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
Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operatio...Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operations,owing to their malleability and versatility.However,the computation-intensive and latency-sensitive natures of CNNs present a formidable obstacle to their deployment on resource-constrained UAVs.Some early studies have explored a hybrid approach that dynamically switches between lightweight and complex models to balance accuracy and latency.However,they often overlook scenarios involving multiple concurrent CNN streams,where competition for resources between streams can substantially impact latency and overall system performance.In this paper,we first investigate the deployment of both lightweight and complex models for multiple CNN streams in UAV swarm.Specifically,we formulate an optimization problem to minimize the total latency across multiple CNN streams,under the constraints on UAV memory and the accuracy requirement of each stream.To address this problem,we propose an algorithm called Adaptive Model Switching of collaborative inference for MultiCNN streams(AMSM)to identify the inference strategy with a low latency.Simulation results demonstrate that the proposed AMSM algorithm consistently achieves the lowest latency while meeting the accuracy requirements compared to benchmark algorithms.展开更多
Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions i...Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.展开更多
For oviparous species with little or no parental care, oviposition site choice may be a powerful agent of natural selection, playing an important role in the evolution of species fitness and life history. Identifying ...For oviparous species with little or no parental care, oviposition site choice may be a powerful agent of natural selection, playing an important role in the evolution of species fitness and life history. Identifying and clarifying the microhabitat characteristics of oviposition sites are key to understanding life history strategies and local adaptations for amphibians with complex biphasic life cycles. However, oviposition strategies for amphibians in alpine streams remain poorly understood. Here, we focused on oviposition site selection for the Chinting alpine toad(Scutiger chintingensis) on Mount Wawu on the eastern margin of the Qinghai-Xizang Plateau. We identified the microhabitat differences between oviposition sites and non-oviposition sites for this species, predicted oviposition site selection patterns,and evaluated the relative importance of each microhabitat variable as well as the effects of the variables on the model prediction probabilities. This study revealed that the microhabitat characteristics of oviposition sites in this species included high water velocity, shallow water, and high-water surface coverage. Water velocity and rock volume were the most important variables explaining the categories of oviposition sites. Additionally, we observed nearly half the proportion of overlapping oviposition in this species, and the benefits of this behavior in terms of microhabitat characteristics need to be explored in the future. Our study revealed an oviposition site selection strategy for amphibians within alpine streams, and contributed to understanding the life history and environmental adaptation strategies of an endemic alpine stream species, and thereby providing guidance for effective conservation strategies for alpine stream amphibians under global change.展开更多
This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belo...This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belonging to 34 taxonomic families were found. Endemism has an important presence in the streams on both islands, especially regarding Trichoptera and Coleoptera. The overall status of freshwater macroinvertebrates is rather uncertain as recent data on these communities are scarce and focused on a limited number of sites. Overexploitation of aquifers and the diversion of natural water flows for irrigation have resulted in the drying up of numerous natural streams, inevitably endangering the fauna that inhabits them. A reduction in number and abundance of endemic and sensitive species was observed in the majority of the sampled streams resulting in a lower ecological rating. Therefore, it is proposed that the protection of streams of high conservation value is essential to conserve freshwater macroinvertebrate fauna native to the Canary Islands.展开更多
A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR...A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.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
A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,wh...A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases：one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing.展开更多
In order to avoid the redundant and inconsistent information in distributed data streams, a sampling method based on min-wise hash functions is designed and the practical semantics of the union of distributed data str...In order to avoid the redundant and inconsistent information in distributed data streams, a sampling method based on min-wise hash functions is designed and the practical semantics of the union of distributed data streams is defined. First, for each family of min-wise hash functions, the data with the minimum hash value are selected as local samples and the biased effect caused by frequent updates in a single data stream is filtered out. Secondly, for the same hash function, the sample with the minimum hash value is selected as the global sample and the local samples are combined at the center node to filter out the biased effect of duplicated updates. Finally, based on the obtained uniform samples, several aggregations on the defined semantics of the union of data streams are precisely estimated. The results of comparison tests on synthetic and real-life data streams demonstrate the effectiveness of this method.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
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
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
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
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
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supported by the National Natural Science Foundation of China(No.61931011)the Jiangsu Provincial Key Research and Development Program,China(No.BE2021013-4)the Fundamental Research Project in University Characteristic Disciplines,China(No.ILF240071A24)。
文摘Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operations,owing to their malleability and versatility.However,the computation-intensive and latency-sensitive natures of CNNs present a formidable obstacle to their deployment on resource-constrained UAVs.Some early studies have explored a hybrid approach that dynamically switches between lightweight and complex models to balance accuracy and latency.However,they often overlook scenarios involving multiple concurrent CNN streams,where competition for resources between streams can substantially impact latency and overall system performance.In this paper,we first investigate the deployment of both lightweight and complex models for multiple CNN streams in UAV swarm.Specifically,we formulate an optimization problem to minimize the total latency across multiple CNN streams,under the constraints on UAV memory and the accuracy requirement of each stream.To address this problem,we propose an algorithm called Adaptive Model Switching of collaborative inference for MultiCNN streams(AMSM)to identify the inference strategy with a low latency.Simulation results demonstrate that the proposed AMSM algorithm consistently achieves the lowest latency while meeting the accuracy requirements compared to benchmark algorithms.
文摘Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.
基金supported by the National Natural Science Foundation of China (32271737, 32071544)the Interdisciplinary Innovation Team of the Chinese Academy of Sciences (CAS) “Light of West China” Program (xbzg-zdsys-202207)+1 种基金the Shenzhen Zhilan Foundation (2023010291A)Nature Science Foundation of Sichuan Province (24NSFSC2934)。
文摘For oviparous species with little or no parental care, oviposition site choice may be a powerful agent of natural selection, playing an important role in the evolution of species fitness and life history. Identifying and clarifying the microhabitat characteristics of oviposition sites are key to understanding life history strategies and local adaptations for amphibians with complex biphasic life cycles. However, oviposition strategies for amphibians in alpine streams remain poorly understood. Here, we focused on oviposition site selection for the Chinting alpine toad(Scutiger chintingensis) on Mount Wawu on the eastern margin of the Qinghai-Xizang Plateau. We identified the microhabitat differences between oviposition sites and non-oviposition sites for this species, predicted oviposition site selection patterns,and evaluated the relative importance of each microhabitat variable as well as the effects of the variables on the model prediction probabilities. This study revealed that the microhabitat characteristics of oviposition sites in this species included high water velocity, shallow water, and high-water surface coverage. Water velocity and rock volume were the most important variables explaining the categories of oviposition sites. Additionally, we observed nearly half the proportion of overlapping oviposition in this species, and the benefits of this behavior in terms of microhabitat characteristics need to be explored in the future. Our study revealed an oviposition site selection strategy for amphibians within alpine streams, and contributed to understanding the life history and environmental adaptation strategies of an endemic alpine stream species, and thereby providing guidance for effective conservation strategies for alpine stream amphibians under global change.
文摘This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belonging to 34 taxonomic families were found. Endemism has an important presence in the streams on both islands, especially regarding Trichoptera and Coleoptera. The overall status of freshwater macroinvertebrates is rather uncertain as recent data on these communities are scarce and focused on a limited number of sites. Overexploitation of aquifers and the diversion of natural water flows for irrigation have resulted in the drying up of numerous natural streams, inevitably endangering the fauna that inhabits them. A reduction in number and abundance of endemic and sensitive species was observed in the majority of the sampled streams resulting in a lower ecological rating. Therefore, it is proposed that the protection of streams of high conservation value is essential to conserve freshwater macroinvertebrate fauna native to the Canary Islands.
基金The National Natural Science Foundation of China(No.60673060)the Natural Science Foundation of Jiangsu Province(No.BK2005047)
文摘A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.
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The High Technology Research Plan of Jiangsu Prov-ince (No.BG2004034)the Foundation of Graduate Creative Program ofJiangsu Province (No.xm04-36).
文摘A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases：one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing.
基金The National Natural Science Foundation of China(No60973023,60603040)the Natural Science Foundation of Southeast University(NoKJ2009362)
文摘In order to avoid the redundant and inconsistent information in distributed data streams, a sampling method based on min-wise hash functions is designed and the practical semantics of the union of distributed data streams is defined. First, for each family of min-wise hash functions, the data with the minimum hash value are selected as local samples and the biased effect caused by frequent updates in a single data stream is filtered out. Secondly, for the same hash function, the sample with the minimum hash value is selected as the global sample and the local samples are combined at the center node to filter out the biased effect of duplicated updates. Finally, based on the obtained uniform samples, several aggregations on the defined semantics of the union of data streams are precisely estimated. The results of comparison tests on synthetic and real-life data streams demonstrate the effectiveness of this method.
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 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.
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.
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.
