This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relativ...This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.展开更多
Eukaryotic mRNAs are polyadenylated at their 3’-ends,and the poly(A)tails play critical roles in post-transcriptional gene regulation by influencing mRNA stability and translation.Here,we describe the biological proc...Eukaryotic mRNAs are polyadenylated at their 3’-ends,and the poly(A)tails play critical roles in post-transcriptional gene regulation by influencing mRNA stability and translation.Here,we describe the biological processes and major protein factors that control poly(A)tail synthesis and shortening.We also discuss recent breakthroughs in poly(A)tail sequencing methods that enable high throughput and accurate measurement of poly(A)tail lengths.Finally,we review how poly(A)-tail regulators and poly(A)-tail-mediated post-transcriptional mechanisms affect stem cell fate and early embryonic development.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the...The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage.展开更多
Background:Minimally invasive distal pancreatectomy(MIDP)is increasingly being used,although its oncologic safety for pancreatic ductal adenocarcinoma(PDAC)remains controversial.In Japan,MIDP for PDAC has limited endo...Background:Minimally invasive distal pancreatectomy(MIDP)is increasingly being used,although its oncologic safety for pancreatic ductal adenocarcinoma(PDAC)remains controversial.In Japan,MIDP for PDAC has limited endorsement due to insufficient data.This study aimed to compare the perioperative and long-term outcomes of open distal pancreatectomy(ODP)and MIDP for PDAC.Methods:We retrospectively analyzed patients with resectable pancreatic body and tail cancer treated with ODP or MIDP(laparoscopic or robotic)between January 2007 and July 2022.The surgical procedures(ODP and MIDP)were compared and the patient characteristics,perioperative outcomes,and long-term outcomes were analyzed.We also compared the outcomes of patients with neoadjuvant chemotherapy(NAC)and without NAC.Results:A total of 72 distal pancreatectomies were performed(37 ODPs and 35 MIDPs).In the upfront group,MIDP resulted in significantly less blood loss than ODP(P<0.01),despite similar operative time.There was no statistically significant difference in the 2-year recurrence-free survival(RFS)rates between ODP and MIDP(39.7%vs.57.8%,P=0.60)or in the overall survival(OS)rates(66.7%vs.74.1%,P=0.43).Similarly,in the NAC group,MIDP resulted in significantly less blood loss than ODP(P=0.01);ODP and MIDP had similar 2-year RFS rates(41.7%and 60.0%,P=0.75)and OS rates(50.0%and 70.0%,P=0.36).The interval from surgery to adjuvant chemotherapy initiation did not significantly differ between the ODP and MIDP subgroups in both the upfront group(P=0.13)and the NAC group(P=0.14).The incidence of recurrence was 64.8%for ODP and 42.8%for MIDP(P=0.06).Both procedures showed similar distributions of local and distant recurrence.Conclusions:MIDP caused less blood loss and had similar oncologic safety compared with ODP.MIDP could become a feasible,minimally invasive option with sufficient oncologic safety for pancreatic body and tail cancers.展开更多
This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality...This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality classification were determined based on wave theory of Kelvin medium.Then,a calculation model was obtained for the millisecond-delay cut blast vibration in Kelvin media using the Starfield charge superposition principle.Further,the influence of the delay time on the cut blast vibration was quantitatively analyzed and a method for calculating the reasonable cut blasting millisecond delay time is proposed according to the principle of dimensional analysis.Finally,field tests were used to verify the applicability of the method.The results show that 5 ms to 20 ms is a better detonator delay time range and cut blasting vibration can be effectively controlled using the delay time calculated by the calculation model described in this paper.展开更多
Flowering time is a critical agronomic trait with a profound effect on the productivity and adaptabillity of rapeseed(Brassica napus L.).Strategically advancing flowering time can reduce the risk of yield losses due t...Flowering time is a critical agronomic trait with a profound effect on the productivity and adaptabillity of rapeseed(Brassica napus L.).Strategically advancing flowering time can reduce the risk of yield losses due to extreme climatic conditions and facilitate the cultivation of subsequent crops on the same land,thereby enhancing overall agricultural efficiency.In this review,we synthesize current information on flowering time regulation in rapeseed through an integrated analysis of its genetic,hormonal,and environmental dimensions,emphasizing their crosstalk and implications for yield.We consolidate multi-omics evidence from population genetics,functional genomics,and systems biology to create a haplotype-based framework that overcomes the trade-off between flowering time and yield,providing support for the precision breeding of early-maturing cultivars.The insights presented here could inform future research on flowering time regulation and guide strategies for increasing rapeseed productivity.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a mult...To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.展开更多
Severe trauma often involves complex injuries,leading to high disability and fatality rates.Effective treatment requires prompt and coordinated efforts across multiple disciplines to enhance success rates.Time-based c...Severe trauma often involves complex injuries,leading to high disability and fatality rates.Effective treatment requires prompt and coordinated efforts across multiple disciplines to enhance success rates.Time-based chain rescue is crucial in managing severe trauma.A patient with chest and abdominal injuries and hemorrhagic shock was transferred from an ambulance to our hospital.Our trauma team-initiated pre-hospital first aid,utilized an emergency green channel,and conducted rapid ultrasound,collaborating across disciplines.The patient eventually recovered and was discharged.展开更多
This article studies the consensus problem with directed graphs for general linear multi-agent systems.New distributed state-feedback protocols with dynamic event-triggered(DET)mechanisms are proposed for directed gra...This article studies the consensus problem with directed graphs for general linear multi-agent systems.New distributed state-feedback protocols with dynamic event-triggered(DET)mechanisms are proposed for directed graphs that are strongly connected and weight-balanced,general strongly connected,and have spanning trees,respectively.It is proven that strictly positive minimum inter-event times(MIETs)are ensured using the designed DET mechanisms.Several numerical examples are presented to illustrate the effectiveness of the theoretical results.Compared with existing results,our results have the following merits:1)DET mechanisms are designed to determine the sampling instants,which can reduce the communication frequency between agents compared with static mechanisms;2)We focus on the consensus problem on directed graphs,which is more general than existing related results on undirected graphs;3)The existence of positive MIETs is shown to be guaranteed by the designed DET sampling strategies while existing related results can only exclude Zeno behavior.展开更多
Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learni...Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.展开更多
UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple...UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple UAVs-mounted IRS(U-IRS),where the data from ground SNs are transmitted to the FC.In practice,energy efficiency(EE)and mission completion time are crucial metrics for evaluating system performance and operational costs.Recognizing their importance during data collection,we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously.To characterize this tradeoff while considering optimization objective consistency,we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE.Due to the non-convex nature of the formulated problem,obtaining optimal solutions is generally challenging.To tackle this issue,we decompose it into three subproblems:UAV-SN association,number of reflecting elements allocation,andUAVtrajectory optimization.An iterative algorithmcombining genetic algorithm,CS-BJ algorithm,and successive convex approximation technique is proposed to solve these sub-problems.Simulation results demonstrate that when the transmitted data amount is 10 and 30Mbits,compared to the static collection benchmark(the UAV hovers directly above each SN),the EE of the proposed method improves by more than 10.4% and 5.2%,while the total mission completion time is reduced by more than 5.4% and 3.3%,respectively.展开更多
Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,transl...Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.展开更多
Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total ...Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total lag and their response mechanisms under drought remain unclear.This study aimed to quantitatively partition the total SPAC water transport time lag through controlled experiments,identify the dominant component driving the drought response,and compare coexisting tree species with contrasting hydraulic strategies:Platycladus orientalis and Quercus variabilis.We conducted potted plant isotope(δ^(2)H)labeling experiments under normal water and drought stress treatments for both species.Using high-frequency isotope sampling and synchronous sap flow monitoring,we quantified the total water transport time lag from the soil surface to canopy branches(T_(iso),based on initial isotope arrival)and the internal plant transport time lag(T_(sap),based on sap flow path integration).An independent laboratory soil mixing experiment determined the baseline soil mixing time lag(T_(mix)),and the lag associated with soil infiltration and root uptake initiation was estimated(T_(soil)=T_(iso)−T_(sap)).The physical mixing of old and new soil water introduced a baseline time lag(T_(mix))of approximately 8-12 h.Under normal water conditions,the internal plant lag(T_(sap):37-40 h)constituted the major part of the total lag(T_(iso):43-46 h),with the estimated soil process lag(T_(soil))being relatively short(3-9 h).Drought stress significantly prolonged the total time lag.Crucially,this increase was primarily driven by a dramatic increase in the internal plant transport time lag(T_(sap)):T_(sap) increased by 77 h(193%)for P.orientalis and 33 h(89%)for Q.variabilis.In contrast,the estimated soil process lag(T_(soil))showed minimal increase(or even decreased)under drought.Consequently,the increase in T_(sap) almost entirely accounted for the prolongation of T_(iso)(T_(iso) increased by 188%for P.orientalis and 63%for Q.variabilis).Furthermore,the shallow-rooted P.orientalis was more sensitive to drought in terms of internal time lag increase compared to the deep-rooted Q.variabilis.Our direct experimental evidence demonstrates that internal plant physiological and hydraulic processes,rather than soil processes,dominantly regulate the response of total SPAC water transport time lag to drought stress.Tree species with different hydraulic strategies exhibit distinct time lag response mechanisms.These findings challenge traditional perspectives potentially overemphasizing soil limitations and highlight the critical importance of understanding internal plant dynamics for accurately predicting the temporal responses of ecosystem water relations.展开更多
文摘This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.
基金supported in part by the National Institute of Environmental Health Sciences(Z01ES102745 to GH).
文摘Eukaryotic mRNAs are polyadenylated at their 3’-ends,and the poly(A)tails play critical roles in post-transcriptional gene regulation by influencing mRNA stability and translation.Here,we describe the biological processes and major protein factors that control poly(A)tail synthesis and shortening.We also discuss recent breakthroughs in poly(A)tail sequencing methods that enable high throughput and accurate measurement of poly(A)tail lengths.Finally,we review how poly(A)-tail regulators and poly(A)-tail-mediated post-transcriptional mechanisms affect stem cell fate and early embryonic development.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
基金Projects(52374138,51764013)supported by the National Natural Science Foundation of ChinaProject(20204BCJ22005)supported by the Training Plan for Academic and Technical Leaders of Major Disciplines of Jiangxi Province,China+1 种基金Project(2019M652277)supported by the China Postdoctoral Science FoundationProject(20192ACBL21014)supported by the Natural Science Youth Foundation Key Projects of Jiangxi Province,China。
文摘The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage.
文摘Background:Minimally invasive distal pancreatectomy(MIDP)is increasingly being used,although its oncologic safety for pancreatic ductal adenocarcinoma(PDAC)remains controversial.In Japan,MIDP for PDAC has limited endorsement due to insufficient data.This study aimed to compare the perioperative and long-term outcomes of open distal pancreatectomy(ODP)and MIDP for PDAC.Methods:We retrospectively analyzed patients with resectable pancreatic body and tail cancer treated with ODP or MIDP(laparoscopic or robotic)between January 2007 and July 2022.The surgical procedures(ODP and MIDP)were compared and the patient characteristics,perioperative outcomes,and long-term outcomes were analyzed.We also compared the outcomes of patients with neoadjuvant chemotherapy(NAC)and without NAC.Results:A total of 72 distal pancreatectomies were performed(37 ODPs and 35 MIDPs).In the upfront group,MIDP resulted in significantly less blood loss than ODP(P<0.01),despite similar operative time.There was no statistically significant difference in the 2-year recurrence-free survival(RFS)rates between ODP and MIDP(39.7%vs.57.8%,P=0.60)or in the overall survival(OS)rates(66.7%vs.74.1%,P=0.43).Similarly,in the NAC group,MIDP resulted in significantly less blood loss than ODP(P=0.01);ODP and MIDP had similar 2-year RFS rates(41.7%and 60.0%,P=0.75)and OS rates(50.0%and 70.0%,P=0.36).The interval from surgery to adjuvant chemotherapy initiation did not significantly differ between the ODP and MIDP subgroups in both the upfront group(P=0.13)and the NAC group(P=0.14).The incidence of recurrence was 64.8%for ODP and 42.8%for MIDP(P=0.06).Both procedures showed similar distributions of local and distant recurrence.Conclusions:MIDP caused less blood loss and had similar oncologic safety compared with ODP.MIDP could become a feasible,minimally invasive option with sufficient oncologic safety for pancreatic body and tail cancers.
基金National Natural Science Foundation of China under Grant Nos.51979205 and 51939008。
文摘This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality classification were determined based on wave theory of Kelvin medium.Then,a calculation model was obtained for the millisecond-delay cut blast vibration in Kelvin media using the Starfield charge superposition principle.Further,the influence of the delay time on the cut blast vibration was quantitatively analyzed and a method for calculating the reasonable cut blasting millisecond delay time is proposed according to the principle of dimensional analysis.Finally,field tests were used to verify the applicability of the method.The results show that 5 ms to 20 ms is a better detonator delay time range and cut blasting vibration can be effectively controlled using the delay time calculated by the calculation model described in this paper.
基金supported by the National Key Research and Development Program of China(2022YFD1200400)the National Natural Science Foundation of China(32272111)+4 种基金Special fund for youth team of the Southwest Universities(SWU-XJPY202306)Chongqing Natural Science Foundation(CSTB2024NSCQLZX0012)Modern Agro-industry Technology Research System(CARS-12)Chongqing Modern Agricultural Industry Technology System(COMAITS202504)Biological Breeding-National Science and Technology Major Project(2022ZD04008).We sincerely appreciate the Plant Editors team for English language editing of the manuscript,which significantly improved its clarity and overall quality.
文摘Flowering time is a critical agronomic trait with a profound effect on the productivity and adaptabillity of rapeseed(Brassica napus L.).Strategically advancing flowering time can reduce the risk of yield losses due to extreme climatic conditions and facilitate the cultivation of subsequent crops on the same land,thereby enhancing overall agricultural efficiency.In this review,we synthesize current information on flowering time regulation in rapeseed through an integrated analysis of its genetic,hormonal,and environmental dimensions,emphasizing their crosstalk and implications for yield.We consolidate multi-omics evidence from population genetics,functional genomics,and systems biology to create a haplotype-based framework that overcomes the trade-off between flowering time and yield,providing support for the precision breeding of early-maturing cultivars.The insights presented here could inform future research on flowering time regulation and guide strategies for increasing rapeseed productivity.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
基金supported by Natural Science Foundation of China(NSFC),Grant number 5247052693.
文摘To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.
基金Jiangsu Provincial Hospital Association Hospital Management Innovation Research Fund(Project Ni.:JSYGY-3-2025-267)。
文摘Severe trauma often involves complex injuries,leading to high disability and fatality rates.Effective treatment requires prompt and coordinated efforts across multiple disciplines to enhance success rates.Time-based chain rescue is crucial in managing severe trauma.A patient with chest and abdominal injuries and hemorrhagic shock was transferred from an ambulance to our hospital.Our trauma team-initiated pre-hospital first aid,utilized an emergency green channel,and conducted rapid ultrasound,collaborating across disciplines.The patient eventually recovered and was discharged.
基金supported in part by the Natural Science Foundation of China(62273227,92367203)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(ICT2024B68)。
文摘This article studies the consensus problem with directed graphs for general linear multi-agent systems.New distributed state-feedback protocols with dynamic event-triggered(DET)mechanisms are proposed for directed graphs that are strongly connected and weight-balanced,general strongly connected,and have spanning trees,respectively.It is proven that strictly positive minimum inter-event times(MIETs)are ensured using the designed DET mechanisms.Several numerical examples are presented to illustrate the effectiveness of the theoretical results.Compared with existing results,our results have the following merits:1)DET mechanisms are designed to determine the sampling instants,which can reduce the communication frequency between agents compared with static mechanisms;2)We focus on the consensus problem on directed graphs,which is more general than existing related results on undirected graphs;3)The existence of positive MIETs is shown to be guaranteed by the designed DET sampling strategies while existing related results can only exclude Zeno behavior.
基金supported in part by the National Natural Science Foundation of China(62472146)the Key Technologies Research Development Joint Foundation of Henan Province of China(225101610001)。
文摘Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.
基金supported in part by the Opening Project of Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory under Grant AD25069102in part by the Basic Ability Improvement Project of Young and Middle Aged Teachers in Guangxi Universities,under Grant 2023KY0226+6 种基金in part by Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education of China,underGrant CRKL220108in part by the Innovation Project of Guangxi Graduate Education,under Grant YCBZ2023131in part by the Doctoral Research Foundation of Guilin University of Electronic Technology,under Grant UF23038Yin part by the Bagui Youth Top Talent Projectin part by the Guangxi Key Research and Development Program under Grant AB25069510in part by Open Fund of IPOC(BUPT),No.IPOC2024B07in part by Guangxi Key Laboratory of Precision Navigation Technology and Application,under Grant DH202309.
文摘UAV-mounted intelligent reflecting surface(IRS)helps address the line-of-sight(LoS)blockage between sensor nodes(SNs)and the fusion center(FC)in Internet of Things(IoT).This paper considers an IoT assisted by multiple UAVs-mounted IRS(U-IRS),where the data from ground SNs are transmitted to the FC.In practice,energy efficiency(EE)and mission completion time are crucial metrics for evaluating system performance and operational costs.Recognizing their importance during data collection,we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously.To characterize this tradeoff while considering optimization objective consistency,we construct an optimization problem that minimizes the weighted sum of the total mission completion time and the reciprocal of EE.Due to the non-convex nature of the formulated problem,obtaining optimal solutions is generally challenging.To tackle this issue,we decompose it into three subproblems:UAV-SN association,number of reflecting elements allocation,andUAVtrajectory optimization.An iterative algorithmcombining genetic algorithm,CS-BJ algorithm,and successive convex approximation technique is proposed to solve these sub-problems.Simulation results demonstrate that when the transmitted data amount is 10 and 30Mbits,compared to the static collection benchmark(the UAV hovers directly above each SN),the EE of the proposed method improves by more than 10.4% and 5.2%,while the total mission completion time is reduced by more than 5.4% and 3.3%,respectively.
基金supported by the National Natural Science Foundation of China under Grant No.72293575.
文摘Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.
基金financial supports from the National Science Foundation of China(42277062,41977149 and 42230714).
文摘Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total lag and their response mechanisms under drought remain unclear.This study aimed to quantitatively partition the total SPAC water transport time lag through controlled experiments,identify the dominant component driving the drought response,and compare coexisting tree species with contrasting hydraulic strategies:Platycladus orientalis and Quercus variabilis.We conducted potted plant isotope(δ^(2)H)labeling experiments under normal water and drought stress treatments for both species.Using high-frequency isotope sampling and synchronous sap flow monitoring,we quantified the total water transport time lag from the soil surface to canopy branches(T_(iso),based on initial isotope arrival)and the internal plant transport time lag(T_(sap),based on sap flow path integration).An independent laboratory soil mixing experiment determined the baseline soil mixing time lag(T_(mix)),and the lag associated with soil infiltration and root uptake initiation was estimated(T_(soil)=T_(iso)−T_(sap)).The physical mixing of old and new soil water introduced a baseline time lag(T_(mix))of approximately 8-12 h.Under normal water conditions,the internal plant lag(T_(sap):37-40 h)constituted the major part of the total lag(T_(iso):43-46 h),with the estimated soil process lag(T_(soil))being relatively short(3-9 h).Drought stress significantly prolonged the total time lag.Crucially,this increase was primarily driven by a dramatic increase in the internal plant transport time lag(T_(sap)):T_(sap) increased by 77 h(193%)for P.orientalis and 33 h(89%)for Q.variabilis.In contrast,the estimated soil process lag(T_(soil))showed minimal increase(or even decreased)under drought.Consequently,the increase in T_(sap) almost entirely accounted for the prolongation of T_(iso)(T_(iso) increased by 188%for P.orientalis and 63%for Q.variabilis).Furthermore,the shallow-rooted P.orientalis was more sensitive to drought in terms of internal time lag increase compared to the deep-rooted Q.variabilis.Our direct experimental evidence demonstrates that internal plant physiological and hydraulic processes,rather than soil processes,dominantly regulate the response of total SPAC water transport time lag to drought stress.Tree species with different hydraulic strategies exhibit distinct time lag response mechanisms.These findings challenge traditional perspectives potentially overemphasizing soil limitations and highlight the critical importance of understanding internal plant dynamics for accurately predicting the temporal responses of ecosystem water relations.
