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
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
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
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
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
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
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
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
SnS has emerged as an attractive catalyst for the electrochemical CO_(2)reduction reaction(CO_(2)RR)to formate,while its long-term operational stability is hindered by the self-reduction of Sn^(2+) and sulfur dissolut...SnS has emerged as an attractive catalyst for the electrochemical CO_(2)reduction reaction(CO_(2)RR)to formate,while its long-term operational stability is hindered by the self-reduction of Sn^(2+) and sulfur dissolution.Thus,maintaining high current efficiency across a wide negative potential range to achieve high production rates of formate remains a significant challenge.In this study,we present a heterostructure constructed with SnS and CuS for efficient CO_(2)RR to formate.The SnS-CuS(30)exhibits a remarkable formate Faradaic efficiency(FE_(f))of 93.94%at−1 V vs.reversible hydrogen electrode(RHE)and demonstrates long-term stability for 7.5 h,maintaining high activity(with an average FE_(f)of 85.6%)across a wide negative potential range(from-0.8 to-1.2 V(vs.RHE)).The results reveal that the heterogeneous interface between SnS and CuS mitigates the self-reduction issue of SnS by sacrificing Cu^(2+),highlighting that the true active species is SnS,which effectively resists structural changes during the electrolysis process under the protection of CuS.The synergistic interaction within the CuS and SnS heterostructure,combined with the tendency for electron self-conduction,enables the catalyst to maintain high formate activity and selectivity across a wide potential range.Furthermore,theoretical results further indicate that the incorporation of CuS enhances CO_(2)adsorption and lowers the energy barrier for the formation of formate intermediates.This study inspires the concept of applying protective layers to active species,promoting high selectivity in Sn-based electrocatalysts.展开更多
Within the context of the"Window of Qingtian"initiative for constructing beautiful countryside in the new era,this study focuses on Malingjiao Village in Zhenbu Town,Qingtian County,exploring innovative appr...Within the context of the"Window of Qingtian"initiative for constructing beautiful countryside in the new era,this study focuses on Malingjiao Village in Zhenbu Town,Qingtian County,exploring innovative approaches and practical models for rural revitalization in mountainous areas.By employing a combination of field research,policy analysis,and case studies,this research deeply analyzes the specific measures implemented in Malingjiao Village,including infrastructure enhancement,integration of primary,secondary,and tertiary industries,revitalization of collective assets,ecological conservation,and cultural heritage preservation.The results indicate that,by pursuing strategies of shared prosperity,agricultural-tourism integration,and integrated development of primary,secondary,and tertiary industries,the village has successfully optimized its industrial structure,expanded the collective economy,increased villager incomes,and improved the ecological environment.This study not only provides a referential demonstration model for beautiful countryside construction throughout Qingtian County but also offers empirical and theoretical support for rural revitalization in mountainous regions across southwestern Zhejiang and even the entire province.展开更多
As a type of charge-balanced power device,the performance of super-junction MOSFETs(SJ-MOS)is significantly influ-enced by fluctuations in the fabrication process.To overcome the relatively narrow process window of co...As a type of charge-balanced power device,the performance of super-junction MOSFETs(SJ-MOS)is significantly influ-enced by fluctuations in the fabrication process.To overcome the relatively narrow process window of conventional SJ-MOS,an optimized structure"vertical variable doping super-junction MOSFET(VVD-SJ)"is proposed.Based on the analysis using the charge superposition principle,it is observed that the VVD-SJ,in which the impurity concentration of the P-pillar gradually decreases while that of the N-pillar increases from top to bottom,improves the electric field distribution and mitigates charge imbalance(CIB).Experimental results demonstrate that the optimized 600 V VVD-SJ achieves a 35.90%expansion of the pro-cess window.展开更多
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 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 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 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 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(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.
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 the National Key Research and Development Program of China(No.2018YFB1501405)the National Natural Science Foundation of China(No.52476185).
文摘SnS has emerged as an attractive catalyst for the electrochemical CO_(2)reduction reaction(CO_(2)RR)to formate,while its long-term operational stability is hindered by the self-reduction of Sn^(2+) and sulfur dissolution.Thus,maintaining high current efficiency across a wide negative potential range to achieve high production rates of formate remains a significant challenge.In this study,we present a heterostructure constructed with SnS and CuS for efficient CO_(2)RR to formate.The SnS-CuS(30)exhibits a remarkable formate Faradaic efficiency(FE_(f))of 93.94%at−1 V vs.reversible hydrogen electrode(RHE)and demonstrates long-term stability for 7.5 h,maintaining high activity(with an average FE_(f)of 85.6%)across a wide negative potential range(from-0.8 to-1.2 V(vs.RHE)).The results reveal that the heterogeneous interface between SnS and CuS mitigates the self-reduction issue of SnS by sacrificing Cu^(2+),highlighting that the true active species is SnS,which effectively resists structural changes during the electrolysis process under the protection of CuS.The synergistic interaction within the CuS and SnS heterostructure,combined with the tendency for electron self-conduction,enables the catalyst to maintain high formate activity and selectivity across a wide potential range.Furthermore,theoretical results further indicate that the incorporation of CuS enhances CO_(2)adsorption and lowers the energy barrier for the formation of formate intermediates.This study inspires the concept of applying protective layers to active species,promoting high selectivity in Sn-based electrocatalysts.
基金Basic Scientific Research Operating Funds Program for Provincial Higher Education Institutions of Zhejiang College of Construction.
文摘Within the context of the"Window of Qingtian"initiative for constructing beautiful countryside in the new era,this study focuses on Malingjiao Village in Zhenbu Town,Qingtian County,exploring innovative approaches and practical models for rural revitalization in mountainous areas.By employing a combination of field research,policy analysis,and case studies,this research deeply analyzes the specific measures implemented in Malingjiao Village,including infrastructure enhancement,integration of primary,secondary,and tertiary industries,revitalization of collective assets,ecological conservation,and cultural heritage preservation.The results indicate that,by pursuing strategies of shared prosperity,agricultural-tourism integration,and integrated development of primary,secondary,and tertiary industries,the village has successfully optimized its industrial structure,expanded the collective economy,increased villager incomes,and improved the ecological environment.This study not only provides a referential demonstration model for beautiful countryside construction throughout Qingtian County but also offers empirical and theoretical support for rural revitalization in mountainous regions across southwestern Zhejiang and even the entire province.
基金supported by the National Science Foundation of Guangdong Province under Grant 2023A1515012652School-enterprise cooperation projects of ZTE Corporation.
文摘As a type of charge-balanced power device,the performance of super-junction MOSFETs(SJ-MOS)is significantly influ-enced by fluctuations in the fabrication process.To overcome the relatively narrow process window of conventional SJ-MOS,an optimized structure"vertical variable doping super-junction MOSFET(VVD-SJ)"is proposed.Based on the analysis using the charge superposition principle,it is observed that the VVD-SJ,in which the impurity concentration of the P-pillar gradually decreases while that of the N-pillar increases from top to bottom,improves the electric field distribution and mitigates charge imbalance(CIB).Experimental results demonstrate that the optimized 600 V VVD-SJ achieves a 35.90%expansion of the pro-cess window.
