[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis...[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.展开更多
This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raph...This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.展开更多
To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per...To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.展开更多
Bread wheat(Triticum aestivum L.)is a staple hexaploid crop with numerous wild relatives.However,domestication and modern breeding have significantly narrowed its genetic diversity,diminishing its capacity to adapt to...Bread wheat(Triticum aestivum L.)is a staple hexaploid crop with numerous wild relatives.However,domestication and modern breeding have significantly narrowed its genetic diversity,diminishing its capacity to adapt to climate change.Wild relatives of wheat serve as a vital reservoir of genetic diversity,offering traits thatenhance its resistance to various biotic and abiotic stresses.Over recent decades,remarkable progress has been made in utilizing superior genes from wild relatives to bolster wheat's defenses against diseases and pests,though the exploration of genes conferring abiotic stress tolerance has lagged behind.In this review,we summarize key advancements in the utilization of wild relatives for wheat enhancement over the past century,emphasizing both theoretical and technological innovations.Furthermore,we evaluate the potential contributions of wild relatives to address production challenges posed by climate change.We also explore strategies for isolating superior genes and developing prebreeding germplasm to support the future development of climate-resilient wheat varieties.展开更多
The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliab...The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliability,and insufficient measurement stability.Micro-CT enables the observation of the internal structures of the sample without the need for slicing,thereby presenting a promising new method for assessing the quality of microholes in PCBs.This study integrates computer vision technology with computed tomography(CT)to propose a method for detecting microhole wall roughness using a U-Net model and image processing algorithms.This study established an unplated copper PCB CT image dataset and trained an improved U-Net model.Validation of the test set demonstrated that the improved model effectively segmented microholes in the PCB CT images.Subsequently,the roughness of the holes’walls was assessed using a customized image-processing algorithm.Comparative analysis between CT detection based on various edge detection algorithms and slice detection revealed that CT detection employing the Canny algorithm closely approximates slice detection,yielding range and average errors of 2.92 and 1.64μm,respectively.Hence,the detection method proposed in this paper offers a novel approach for nondestructive testing of hole wall roughness in the PCB industry.展开更多
A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are sys...A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.展开更多
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors pr...Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vi...Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.展开更多
Photosynthesis is one the most important chemical reaction in plants,and it is the ultimate energy source of any living organisms.The light and dark reactions are two essential phases of photosynthesis.Light reaction ...Photosynthesis is one the most important chemical reaction in plants,and it is the ultimate energy source of any living organisms.The light and dark reactions are two essential phases of photosynthesis.Light reaction harvests light energy to synthesize ATP and NADPH through an electron transport chain,and as well as giving out O_(2);dark reaction fixes CO_(2) into six carbon sugars by utilizing NADPH and energy from ATP.Subsequently,plants convert optical energy into chemical energy for maintaining growth and development through absorbing light energy.Here,firstly,we highlighted the biological importance of photosynthesis,and hormones and metabolites,photosynthetic and regulating enzymes,and signaling components that collectively regulate photosynthesis in tomato.Next,we reviewed the advances in tomato photosynthesis,including two aspects of genetic basis and genetic improvement.Numerous genes regulating tomato photosynthesis are gradually uncovered,and the interaction network among those genes remains to be constructed.Finally,the photosynthesis occurring in fruit of tomato and the relationship between photosynthesis in leaf and fruit were discussed.Leaves and fruits are photosynthate sources and sinks of tomato respectively,and interaction between photosynthesis in leaf and fruit exists.Additionally,future perspectives that needs to be addressed on tomato photosynthesis were proposed.展开更多
Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the ne...Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the next-generation commercial solar cells.However,critical challenges remain in preserving high efficiency practical large-scale commercialized PSCs:a)the long-term stability of the cell materials and devices,b)lead leakage,and c)methods to scale the cells for larger area applications.This paper summarizes the prior-art strategies to address the above challenges,including the latest studies on the traditional glass-glass and thin-film encapsulation methods to better improve the reliability of PSCs,new technologies for preventing lead leakage,and geometric improvement strategies to enhance the reliability,efficiency,and performance of perovskite solar modules(PSMs).Through these strategies,the device achieved enhanced performance in long-term stability tests.The encapsulation resulted in a high lead leakage inhibition rate of up to 99%,and the PSMs possessed a geometric fill factor of 99.6%and a power conversion efficiency(PCE)of 20.7%.The dramatic improvement of efficiency and reliability of perovskite solar cells and modules indicate the great potential for mass production and commer-cialization of perovskite solar applications in the near future.展开更多
Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessi...Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.展开更多
To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,t...To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.展开更多
文摘[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-82-1445)。
文摘This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.
基金supported by the Jiangsu Engineering Research Center of the Key Technology for Intelligent Manufacturing Equipment and the Suqian Key Laboratory of Intelligent Manufacturing(Grant No.M202108).
文摘To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04071)the National Key Research and Development Program of China(2023YFF1000600)and the National Natural Science Foundation of China(32272084,32372089,and 31971887).
文摘Bread wheat(Triticum aestivum L.)is a staple hexaploid crop with numerous wild relatives.However,domestication and modern breeding have significantly narrowed its genetic diversity,diminishing its capacity to adapt to climate change.Wild relatives of wheat serve as a vital reservoir of genetic diversity,offering traits thatenhance its resistance to various biotic and abiotic stresses.Over recent decades,remarkable progress has been made in utilizing superior genes from wild relatives to bolster wheat's defenses against diseases and pests,though the exploration of genes conferring abiotic stress tolerance has lagged behind.In this review,we summarize key advancements in the utilization of wild relatives for wheat enhancement over the past century,emphasizing both theoretical and technological innovations.Furthermore,we evaluate the potential contributions of wild relatives to address production challenges posed by climate change.We also explore strategies for isolating superior genes and developing prebreeding germplasm to support the future development of climate-resilient wheat varieties.
基金Supported by National Natural Science Foundation of China(Grant Nos.52122510 and 52375415).
文摘The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliability,and insufficient measurement stability.Micro-CT enables the observation of the internal structures of the sample without the need for slicing,thereby presenting a promising new method for assessing the quality of microholes in PCBs.This study integrates computer vision technology with computed tomography(CT)to propose a method for detecting microhole wall roughness using a U-Net model and image processing algorithms.This study established an unplated copper PCB CT image dataset and trained an improved U-Net model.Validation of the test set demonstrated that the improved model effectively segmented microholes in the PCB CT images.Subsequently,the roughness of the holes’walls was assessed using a customized image-processing algorithm.Comparative analysis between CT detection based on various edge detection algorithms and slice detection revealed that CT detection employing the Canny algorithm closely approximates slice detection,yielding range and average errors of 2.92 and 1.64μm,respectively.Hence,the detection method proposed in this paper offers a novel approach for nondestructive testing of hole wall roughness in the PCB industry.
基金supported by the National Natural Science Foundation of China(72071106)Jiangsu Provincial Social Science Fund(23EYA001)+1 种基金Jiangsu Provincial Education Science Planning Fund(Ba/2024/08)Jiangsu Higher Education Association Fund(24FYHLX090)。
文摘A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金the National Science and Technology Council(NSTC)of the Republic of China,Taiwan,for financially supporting this research under Contract No.NSTC 112-2637-M-131-001.
文摘Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金funded by the National Natural Science Foundation of China(No.41962016)the Natural Science Foundation of NingXia(Nos.2023AAC02023,2023A1218,and 2021AAC02006).
文摘Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.
基金supported by grants from the National Key Research&Development Plan(Grants Nos.2022YFF10030022022YFD1200502)+7 种基金National Natural Science Foundation of China(Grant Nos.3237269631991182)Wuhan Biological Breeding Major Project(Grant No.2022021302024852)Key Project of Hubei Hongshan Laboratory(2021hszd007)HZAU-AGIS Cooperation Fund(Grant No.SZYJY2023022)Funds for High Quality Development of Hubei Seed Industry(HBZY2023B004)Hubei Agriculture Research System(2023HBSTX4-06)Hubei Key Research&Development Plan(Grants Nos.2022BBA0066,2022BBA0062)。
文摘Photosynthesis is one the most important chemical reaction in plants,and it is the ultimate energy source of any living organisms.The light and dark reactions are two essential phases of photosynthesis.Light reaction harvests light energy to synthesize ATP and NADPH through an electron transport chain,and as well as giving out O_(2);dark reaction fixes CO_(2) into six carbon sugars by utilizing NADPH and energy from ATP.Subsequently,plants convert optical energy into chemical energy for maintaining growth and development through absorbing light energy.Here,firstly,we highlighted the biological importance of photosynthesis,and hormones and metabolites,photosynthetic and regulating enzymes,and signaling components that collectively regulate photosynthesis in tomato.Next,we reviewed the advances in tomato photosynthesis,including two aspects of genetic basis and genetic improvement.Numerous genes regulating tomato photosynthesis are gradually uncovered,and the interaction network among those genes remains to be constructed.Finally,the photosynthesis occurring in fruit of tomato and the relationship between photosynthesis in leaf and fruit were discussed.Leaves and fruits are photosynthate sources and sinks of tomato respectively,and interaction between photosynthesis in leaf and fruit exists.Additionally,future perspectives that needs to be addressed on tomato photosynthesis were proposed.
基金supported by the National Natural Science Foundation of China(No.62404041)the Natural Science Foundation of Jiangsu Province of China(No.BK20230830).
文摘Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the next-generation commercial solar cells.However,critical challenges remain in preserving high efficiency practical large-scale commercialized PSCs:a)the long-term stability of the cell materials and devices,b)lead leakage,and c)methods to scale the cells for larger area applications.This paper summarizes the prior-art strategies to address the above challenges,including the latest studies on the traditional glass-glass and thin-film encapsulation methods to better improve the reliability of PSCs,new technologies for preventing lead leakage,and geometric improvement strategies to enhance the reliability,efficiency,and performance of perovskite solar modules(PSMs).Through these strategies,the device achieved enhanced performance in long-term stability tests.The encapsulation resulted in a high lead leakage inhibition rate of up to 99%,and the PSMs possessed a geometric fill factor of 99.6%and a power conversion efficiency(PCE)of 20.7%.The dramatic improvement of efficiency and reliability of perovskite solar cells and modules indicate the great potential for mass production and commer-cialization of perovskite solar applications in the near future.
基金funded by Changzhou Science and Technology Project(No.CZ20230025)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.XSJCX23_36).
文摘Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_4084).
文摘To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.
