Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of upper and lower motor neurons in the brainstem and spinal cord,leading to muscle weakness,para...Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of upper and lower motor neurons in the brainstem and spinal cord,leading to muscle weakness,paralysis,and respiratory failure (Morgan and Orrell,2016).展开更多
Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light grad...Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.展开更多
Do you like animals?Animals are cute.Some people like loyal dogs,some like adorable cats,and others prefer fluffy bunnies.But my favorite animals are naughty hamsters because they are full of energy.With just a little...Do you like animals?Animals are cute.Some people like loyal dogs,some like adorable cats,and others prefer fluffy bunnies.But my favorite animals are naughty hamsters because they are full of energy.With just a little food and water,they can thrive.Plus,they are really affordable,unlike cats and dogs that can cost several hundred or even over a thousand yuan.展开更多
Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of di...Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model.展开更多
Dear Editor,I am responding to Zou and Li's,The missing perilymph sign on MRI indicates a perilymphatic fistula:A case report Zou J,Li H.Journal of Otology,2025, 20(1):1-4.https://doi.org/10.26599/JOTO.2025.954000...Dear Editor,I am responding to Zou and Li's,The missing perilymph sign on MRI indicates a perilymphatic fistula:A case report Zou J,Li H.Journal of Otology,2025, 20(1):1-4.https://doi.org/10.26599/JOTO.2025.9540001 proposing the"missing perilymph"sign on MRI as a novel radiological indicator of perilymphatic fistula(PLF).This study adds to the growing body of work seeking objective,non-invasive diagnostic methods for PLF,a condition that has long eluded definitive radiological confirmation.The avoidance of gadolinium contrast in the imaging technique is an additional strength,given increasing awareness of gadoliniumassociated risks (Starekova et al.,2024).展开更多
Background Chickens and ducks are vital sources of animal protein for humans.Recent pangenome studies suggest that a single genome is insufficient to represent the genetic information of a species,highlighting the nee...Background Chickens and ducks are vital sources of animal protein for humans.Recent pangenome studies suggest that a single genome is insufficient to represent the genetic information of a species,highlighting the need for more comprehensive genomes.The bird genome has more than tens of microchromosomes,but comparative genomics,annotations,and the discovery of variations are hindered by inadequate telomere-to-telomere level assemblies.We aim to complete the chicken and duck genomes,recover missing genes,and reveal common and unique chromosomal features between birds.Results The near telomere-to-telomere genomes of Silkie Gallus gallus and Mallard Anas platyrhynchos were successfully assembled via multiple high-coverage complementary technologies,with quality values of 36.65 and 44.17 for Silkie and Mallard,respectively;and BUSCO scores of 96.55%and 96.97%for Silkie and Mallard,respectively;the mapping rates reached over 99.52%for both assembled genomes,these evaluation results ensured high completeness and accuracy.We successfully annotated 20,253 and 19,621 protein-coding genes for Silkie and Mallard,respectively,and assembled gap-free sex chromosomes in Mallard for the first time.Comparative analysis revealed that microchromosomes differ from macrochromosomes in terms of GC content,repetitive sequence abundance,gene density,and levels of 5mC methylation.Different types of arrangements of centromeric repeat sequence centromeres exist in both Silkie and the Mallard genomes,with Mallard centromeres being invaded by CR1.The highly heterochromatic W chromosome,which serves as a refuge for ERVs,contains disproportionately long ERVs.Both Silkie and the Mallard genomes presented relatively high 5mC methylation levels on sex chromosomes and microchromosomes,and the telomeres and centromeres presented significantly higher 5mC methylation levels than the whole genome.Finally,we recovered 325 missing genes via our new genomes and annotated TNFA in Mallard for the first time,revealing conserved protein structures and tissue-specific expression.Conclusions The near telomere-to-telomere assemblies in Mallard and Silkie,with the first gap-free sex chromosomes in ducks,significantly enhanced our understanding of genetic structures in birds,specifically highlighting the distinctive chromosome features between the chicken and duck genomes.This foundational work also provides a series of newly identified missing genes for further investigation.展开更多
0 INTRODUCTION Changbaishan volcanism,located on the border of China and North Korea,has been a subject of extensive research due to its unique geological features and active volcanic history(Wan et al.,2024).Two prim...0 INTRODUCTION Changbaishan volcanism,located on the border of China and North Korea,has been a subject of extensive research due to its unique geological features and active volcanic history(Wan et al.,2024).Two primary models have been proposed to explain the origin of Changbaishan volcanism(CV).展开更多
Background:As the digital age progresses,fear of missing out(FoMO)is becoming increasingly common,and the impact factor of FOMO needs to be further investigated.This study aims to explore the relationship between psyc...Background:As the digital age progresses,fear of missing out(FoMO)is becoming increasingly common,and the impact factor of FOMO needs to be further investigated.This study aims to explore the relationship between psychological security(PS)and FoMO by analyzing the mediating role of social networking addiction(SNA)and the moderating role of social self-efficacy(SSE).Methods:We collected a sample of 1181 college students(with a mean age of 19.671.38 years)from five universities in a province of China's Mainland through cluster sampling.Data±were gathered using the psychological security questionnaire(PSQ),the FoMO scale,the SNA scale,and the perceived social self-efficacy(PSSE)scale.Data analysis employed independent-sample t-tests,one-way analysis of variance(ANOVA),Harman’s single-factor test,confirmatory factor analysis,and moderated mediation analysis.Results:The results of the mediation model and moderated mediation model analyses showed the following key findings:(1)PS is significantly negatively correlated with FoMO;(2)SNA mediates the relationship between PS and FoMO;(3)SSE positively moderates the relationship between PS and FoMO;and(4)SSE also positively moderates the relationship between PS and SNA.Conclusion:University students’PS not only directly impacts FoMO but also indirectly influences it through SNA.Additionally,SSE positively moderates both the direct path and the first half of the mediation path,indicating that enhancing students’PS and SSE can help alleviate their SNA and FoMO,promoting their psychological and behavioral well-being.展开更多
Dear Editor,I am writing in response to Jamil's letter,"Interpretative Challenges of the Missing Perilymph'Sign in PLF Diagnosis."I concur with the author's emphasis on the necessity for cautious...Dear Editor,I am writing in response to Jamil's letter,"Interpretative Challenges of the Missing Perilymph'Sign in PLF Diagnosis."I concur with the author's emphasis on the necessity for cautious interpretation of low-signal areas as evidence of active perilymph leakage,requiring correlation with clinical findings,surgical confirmation,and longitudinal imaging changes.展开更多
Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.T...Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.These studies,however,have the following limitations:1)effective supervision is neglected for missing data across different fault types and 2)imbalance in missing rates among fault types results in inadequate learning during model training.To overcome the above limitations,this paper proposes a dynamic relative advantagedriven multi-fault synergistic diagnosis method to accomplish accurate fault diagnosis of motors under imbalanced missing data rates.Firstly,a cross-fault-type generalized synergistic diagnostic strategy is established based on variational information bottleneck theory,which is able to ensure sufficient supervision in handling missing data.Then,a dynamic relative advantage assessment technique is designed to reduce diagnostic accuracy decay caused by imbalanced missing data rates.The proposed method is validated using multi-sensor data from motor fault simulation experiments,and experimental results demonstrate its effectiveness and superiority in improving diagnostic accuracy and generalization under imbalanced missing data rates.展开更多
The physiological structure and growth of trees in extreme environments(freezing temperatures,prolonged drought,wildfires,pest infestations,and diseases)can be inhibited,including radial growth,and stagnant growth or ...The physiological structure and growth of trees in extreme environments(freezing temperatures,prolonged drought,wildfires,pest infestations,and diseases)can be inhibited,including radial growth,and stagnant growth or missing annual rings is highly possible.In this study,we analyzed the radial growth of Siberian larch(Larix sibirica)in the Hongshanzui area of the Altai Mountains,China.The overall missing ring rate at the sampling point was 2.39%,with years with the highest missing rings since meteorological site data were available(1960)identified as 1960,1961,1971,1973,1985,1987,and 1995.Radial growth in high altitudes was mainly affected by temperatures in May and June(average temperature,average minimum temperature,and average maximum temperature).Frequent periods of freezing may lead to missing annual rings.However,while Larix sibirica shows resilience after prolonged freezing temperatures,it still requires time for the trees to return to normal growth levels.展开更多
Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a sign...Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.展开更多
Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This s...Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This study aims to investigate the issue of missing data in extensive TBM datasets.Through a comprehensive literature review,we analyze the mechanism of missing TBM data and compare different imputation methods,including statistical analysis and machine learning algorithms.We also examine the impact of various missing patterns and rates on the efficacy of these methods.Finally,we propose a dynamic interpolation strategy tailored for TBM engineering sites.The research results show that K-Nearest Neighbors(KNN)and Random Forest(RF)algorithms can achieve good interpolation results;As the missing rate increases,the interpolation effect of different methods will decrease;The interpolation effect of block missing is poor,followed by mixed missing,and the interpolation effect of sporadic missing is the best.On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects,applicable in ML scenarios such as parameter optimization,attitude warning,and pressure prediction.These findings contribute to enhancing the efficiency of TBM missing data processing,offering more effective support for large-scale TBM monitoring datasets.展开更多
Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attentio...Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.展开更多
The Central Institute of Forensic Science(CIFS)has been providing DNA testing services to Thai people since 2002.Bone accounts for majority of the biological specimens tested,constituting approximately 26%in total evi...The Central Institute of Forensic Science(CIFS)has been providing DNA testing services to Thai people since 2002.Bone accounts for majority of the biological specimens tested,constituting approximately 26%in total evidence.DNA recovery from the bone is challenging owing to degradation and the presence of inhibitors.Therefore,guidelines for bone selection,extraction,and DNA typing are essential for the routine laboratory of CIFS to maximize DNA yield,and minimize time and cost.In this study,we extracted three types of bones:femur,occipital,and petrous,from 12 bodies using a modified organic extraction and silica-based method.The success rate of the Short Tandem Repeat(STR)typing was determined through the number of reportable loci.Furthermore,analysis of mitochondrial DNA(mtDNA)was performed using the massively parallel sequencing technique.Coverage and variant analyses of all samples were evaluated.The results indicate that the femur exhibits the highest success rate in STR typing.The results,in decreasing order,are as follows:femur>petrous>occipital.We determined that silica-based extraction is the most efficient technique for the STR typing;however,modified organic extraction can be used as an alternative method in obtaining mtDNA.The outcome from this study could serve as a guide for identifying human remains and missing persons in the CIFS laboratory,as well as other Thai forensic laboratories.展开更多
Fear of missing out(FoMO)is a unique term introduced in 2004 to describe a phenomenon observed on social networking sites.FoMO includes two processes;firstly,perception of missing out,followed up with a compulsive beh...Fear of missing out(FoMO)is a unique term introduced in 2004 to describe a phenomenon observed on social networking sites.FoMO includes two processes;firstly,perception of missing out,followed up with a compulsive behavior to maintain these social connections.We are interested in understanding the complex construct of FoMO and its relations to the need to belong and form stable interpersonal relationships.It is associated with a range of negative life experiences and feelings,due to it being considered a problematic attachment to social media.We have provided a general review of the literature and have summarized the findings in relation to mental health,social functioning,sleep,academic performance and productivity,neuro-developmental disorders,and physical well-being.We have also discussed the treatment options available for FoMo based on cognitive behavior therapy.It imperative that new findings on FoMO are communicated to the clinical community as it has diagnostic implications and could be a confounding variable in those who do not respond to treatment as usual.展开更多
Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real proc...Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real processes, the available data set is usually obtained with missing values. To overcome the shortcomings of global modeling and missing data values, a new modeling method is proposed. Firstly, an incomplete data set with missing values is partitioned into several clusters by a K-means with soft constraints （KSC） algorithm, which incorporates soft constraints to enable clustering with missing values. Then a local model based on each group is developed by using SVR algorithm, which adopts a missing value insensitive （MVI） kernel to investigate the missing value estimation problem. For each local model, its valid area is gotten as well. Simulation results prove the effectiveness of the current local model and the estimation algorithm.展开更多
The first thunderstorm weather appeared in southern Shenyang on May 2,2010 and did not bring about severe lightning disaster for Shenyang region,but forecast service had poor effect without forecasting thunderstorm we...The first thunderstorm weather appeared in southern Shenyang on May 2,2010 and did not bring about severe lightning disaster for Shenyang region,but forecast service had poor effect without forecasting thunderstorm weather accurately.In our paper,the reasons for missing report of this thunderstorm weather were analyzed,and analysis on thunderstorm potential was carried out by means of mesoscale analysis technique,providing technical index and vantage point for the prediction of thunderstorm potential.The results showed that the reasons for missing report of this weather process were as follows:surface temperature at prophase was constantly lower going against the development of convective weather;the interpreting and analyzing ability of numerical forecast product should be improved;the forecast result of T639 model was better than that of Japanese numerical forecast;the study and application of mesoscale analysis technique should be strengthened,and this service was formally developed after thunderstorm weather on June 1,2010.展开更多
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
文摘Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of upper and lower motor neurons in the brainstem and spinal cord,leading to muscle weakness,paralysis,and respiratory failure (Morgan and Orrell,2016).
基金supported by the National Natural Science Foundation of China(No.12475340 and 12375350)Special Branch project of South Taihu Lakethe Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202456326).
文摘Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.
文摘Do you like animals?Animals are cute.Some people like loyal dogs,some like adorable cats,and others prefer fluffy bunnies.But my favorite animals are naughty hamsters because they are full of energy.With just a little food and water,they can thrive.Plus,they are really affordable,unlike cats and dogs that can cost several hundred or even over a thousand yuan.
基金supported by the National Natural Science Foundation of China(Grant Nos.51991395 and 42293355)geological survey project of China Geological Survey:Support for Geo-hazard monitoring,early warning and prevention(Grant No.DD20230085).
文摘Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model.
文摘Dear Editor,I am responding to Zou and Li's,The missing perilymph sign on MRI indicates a perilymphatic fistula:A case report Zou J,Li H.Journal of Otology,2025, 20(1):1-4.https://doi.org/10.26599/JOTO.2025.9540001 proposing the"missing perilymph"sign on MRI as a novel radiological indicator of perilymphatic fistula(PLF).This study adds to the growing body of work seeking objective,non-invasive diagnostic methods for PLF,a condition that has long eluded definitive radiological confirmation.The avoidance of gadolinium contrast in the imaging technique is an additional strength,given increasing awareness of gadoliniumassociated risks (Starekova et al.,2024).
基金supported by the National Key R&D Program of China(2022YFF1000100,2023YFD1300300)the National Natural Science Foundation of China(31572388,31972525)the China Agriculture Research System of MOF and MARA(CARS-41)。
文摘Background Chickens and ducks are vital sources of animal protein for humans.Recent pangenome studies suggest that a single genome is insufficient to represent the genetic information of a species,highlighting the need for more comprehensive genomes.The bird genome has more than tens of microchromosomes,but comparative genomics,annotations,and the discovery of variations are hindered by inadequate telomere-to-telomere level assemblies.We aim to complete the chicken and duck genomes,recover missing genes,and reveal common and unique chromosomal features between birds.Results The near telomere-to-telomere genomes of Silkie Gallus gallus and Mallard Anas platyrhynchos were successfully assembled via multiple high-coverage complementary technologies,with quality values of 36.65 and 44.17 for Silkie and Mallard,respectively;and BUSCO scores of 96.55%and 96.97%for Silkie and Mallard,respectively;the mapping rates reached over 99.52%for both assembled genomes,these evaluation results ensured high completeness and accuracy.We successfully annotated 20,253 and 19,621 protein-coding genes for Silkie and Mallard,respectively,and assembled gap-free sex chromosomes in Mallard for the first time.Comparative analysis revealed that microchromosomes differ from macrochromosomes in terms of GC content,repetitive sequence abundance,gene density,and levels of 5mC methylation.Different types of arrangements of centromeric repeat sequence centromeres exist in both Silkie and the Mallard genomes,with Mallard centromeres being invaded by CR1.The highly heterochromatic W chromosome,which serves as a refuge for ERVs,contains disproportionately long ERVs.Both Silkie and the Mallard genomes presented relatively high 5mC methylation levels on sex chromosomes and microchromosomes,and the telomeres and centromeres presented significantly higher 5mC methylation levels than the whole genome.Finally,we recovered 325 missing genes via our new genomes and annotated TNFA in Mallard for the first time,revealing conserved protein structures and tissue-specific expression.Conclusions The near telomere-to-telomere assemblies in Mallard and Silkie,with the first gap-free sex chromosomes in ducks,significantly enhanced our understanding of genetic structures in birds,specifically highlighting the distinctive chromosome features between the chicken and duck genomes.This foundational work also provides a series of newly identified missing genes for further investigation.
基金support from the National Natural Science Foundation of China(No.42276049)。
文摘0 INTRODUCTION Changbaishan volcanism,located on the border of China and North Korea,has been a subject of extensive research due to its unique geological features and active volcanic history(Wan et al.,2024).Two primary models have been proposed to explain the origin of Changbaishan volcanism(CV).
基金supported by the Jiangxi Province Think Tank Research Project(ZK202406)the 2023 Jiangxi Provincial Health Commission Research Project(52524010)。
文摘Background:As the digital age progresses,fear of missing out(FoMO)is becoming increasingly common,and the impact factor of FOMO needs to be further investigated.This study aims to explore the relationship between psychological security(PS)and FoMO by analyzing the mediating role of social networking addiction(SNA)and the moderating role of social self-efficacy(SSE).Methods:We collected a sample of 1181 college students(with a mean age of 19.671.38 years)from five universities in a province of China's Mainland through cluster sampling.Data±were gathered using the psychological security questionnaire(PSQ),the FoMO scale,the SNA scale,and the perceived social self-efficacy(PSSE)scale.Data analysis employed independent-sample t-tests,one-way analysis of variance(ANOVA),Harman’s single-factor test,confirmatory factor analysis,and moderated mediation analysis.Results:The results of the mediation model and moderated mediation model analyses showed the following key findings:(1)PS is significantly negatively correlated with FoMO;(2)SNA mediates the relationship between PS and FoMO;(3)SSE positively moderates the relationship between PS and FoMO;and(4)SSE also positively moderates the relationship between PS and SNA.Conclusion:University students’PS not only directly impacts FoMO but also indirectly influences it through SNA.Additionally,SSE positively moderates both the direct path and the first half of the mediation path,indicating that enhancing students’PS and SSE can help alleviate their SNA and FoMO,promoting their psychological and behavioral well-being.
文摘Dear Editor,I am writing in response to Jamil's letter,"Interpretative Challenges of the Missing Perilymph'Sign in PLF Diagnosis."I concur with the author's emphasis on the necessity for cautious interpretation of low-signal areas as evidence of active perilymph leakage,requiring correlation with clinical findings,surgical confirmation,and longitudinal imaging changes.
文摘Missing data handling is vital for multi-sensor information fusion fault diagnosis of motors to prevent the accuracy decay or even model failure,and some promising results have been gained in several current studies.These studies,however,have the following limitations:1)effective supervision is neglected for missing data across different fault types and 2)imbalance in missing rates among fault types results in inadequate learning during model training.To overcome the above limitations,this paper proposes a dynamic relative advantagedriven multi-fault synergistic diagnosis method to accomplish accurate fault diagnosis of motors under imbalanced missing data rates.Firstly,a cross-fault-type generalized synergistic diagnostic strategy is established based on variational information bottleneck theory,which is able to ensure sufficient supervision in handling missing data.Then,a dynamic relative advantage assessment technique is designed to reduce diagnostic accuracy decay caused by imbalanced missing data rates.The proposed method is validated using multi-sensor data from motor fault simulation experiments,and experimental results demonstrate its effectiveness and superiority in improving diagnostic accuracy and generalization under imbalanced missing data rates.
基金supported by the Natural Science Foundation of Xinjiang Uigur Autonomous Region−Science Fund for Distinguished Young Scholars(2022D01E105)the Natural Key Research and Development Program(Inter-governmental Key and Special Project,2023YFE0102700)+2 种基金Tianshan Talent Training Program−Young Scientific and Technological Innovation Talent(2023TSYCCX0076)the Science and Technology Development Fund Project of Institute of Desert Meteorology,China Meteorological Administration(KJFZ202306,KJFZ202406)Xinjiang Regional collaborative innovation project(2022E01045)。
文摘The physiological structure and growth of trees in extreme environments(freezing temperatures,prolonged drought,wildfires,pest infestations,and diseases)can be inhibited,including radial growth,and stagnant growth or missing annual rings is highly possible.In this study,we analyzed the radial growth of Siberian larch(Larix sibirica)in the Hongshanzui area of the Altai Mountains,China.The overall missing ring rate at the sampling point was 2.39%,with years with the highest missing rings since meteorological site data were available(1960)identified as 1960,1961,1971,1973,1985,1987,and 1995.Radial growth in high altitudes was mainly affected by temperatures in May and June(average temperature,average minimum temperature,and average maximum temperature).Frequent periods of freezing may lead to missing annual rings.However,while Larix sibirica shows resilience after prolonged freezing temperatures,it still requires time for the trees to return to normal growth levels.
文摘Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.
基金supported by the National Natural Science Foundation of China(Grant No.52409151)the Programme of Shenzhen Key Laboratory of Green,Efficient and Intelligent Construction of Underground Metro Station(Programme No.ZDSYS20200923105200001)the Science and Technology Major Project of Xizang Autonomous Region of China(XZ202201ZD0003G).
文摘Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This study aims to investigate the issue of missing data in extensive TBM datasets.Through a comprehensive literature review,we analyze the mechanism of missing TBM data and compare different imputation methods,including statistical analysis and machine learning algorithms.We also examine the impact of various missing patterns and rates on the efficacy of these methods.Finally,we propose a dynamic interpolation strategy tailored for TBM engineering sites.The research results show that K-Nearest Neighbors(KNN)and Random Forest(RF)algorithms can achieve good interpolation results;As the missing rate increases,the interpolation effect of different methods will decrease;The interpolation effect of block missing is poor,followed by mixed missing,and the interpolation effect of sporadic missing is the best.On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects,applicable in ML scenarios such as parameter optimization,attitude warning,and pressure prediction.These findings contribute to enhancing the efficiency of TBM missing data processing,offering more effective support for large-scale TBM monitoring datasets.
基金supported by the Intelligent System Research Group(ISysRG)supported by Universitas Sriwijaya funded by the Competitive Research 2024.
文摘Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.
文摘The Central Institute of Forensic Science(CIFS)has been providing DNA testing services to Thai people since 2002.Bone accounts for majority of the biological specimens tested,constituting approximately 26%in total evidence.DNA recovery from the bone is challenging owing to degradation and the presence of inhibitors.Therefore,guidelines for bone selection,extraction,and DNA typing are essential for the routine laboratory of CIFS to maximize DNA yield,and minimize time and cost.In this study,we extracted three types of bones:femur,occipital,and petrous,from 12 bodies using a modified organic extraction and silica-based method.The success rate of the Short Tandem Repeat(STR)typing was determined through the number of reportable loci.Furthermore,analysis of mitochondrial DNA(mtDNA)was performed using the massively parallel sequencing technique.Coverage and variant analyses of all samples were evaluated.The results indicate that the femur exhibits the highest success rate in STR typing.The results,in decreasing order,are as follows:femur>petrous>occipital.We determined that silica-based extraction is the most efficient technique for the STR typing;however,modified organic extraction can be used as an alternative method in obtaining mtDNA.The outcome from this study could serve as a guide for identifying human remains and missing persons in the CIFS laboratory,as well as other Thai forensic laboratories.
文摘Fear of missing out(FoMO)is a unique term introduced in 2004 to describe a phenomenon observed on social networking sites.FoMO includes two processes;firstly,perception of missing out,followed up with a compulsive behavior to maintain these social connections.We are interested in understanding the complex construct of FoMO and its relations to the need to belong and form stable interpersonal relationships.It is associated with a range of negative life experiences and feelings,due to it being considered a problematic attachment to social media.We have provided a general review of the literature and have summarized the findings in relation to mental health,social functioning,sleep,academic performance and productivity,neuro-developmental disorders,and physical well-being.We have also discussed the treatment options available for FoMo based on cognitive behavior therapy.It imperative that new findings on FoMO are communicated to the clinical community as it has diagnostic implications and could be a confounding variable in those who do not respond to treatment as usual.
基金supported by Key Discipline Construction Program of Beijing Municipal Commission of Education (XK10008043)
文摘Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to estimate a model by assuming that the data set is governed by a global model. Moreover, in real processes, the available data set is usually obtained with missing values. To overcome the shortcomings of global modeling and missing data values, a new modeling method is proposed. Firstly, an incomplete data set with missing values is partitioned into several clusters by a K-means with soft constraints （KSC） algorithm, which incorporates soft constraints to enable clustering with missing values. Then a local model based on each group is developed by using SVR algorithm, which adopts a missing value insensitive （MVI） kernel to investigate the missing value estimation problem. For each local model, its valid area is gotten as well. Simulation results prove the effectiveness of the current local model and the estimation algorithm.
文摘The first thunderstorm weather appeared in southern Shenyang on May 2,2010 and did not bring about severe lightning disaster for Shenyang region,but forecast service had poor effect without forecasting thunderstorm weather accurately.In our paper,the reasons for missing report of this thunderstorm weather were analyzed,and analysis on thunderstorm potential was carried out by means of mesoscale analysis technique,providing technical index and vantage point for the prediction of thunderstorm potential.The results showed that the reasons for missing report of this weather process were as follows:surface temperature at prophase was constantly lower going against the development of convective weather;the interpreting and analyzing ability of numerical forecast product should be improved;the forecast result of T639 model was better than that of Japanese numerical forecast;the study and application of mesoscale analysis technique should be strengthened,and this service was formally developed after thunderstorm weather on June 1,2010.
