Image-based similar trademark retrieval is a time-consuming and labor-intensive task in the trademark examination process.This paper aims to support trademark examiners by training Deep Convolutional Neural Network(DC...Image-based similar trademark retrieval is a time-consuming and labor-intensive task in the trademark examination process.This paper aims to support trademark examiners by training Deep Convolutional Neural Network(DCNN)models for effective Trademark Image Retrieval(TIR).To achieve this goal,we first develop a novel labeling method that automatically generates hundreds of thousands of labeled similar and dissimilar trademark image pairs using accompanying data fields such as citation lists,Vienna classification(VC)codes,and trademark ownership information.This approach eliminates the need for manual labeling and provides a large-scale dataset suitable for training deep learning models.We then train DCNN models based on Siamese and Triplet architectures,evaluating various feature extractors to determine the most effective configuration.Furthermore,we present an Adapted Contrastive Loss Function(ACLF)for the trademark retrieval task,specifically engineered to mitigate the influence of noisy labels found in automatically created datasets.Experimental results indicate that our proposed model(Efficient-Net_v21_Siamese)performs best at both True Negative Rate(TNR)threshold levels,TNR 0.9 and TNR 0.95,with==respective True Positive Rates(TPRs)of 77.7%and 70.8%and accuracies of 83.9%and 80.4%.Additionally,when testing on the public trademark dataset METU_v2,our model achieves a normalized average rank(NAR)of 0.0169,outperforming the current state-of-the-art(SOTA)model.Based on these findings,we estimate that considering only approximately 10%of the returned trademarks would be sufficient,significantly reducing the review time.Therefore,the paper highlights the potential of utilizing national trademark data to enhance the accuracy and efficiency of trademark retrieval systems,ultimately supporting trademark examiners in their evaluation tasks.展开更多
DDeeaarr EEddiittoorr,,The encoding and retrieval of emotional memories demands intricate interplay within the limbic network,where the network state is subject to significant reconfiguration by learning-induced plast...DDeeaarr EEddiittoorr,,The encoding and retrieval of emotional memories demands intricate interplay within the limbic network,where the network state is subject to significant reconfiguration by learning-induced plasticity,behavioral state,and contextual information[1].展开更多
Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various domains.The image retrieval sy...Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various domains.The image retrieval system mainly relies on the efficiency and accuracy of the classification models.This research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and CBIR.The novelty of the proposed EffNet-CNN model includes the integration of different techniques and modifications.The model includes the Mahalanobis distance metric for feature matching,which enhances the similarity measurements.The model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature representations.The EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 datasets.The model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior performance.The results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.展开更多
Retrieval analysis in total knee arthroplasty(TKA)has been little studied in the literature.A narrative review of the literature to understand the current importance of retrieval analysis in TKA has been conducted.On ...Retrieval analysis in total knee arthroplasty(TKA)has been little studied in the literature.A narrative review of the literature to understand the current importance of retrieval analysis in TKA has been conducted.On August 27,2024,a literature search was performed in PubMed using“TKA retrieval analysis”as keywords.A total of 160 articles were found,of which only 19 were analyzed because they were directly related to the subject of this article.Rotating-platform(mobile-bearing)TKA has no surface damage advantage over fixed-bearing TKA.TKAs with central locking mechanisms are more prone to debond from the cement mantle.No major wear of the polyethylene(PE)component in TKA using oxidized zirconium components occurs.Femoral components of cobalt-chromium roughen more than oxidized zirconium femoral components.The use of a polished tibial tray over an unpolished design is advised.At short-run assessment(15 months on average),antioxidant-stabilized highly crosslinked PE components are not clinically different in surface damage,density of crosslinking,or oxidation compared to standard remelted highly crosslinked PE components.A correlation between implant position and PE component surface damage has been reported.It shows the importance of optimizing component position to reduce PE component damage.Contemporary knee tumor megaendoprostheses show notable volumetric metal wear originated at the rotating hinge.Retrieval analysis in TKA renders relevant data on how different prosthetic designs described in the literature perform.Such information can help to improve future prosthetic designs to increase prosthetic survival.展开更多
Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–...Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.展开更多
This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi...This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.展开更多
Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation.Memory retrieval,as the only representation of memory content ...Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation.Memory retrieval,as the only representation of memory content and an active form of memory processing that induces memory reconsolidation,has attracted increasing attention in recent years.Although the molecular mechanisms specifc to memory retrievalinduced reconsolidation have been gradually revealed,an understanding of the time-dependent regulatory mechanisms of this process is still lacking.In this study,we applied a transcriptome analysis of memory retrieval at diferent time points in the recent memory stage.Diferential expression analysis and Short Time-series Expression Miner(STEM)depicting temporal gene expression patterns indicated that most diferential gene expression occurred at 48 h,and the STEM cluster showing the greatest transcriptional upregulation at 48 h demonstrated the most significant diference.We then screened the diferentially-expressed genes associated with that met the expression patterns of those cluster-identifed genes that have been reported to be involved in learning and memory processes in addition to dipeptidyl peptidase 9(DPP9).Further quantitative polymerase chain reaction verifcation and pharmacological intervention suggested that DPP9 is involved in 48-h fear memory retrieval and viral vector-mediated overexpression of DPP9 countered the 48-h retrieval-induced attenuation of fear memory.Taken together,our fndings suggest that temporal gene expression patterns are induced by recent memory retrieval and provide hitherto undocumented evidence of the role of DPP9 in the retrieval-induced reconsolidation of fear memory.展开更多
At present,the polymerase chain reaction(PCR)amplification-based file retrieval method is the mostcommonly used and effective means of DNA file retrieval.The number of orthogonal primers limitsthe number of files that...At present,the polymerase chain reaction(PCR)amplification-based file retrieval method is the mostcommonly used and effective means of DNA file retrieval.The number of orthogonal primers limitsthe number of files that can be accurately accessed,which in turn affects the density in a single oligo poolof digital DNA storage.In this paper,a multi-mode DNA sequence design method based on PCR file retrie-val in a single oligonucleotide pool is proposed for high-capacity DNA data storage.Firstly,by analyzingthe maximum number of orthogonal primers at each predicted primer length,it was found that the rela-tionship between primer length and the maximum available primer number does not increase linearly,and the maximum number of orthogonal primers is on the order of 10^(4).Next,this paper analyzes themaximum address space capacity of DNA sequences with different types of primer binding sites for filemapping.In the case where the capacity of the primer library is R(where R is even),the number ofaddress spaces that can be mapped by the single-primer DNA sequence design scheme proposed in thispaper is four times that of the previous one,and the two-level primer DNA sequence design scheme can reach [R/2·(R/2-1)]^(2)times.Finally,a multi-mode DNA sequence generation method is designed based onthe number of files to be stored in the oligonucleotide pool,in order to meet the requirements of the ran-dom retrieval of target files in an oligonucleotide pool with large-scale file numbers.The performance ofthe primers generated by the orthogonal primer library generator proposed in this paper is verified,andthe average Gibbs free energy of the most stable heterodimer formed between the orthogonal primersproduced is−1 kcal·(mol·L^(−1))^(−1)(1 kcal=4.184 kJ).At the same time,by selectively PCR-amplifying theDNA sequences of the two-level primer binding sites for random access,the target sequence can be accu-rately read with a minimum of 10^(3) reads,when the primer binding site sequences at different positionsare mutually different.This paper provides a pipeline for orthogonal primer library generation and multi-mode mapping schemes between files and primers,which can help achieve precise random access to filesin large-scale DNA oligo pools.展开更多
This study describes the use of the weighted multiplicative algebraic reconstruction technique(WMART)to obtain vertical ozone profiles from limb observations performed by the scanning imaging absorption spectrometer f...This study describes the use of the weighted multiplicative algebraic reconstruction technique(WMART)to obtain vertical ozone profiles from limb observations performed by the scanning imaging absorption spectrometer for atmospheric chartography(SCIAMACHY).This technique is based on SaskMART(the combination of the multiplicative algebraic reconstruction technique and SaskTRAN radiative transfer model),which was originally developed for optical spectrometer and infrared imaging system(OSIRIS)data.One of the objectives of this study was to obtain consistent ozone profiles from the two satellites.In this study,the WMART algorithm is combined with a radiative transfer model(SCIATRAN),as well as a set of measurement vectors comprising five Hartley pairing vectors(HPVs)and one Chappuis triplet vector(CTV),to retrieve ozone profiles in the altitude range of 10–69 km.Considering that the weighting factors in WMART have a significant effect on the retrievals,we propose a novel approach to calculate the pair/triplet weighting factors using wavelength weighting functions.The results of the application of the proposed ozone retrieval scheme are compared with the SCIAMACHY v3.5 ozone product by University of Bremen and validated against profiles derived from other passive satellite observations or measured by ozonesondes.Between 18 and 55 km,the retrieved ozone profiles typically agree with data from the SCIAMACHY ozone product within 5%for tropics and middle latitudes,whereas a negative deviation exists between 35 and 50 km for northern high latitudes,with a deviation of less than 10%above 50 km.Comparison of the retrieved profiles with microwave limb sounder(MLS)v5.0 indicates that the difference is within±5%between 18 and 55 km,and an agreement within 10%is achieved in other altitudes for tropics and middle latitudes.Comparison of the retrieved profiles with OSIRIS v7.1 indicates that the average deviation is within±5%between 20 and 59 km,and difference of approximately 10%is achieved below 20 km.Compared with ozonesondes data,a general validity of the retrievals is no more than 5%between 15 and 30 km.展开更多
Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To ...Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To address these problems,this paper proposes an enhanced Fully Homomorphic Encryption(FHE)algorithm based on an improved DGHV algorithm,coupled with an optimized ciphertext retrieval scheme.Our specific contributions are outlined as follows:First,we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data.Second,a triple-key encryption mechanism,which separates the data encryption key,retrieval authorization key,and retrieval key,is designed.Different keys are provided to different entities to run corresponding system functions.The key separation architecture proves particularly advantageous in multi-verifier coexistence scenarios,environments involving untrusted third-party retrieval services.Finally,the enhanced DGHV-based retrieval mechanism extends conventional functionality by enabling multi-keyword queries with similarity-ranked results,thereby significantly improving both the functionality and usability of the FHE system.展开更多
Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and...Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.展开更多
Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third...Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.展开更多
The advent of intracytoplasmic sperm injection,along with the realization that many men with azoospermia due to primary testicular failure may have a few spermatozoa in their testes,has resulted in the revolutionary p...The advent of intracytoplasmic sperm injection,along with the realization that many men with azoospermia due to primary testicular failure may have a few spermatozoa in their testes,has resulted in the revolutionary possibility of azoospermic men fathering their own genetic offspring.展开更多
Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention recently.However,with the increasi...Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention recently.However,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage resources.Moreover,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity relationships.To address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial relationships.The spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel dimension.We conduct quantitative and qualitative experiments on two different commonly used RSCIR datasets.Compared with traditional methods,our approach achieves an improvement of 3%-11% in sumR metric.Compared with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.展开更多
Oncological microdissection testicular sperm extraction(onco-micro-TESE)represents a significant breakthrough for patients with nonobstructive azoospermia(NOA)and a concomitant in situ testicular tumor,to be managed a...Oncological microdissection testicular sperm extraction(onco-micro-TESE)represents a significant breakthrough for patients with nonobstructive azoospermia(NOA)and a concomitant in situ testicular tumor,to be managed at the time of sperm retrieval.Onco-micro-TESE addresses the dual objectives of treating both infertility and the testicular tumor simultaneously.The technique is intricate,necessitating a comprehensive understanding of testicular anatomy,physiology,tumor biology,and advanced microsurgical methods.It aims to carefully extract viable spermatozoa while minimizing the risk of tumor dissemination.This review encapsulates the procedural intricacies,evaluates success determinants,including tumor pathology and spermatogenic tissue health,and discusses the implementation of imaging techniques for enhanced surgical precision.Ethical considerations are paramount,as the procedure implicates complex decision-making that weighs the potential oncological risks against the profound desire for fatherhood using the male gametes.The review aims to provide a holistic overview of onco-micro-TESE,detailing methodological advances,clinical outcomes,and the ethical landscape,thus offering an indispensable resource for clinicians navigating this multifaceted clinical scenario.展开更多
Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd cou...Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering behaviors.We believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and emergencies.Therefore,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective responses.To address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first time.Traditional image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval accuracy.While local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model performance.To overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event type.By calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval efficiency.To validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent studies.We conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of EDAN.In the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,while in the text-to-image retrieval task,it showed superior results on both R@10 and R@5 metrics.Additionally,EDAN excelled in the overall Rsummetric,achieving the best performance.Finally,ablation studies further verified the effectiveness of event-driven attention module.展开更多
Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moist...Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.展开更多
Literature Retrieval and writing is a fundamental course in information literacy.In the context of engineering education accreditation,this paper presents an Outcome-Based Education(OBE)teaching design for the course....Literature Retrieval and writing is a fundamental course in information literacy.In the context of engineering education accreditation,this paper presents an Outcome-Based Education(OBE)teaching design for the course.The instructional content is modularized,specific student learning outcomes are articulated,and a comprehensive evaluation system is established.This teaching design aims and contributes the enhancement of students’academic competencies and foster improved learning achievements.展开更多
We investigated the prognostic importance of noninvasive factors in predicting sperm retrieval failure in idiopathic nonobstructive azoospermia(iNOA).We studied 193 patients with nonobstructive azoospermia who underwe...We investigated the prognostic importance of noninvasive factors in predicting sperm retrieval failure in idiopathic nonobstructive azoospermia(iNOA).We studied 193 patients with nonobstructive azoospermia who underwent microsurgical testicular sperm extraction.The Chi-square test and Mann–Whitney U tests for clinical parameters and seminiferous tubule distribution were used for between-group comparisons.A logistic regression analysis was conducted to identify predictors of retrieval failure.Area under the receiver operating characteristic curve for each variable was evaluated,and the net clinical benefit was calculated using a clinical decision curve.Patients with iNOA had a lower sperm retrieval rate than those with known causes.Moreover,testicular volume was an independent factor affecting sperm extraction outcomes(odds ratio=0.79,P<0.05).The testicular volume cut-off value was 6.5 ml(area under the curve:0.694).The patients with iNOA were categorized into two groups on the basis of the distribution of seminiferous tubules observed.The sperm retrieval rate and testicular volume were significantly different between the groups with a uniform or heterogeneous tubule distribution.There was also a significant association between a uniform tubule distribution and testicular volume.In conclusion,a testicular volume of more than 6.5 ml effectively predicts microsurgical testicular sperm extraction failure due to a uniform tubule distribution in patients with iNOA.展开更多
Background Transvaginal oocyte retrieval is frequently followed by adverse events related to anesthesia and the procedure.Some research showed that transcutaneous electrical acupoint stimulation(TEAS)can relieve intra...Background Transvaginal oocyte retrieval is frequently followed by adverse events related to anesthesia and the procedure.Some research showed that transcutaneous electrical acupoint stimulation(TEAS)can relieve intraoperative pain and postoperative nausea.Objective This study examined whether TEAS can alleviate pain and relieve adverse symptoms after oocyte retrieval.Design,setting,participants and interventions Altogether 128 patients were randomly divided into the TEAS group and the mock TEAS group.The two groups received a 30-minute-long TEAS or mock TEAS treatment that began 30 min after oocyte retrieval.Main outcome measures The primary outcome was the visual analog scale(VAS)pain score.Secondary outcomes were pressure pain threshold,McGill score,pain rating index(PRI),present pain intensity(PPI),VAS stress score,VAS anxiety score,and postoperative adverse symptoms.Results The baseline characteristics of the two groups were comparable(P>0.05).The VAS pain scores of the TEAS group were lower than those of the mock TEAS group at 60 and 90 min after oocyte retrieval(P<0.05).The McGill score,PRI and PPI in the TEAS group were significantly lower than those in the control group at 60 min after oocyte retrieval(P<0.05).However,the two groups had equivalent beneficial effects regarding the negative emotions,such as nervousness and anxiety(P>0.05).The TEAS group was superior to the mock TEAS group for relieving postoperative adverse symptoms(P<0.05).Conclusion TEAS treatment can relieve postoperative pain and postoperative adverse symptoms for patients undergoing oocyte retrieval.展开更多
基金funded by the Institute of InformationTechnology,VietnamAcademy of Science and Technology(project number CSCL02.02/22-23)“Research and Development of Methods for Searching Similar Trademark Images Using Machine Learning to Support Trademark Examination in Vietnam”.
文摘Image-based similar trademark retrieval is a time-consuming and labor-intensive task in the trademark examination process.This paper aims to support trademark examiners by training Deep Convolutional Neural Network(DCNN)models for effective Trademark Image Retrieval(TIR).To achieve this goal,we first develop a novel labeling method that automatically generates hundreds of thousands of labeled similar and dissimilar trademark image pairs using accompanying data fields such as citation lists,Vienna classification(VC)codes,and trademark ownership information.This approach eliminates the need for manual labeling and provides a large-scale dataset suitable for training deep learning models.We then train DCNN models based on Siamese and Triplet architectures,evaluating various feature extractors to determine the most effective configuration.Furthermore,we present an Adapted Contrastive Loss Function(ACLF)for the trademark retrieval task,specifically engineered to mitigate the influence of noisy labels found in automatically created datasets.Experimental results indicate that our proposed model(Efficient-Net_v21_Siamese)performs best at both True Negative Rate(TNR)threshold levels,TNR 0.9 and TNR 0.95,with==respective True Positive Rates(TPRs)of 77.7%and 70.8%and accuracies of 83.9%and 80.4%.Additionally,when testing on the public trademark dataset METU_v2,our model achieves a normalized average rank(NAR)of 0.0169,outperforming the current state-of-the-art(SOTA)model.Based on these findings,we estimate that considering only approximately 10%of the returned trademarks would be sufficient,significantly reducing the review time.Therefore,the paper highlights the potential of utilizing national trademark data to enhance the accuracy and efficiency of trademark retrieval systems,ultimately supporting trademark examiners in their evaluation tasks.
基金supported by the National Natural Science Foundation of China(T2394531)the National Key R&D Program of China(2024YFF1206500)+1 种基金the Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)ZJ Lab,and the Shanghai Center for Brain Science and Brain-Inspired Technology,China.
文摘DDeeaarr EEddiittoorr,,The encoding and retrieval of emotional memories demands intricate interplay within the limbic network,where the network state is subject to significant reconfiguration by learning-induced plasticity,behavioral state,and contextual information[1].
基金The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University,Kingdom of Saudi Arabia,for funding this work through the Small Research Group Project under Grant Number RGP.1/316/45.
文摘Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various domains.The image retrieval system mainly relies on the efficiency and accuracy of the classification models.This research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and CBIR.The novelty of the proposed EffNet-CNN model includes the integration of different techniques and modifications.The model includes the Mahalanobis distance metric for feature matching,which enhances the similarity measurements.The model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature representations.The EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 datasets.The model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior performance.The results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.
文摘Retrieval analysis in total knee arthroplasty(TKA)has been little studied in the literature.A narrative review of the literature to understand the current importance of retrieval analysis in TKA has been conducted.On August 27,2024,a literature search was performed in PubMed using“TKA retrieval analysis”as keywords.A total of 160 articles were found,of which only 19 were analyzed because they were directly related to the subject of this article.Rotating-platform(mobile-bearing)TKA has no surface damage advantage over fixed-bearing TKA.TKAs with central locking mechanisms are more prone to debond from the cement mantle.No major wear of the polyethylene(PE)component in TKA using oxidized zirconium components occurs.Femoral components of cobalt-chromium roughen more than oxidized zirconium femoral components.The use of a polished tibial tray over an unpolished design is advised.At short-run assessment(15 months on average),antioxidant-stabilized highly crosslinked PE components are not clinically different in surface damage,density of crosslinking,or oxidation compared to standard remelted highly crosslinked PE components.A correlation between implant position and PE component surface damage has been reported.It shows the importance of optimizing component position to reduce PE component damage.Contemporary knee tumor megaendoprostheses show notable volumetric metal wear originated at the rotating hinge.Retrieval analysis in TKA renders relevant data on how different prosthetic designs described in the literature perform.Such information can help to improve future prosthetic designs to increase prosthetic survival.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030708,42375138,42030608,42105128,42075079)the Opening Foundation of Key Laboratory of Atmospheric Sounding,China Meteorological Administration(CMA),and the CMA Research Center on Meteorological Observation Engineering Technology(Grant No.U2021Z03),and the Opening Foundation of the Key Laboratory of Atmospheric Chemistry,CMA(Grant No.2022B02)。
文摘Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.
基金funded by the Deanship of Research and Graduate Studies at King Khalid University through small group research under grant number RGP1/278/45.
文摘This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.
基金supported by the STI2030-Major Projects(2022ZD0204900)the National Natural Science Foundation of China(32071029 and 32271080)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32020200)the Yunnan Provincial Science and Technology Department(202402AA310014).
文摘Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation.Memory retrieval,as the only representation of memory content and an active form of memory processing that induces memory reconsolidation,has attracted increasing attention in recent years.Although the molecular mechanisms specifc to memory retrievalinduced reconsolidation have been gradually revealed,an understanding of the time-dependent regulatory mechanisms of this process is still lacking.In this study,we applied a transcriptome analysis of memory retrieval at diferent time points in the recent memory stage.Diferential expression analysis and Short Time-series Expression Miner(STEM)depicting temporal gene expression patterns indicated that most diferential gene expression occurred at 48 h,and the STEM cluster showing the greatest transcriptional upregulation at 48 h demonstrated the most significant diference.We then screened the diferentially-expressed genes associated with that met the expression patterns of those cluster-identifed genes that have been reported to be involved in learning and memory processes in addition to dipeptidyl peptidase 9(DPP9).Further quantitative polymerase chain reaction verifcation and pharmacological intervention suggested that DPP9 is involved in 48-h fear memory retrieval and viral vector-mediated overexpression of DPP9 countered the 48-h retrieval-induced attenuation of fear memory.Taken together,our fndings suggest that temporal gene expression patterns are induced by recent memory retrieval and provide hitherto undocumented evidence of the role of DPP9 in the retrieval-induced reconsolidation of fear memory.
基金supported by the fund from Tianjin Municipal Science and Technology Bureau(22JCYBJC01390).
文摘At present,the polymerase chain reaction(PCR)amplification-based file retrieval method is the mostcommonly used and effective means of DNA file retrieval.The number of orthogonal primers limitsthe number of files that can be accurately accessed,which in turn affects the density in a single oligo poolof digital DNA storage.In this paper,a multi-mode DNA sequence design method based on PCR file retrie-val in a single oligonucleotide pool is proposed for high-capacity DNA data storage.Firstly,by analyzingthe maximum number of orthogonal primers at each predicted primer length,it was found that the rela-tionship between primer length and the maximum available primer number does not increase linearly,and the maximum number of orthogonal primers is on the order of 10^(4).Next,this paper analyzes themaximum address space capacity of DNA sequences with different types of primer binding sites for filemapping.In the case where the capacity of the primer library is R(where R is even),the number ofaddress spaces that can be mapped by the single-primer DNA sequence design scheme proposed in thispaper is four times that of the previous one,and the two-level primer DNA sequence design scheme can reach [R/2·(R/2-1)]^(2)times.Finally,a multi-mode DNA sequence generation method is designed based onthe number of files to be stored in the oligonucleotide pool,in order to meet the requirements of the ran-dom retrieval of target files in an oligonucleotide pool with large-scale file numbers.The performance ofthe primers generated by the orthogonal primer library generator proposed in this paper is verified,andthe average Gibbs free energy of the most stable heterodimer formed between the orthogonal primersproduced is−1 kcal·(mol·L^(−1))^(−1)(1 kcal=4.184 kJ).At the same time,by selectively PCR-amplifying theDNA sequences of the two-level primer binding sites for random access,the target sequence can be accu-rately read with a minimum of 10^(3) reads,when the primer binding site sequences at different positionsare mutually different.This paper provides a pipeline for orthogonal primer library generation and multi-mode mapping schemes between files and primers,which can help achieve precise random access to filesin large-scale DNA oligo pools.
基金supported by the National Science Foundations of China(No.61905256)the National Key Research and Development Program of China(No.2019YFC0214702)the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020439)。
文摘This study describes the use of the weighted multiplicative algebraic reconstruction technique(WMART)to obtain vertical ozone profiles from limb observations performed by the scanning imaging absorption spectrometer for atmospheric chartography(SCIAMACHY).This technique is based on SaskMART(the combination of the multiplicative algebraic reconstruction technique and SaskTRAN radiative transfer model),which was originally developed for optical spectrometer and infrared imaging system(OSIRIS)data.One of the objectives of this study was to obtain consistent ozone profiles from the two satellites.In this study,the WMART algorithm is combined with a radiative transfer model(SCIATRAN),as well as a set of measurement vectors comprising five Hartley pairing vectors(HPVs)and one Chappuis triplet vector(CTV),to retrieve ozone profiles in the altitude range of 10–69 km.Considering that the weighting factors in WMART have a significant effect on the retrievals,we propose a novel approach to calculate the pair/triplet weighting factors using wavelength weighting functions.The results of the application of the proposed ozone retrieval scheme are compared with the SCIAMACHY v3.5 ozone product by University of Bremen and validated against profiles derived from other passive satellite observations or measured by ozonesondes.Between 18 and 55 km,the retrieved ozone profiles typically agree with data from the SCIAMACHY ozone product within 5%for tropics and middle latitudes,whereas a negative deviation exists between 35 and 50 km for northern high latitudes,with a deviation of less than 10%above 50 km.Comparison of the retrieved profiles with microwave limb sounder(MLS)v5.0 indicates that the difference is within±5%between 18 and 55 km,and an agreement within 10%is achieved in other altitudes for tropics and middle latitudes.Comparison of the retrieved profiles with OSIRIS v7.1 indicates that the average deviation is within±5%between 20 and 59 km,and difference of approximately 10%is achieved below 20 km.Compared with ozonesondes data,a general validity of the retrievals is no more than 5%between 15 and 30 km.
基金supported by the Innovation Program for Quantum Science and technology(2021ZD0301300)supported by the Fundamental Research Funds for the Central Universities(Nos.3282024046,3282024052,3282024058,3282023017).
文摘Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To address these problems,this paper proposes an enhanced Fully Homomorphic Encryption(FHE)algorithm based on an improved DGHV algorithm,coupled with an optimized ciphertext retrieval scheme.Our specific contributions are outlined as follows:First,we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data.Second,a triple-key encryption mechanism,which separates the data encryption key,retrieval authorization key,and retrieval key,is designed.Different keys are provided to different entities to run corresponding system functions.The key separation architecture proves particularly advantageous in multi-verifier coexistence scenarios,environments involving untrusted third-party retrieval services.Finally,the enhanced DGHV-based retrieval mechanism extends conventional functionality by enabling multi-keyword queries with similarity-ranked results,thereby significantly improving both the functionality and usability of the FHE system.
基金supported by the Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences(2021-I2M-1-033)the National Key Research and Development Program of China(2022YFF0711900).
文摘Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.
基金supported by the NationalNatural Science Foundation of China(No.61862041).
文摘Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.
文摘The advent of intracytoplasmic sperm injection,along with the realization that many men with azoospermia due to primary testicular failure may have a few spermatozoa in their testes,has resulted in the revolutionary possibility of azoospermic men fathering their own genetic offspring.
基金supported by the National Key R&D Program of China(No.2022ZD0118402)。
文摘Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention recently.However,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage resources.Moreover,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity relationships.To address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial relationships.The spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel dimension.We conduct quantitative and qualitative experiments on two different commonly used RSCIR datasets.Compared with traditional methods,our approach achieves an improvement of 3%-11% in sumR metric.Compared with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.
基金supported by the National Natural Science Foundation of China(No.82371633)Peking University Clinical Scientist Training Program and the Fundamental Research Funds for the Central University(BMU2023PYJ H012).
文摘Oncological microdissection testicular sperm extraction(onco-micro-TESE)represents a significant breakthrough for patients with nonobstructive azoospermia(NOA)and a concomitant in situ testicular tumor,to be managed at the time of sperm retrieval.Onco-micro-TESE addresses the dual objectives of treating both infertility and the testicular tumor simultaneously.The technique is intricate,necessitating a comprehensive understanding of testicular anatomy,physiology,tumor biology,and advanced microsurgical methods.It aims to carefully extract viable spermatozoa while minimizing the risk of tumor dissemination.This review encapsulates the procedural intricacies,evaluates success determinants,including tumor pathology and spermatogenic tissue health,and discusses the implementation of imaging techniques for enhanced surgical precision.Ethical considerations are paramount,as the procedure implicates complex decision-making that weighs the potential oncological risks against the profound desire for fatherhood using the male gametes.The review aims to provide a holistic overview of onco-micro-TESE,detailing methodological advances,clinical outcomes,and the ethical landscape,thus offering an indispensable resource for clinicians navigating this multifaceted clinical scenario.
基金sponsored by Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01A19).
文摘Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering behaviors.We believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and emergencies.Therefore,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective responses.To address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first time.Traditional image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval accuracy.While local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model performance.To overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event type.By calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval efficiency.To validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent studies.We conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of EDAN.In the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,while in the text-to-image retrieval task,it showed superior results on both R@10 and R@5 metrics.Additionally,EDAN excelled in the overall Rsummetric,achieving the best performance.Finally,ablation studies further verified the effectiveness of event-driven attention module.
基金Foundation of Key Laboratory of Smart Earth(KF2023ZD03-02)China Meteorological Administration Innovation development project(CXFZ2025J116)+1 种基金National Natural Science Foundation of China(42205197)Basic Research Fund of CAMS(2022Y023,2022Y025)。
文摘Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.
文摘Literature Retrieval and writing is a fundamental course in information literacy.In the context of engineering education accreditation,this paper presents an Outcome-Based Education(OBE)teaching design for the course.The instructional content is modularized,specific student learning outcomes are articulated,and a comprehensive evaluation system is established.This teaching design aims and contributes the enhancement of students’academic competencies and foster improved learning achievements.
文摘We investigated the prognostic importance of noninvasive factors in predicting sperm retrieval failure in idiopathic nonobstructive azoospermia(iNOA).We studied 193 patients with nonobstructive azoospermia who underwent microsurgical testicular sperm extraction.The Chi-square test and Mann–Whitney U tests for clinical parameters and seminiferous tubule distribution were used for between-group comparisons.A logistic regression analysis was conducted to identify predictors of retrieval failure.Area under the receiver operating characteristic curve for each variable was evaluated,and the net clinical benefit was calculated using a clinical decision curve.Patients with iNOA had a lower sperm retrieval rate than those with known causes.Moreover,testicular volume was an independent factor affecting sperm extraction outcomes(odds ratio=0.79,P<0.05).The testicular volume cut-off value was 6.5 ml(area under the curve:0.694).The patients with iNOA were categorized into two groups on the basis of the distribution of seminiferous tubules observed.The sperm retrieval rate and testicular volume were significantly different between the groups with a uniform or heterogeneous tubule distribution.There was also a significant association between a uniform tubule distribution and testicular volume.In conclusion,a testicular volume of more than 6.5 ml effectively predicts microsurgical testicular sperm extraction failure due to a uniform tubule distribution in patients with iNOA.
基金funded by the Science and Technology Foundation of Sichuan Province (No.2020JDJQ0051)National Natural Science Foundation of China (No.82174517)
文摘Background Transvaginal oocyte retrieval is frequently followed by adverse events related to anesthesia and the procedure.Some research showed that transcutaneous electrical acupoint stimulation(TEAS)can relieve intraoperative pain and postoperative nausea.Objective This study examined whether TEAS can alleviate pain and relieve adverse symptoms after oocyte retrieval.Design,setting,participants and interventions Altogether 128 patients were randomly divided into the TEAS group and the mock TEAS group.The two groups received a 30-minute-long TEAS or mock TEAS treatment that began 30 min after oocyte retrieval.Main outcome measures The primary outcome was the visual analog scale(VAS)pain score.Secondary outcomes were pressure pain threshold,McGill score,pain rating index(PRI),present pain intensity(PPI),VAS stress score,VAS anxiety score,and postoperative adverse symptoms.Results The baseline characteristics of the two groups were comparable(P>0.05).The VAS pain scores of the TEAS group were lower than those of the mock TEAS group at 60 and 90 min after oocyte retrieval(P<0.05).The McGill score,PRI and PPI in the TEAS group were significantly lower than those in the control group at 60 min after oocyte retrieval(P<0.05).However,the two groups had equivalent beneficial effects regarding the negative emotions,such as nervousness and anxiety(P>0.05).The TEAS group was superior to the mock TEAS group for relieving postoperative adverse symptoms(P<0.05).Conclusion TEAS treatment can relieve postoperative pain and postoperative adverse symptoms for patients undergoing oocyte retrieval.
