With the advent of modern devices,such as smartphones and wearable devices,high-dimensional data are collected on many participants for a period of time or even in perpetuity.For this type of data,dependencies between...With the advent of modern devices,such as smartphones and wearable devices,high-dimensional data are collected on many participants for a period of time or even in perpetuity.For this type of data,dependencies between and within data batches exist because data are collected from the same individual over time.Under the framework of streamed data,individual historical data are not available due to the storage and computation burden.It is urgent to develop computationally efficient methods with statistical guarantees to analyze high-dimensional streamed data and make reliable inferences in practice.In addition,the homogeneity assumption on the model parameters may not be valid in practice over time.To address the above issues,in this paper,we develop a new renewable debiased-lasso inference method for high-dimensional streamed data allowing dependences between and within data batches to exist and model parameters to gradually change.We establish the large sample properties of the proposed estimators,including consistency and asymptotic normality.The numerical results,including simulations and real data analysis,show the superior performance of the proposed method.展开更多
Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source...Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source often significantly impacts end-to-end(E2E)latency.However,designing an optimized GOP structure to reduce this effect remains a significant challenge.This paper presents two key contributions.First,it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services.Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths.Second,this paper proposes EGOP(an Enhanced GOP structure)that can be implemented in streaming media servers.Experiments demonstrate that EGOP maintains a consistent E2E latency,unaffected by the GOP length of the video source.Specifically,even with a GOP length of 10 s,the E2E latency remains at 1.35 s,achieving a reduction of 6.98 s compared to Volcano-Engine(the live streaming service provider for TikTok).This makes EGOP a promising solution for low-latency live streaming.展开更多
RENEWING THE FORBIDDEN CITY’S CENTURY-OLD LEGACY.Oriental Outlook.27 November 2025.At sunrise,the Forbidden City glows under a veil of gold;at night,it retreats into quiet dignity.But the palace never really sleeps.A...RENEWING THE FORBIDDEN CITY’S CENTURY-OLD LEGACY.Oriental Outlook.27 November 2025.At sunrise,the Forbidden City glows under a veil of gold;at night,it retreats into quiet dignity.But the palace never really sleeps.As visitors depart,the“digital relic vault”awakens online,where porcelain,calligraphy,jade and timepieces reveal their beauty in virtual form.History continues to breathe in the data stream.展开更多
By encouraging collaboration,attracting young entrepreneurs,and setting up village-invested enterprises,rural communities in Zhejiang Province are growing their income streams.“Over the past years,we have seen more a...By encouraging collaboration,attracting young entrepreneurs,and setting up village-invested enterprises,rural communities in Zhejiang Province are growing their income streams.“Over the past years,we have seen more and more tourists coming to our village,and their stay here has grown longer.Many even said they don’t want to leave,”Pan Chunlin,a resident of Yucun Village in Anji County of Huzhou City,east China’s Zhejiang Province,told China Today.Pan is the owner of the Chunlin Lodge,a bed&breakfast(B&B)which received more than 70,000 visitors last year,generating over RMB 4 million in revenue.展开更多
Groundwater and surface water contamination by PTE(Potentially Toxic Elements)was assessed in Ruashi and Annexe municipalities of Lubumbashi city.Analyses of seventy water samples collected from six drilled wells,eigh...Groundwater and surface water contamination by PTE(Potentially Toxic Elements)was assessed in Ruashi and Annexe municipalities of Lubumbashi city.Analyses of seventy water samples collected from six drilled wells,eight spade-sunk wells,one river and one spring in both municipalities in 2017 and 2018 were carried out by ICP-SF-MS(Inductively Coupled Plasma-Sector Field Mass Spectrometry).Twenty PTEs including aluminum,arsenic,barium,bismuth,cadmium,cesium,chromium,cobalt,copper,iron,lead,manganese,molybdenum,nickel,strontium,thallium,tungsten,uranium,vanadium and zinc were detected at various concentrations in each one of the samples.Many samples had concentrations and mean concentrations of PTEs,such as aluminum,cadmium,copper,iron,lead,manganese,nickel and zinc,higher than the respective acceptable limits set for drinking water by the EU(European Union),the USEPA(United States Environmental Protection Agency),and the WHO(World Health Organization)standards.Most PTEs being deleterious to human health even at very low concentrations,people who use the groundwater and surface water to meet their water needs in both Ruashi and Annexe municipalities are at risk.展开更多
Depleting fossil energy sources and conventional polluting power generation pose a threat to sustainable development.Hydroelectricity generation from ubiquitous and spontaneous phase transitions between liquid and gas...Depleting fossil energy sources and conventional polluting power generation pose a threat to sustainable development.Hydroelectricity generation from ubiquitous and spontaneous phase transitions between liquid and gaseous water has been considered a promising strategy for mitigating the energy crisis.Fibrous materials with unique flexibility,processability,multifunctionality,and practicability have been widely applied for fibrous materials-based hydroelectricity generation(FHG).In this review,the power generation mechanisms,design principles,and electricity enhancement factors of FHG are first introduced.Then,the fabrication strategies and characteristics of varied constructions including 1D fiber,1D yarn,2D fabric,2D membrane,3D fibrous framework,and 3D fibrous gel are demonstrated.Afterward,the advanced functions of FHG during water harvesting,proton dissociation,ion separation,and charge accumulation processes are analyzed in detail.Moreover,the potential applications including power supply,energy storage,electrical sensor,and information expression are also discussed.Finally,some existing challenges are considered and prospects for future development are sincerely proposed.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
Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been pr...Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.展开更多
Over 100 reaches of perennial streams(PS) and non-perennial streams(NPS) were classified based on the Rosgen stream classification. NPS were mainly type B(39%), characterized by moderate entrenchment and low sinuosity...Over 100 reaches of perennial streams(PS) and non-perennial streams(NPS) were classified based on the Rosgen stream classification. NPS were mainly type B(39%), characterized by moderate entrenchment and low sinuosity. The remainder were almost equally split between three different classes, highlighting the morphological diversity of NPS. Fiftynine percent of PS belonged to type C;such streams are slightly entrenched, less sinuous and have a sequential riffle-pool configuration. Surface particles were significantly coarser than the subsurface in both stream types in thalweg and low flow areas, whereas in NPS, this was prominent, showing 4-5 times more armoring than PS. Even though the NPS had a significantly coarser surface sediment layer than PS in thalweg and low flow areas, its subsurface sediment showed similar particle sizes to PS;this is an indication of surface armoring and provision of more infiltration of fine particles in NPS. A two-year return period flow event did not result in a change of the cross-section profiles. In both stream types, the horizontal force required to uproot herbaceous vegetation with unexposed roots under moist conditions manually was higher than the tractive force at high flows;however, at bankfull flows, it was lower. Also, the uprooting force in PS was lower than that of NPS, indicating that NPS banks are more stable, and numerical analyses showed they are stable under the self-weight.展开更多
The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-g...The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-guided actions.However,recent evidence suggests a more integrated function of these streams.We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG.We tracked neural activity in the inferior parietal lobule in the dorsal stream,and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory.We found increased alpha power in both streams during the delay,indicating their role in maintaining spatial visual information.In addition,we recorded increased alpha power in the hippocampus during the delay,but only when both object identity and location needed to be remembered.We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay.Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule,ventral temporal cortex,and hippocampus that varied across task phases.Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams,supporting an integrated processing model in which both streams contribute to memory-guided actions.展开更多
Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for...Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
While feeding her ostriches,Wang Xue swiftly responds to fans'questions with a sense of humor through her popular live‑streaming channel.Since Wang,30,an expert in ostrich farming,started live broadcasting on a sh...While feeding her ostriches,Wang Xue swiftly responds to fans'questions with a sense of humor through her popular live‑streaming channel.Since Wang,30,an expert in ostrich farming,started live broadcasting on a short‑video platform with her husband Han Peng,their ostrich farming business has flourished,garnering nearly 3 million fans on the platform.“The highest viewership for one single live broadcast reached over 6 million,”said Wang,adding that riding an ostrich at high speed was an essential part of her broadcasts.Through her videos,Wang brought fans close to the ostriches,providing details of their habits,farming methods and economic value.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
The expanding field of alternative proteins represents a transformative approach to addressing global food security and sustainability challenges.Among these,fermentation-derived alternative proteins cultivated from m...The expanding field of alternative proteins represents a transformative approach to addressing global food security and sustainability challenges.Among these,fermentation-derived alternative proteins cultivated from microorganisms such as fungi,bacteria,and algae offer a promising avenue for sustainable protein production.This review explores the selection and utilization of raw materials to produce microbial proteins through fermentation processes.Critical raw materials include agricultural byproducts,industrial waste streams,and specifically designed feedstocks,which not only mitigate environmental footprint but also enhance the economic viability of production systems.The utilization of lignocellulosic biomass and molasses has demonstrated considerable promise,attributed to their abundant and renewable nature.The review underscored the necessity of exploring specific areas to enhance the viability of producing microbial protein from diverse raw materials.These areas include improving pre-treatment strategies to enhance substrate suitability for fermentation,optimizing fermentation processes for increased yield and reduced costs,and developing more resilient microorganisms capable of thriving on varied substrates.These strategies are crucial for advancing the production of alternative proteins through fermentation,in addition to raw material selection,which is vital in the scalability and sustainability of alternative protein production through fermentation,emphasizing the need for continued research and innovation in this field.展开更多
Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in hum...Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.展开更多
基金Supported by National Key R&D Program of China(Grant No.2022YFA1003702)National Natural Science Foundation of China(Grant No.12271441)。
文摘With the advent of modern devices,such as smartphones and wearable devices,high-dimensional data are collected on many participants for a period of time or even in perpetuity.For this type of data,dependencies between and within data batches exist because data are collected from the same individual over time.Under the framework of streamed data,individual historical data are not available due to the storage and computation burden.It is urgent to develop computationally efficient methods with statistical guarantees to analyze high-dimensional streamed data and make reliable inferences in practice.In addition,the homogeneity assumption on the model parameters may not be valid in practice over time.To address the above issues,in this paper,we develop a new renewable debiased-lasso inference method for high-dimensional streamed data allowing dependences between and within data batches to exist and model parameters to gradually change.We establish the large sample properties of the proposed estimators,including consistency and asymptotic normality.The numerical results,including simulations and real data analysis,show the superior performance of the proposed method.
基金supported by Henan Province Major Science and Technology Project(241100210100).
文摘Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source often significantly impacts end-to-end(E2E)latency.However,designing an optimized GOP structure to reduce this effect remains a significant challenge.This paper presents two key contributions.First,it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services.Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths.Second,this paper proposes EGOP(an Enhanced GOP structure)that can be implemented in streaming media servers.Experiments demonstrate that EGOP maintains a consistent E2E latency,unaffected by the GOP length of the video source.Specifically,even with a GOP length of 10 s,the E2E latency remains at 1.35 s,achieving a reduction of 6.98 s compared to Volcano-Engine(the live streaming service provider for TikTok).This makes EGOP a promising solution for low-latency live streaming.
文摘RENEWING THE FORBIDDEN CITY’S CENTURY-OLD LEGACY.Oriental Outlook.27 November 2025.At sunrise,the Forbidden City glows under a veil of gold;at night,it retreats into quiet dignity.But the palace never really sleeps.As visitors depart,the“digital relic vault”awakens online,where porcelain,calligraphy,jade and timepieces reveal their beauty in virtual form.History continues to breathe in the data stream.
文摘By encouraging collaboration,attracting young entrepreneurs,and setting up village-invested enterprises,rural communities in Zhejiang Province are growing their income streams.“Over the past years,we have seen more and more tourists coming to our village,and their stay here has grown longer.Many even said they don’t want to leave,”Pan Chunlin,a resident of Yucun Village in Anji County of Huzhou City,east China’s Zhejiang Province,told China Today.Pan is the owner of the Chunlin Lodge,a bed&breakfast(B&B)which received more than 70,000 visitors last year,generating over RMB 4 million in revenue.
基金funding received from UNESCO-SIDA Project as well as Professor Martine Leermakers and Professor Willy Baeyens for their financial help to analyze the water samples in their laboratory at VUB.Acknowledgements
文摘Groundwater and surface water contamination by PTE(Potentially Toxic Elements)was assessed in Ruashi and Annexe municipalities of Lubumbashi city.Analyses of seventy water samples collected from six drilled wells,eight spade-sunk wells,one river and one spring in both municipalities in 2017 and 2018 were carried out by ICP-SF-MS(Inductively Coupled Plasma-Sector Field Mass Spectrometry).Twenty PTEs including aluminum,arsenic,barium,bismuth,cadmium,cesium,chromium,cobalt,copper,iron,lead,manganese,molybdenum,nickel,strontium,thallium,tungsten,uranium,vanadium and zinc were detected at various concentrations in each one of the samples.Many samples had concentrations and mean concentrations of PTEs,such as aluminum,cadmium,copper,iron,lead,manganese,nickel and zinc,higher than the respective acceptable limits set for drinking water by the EU(European Union),the USEPA(United States Environmental Protection Agency),and the WHO(World Health Organization)standards.Most PTEs being deleterious to human health even at very low concentrations,people who use the groundwater and surface water to meet their water needs in both Ruashi and Annexe municipalities are at risk.
基金funding support from the National Key Research and Development Program of China(No.2022YFB3805800)the National Natural Science Foundation of China(52173059)+1 种基金The Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions(21KJA540002)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB555).
文摘Depleting fossil energy sources and conventional polluting power generation pose a threat to sustainable development.Hydroelectricity generation from ubiquitous and spontaneous phase transitions between liquid and gaseous water has been considered a promising strategy for mitigating the energy crisis.Fibrous materials with unique flexibility,processability,multifunctionality,and practicability have been widely applied for fibrous materials-based hydroelectricity generation(FHG).In this review,the power generation mechanisms,design principles,and electricity enhancement factors of FHG are first introduced.Then,the fabrication strategies and characteristics of varied constructions including 1D fiber,1D yarn,2D fabric,2D membrane,3D fibrous framework,and 3D fibrous gel are demonstrated.Afterward,the advanced functions of FHG during water harvesting,proton dissociation,ion separation,and charge accumulation processes are analyzed in detail.Moreover,the potential applications including power supply,energy storage,electrical sensor,and information expression are also discussed.Finally,some existing challenges are considered and prospects for future development are sincerely proposed.
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 Key Research and Development Program of Jiangsu Province under Grant BE2022059-3,CTBC Bank through the Industry-Academia Cooperation Project,as well as by the Ministry of Science and Technology of Taiwan through Grants MOST-108-2218-E-002-055,MOST-109-2223-E-009-002-MY3,MOST-109-2218-E-009-025,and MOST431109-2218-E-002-015.
文摘Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.
文摘Over 100 reaches of perennial streams(PS) and non-perennial streams(NPS) were classified based on the Rosgen stream classification. NPS were mainly type B(39%), characterized by moderate entrenchment and low sinuosity. The remainder were almost equally split between three different classes, highlighting the morphological diversity of NPS. Fiftynine percent of PS belonged to type C;such streams are slightly entrenched, less sinuous and have a sequential riffle-pool configuration. Surface particles were significantly coarser than the subsurface in both stream types in thalweg and low flow areas, whereas in NPS, this was prominent, showing 4-5 times more armoring than PS. Even though the NPS had a significantly coarser surface sediment layer than PS in thalweg and low flow areas, its subsurface sediment showed similar particle sizes to PS;this is an indication of surface armoring and provision of more infiltration of fine particles in NPS. A two-year return period flow event did not result in a change of the cross-section profiles. In both stream types, the horizontal force required to uproot herbaceous vegetation with unexposed roots under moist conditions manually was higher than the tractive force at high flows;however, at bankfull flows, it was lower. Also, the uprooting force in PS was lower than that of NPS, indicating that NPS banks are more stable, and numerical analyses showed they are stable under the self-weight.
基金supported by European Union–Next Generation EU(LX22NPO5107(MEYS))the Czech Science Foundation(20-21339S)+2 种基金the Grant Agency of Charles University(GAUK 248122 and 272221)ERDF-Project Brain Dynamics(CZ.02.01.01/00/22_008/0004643)the Ministry of Health of the Czech Republic Project NU21J-08-00081.
文摘The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-guided actions.However,recent evidence suggests a more integrated function of these streams.We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG.We tracked neural activity in the inferior parietal lobule in the dorsal stream,and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory.We found increased alpha power in both streams during the delay,indicating their role in maintaining spatial visual information.In addition,we recorded increased alpha power in the hippocampus during the delay,but only when both object identity and location needed to be remembered.We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay.Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule,ventral temporal cortex,and hippocampus that varied across task phases.Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams,supporting an integrated processing model in which both streams contribute to memory-guided actions.
基金supported by the National Language Commission to research on sign language data specifications for artificial intelligence applications and test standards for language service translation systems (No.ZDI145-70)。
文摘Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
文摘While feeding her ostriches,Wang Xue swiftly responds to fans'questions with a sense of humor through her popular live‑streaming channel.Since Wang,30,an expert in ostrich farming,started live broadcasting on a short‑video platform with her husband Han Peng,their ostrich farming business has flourished,garnering nearly 3 million fans on the platform.“The highest viewership for one single live broadcast reached over 6 million,”said Wang,adding that riding an ostrich at high speed was an essential part of her broadcasts.Through her videos,Wang brought fans close to the ostriches,providing details of their habits,farming methods and economic value.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金funded by the Natural Sciences and Engineering Research Council of Canada(NSERC)CREATE Program 554777-2021Training in Advanced Biotechnology for Environmental Sustainability(TABES)James and Joanne Love Chair in Environmental Engineering.
文摘The expanding field of alternative proteins represents a transformative approach to addressing global food security and sustainability challenges.Among these,fermentation-derived alternative proteins cultivated from microorganisms such as fungi,bacteria,and algae offer a promising avenue for sustainable protein production.This review explores the selection and utilization of raw materials to produce microbial proteins through fermentation processes.Critical raw materials include agricultural byproducts,industrial waste streams,and specifically designed feedstocks,which not only mitigate environmental footprint but also enhance the economic viability of production systems.The utilization of lignocellulosic biomass and molasses has demonstrated considerable promise,attributed to their abundant and renewable nature.The review underscored the necessity of exploring specific areas to enhance the viability of producing microbial protein from diverse raw materials.These areas include improving pre-treatment strategies to enhance substrate suitability for fermentation,optimizing fermentation processes for increased yield and reduced costs,and developing more resilient microorganisms capable of thriving on varied substrates.These strategies are crucial for advancing the production of alternative proteins through fermentation,in addition to raw material selection,which is vital in the scalability and sustainability of alternative protein production through fermentation,emphasizing the need for continued research and innovation in this field.
基金Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)the Competitive Research Fund of The University of Aizu,Japan.
文摘Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.
