FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku prod...FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.展开更多
Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the re...Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks.展开更多
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects...Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications.展开更多
IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal ...IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal traffic often degrades model performance.We propose a novel multi-generator adversarial data augmentation method that blends the strengths of TMG-GAN(Tabular Multi-Generator Generative Adversarial Network)and R3GAN(Re-GAN).Our approach uses multiple class-specific generators to create diverse,high-quality synthetic samples,improving training stability and minority-class detection.A dual-branch discriminator-classifier enhances authenticity and class prediction,while feature similarity and decoupling techniques ensure clear class separation.Experiments on TON-IoT and Edge-IIoTset datasets show our method outperforms existing techniques like hybrid sampling,SNGAN(Spectral Normalization GAN),and TMG-GAN,achieving higher detection accuracy and better minority-class recall for imbalanced IoT intrusion detection.展开更多
To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detecti...To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components.展开更多
The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflect...The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.展开更多
Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in ord...Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
Peri-implant keratinized mucosa(PIKM)augmentation refers to surgical procedures aimed at increasing the width of PIKM.Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-ter...Peri-implant keratinized mucosa(PIKM)augmentation refers to surgical procedures aimed at increasing the width of PIKM.Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health.Currently,several surgical techniques have been validated for their effectiveness in increasing PIKM.However,the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques,variations in clinical scenarios,and anatomical differences.Therefore,clear guidelines and considerations for PIKM augmentation are needed.This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery.It aims to establish a standardized framework for assessing,planning,and executing PIKM augmentation procedures,with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.展开更多
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th...In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aim...Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.展开更多
Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning b...Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.展开更多
An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect s...An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.展开更多
High-quality data is essential for the success of data-driven learning tasks.The characteristics,precision,and completeness of the datasets critically determine the reliability,interpretability,and effectiveness of su...High-quality data is essential for the success of data-driven learning tasks.The characteristics,precision,and completeness of the datasets critically determine the reliability,interpretability,and effectiveness of subsequent analyzes and applications,such as fault detection,predictive maintenance,and process optimization.However,for many industrial processes,obtaining sufficient high-quality data remains a significant challenge due to high costs,safety concerns,and practical constraints.To overcome these challenges,data augmentation has emerged as a rapidly growing research area,attracting considerable attention across both academia and industry.By expanding datasets,data augmentation techniques improve greater generalization and more robust performance in actual applications.This paper provides a comprehensive,multi-perspective review of data augmentation methods for industrial processes.For clarity and organization,existing studies are systematically grouped into four categories:small sample with low dimension,small sample with high dimension,large sample with low dimension,and large sample with high dimension.Within this framework,the review examines current research from both methodological and application-oriented perspectives,highlighting main methods,advantages,and limitations.By synthesizing these findings,this review offers a structured overview for scholars and practitioners,serving as a valuable reference for newcomers and experienced researchers seeking to explore and advance data augmentation techniques in industrial processes.展开更多
Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation...Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.展开更多
Breast augmentation with implants is a popular cosmetic surgery that enhances breast volume and contour through various placement planes.In this review,we examine the impact of subglandular,subpectoral,and subfascial ...Breast augmentation with implants is a popular cosmetic surgery that enhances breast volume and contour through various placement planes.In this review,we examine the impact of subglandular,subpectoral,and subfascial implant planes on postoperative outcomes and complication rates.Subglandular placement offers simplicity but is associated with higher risks of capsular contracture,hematoma,and rippling in patients with low tissue coverage.The subpectoral plane,widely adopted for its natural appearance and reduced capsular contracture risk,may cause dynamic deformity due to muscle contraction.Although technically challenging,the subfascial plane combines the benefits of soft tissue support and reduced implant displacement.We highlight the importance of choosing an optimal implant plane tailored to each patient’s anatomical and aesthetic needs to enhance surgical outcomes and minimize complications.Further research is needed to validate long-term efficacy,particularly for subfascial placement.展开更多
Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of ...Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.展开更多
Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on opt...Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on optimizing machine learning(ML)algorithms,very limited attention has been paid to enhancing data quality for improved predictive performance.This study employs strategic data augmentation(DA)techniques to enhance the accuracy of shallow landslide prediction.Using five DA methods including singular spectrum analysis(SSA),moving averages(MA),wavelet denoising(WD),variational mode decomposition(VMD),and linear interpolation(LI),we utilize strategies such as smoothing,denoising,trend decomposition,and synthetic data generation to improve the training dataset.Four machine learning algorithms,i.e.artificial neural network(ANN),recurrent neural network(RNN),one-dimensional convolutional neural network(CNN1D),and long short-term memory(LSTM),are used to forecast landslide displacement.The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements,despite the inherent limitations of the monitoring dataset.VMD proves the most effective for smoothing and denoising,improving R^(2),RMSE,and MAPE by 172.16%,71.82%,and 98.9%,respectively.SSA addresses missing data,while LI is effective with limited data samples,improving metrics by 21.6%,52.59%,and 47.87%,respectively.This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy,with implications for similar cases.展开更多
基金supported by the Innovation and Development Special Project of the China Meteorological Administration(Grant No.CXFZ2024J058)the Guangdong Province Basic and Applied Basic Research Foundation Meteorological Joint Fund Project(Grant No.2024A1515510036)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3004101)the Technical Innovation Team Project of Guangzhou Meteorological Satellite Ground Station(Grant No.CXTD202401).
文摘FY-3G is the first polar-orbiting satellite equipped with a precipitation measurement radar(PMR)operating at Ku-andKa-band frequencies in China.In this study,we compare the reflectivity data from the FY-3G PMR Ku product and groundbasedradars(GRs)during 2024.Also,the FY-3G PMR is used as a third-party reference to evaluate the reflectivityconsistency among different GRs.The FY-3G PMR and GRs share similarities in their general distribution,characteristics,and intensity of reflectivity in strong precipitation cloud systems,though the former presents less detailed system structure.Systematic deviations between the FY-3G PMR and GRs and between GRs are comparable,albeit the reflectivity of the FY-3G PMR is generally slightly stronger than that of GRs(especially X-band GRs),with a mean bias ranging from 0.7 to 1.7dB.S-band GRs exhibit the smallest systematic deviation(STD=3.09 dB)from the FY-3G PMR,whereas the X-band GRsshow the largest(STD=3.61 dB),indirectly indicating the highest internal consistency among S-band GRs and the lowestamong X-band GRs.Besides,both S-and C-band GRs display similar deviations when paired with the FY-3G PMR as wellas when paired with their adjacent S/C-band GRs,suggesting good consistency between these two bands.In contrast,XbandGRs exhibit relatively poor consistency with S-band GRs and the FY-3G PMR,showing a deviation ranging from 3.0to 4.6 dB.
基金supported by the Institute of Information&Communications Technoljogy Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program grant funded by the Korea government(MSIT)(IITP-2026-RS-2022-00156334,50%)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1C1C2011105,50%).
文摘Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金funded by the National Science and Technology Council(NSTC),Taiwan,grant number NSTC 114-2218-E-167-001.
文摘Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications.
基金Supported by the Key R&D Projects in Hubei Province(2025BAB018,2022BAA041)and Wuhan University Comprehensive Undergraduate Education Quality Reform Project。
文摘IoT devices are highly vulnerable to cyberattacks due to their widespread,distributed nature and limited security features.Intrusion detection can counter these threats,but class imbalance between normal and abnormal traffic often degrades model performance.We propose a novel multi-generator adversarial data augmentation method that blends the strengths of TMG-GAN(Tabular Multi-Generator Generative Adversarial Network)and R3GAN(Re-GAN).Our approach uses multiple class-specific generators to create diverse,high-quality synthetic samples,improving training stability and minority-class detection.A dual-branch discriminator-classifier enhances authenticity and class prediction,while feature similarity and decoupling techniques ensure clear class separation.Experiments on TON-IoT and Edge-IIoTset datasets show our method outperforms existing techniques like hybrid sampling,SNGAN(Spectral Normalization GAN),and TMG-GAN,achieving higher detection accuracy and better minority-class recall for imbalanced IoT intrusion detection.
基金Supported by the Science and Technology Project from State Grid Corporation of China (No.5700-202490330A-2-1-ZX)。
文摘To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components.
基金funded by the National Key Research and Development Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation(Grant No.42330602)the“Fengyun Satellite Remote Sensing Product Validation and Verification”Youth Innovation Team of the China Meteorological Administration(Grant No.CMA2023QN12)。
文摘The Global Precipitation Measurement(GPM)dual-frequency precipitation radar(DPR)products(Version 07A)are employed for a rigorous comparative analysis with ground-based operational weather radar(GR)networks.The reflectivity observed by GPM Ku PR is compared quantitatively against GR networks from CINRAD of China and NEXRAD of the United States,and the volume matching method is used for spatial matching.Additionally,a novel frequency correction method for all phases as well as precipitation types is used to correct the GPM Ku PR radar frequency to the GR frequency.A total of 20 GRs(including 10 from CINRAD and 10 from NEXRAD)are included in this comparative analysis.The results indicate that,compared with CINRAD matched data,NEXRAD exhibits larger biases in reflectivity when compared with the frequency-corrected Ku PR.The root-mean-square difference for CINRAD is calculated at 2.38 d B,whereas for NEXRAD it is 3.23 d B.The mean bias of CINRAD matched data is-0.16 d B,while the mean bias of NEXRAD is-2.10 d B.The mean standard deviation of bias for CINRAD is 2.15 d B,while for NEXRAD it is 2.29 d B.This study effectively assesses weather radar data in both the United States and China,which is crucial for improving the overall consistency of global precipitation estimates.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020025Science and Technology Commissioner Program of Huzhou,Grant/Award Number:2023GZ42Sichuan Provincial Science and Technology Support Program,Grant/Award Numbers:2023ZHCG0005,2023ZHCG0008。
文摘Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
基金supported by the Natural Science Foundation of Sichuan Province(grant number:25NSFSC0265).
文摘Peri-implant keratinized mucosa(PIKM)augmentation refers to surgical procedures aimed at increasing the width of PIKM.Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health.Currently,several surgical techniques have been validated for their effectiveness in increasing PIKM.However,the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques,variations in clinical scenarios,and anatomical differences.Therefore,clear guidelines and considerations for PIKM augmentation are needed.This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery.It aims to establish a standardized framework for assessing,planning,and executing PIKM augmentation procedures,with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
基金funded by the Bavarian State Ministry of Science,Research and Art(Grant number:H.2-F1116.WE/52/2)。
文摘In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.
基金funded by Innovation and Development Special Project of China Meteorological Administration(CXFZ2022J038,CXFZ2024J035)Sichuan Science and Technology Program(No.2023YFQ0072)+1 种基金Key Laboratory of Smart Earth(No.KF2023YB03-07)Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province(CUIT-SAG202210).
文摘Accurate cloud classification plays a crucial role in aviation safety,climate monitoring,and localized weather forecasting.Current research has been focusing on machine learning techniques,particularly deep learning based model,for the types identification.However,traditional approaches such as convolutional neural networks(CNNs)encounter difficulties in capturing global contextual information.In addition,they are computationally expensive,which restricts their usability in resource-limited environments.To tackle these issues,we present the Cloud Vision Transformer(CloudViT),a lightweight model that integrates CNNs with Transformers.The integration enables an effective balance between local and global feature extraction.To be specific,CloudViT comprises two innovative modules:Feature Extraction(E_Module)and Downsampling(D_Module).These modules are able to significantly reduce the number of model parameters and computational complexity while maintaining translation invariance and enhancing contextual comprehension.Overall,the CloudViT includes 0.93×10^(6)parameters,which decreases more than ten times compared to the SOTA(State-of-the-Art)model CloudNet.Comprehensive evaluations conducted on the HBMCD and SWIMCAT datasets showcase the outstanding performance of CloudViT.It achieves classification accuracies of 98.45%and 100%,respectively.Moreover,the efficiency and scalability of CloudViT make it an ideal candidate for deployment inmobile cloud observation systems,enabling real-time cloud image classification.The proposed hybrid architecture of CloudViT offers a promising approach for advancing ground-based cloud image classification.It holds significant potential for both optimizing performance and facilitating practical deployment scenarios.
基金supported by the Jiangsu Province IUR Cooperation Project (No.BY2021258)the Wuxi Science and Technology Development Fund Project (No.G20212028)。
文摘An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.
基金supported by the Postdoctoral Fellowship Program(Grade B)of China(GZB20250435)the National Natural Science Foundation of China(62403270).
文摘High-quality data is essential for the success of data-driven learning tasks.The characteristics,precision,and completeness of the datasets critically determine the reliability,interpretability,and effectiveness of subsequent analyzes and applications,such as fault detection,predictive maintenance,and process optimization.However,for many industrial processes,obtaining sufficient high-quality data remains a significant challenge due to high costs,safety concerns,and practical constraints.To overcome these challenges,data augmentation has emerged as a rapidly growing research area,attracting considerable attention across both academia and industry.By expanding datasets,data augmentation techniques improve greater generalization and more robust performance in actual applications.This paper provides a comprehensive,multi-perspective review of data augmentation methods for industrial processes.For clarity and organization,existing studies are systematically grouped into four categories:small sample with low dimension,small sample with high dimension,large sample with low dimension,and large sample with high dimension.Within this framework,the review examines current research from both methodological and application-oriented perspectives,highlighting main methods,advantages,and limitations.By synthesizing these findings,this review offers a structured overview for scholars and practitioners,serving as a valuable reference for newcomers and experienced researchers seeking to explore and advance data augmentation techniques in industrial processes.
基金supported by Open Fund of National Key Laboratory of Deep Space Exploration(NKDSEL2024014)by Civil Aerospace Pre-research Project of State Administration of Science,Technology and Industry for National Defence,PRC(D040103).
文摘Space target imaging simulation technology is an important tool for space target detection and identification,with advantages that include high flexibility and low cost.However,existing space target imaging simulation technologies are mostly based on target magnitudes for simulations,making it difficult to meet image simulation requirements for different signal-to-noise ratio(SNR)needs.Therefore,design of a simulation method that generates target image sequences with various SNRs based on the optical detection system parameters will be important for faint space target detection research.Addressing the SNR calculation issue in optical observation systems,this paper proposes a ground-based detection image SNR calculation method using the optical system parameters.This method calculates the SNR of an observed image precisely using radiative transfer theory,the optical system parameters,and the observation environment parameters.An SNR-based target sequence image simulation method for ground-based detection scenarios is proposed.This method calculates the imaging SNR using the optical system parameters and establishes a model for conversion between the target’s apparent magnitude and image grayscale values,thereby enabling generation of target sequence simulation images with corresponding SNRs for different system parameters.Experiments show that the SNR obtained using this calculation method has an average calculation error of<1 dB when compared with the theoretical SNR of the actual optical system.Additionally,the simulation images generated by the imaging simulation method show high consistency with real images,which meets the requirements of faint space target detection algorithm research and provides reliable data support for development of related technologies.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(grant no.LQ22H150005).
文摘Breast augmentation with implants is a popular cosmetic surgery that enhances breast volume and contour through various placement planes.In this review,we examine the impact of subglandular,subpectoral,and subfascial implant planes on postoperative outcomes and complication rates.Subglandular placement offers simplicity but is associated with higher risks of capsular contracture,hematoma,and rippling in patients with low tissue coverage.The subpectoral plane,widely adopted for its natural appearance and reduced capsular contracture risk,may cause dynamic deformity due to muscle contraction.Although technically challenging,the subfascial plane combines the benefits of soft tissue support and reduced implant displacement.We highlight the importance of choosing an optimal implant plane tailored to each patient’s anatomical and aesthetic needs to enhance surgical outcomes and minimize complications.Further research is needed to validate long-term efficacy,particularly for subfascial placement.
文摘Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.
基金supported by the National Natural Science Foundation of China(Grant No.42101089)Sichuan Science and Technology Program(2022YFS0586)the Open Fund of Key Laboratory of Mountain Hazards and Earth Surface Processes Chinese Academy of Sciences.
文摘Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on optimizing machine learning(ML)algorithms,very limited attention has been paid to enhancing data quality for improved predictive performance.This study employs strategic data augmentation(DA)techniques to enhance the accuracy of shallow landslide prediction.Using five DA methods including singular spectrum analysis(SSA),moving averages(MA),wavelet denoising(WD),variational mode decomposition(VMD),and linear interpolation(LI),we utilize strategies such as smoothing,denoising,trend decomposition,and synthetic data generation to improve the training dataset.Four machine learning algorithms,i.e.artificial neural network(ANN),recurrent neural network(RNN),one-dimensional convolutional neural network(CNN1D),and long short-term memory(LSTM),are used to forecast landslide displacement.The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements,despite the inherent limitations of the monitoring dataset.VMD proves the most effective for smoothing and denoising,improving R^(2),RMSE,and MAPE by 172.16%,71.82%,and 98.9%,respectively.SSA addresses missing data,while LI is effective with limited data samples,improving metrics by 21.6%,52.59%,and 47.87%,respectively.This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy,with implications for similar cases.
