Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.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
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features i...Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.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
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
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
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
Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of intersp...Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of interspecies similarity,multi-scale,and background complexity of pests.To address these problems,this study proposes an FD-YOLO pest target detection model.The FD-YOLO model uses a Fully Connected Feature Pyramid Network(FC-FPN)instead of a PANet in the neck,which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer,enhance large-scale target features in the shallow layer,and enhance the multiplexing of effective features.A dual self-attention module(DSA)is then embedded in the C3 module of the neck,which captures the dependencies between the information in both spatial and channel dimensions,effectively enhancing global features.We selected 16 types of pests that widely damage field crops in the IP102 pest dataset,which were used as our dataset after data supplementation and enhancement.The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8%compared to YOLOv5,reaching 82.6%and 19.1%–5%better than other state-of-the-art models.This method provides an effective new approach for detecting similar or multiscale pests in field crops.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
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
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.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
基金funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabiaby the Science and Technology Research Programof Chongqing Municipal Education Commission(Grant No.KJQN202400813)by the Graduate Research Innovation Project(Grant Nos.yjscxx2025-269-193 and CYS25618).
文摘Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
基金supported by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
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 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 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 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funded by Liaoning Provincial Department of Education Project,Award number JYTMS20230418.
文摘Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks;however,their application in the actual agricultural production process is still challenging owing to the problems of interspecies similarity,multi-scale,and background complexity of pests.To address these problems,this study proposes an FD-YOLO pest target detection model.The FD-YOLO model uses a Fully Connected Feature Pyramid Network(FC-FPN)instead of a PANet in the neck,which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer,enhance large-scale target features in the shallow layer,and enhance the multiplexing of effective features.A dual self-attention module(DSA)is then embedded in the C3 module of the neck,which captures the dependencies between the information in both spatial and channel dimensions,effectively enhancing global features.We selected 16 types of pests that widely damage field crops in the IP102 pest dataset,which were used as our dataset after data supplementation and enhancement.The experimental results showed that FD-YOLO’s mAP@0.5 improved by 6.8%compared to YOLOv5,reaching 82.6%and 19.1%–5%better than other state-of-the-art models.This method provides an effective new approach for detecting similar or multiscale pests in field crops.
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.
