Previous works were mainly concentrated on long-term average runoff alterations,and extreme temperatures and watershed conditions are little analyzed.In this study,we collected gauged river flow and meteorological dat...Previous works were mainly concentrated on long-term average runoff alterations,and extreme temperatures and watershed conditions are little analyzed.In this study,we collected gauged river flow and meteorological data time series from 1916 to 2015 and 1941 to 2015 across the contiguous United States(CONUS)for 188 catchments to investigate the temporal trends and spatial features of runoff changes at multi-time scales.We also analyzed the relationships between runoff changes and climatic factors.Median descriptive statistics and Budyko coupled climate elasticity methods were used to calculate runoff elasticity in each time scale.The original Mann-Kendall trend test was used to test their trend significance in four time-scale(11,20,40,and 60 a),respectively.The results show that the trend of runoff changes is more significant in high time scales;total changes are heterogeneous over CONUS.After the 1970s,increases of up to 27%decade-1 were mainly concentrated in the mid-northern regions.Maximum temperature and catchment characteristics are vital factors for runoff alteration;runoff changes are independent of rainfall,and wet regions tend to have lower changes.These findings could help develop better regional water resource planning and management.展开更多
Coal is a solid combustible mineral,and coal-bearing strata have important hydrocarbon generation potential and contribute to more than 12%of the global hydrocarbon resources.However,the deposition and hydrocarbon evo...Coal is a solid combustible mineral,and coal-bearing strata have important hydrocarbon generation potential and contribute to more than 12%of the global hydrocarbon resources.However,the deposition and hydrocarbon evolution process of ancient coal-bearing strata is characterized by multiple geological times,leading to obvious distinctions in their hydrocarbon generation potential,geological processes,and production,which affect the evaluation and exploration of hydrocarbon resources derived from coaly source rocks worldwide.This study aimed to identify the differences on oil-generated parent macerals and the production of oil generated from different coaly source rocks and through different oil generation processes.Integrating with the analysis of previous tectonic burial history and hydrocarbon generation history,high-temperature and high-pressure thermal simulation experiments,organic geochemistry,and organic petrology were performed on the Carboniferous-Permian(C-P)coaly source rocks in the Huanghua Depression,Bohai Bay Basin.The oil-generated parent macerals of coal's secondary oil generation process(SOGP)were mainly hydrogen-rich collotelinite,collodetrinite,sporinite,and cutinite,while the oil-generated parent macerals of tertiary oil generation process(TOGP)were the remaining small amount of hydrogen-rich collotelinite,sporinite,and cutinite,as well as dispersed soluble organic matter and unexhausted residual hydrocarbons.Compared with coal,the oil-generated parent macerals of coaly shale SOGP were mostly sporinite and cutinite.And part of hydrogen-poor vitrinite,lacking hydrocarbon-rich macerals,and macerals of the TOGP,in addition to some remaining cutinite and a small amount of crude oil and bitumen from SOGP contributed to the oil yield.The results indicated that the changes in oil yield had a good junction between SOGP and TOGP,both coal and coaly shale had higher SOGP aborted oil yield than TOGP starting yield,and coaly shale TOGP peak oil yield was lower than SOGP peak oil yield.There were significant differences in saturated hydrocarbon and aromatic parameters in coal and coaly shale.Coal SOGP was characterized by a lower Ts/Tm and C31-homohopane22S/(22S+22R)and a higher Pr/n C17compared to coal TOGP,while the aromatic parameter methyl dibenzothiophene ratio(MDR)exhibited coaly shale TOGP was higher than coaly shale SOGP than coaly TOGP than coaly SOGP,and coal trimethylnaphthalene ratio(TNR)was lower than coaly shale TNR.Thus,we established oil generation processes and discriminative plates.In this way,we distinguished the differences between oil generation parent maceral,oil generation time,and oil production of coaly source rocks,and therefore,we provided important support for the evaluation,prediction,and exploration of oil resources from global ancient coaly source rocks.展开更多
Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a ...Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.展开更多
With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation wind...With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.展开更多
Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally...Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally,approximately 15 million PTB cases are reported annually,posing a huge burden on individual families and the community economy[2].In the context of climate warming,O_(3) pollution has continuously increased in many countries in recent years,including China;therefore,scientific communities and government agencies must strive to mitigate ozone pollution.展开更多
With the rapid advancement of Voice over Internet Protocol(VoIP)technology,speech steganography techniques such as Quantization Index Modulation(QIM)and Pitch Modulation Steganography(PMS)have emerged as significant c...With the rapid advancement of Voice over Internet Protocol(VoIP)technology,speech steganography techniques such as Quantization Index Modulation(QIM)and Pitch Modulation Steganography(PMS)have emerged as significant challenges to information security.These techniques embed hidden information into speech streams,making detection increasingly difficult,particularly under conditions of low embedding rates and short speech durations.Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals.To address these challenges,this paper proposes an Efficient Sliding Window Analysis Network(E-SWAN),a novel deep learning model specifically designed for real-time speech steganalysis.E-SWAN integrates two core modules:the LSTM Temporal Feature Miner(LTFM)and the Convolutional Key Feature Miner(CKFM).LTFM captures long-range temporal dependencies using Long Short-Term Memory networks,while CKFM identifies local spatial variations caused by steganographic embedding through convolutional operations.These modules operate within a sliding window framework,enabling efficient extraction of temporal and spatial features.Experimental results on the Chinese CNV and PMS datasets demonstrate the superior performance of E-SWAN.Under conditions of a ten-second sample duration and an embedding rate of 10%,E-SWAN achieves a detection accuracy of 62.09%on the PMS dataset,surpassing existing methods by 4.57%,and an accuracy of 82.28%on the CNV dataset,outperforming state-of-the-art methods by 7.29%.These findings validate the robustness and efficiency of E-SWAN under low embedding rates and short durations,offering a promising solution for real-time VoIP steganalysis.This work provides significant contributions to enhancing information security in digital communications.展开更多
In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes u...In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes unreliable messages along the edges of belief propagation(BP)decoding in the current window to be kept for subsequent window decoding.To improve the reliability of the retained messages during the window transition,a reliable termination method is embedded,where the retained messages undergo more reliable parity checks.Additionally,decoding failure is unavoidable and even causes error propagation when the number of errors exceeds the error-correcting capability of the window.To mitigate this problem,a channel value reuse mechanism is designed,where the received channel values are utilized to reinitialize the window.Furthermore,considering the complexity and performance of decoding,a feasible sliding optimized window decoding(SOWD)scheme is introduced.Finally,simulation results confirm the superior performance of the proposed SOWD scheme in both the waterfall and error floor regions.This work has great potential in the applications of wireless optical communication and fiber optic communication.展开更多
Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security...Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security threats.Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments,yet they are fundamentally limited in computational and memory resources.Accurate and memoryefficient persistent flow detection on programmable switches is therefore essential.However,existing approaches often rely on fixed-window sketches or multiple sketches instances,which either suffer from insufficient temporal precision or incur substantial memory overhead,making them ineffective on programmable switches.To address these challenges,we propose SP-Sketch,an innovative sliding-window-based sketch that leverages a probabilistic update mechanism to emulate slot expiration without maintaining multiple sketch instances.This innovative design significantly reduces memory consumption while preserving high detection accuracy across multiple time intervals.We provide rigorous theoretical analyses of the estimation errors,deriving precise error bounds for the proposed method,and validate our approach through comprehensive implementations on both P4 hardware switches(with Intel Tofino ASIC)and software switches(i.e.,BMv2).Experimental evaluations using real-world traffic traces demonstrate that SP-Sketch outperforms traditional methods,improving accuracy by up to 20%over baseline sliding window approaches and enhancing recall by 5%compared to non-sliding alternatives.Furthermore,SP-Sketch achieves a significant reduction in memory utilization,reducing memory consumption by up to 65%compared to traditional methods,while maintaining a robust capability to accurately track persistent flow behavior over extended time periods.展开更多
Transparent materials utilized as underwater optical windows are highly vulnerable to various forms of pollution or abrasion due to their intrinsic hydrophilic properties.This susceptibility is particularly pronounced...Transparent materials utilized as underwater optical windows are highly vulnerable to various forms of pollution or abrasion due to their intrinsic hydrophilic properties.This susceptibility is particularly pronounced in underwater environments where pollutants can impede the operation of these optical devices,significantly degrading or even compromising their optical properties.The glass catfish,known for its remarkable transparency in water,maintains surface cleanliness and clarity despite exposure to contaminants,impurities abrasion,and hydraulic pressure.Inspired by the glass catfish’s natural attributes,this study introduces a new solution named subaquatic abrasion-resistant and anti-fouling window(SAAW).Utilizing femtosecond laser ablation and electrodeposition,the SAAW is engineered by embedding fine metal bone structures into a transparent substrate and anti-fouling sliding layer,akin to the sturdy bones among catfish’s body.This approach significantly bolsters the window’s abrasion resistance and anti-fouling performance while maintaining high light transmittance.The sliding layer on the SAAW’s surface remarkably reduces the friction of various liquids,which is the reason that SAAW owns the great anti-fouling property.The SAAW demonstrates outstanding optical clarity even after enduring hundreds of sandpaper abrasions,attributing to the fine metal bone structures bearing all external forces and protecting the sliding layer of SAAW.Furthermore,it exhibits exceptional resistance to biological adhesion and underwater pressure.In a green algae environment,the window remains clean with minimal change in transmittance over one month.Moreover,it retains its wettability and anti-fouling properties when subjected to a depth of 30 m of underwater pressure for 30 d.Hence,the SAAW prepared by femtosecond laser ablation and electrodeposition presents a promising strategy for developing stable optical windows in liquid environments.展开更多
Memory is a cognitive process through which past experiences are encoded,stored,and retrieved,playing a crucial role in intelligent behavior.It is well established that the hippocampus continues to reactivate memories...Memory is a cognitive process through which past experiences are encoded,stored,and retrieved,playing a crucial role in intelligent behavior.It is well established that the hippocampus continues to reactivate memories for several days after learning,and this process primarily occurs during sleep[1,2].The prevailing view suggests that sharp-wave ripples(SWRs)during non-rapid eye movement(NREM)sleep serve as key electrophysiological signatures of memory replay[3,4].However,only a small portion of SWRs contain memory replay[5].The direct relationship among SWRs,memory replay,and memory consolidation remains an open question.Another unresolved issue is how the hippocampus simultaneously reactivates both new and old memories while preventing interference.展开更多
Regulating the freedom and distribution of H_(2)O molecules has become the decisive factor in enlarging the electrochemical stability window(ESW)of aqueous electrolytes.Compared with the water in a bulk electrolyte,H_...Regulating the freedom and distribution of H_(2)O molecules has become the decisive factor in enlarging the electrochemical stability window(ESW)of aqueous electrolytes.Compared with the water in a bulk electrolyte,H_(2)O molecules at the electrode-electrolyte interface tend to directly split under bias potential.Therefore,the composition and properties of the interfacial microenvironment are the crux for optimizing ESW.Herein,we developed a heterogel electrolyte with wide ESW(4.88 V)and satisfactory ionic conductivity(4.4 mS/cm)inspired by the bicontinuous architecture and surfactant self-assembly behavior in the ionic liquid microemulsion-based template.This electrolyte was capable of expanding the ESW through the dynamic oil/water/electrode interface ternary structure,which enriched the oil phase and assembled the hydrophobic surfactant tails at the interface to prevent H_(2)O molecules from approaching the electrode surface.Moreover,the surfactant Tween 20 and polymer network effectively suppressed the activity of H_(2)O molecules through H-bond interactions,which was beneficial in expanding the operating voltage range and improving the temperature tolerance.The prepared gel electrolyte demonstrated unparalleled adaptability in various aqueous lithium-based energy storage devices.Notably,the lithium-ion capacitor showed an extended operating voltage of 2.2 V and could provide a high power density of 1350.36 W/kg at an energy density of 6 Wh/kg.It maintained normal power output even in the challenging harsh environment,which enabled 11,000 uninterrupted charge-discharge cycles at 0℃.This work focuses on the regulation of the interfacial microdomain and the restriction of the degree of freedom of H_(2)O molecules to boost the ESW of aqueous electrolytes,providing a promising strategy for the advancement of energy storage technologies.展开更多
Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global featu...Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature.The transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be heavy.In this work,a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic levels.The core components of the proposed method include the cross window self-attention based transformer(CWST)and multi-scale local enhanced(MLE)modules.The CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between windows.The MLE module selectively fuses features by computing the voxel attention between different branch features,and uses convolution to strengthen the dense local information.The experiments on the prostate,atrium,and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient,Intersection over Union,95%Hausdorff distance and average symmetric surface distance.展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing opti...This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center,focusing on minimizing vehicle dispatch,loading and unloading,operating,and time window penalty costs.The model incorporates constraints such as vehicle capacity,time windows,and travel distance,and is solved using a genetic algorithm to ensure optimal route planning.Through MATLAB simulations,34 customer points are analyzed,demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%,increases vehicle loading rates by 20.04%,and decreases travel distance compared to delivery-only or pickup-only models.The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization,offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.展开更多
基金supported by National Key R&D Program of China(No.2018YFC0407303)“Young Talents”Project of Northeast Agricultural University(No.20QC13)the Natural Science Foundation of Heilongjiang Province of China(No.E2017009)。
文摘Previous works were mainly concentrated on long-term average runoff alterations,and extreme temperatures and watershed conditions are little analyzed.In this study,we collected gauged river flow and meteorological data time series from 1916 to 2015 and 1941 to 2015 across the contiguous United States(CONUS)for 188 catchments to investigate the temporal trends and spatial features of runoff changes at multi-time scales.We also analyzed the relationships between runoff changes and climatic factors.Median descriptive statistics and Budyko coupled climate elasticity methods were used to calculate runoff elasticity in each time scale.The original Mann-Kendall trend test was used to test their trend significance in four time-scale(11,20,40,and 60 a),respectively.The results show that the trend of runoff changes is more significant in high time scales;total changes are heterogeneous over CONUS.After the 1970s,increases of up to 27%decade-1 were mainly concentrated in the mid-northern regions.Maximum temperature and catchment characteristics are vital factors for runoff alteration;runoff changes are independent of rainfall,and wet regions tend to have lower changes.These findings could help develop better regional water resource planning and management.
基金supported by the Certificate of National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2016ZX05006007-004)the National Natural Science Foundation of China(Nos.42172145,42072130)。
文摘Coal is a solid combustible mineral,and coal-bearing strata have important hydrocarbon generation potential and contribute to more than 12%of the global hydrocarbon resources.However,the deposition and hydrocarbon evolution process of ancient coal-bearing strata is characterized by multiple geological times,leading to obvious distinctions in their hydrocarbon generation potential,geological processes,and production,which affect the evaluation and exploration of hydrocarbon resources derived from coaly source rocks worldwide.This study aimed to identify the differences on oil-generated parent macerals and the production of oil generated from different coaly source rocks and through different oil generation processes.Integrating with the analysis of previous tectonic burial history and hydrocarbon generation history,high-temperature and high-pressure thermal simulation experiments,organic geochemistry,and organic petrology were performed on the Carboniferous-Permian(C-P)coaly source rocks in the Huanghua Depression,Bohai Bay Basin.The oil-generated parent macerals of coal's secondary oil generation process(SOGP)were mainly hydrogen-rich collotelinite,collodetrinite,sporinite,and cutinite,while the oil-generated parent macerals of tertiary oil generation process(TOGP)were the remaining small amount of hydrogen-rich collotelinite,sporinite,and cutinite,as well as dispersed soluble organic matter and unexhausted residual hydrocarbons.Compared with coal,the oil-generated parent macerals of coaly shale SOGP were mostly sporinite and cutinite.And part of hydrogen-poor vitrinite,lacking hydrocarbon-rich macerals,and macerals of the TOGP,in addition to some remaining cutinite and a small amount of crude oil and bitumen from SOGP contributed to the oil yield.The results indicated that the changes in oil yield had a good junction between SOGP and TOGP,both coal and coaly shale had higher SOGP aborted oil yield than TOGP starting yield,and coaly shale TOGP peak oil yield was lower than SOGP peak oil yield.There were significant differences in saturated hydrocarbon and aromatic parameters in coal and coaly shale.Coal SOGP was characterized by a lower Ts/Tm and C31-homohopane22S/(22S+22R)and a higher Pr/n C17compared to coal TOGP,while the aromatic parameter methyl dibenzothiophene ratio(MDR)exhibited coaly shale TOGP was higher than coaly shale SOGP than coaly TOGP than coaly SOGP,and coal trimethylnaphthalene ratio(TNR)was lower than coaly shale TNR.Thus,we established oil generation processes and discriminative plates.In this way,we distinguished the differences between oil generation parent maceral,oil generation time,and oil production of coaly source rocks,and therefore,we provided important support for the evaluation,prediction,and exploration of oil resources from global ancient coaly source rocks.
文摘Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.
文摘With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.
基金supported by the Natural Science Foundation of Henan Province[grant number:242300420115]Key Scientific Research Projects in Universities of Henan Province[grant number:23A330006].
文摘Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally,approximately 15 million PTB cases are reported annually,posing a huge burden on individual families and the community economy[2].In the context of climate warming,O_(3) pollution has continuously increased in many countries in recent years,including China;therefore,scientific communities and government agencies must strive to mitigate ozone pollution.
基金supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQ20F020004in part by the National College Student Innovation and Research Training Program under Grant 202313283002.
文摘With the rapid advancement of Voice over Internet Protocol(VoIP)technology,speech steganography techniques such as Quantization Index Modulation(QIM)and Pitch Modulation Steganography(PMS)have emerged as significant challenges to information security.These techniques embed hidden information into speech streams,making detection increasingly difficult,particularly under conditions of low embedding rates and short speech durations.Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals.To address these challenges,this paper proposes an Efficient Sliding Window Analysis Network(E-SWAN),a novel deep learning model specifically designed for real-time speech steganalysis.E-SWAN integrates two core modules:the LSTM Temporal Feature Miner(LTFM)and the Convolutional Key Feature Miner(CKFM).LTFM captures long-range temporal dependencies using Long Short-Term Memory networks,while CKFM identifies local spatial variations caused by steganographic embedding through convolutional operations.These modules operate within a sliding window framework,enabling efficient extraction of temporal and spatial features.Experimental results on the Chinese CNV and PMS datasets demonstrate the superior performance of E-SWAN.Under conditions of a ten-second sample duration and an embedding rate of 10%,E-SWAN achieves a detection accuracy of 62.09%on the PMS dataset,surpassing existing methods by 4.57%,and an accuracy of 82.28%on the CNV dataset,outperforming state-of-the-art methods by 7.29%.These findings validate the robustness and efficiency of E-SWAN under low embedding rates and short durations,offering a promising solution for real-time VoIP steganalysis.This work provides significant contributions to enhancing information security in digital communications.
基金supported by the National Natural Science Foundation of China (No.62275193)。
文摘In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes unreliable messages along the edges of belief propagation(BP)decoding in the current window to be kept for subsequent window decoding.To improve the reliability of the retained messages during the window transition,a reliable termination method is embedded,where the retained messages undergo more reliable parity checks.Additionally,decoding failure is unavoidable and even causes error propagation when the number of errors exceeds the error-correcting capability of the window.To mitigate this problem,a channel value reuse mechanism is designed,where the received channel values are utilized to reinitialize the window.Furthermore,considering the complexity and performance of decoding,a feasible sliding optimized window decoding(SOWD)scheme is introduced.Finally,simulation results confirm the superior performance of the proposed SOWD scheme in both the waterfall and error floor regions.This work has great potential in the applications of wireless optical communication and fiber optic communication.
基金supported by the National Undergraduate Innovation and Entrepreneurship Training Program of China(Project No.202510559076)at Jinan University,a nationwide initiative administered by the Ministry of Educationthe National Natural Science Foundation of China(NSFC)under Grant No.62172189.
文摘Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security threats.Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments,yet they are fundamentally limited in computational and memory resources.Accurate and memoryefficient persistent flow detection on programmable switches is therefore essential.However,existing approaches often rely on fixed-window sketches or multiple sketches instances,which either suffer from insufficient temporal precision or incur substantial memory overhead,making them ineffective on programmable switches.To address these challenges,we propose SP-Sketch,an innovative sliding-window-based sketch that leverages a probabilistic update mechanism to emulate slot expiration without maintaining multiple sketch instances.This innovative design significantly reduces memory consumption while preserving high detection accuracy across multiple time intervals.We provide rigorous theoretical analyses of the estimation errors,deriving precise error bounds for the proposed method,and validate our approach through comprehensive implementations on both P4 hardware switches(with Intel Tofino ASIC)and software switches(i.e.,BMv2).Experimental evaluations using real-world traffic traces demonstrate that SP-Sketch outperforms traditional methods,improving accuracy by up to 20%over baseline sliding window approaches and enhancing recall by 5%compared to non-sliding alternatives.Furthermore,SP-Sketch achieves a significant reduction in memory utilization,reducing memory consumption by up to 65%compared to traditional methods,while maintaining a robust capability to accurately track persistent flow behavior over extended time periods.
基金supported by the National Science Foundation of China under Grant Nos(Nos.12127806,62175195)the International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies。
文摘Transparent materials utilized as underwater optical windows are highly vulnerable to various forms of pollution or abrasion due to their intrinsic hydrophilic properties.This susceptibility is particularly pronounced in underwater environments where pollutants can impede the operation of these optical devices,significantly degrading or even compromising their optical properties.The glass catfish,known for its remarkable transparency in water,maintains surface cleanliness and clarity despite exposure to contaminants,impurities abrasion,and hydraulic pressure.Inspired by the glass catfish’s natural attributes,this study introduces a new solution named subaquatic abrasion-resistant and anti-fouling window(SAAW).Utilizing femtosecond laser ablation and electrodeposition,the SAAW is engineered by embedding fine metal bone structures into a transparent substrate and anti-fouling sliding layer,akin to the sturdy bones among catfish’s body.This approach significantly bolsters the window’s abrasion resistance and anti-fouling performance while maintaining high light transmittance.The sliding layer on the SAAW’s surface remarkably reduces the friction of various liquids,which is the reason that SAAW owns the great anti-fouling property.The SAAW demonstrates outstanding optical clarity even after enduring hundreds of sandpaper abrasions,attributing to the fine metal bone structures bearing all external forces and protecting the sliding layer of SAAW.Furthermore,it exhibits exceptional resistance to biological adhesion and underwater pressure.In a green algae environment,the window remains clean with minimal change in transmittance over one month.Moreover,it retains its wettability and anti-fouling properties when subjected to a depth of 30 m of underwater pressure for 30 d.Hence,the SAAW prepared by femtosecond laser ablation and electrodeposition presents a promising strategy for developing stable optical windows in liquid environments.
基金supported by the National Natural Science Foundation of China(32371028,32300822,U24A20373,and 82071177)the Shanghai Rising-Star Program(24QA2704800)+2 种基金the Shanghai Jiao Tong University 2030 InitiativeShanghai Municipal Health Commission(202340046)the Fund for Excellent Young Scholars of Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine.
文摘Memory is a cognitive process through which past experiences are encoded,stored,and retrieved,playing a crucial role in intelligent behavior.It is well established that the hippocampus continues to reactivate memories for several days after learning,and this process primarily occurs during sleep[1,2].The prevailing view suggests that sharp-wave ripples(SWRs)during non-rapid eye movement(NREM)sleep serve as key electrophysiological signatures of memory replay[3,4].However,only a small portion of SWRs contain memory replay[5].The direct relationship among SWRs,memory replay,and memory consolidation remains an open question.Another unresolved issue is how the hippocampus simultaneously reactivates both new and old memories while preventing interference.
基金supported by the National Natural Science Foundation of China(Grant Nos.22032003 and 22072073)。
文摘Regulating the freedom and distribution of H_(2)O molecules has become the decisive factor in enlarging the electrochemical stability window(ESW)of aqueous electrolytes.Compared with the water in a bulk electrolyte,H_(2)O molecules at the electrode-electrolyte interface tend to directly split under bias potential.Therefore,the composition and properties of the interfacial microenvironment are the crux for optimizing ESW.Herein,we developed a heterogel electrolyte with wide ESW(4.88 V)and satisfactory ionic conductivity(4.4 mS/cm)inspired by the bicontinuous architecture and surfactant self-assembly behavior in the ionic liquid microemulsion-based template.This electrolyte was capable of expanding the ESW through the dynamic oil/water/electrode interface ternary structure,which enriched the oil phase and assembled the hydrophobic surfactant tails at the interface to prevent H_(2)O molecules from approaching the electrode surface.Moreover,the surfactant Tween 20 and polymer network effectively suppressed the activity of H_(2)O molecules through H-bond interactions,which was beneficial in expanding the operating voltage range and improving the temperature tolerance.The prepared gel electrolyte demonstrated unparalleled adaptability in various aqueous lithium-based energy storage devices.Notably,the lithium-ion capacitor showed an extended operating voltage of 2.2 V and could provide a high power density of 1350.36 W/kg at an energy density of 6 Wh/kg.It maintained normal power output even in the challenging harsh environment,which enabled 11,000 uninterrupted charge-discharge cycles at 0℃.This work focuses on the regulation of the interfacial microdomain and the restriction of the degree of freedom of H_(2)O molecules to boost the ESW of aqueous electrolytes,providing a promising strategy for the advancement of energy storage technologies.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFE0206900China Postdoctoral Science Foundation,Grant/Award Number:2023M731204+2 种基金The Open Project of Key Laboratory for Quality Evaluation of Ultrasound Surgical Equipment of National Medical Products Administration,Grant/Award Number:SMDTKL-2023-1-01The Hubei Province Key Research and Development Project,Grant/Award Number:2023BCB007CAAI-Huawei MindSpore Open Fund。
文摘Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature.The transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be heavy.In this work,a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic levels.The core components of the proposed method include the cross window self-attention based transformer(CWST)and multi-scale local enhanced(MLE)modules.The CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between windows.The MLE module selectively fuses features by computing the voxel attention between different branch features,and uses convolution to strengthen the dense local information.The experiments on the prostate,atrium,and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient,Intersection over Union,95%Hausdorff distance and average symmetric surface distance.
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
文摘This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center,focusing on minimizing vehicle dispatch,loading and unloading,operating,and time window penalty costs.The model incorporates constraints such as vehicle capacity,time windows,and travel distance,and is solved using a genetic algorithm to ensure optimal route planning.Through MATLAB simulations,34 customer points are analyzed,demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%,increases vehicle loading rates by 20.04%,and decreases travel distance compared to delivery-only or pickup-only models.The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization,offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.
