis showed that co-expression of CD24 and CD133 in GC tissues was significantly correlated with tumor size,Lauren classification,T stage,N stage,and vascular invasion(P<0.05),but not with patient age,gender,tumor site,World Health Organization histological classification,or M stage(P>0.05).Further multivariate regression analysis suggested that tumor size,T stage,N stage,and vascular invasion were independent risk factors promoting CD24 and CD133 double positivity.Spearman rank correlation analysis indicated a moderate positive correlation between their expression intensities(r=0.420,P<0.001).During follow-up,29 of 304 patients were lost(loss rate 9.54%);146 deaths occurred.According to expression combination,there were 89 cases of CD24 single positivity(39 deaths),68 cases of CD133 single positivity(31 deaths),81 cases of double negativity(25 deaths),and 66 cases of double positivity(51 deaths).Log-rank test showed significant differences in overall survival among the four groups(χ2=20.89,P<0.001),with CD24+/CD133+group showing the worst prognosis.CONCLUSION CD24 and CD133 exhibit high positive detection rates in GC tissues,and their co-positivity is closely associated with tumor stage progression and significantly indicates unfavorable survival outcomes.The co-expression of CD24/CD133 may reflect higher aggressiveness and metastatic potential of GC,serving as a potential prognostic marker and a direction for targeted therapeutic strategies.However,as this is a single-center retrospective study with limitations such as patient loss to follow-up and sample size,further prospective,multicenter,and mechanistic studies are required to validate its clinical applicability and biological role.
文摘Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.
基金funded by the National Nature Science Foundation of China (31671577)the Natural Science Foundation of Beijing, China (5174033)+2 种基金the Scientific and Technological Innovation Capacity Construction Project of Beijing Academy of Agricultural and Forestry Sciences, China (KJCX20170404)the Scientific and Technological Innovation Team of Beijing Academy of Agricultural and Forestry Sciences, China (JNKYT201604)the Beijing Postdoctoral Research Foundation, China (2016 ZZ-66)
文摘Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.
文摘Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
文摘Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.
基金the financial support from the National Key Research and Development Program of China(2021YFD1201004)the Science and Technology Innovation Project,Chinese Academy of Agricultural Sciences(CAAS-ZDRW202201)the Shandong Provincial Key R&D Program,China(2023LZGC010)。
文摘Starch serves as a critical storage component,significantly influencing the grain yield and quality of maize(Zea mays L.).Understanding the genetic basis of natural variation in kernel starch content(SC)is essential for maize breeding to meet future demands.A genome-wide association study(GWAS)identified 84 and 96 loci associated with kernel SC across two years,overlapping with 185 candidate genes.The candidate gene Zm MYB71,encoding a MYB-related transcription factor,demonstrated the highest co-expression frequency with starch synthesis genes.Analysis revealed that Zm MYB71 functions as a nuclear located transcription repressor,and mutants exhibited increased kernel SC by over 2.32%,with minimal impact on amylose content or 100-grain weight.Sh1,Sh2,and GBSSI exhibited up-regulation in mutants by 1.56-,1.45-and 1.32-fold,respectively,aligning with RNA sequencing results;their promoter activities appear directly repressed by Zm MYB71 through the GATATC and TTAGGG motifs.Additionally,the Zm MYB71 elite haplotype Hap1 occurred in over 55%of the high-starch maize sub-populations Iowa Stiff Stalk Synthetic(BSSS)and Partner B(PB),but only in 7.14%of the low-starch sub-population Partner A(PA).Analysis of Hap1 haplotype frequencies across breeding stages revealed a significant increase to 40.28%in inbred groups released after 2010,compared to 28.57 and 27.94%in 1980 and 1990,and 2000,respectively.These findings enhance understanding of natural variation in maize kernel SC and establish Zm MYB71 as a negative regulator with potential applications in SC improvement.
基金financially supported by the Key Fund of Tianjin Natural Science Foundation,China Project of Tianjin Natural Science Foundation(24JCZDJC00510)the National Natural Science Foundation of China,China(22475147)the Fundamental Research Funds for the Central Universities,China。
文摘The fabrication of efficient and stable flexible perovskite solar modules(F-PSMs)using poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine](PTAA)remains a significant challenge due to its hydrophobic properties and the mismatch in interface energy-level alignment.Here,we introduced[2-(3,6-dimethoxy-9H-carba zol-9-yl)ethyl]phosphonic acid(MeO-2PACz)to modify the PTAA layer,which effectively suppressed surface potential fluctuations and aligned energy levels at the interface of PTAA/perovskite.Additionally,MeO-2PACz enhanced the hydrophilicity of PTAA,facilitating the fabrication of dense,uniform,and pinhole-free perovskite films on large-area flexible substrates.As a result,we achieved an F-PSM with a power conversion efficiency(PCE)of 16.6% and an aperture area of 64 cm^(2),which is the highest reported value among F-PSMs with an active area exceeding 35 cm^(2)based on PTAA.Moreover,the encapsulated module demonstrated outstanding long-term operational stability,retaining 90.2% of its initial efficiency after 1000 bending cycles(5 mm radius),87.2% after 1000 h of continuous illumination,and 80.3% under combined thermal and humid conditions(85℃ and 85% relative humidity),representing one of the most stable F-PSMs reported to date.
基金Supported by the Fundamental Research Program of Shanxi Province(Grant No.202303021212147)the National Natural Science Foundation of China(Grant No.12471038)。
文摘Let D(n)be the finite dimensional non-pointed and non-semisimple Hopf algebra,which is a quotient of a prime Hopf algebras of GK-dimension one for an odd number n>1.In this paper,we investigate the structure of Yetter-Drinfeld simple modules over D(n)and give iso-classes of them.
基金supported by the National Natural Science Foundation of China,(Nos.82272151,82204318)Liaoning Revitalization Talents Program(No.XLYC2203083)+2 种基金Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program(No.RC220389)Postdoctoral Fellowship Program of CPSF(No.GZC20231732)China Postdoctoral Science Foundation(Nos.2023TQ0222,2023MD744229).
文摘Self-assembled prodrug nanomedicine has emerged as an advanced platform for antitumor therapy,mainly comprise drug modules,response modules and modification modules.However,existing studies usually compare the differences between single types of modification modules,neglecting the impact of steric-hindrance effect caused by chemical structure.Herein,single-tailed modification module with low-steric-hindrance effect and two-tailed modification module with high-steric-hindrance effect were selected to construct paclitaxel prodrugs(P-LA_(C18)and P-BAC18),and the in-depth insights of the sterichindrance effect on prodrug nanoassemblies were explored.Notably,the size stability of the two-tailed prodrugs was enhanced due to improved intermolecular interactions and steric hindrance.Single-tailed prodrug nanoassemblies were more susceptible to attack by redox agents,showing faster drug release and stronger antitumor efficacy,but with poorer safety.In contrast,two-tailed prodrug nanoassemblies exhibited significant advantages in terms of pharmacokinetics,tumor accumulation and safety due to the good size stability,thus ensuring equivalent antitumor efficacy at tolerance dose.These findings highlighted the critical role of steric-hindrance effect of the modification module in regulating the structureactivity relationship of prodrug nanoassemblies and proposed new perspectives into the precise design of self-assembled prodrugs for high-performance cancer therapeutics.
基金supported by National Natural Science Foundation of China(Nos.52170086,22308194,U22A20423)Natural Science Foundation of Shandong Province(No.ZR2021ME013)+4 种基金Shandong Provincial Excellent Youth(No.ZR2022YQ47)the doctor research start Foundation of Shaanxi University of Technology(No.SLGRCQD004)Science and Technology Innovation Team Project of Shaanxi Province(No.2025RS-CXTD-040)the General Special Scientific Research Program of the Shaanxi Provincial Department of Education(No.24JK0366)supported by funding from Shandong Provincial Key Laboratory of Monocrystalline Silicon Semiconductor Materials and Technology。
文摘Most carbon-based catalysts utilized in Fenton-like systems face challenges such as structural instability,susceptibility to deactivation,and a tendency to disperse during operation.Wood-derived catalysts have garnered considerable attention due to their well-defined structures,extensive pipeline networks,superior mechanical strength,and adaptability for device customization.However,there remains a paucity of research that systematically summarizes Fenton-like systems based on wood-derived catalysts.In this review,we first summarize the structural designs of wood-derived catalysts based on nano-metal sites and single-atom sites,while also outlining their advantages and limitations applied in Fenton-like systems.Furthermore,we evaluate catalytic modules of wood-derived catalysts for scale-up and continuous Fenton-like systems.Additionally,wood-inspired catalytic materials utilizing commercial textures and their applications in Fenton-like processes are also discussed.This paper aims to comprehensively explore the fundamental mechanisms(e.g.,characteristics of catalytic sites,catalytic performance,and mechanisms)of wood-based catalysts in Fenton-like chemistry,as well as their equipment designs and application scenarios,as well as providing the insights into future developments.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.
基金supported by China Huaneng Group Key R&D Program(HNKJ22-H104)the Science and Technology Programs of Fujian Province(2022H0005)+1 种基金the Fundamental Research Funds for the Central Universities(20720240067)Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(RD2020020101 and RD2022040601).
文摘Currently,perovskite solar cells have achieved commendable progresses in power conversion efficiency(PCE)and operational stability.However,some conventional laboratory-scale fabrication methods become challenging when scaling up material syntheses or device production.Particularly,the prolonged high-temperature annealing process for the crystallization of perovskites requires a substantial amount of energy consumption and impact the modules’throughput.Here,we report a modified near-infrared annealing(NIRA)process,which involves the excess PbI_(2)engineered crystallization,efficiently reduces the preparation time for perovskite active layer to within 20 s compared to dozens of min in conventional hot plate annealing(HPA)process.The study showed that the incorporated PbI_(2)promoted the consistent nucleation of the perovskite film,leading to the subsequent rapid and homogeneous crystallization at the NIRA stage.Thus,highly crystalized perovskite film was realized with even better crystallization performance than conventional HPA-based film.Ultimately,efficient perovskite solar modules of 36 and 100 cm^(2)were readily fabricated with the optimal PCEs of 22.03%and 20.18%,respectively.This study demonstrates,for the first time,the successful achievement of homogeneous and high-quality crystallization in large-area perovskite films through rapid NIRA processing.This approach not only significantly reduces energy consumption during production,but also substantially shortens the manufacturing cycle,paving a new path toward the commercial-scale application of perovskite solar modules.
文摘Modules enable students to engage with content at their own pace,fostering autonomy and deeper understanding.The modular approach ensures clarity in presenting objectives,instructions,and concepts,while having illustrations,activities,and assessments could enhance comprehension and retention.This paper was a developmental study on STS module for college students using the ADDIE Model(Analysis,Design,Development,Implementation,and Evaluation).Sampled 673 first-year students from Northwest Samar State University participated in the study,with 299 participating in a test try-out and 374 in the students’performance evaluation.Three expert evaluators with backgrounds in science,English,and psychology,each with over four years of experience,assessed the modules to ensure alignment with the study’s constructivist learning goals and instructional integrity.The findings revealed that both students and experts had rated the instructional module positively,indicating its effectiveness in facilitating learning and completing lessons.Key aspects such as the style of illustrations and written expressions,the usefulness of learning activities,and the guidance provided by illustrations and captions were especially well-received.The module was praised for its clear objectives,understandable instructions,and engaging tasks like trivia and puzzles.Expert evaluations highlighted relevance,simplicity,and balanced emphasis on topics in the module content.Furthermore,students in test group demonstrated significant improvement in performance,with post-test scores notably higher than pre-test scores,confirming the module’s effectiveness in enhancing learning outcomes.Consequently,this paper provides an opportunity to integrate science learning with initiatives aimed at promoting environmental preservation and driving social change.
基金supported by the National Natural Science Foundation of China (31371557 and 31571574)Wenzhou Basic Scientific Research Project (N20240009)。
文摘Transcription factors(TFs)play key roles in the regulatory network of leaf senescence.However,many nodes in this network remain unclear.To elucidate the mechanism of leaf senescence mediated by a rice TF,WRKY10,the expression of multiple senescence-related genes and physiological phenotypes were monitored in WRKY10-and VQ MOTIF-CONTAINING PROTEIN8(VQ8)-overexpressing plants and the wrky10 and vq8 mutants.Our results showed that WRKY10 positively regulates abscisic acid(ABA)-and dark-induced senescence(DIS)by directly regulating the expression of multiple senescence-related genes.The VQ8 protein,a repressor of WRKY10,negatively regulates WRKY10-mediated DIS.The WRKY10-VQ8 module fine-tunes the progression of DIS.ABA,methyl jasmonate,and H_(2)O_(2) accelerate WRKY10-mediated DIS,whereas ammonium nitrate and dithiothreitol delay WRKY10-mediated DIS.Further analysis revealed that WRKY10 and VQ8 interact with ABA RESPONSIVE ELEMENT BINDING FACTOR1(ABF1)or ABF2.VQ8 represses the transcriptional activity of ABF1 and ABF2.Overexpression of ABF1 or ABF2 accelerates ABA-and dark-induced senescence and H_(2)O_(2) accumulation in N.benthamiana leaves,and WRKY10 and VQ8 can inhibit either ABF1-or ABF2-induced cell necrosis.Taken together,WRKY10 integrates multiple senescence signals to establish an orderly progression of leaf senescence.The VQ8 protein acts as a brake on WRKY10-induced senescence and ABF1/2-induced cell death,preventing uncontrolled cell death.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549).
文摘The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.
基金supported in part by the National Key R&D Program of China under grant no.2022YFB2703000in part by the Young Backbone Teachers Support Plan of BISTU under grant no.YBT202437+1 种基金in part by the R&D Program of Beijing Municipal Education Commission under grant no.KM202211232012in part by the Educational Innovation Program of BISTU under grant no.2025JGYB19。
文摘Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant attention.Compared with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself.Some existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless networks.Additionally,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing attacks.To overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely SE-ConvNet.To be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep learning.Moreover,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication accuracy.Compared with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.
文摘With the rapid development of the new energy automotive industry,the enhancement of lithium battery performance and production efficiency has become critical.This article explores the application of artificial intelligence technology in the lithium battery module PACK line,analyzing how it optimizes the production process and improves production efficiency,and predicts future development trends.The PACK line is an important link in battery manufacturing,involving complex processes such as cell sorting,welding,assembly and testing.The application of AI technology in image recognition,data analysis and predictive maintenance provides new solutions for the intelligent upgrading of the PACK line.This article describes the process of the PACK line in detail,analyzes the challenges under current technological levels,and reviews the application cases of AI technology in the manufacturing industry.The study aims to provide theoretical and practical guidance for the intelligent development of lithium battery module PACK lines,discussing the integration of AI technology,its actual performance,technical challenges,and solutions.It is expected that AI technology will play a greater role in the PACK line,and future research will focus on improving the adaptability of models,developing efficient algorithms,and further integrating into the production line.
基金Supported by National Natural Science Foundation of China(Grant No.52332013)。
文摘The intelligent vehicle corner module system,which integrates four-wheel independent drive,independent steering,independent braking and active suspension,can accurately and efficiently perform vehicle driving tasks and is the best carrier of intelligent vehicles.Nevertheless,too many angle/torque control inputs make control difficult and non-real-time.In this paper,a hierarchical real-time motion control framework for corner module configuration intelligent electric vehicles is proposed.In the trajectory planning module,an improved driving risk field is designed to describe the surrounding environment’s driving risk.Combined with the kinematic vehicle-road model,model predictive control(MPC)method,spline curve method,the local reference trajectory of safety,comfort and smoothness is planned in real time.The optimal steering angle is determined using MPC method in path tracking module.In the motion control module,a feedforward-feedback controller assigns the optimal steering angle to the front/rear axles,and an angle allocation controller distributes the target angles of the front/rear axles to four steered wheels.Finally,the PreScan-Simulink-CarSim joint simulation environment is established for conducting the human-in-the-loop emergency obstacle avoidance experiment.It took only 0.005 s for the hierarchical motion control system to determine its average solution time.This proves the effectiveness of the hierarchical motion control system.
