In this paper, the multi-agent systems(MASs) typically with heterogeneous unknown nonlinearities and nonidentical unknown control coefficients are studied. Although the model information of MASs is coarse, the leader-...In this paper, the multi-agent systems(MASs) typically with heterogeneous unknown nonlinearities and nonidentical unknown control coefficients are studied. Although the model information of MASs is coarse, the leader-following consensus is still pursued, with a prescribed performance and zero consensus errors. Leveraging a powerful funnel control strategy, a fully distributed and completely relative-state-dependent protocol is designed. Distinctively, the time-varying function characterizing the performance boundary is introduced, not only to construct the funnel gains but also as an indispensable part of the protocol,enhancing the control ability and enabling the consensus errors to converge to zero(rather than a residual set). Remark that when control directions are unknown, coexisting with inherent system nonlinearities, it is essential to incorporate an additional compensation mechanism while imposing a hierarchical structure of communication topology for the control design and analysis. Simulation examples are given to illustrate the effectiveness of the theoretical results.展开更多
In this paper we make use of a special procedure on the repro ducing kernel space to give an expansion theorem for the function with two unkno wns and a surface approximation formula. The error of the surface posses...In this paper we make use of a special procedure on the repro ducing kernel space to give an expansion theorem for the function with two unkno wns and a surface approximation formula. The error of the surface possesses mono tonically decreasing and uniformly convergent characteristics in the sense of t he norm on the space.展开更多
Based on the finite difference discretization of partial differential equations, we propose a kind of semi-implicit θ-schemes of incremental unknowns type for the heat equation with time-dependent coefficients. The s...Based on the finite difference discretization of partial differential equations, we propose a kind of semi-implicit θ-schemes of incremental unknowns type for the heat equation with time-dependent coefficients. The stability of the new schemes is carefully studied. Some new types of conditions give better stability when θ is closed to 1/2 even if we have variable coefficients.展开更多
This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater pene...This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater penetration,power grids are becoming increasingly vulnerable to cyber threats,potentially leading to frequency instability and widespread disruptions.We model two significant attack vectors:load-altering attacks(LAAs)and false data injection attacks(FDIAs)that corrupt frequency measurements.These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models,incorporating generation rate constraints and nonlinear loads.A coordinated attack strategy is presented,combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators,thereby maximizing disruption while evading traditional detection.To counteract these threats,we propose an Unknown Input Observer(UIO)-based detection framework for linear and nonlinear LFCs.The UIO is designed using linear matrix inequalities(LMIs)to estimate system states while isolating unknown attack inputs,enabling attack detection through monitoring measurement residuals against a predefined threshold.For mitigation,we leverage BESS capabilities with two adaptive strategies:dynamic mitigation for dynamic LAAs,which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium;and staticmitigation for static LAAs and FDIAs.Simulations show that the UIO achieves high detection accuracy,with residuals exceeding thresholds promptly under coordinated attacks,even in nonlinear models.Mitigation strategies reduce frequency deviations by up to 80%compared to unmitigated cases,restoring stability within seconds.展开更多
For decades,the central dogma of oncology has been that a cancer’s identity is inextricably linked to its anatomical origin.This principle underpins the entire diagnostic and therapeutic framework,from histology-base...For decades,the central dogma of oncology has been that a cancer’s identity is inextricably linked to its anatomical origin.This principle underpins the entire diagnostic and therapeutic framework,from histology-based classification to site-specific treatment guidelines.Yet,this framework catastrophically fails for a substantial population of patients diagnosed with cancer of unknown primary(CUP).These patients present metastatic disease,yet their primary tumors remain elusive despite exhaustive clinical workup1.CUP,accounting for 1%-3%of all cancer diagnoses,is an enigma with devastating consequences;the median overall survival is only 2-12 months2-4.The inability to pinpoint an origin forces clinicians to rely on broad-spectrum empirical chemotherapy,such as taxane-carboplatin regimens,which have limited efficacy and exclude patients from the promise of targeted therapies and clinical trials5.CUP is not only a diagnostic challenge but also an indictment of the siloed approach to understanding malignancy:this cancer highlights the limitations of origin-based diagnostic frameworks.However,the confluence of high-dimensional biological data and advanced artificial intelligence(AI)is now poised to address this long-standing diagnostic limitation and to herald a new era for not only CUP but also oncology as a whole(Figure 1).展开更多
The novel coronavirus disease(COVID-19)outbreak is a major public health crisis unseen in about 100 years.There is also a possibility that the disease may continue to exist for a long time to come.One of the most effe...The novel coronavirus disease(COVID-19)outbreak is a major public health crisis unseen in about 100 years.There is also a possibility that the disease may continue to exist for a long time to come.One of the most effective approaches to tackling the spread of the virus is to adopt widespread lockdowns.展开更多
HONO is a critical precursor of•OH,but its sources are controversial due to its complex formation mechanism.This study conducted comprehensive observations in Zhengzhou from April 26 to May 11,2022.Low NO_(x)concentra...HONO is a critical precursor of•OH,but its sources are controversial due to its complex formation mechanism.This study conducted comprehensive observations in Zhengzhou from April 26 to May 11,2022.Low NO_(x)concentrations were observed during the Covid epidemic period(EP)(10.4±3.0 ppb),compared to the pre-epidemic period(PEP)(12.5±3.8 ppb).The mean HONO concentration during EP(0.53±0.34 ppb)was 0.09 ppb lower than that during PEP(0.62±0.53 ppb).The decrease in HONO concentration during EP came mainly at night due to the reduction in the direct emission(P_(emi))(0.03 ppb/hr),the homogeneous reaction between•OHandNO(P_(OH+NO))(0.02 ppb/hr),and the heterogeneous conversion of NO_(2)on the ground(0.01 ppb/hr).Notably,there was no significant change in daytime HONO concentration.The daytime HONO budget indicated that the primary HONO sources during PEP were the nitrate photolysis(P_(nitrate)),followed by the P_(OH+NO),Pemi,the photo-enhanced reaction of NO_(2)on the ground(P_(ground+hv))and aerosol surface(Paerosol+hv).The primary HONO sources were Pnitrate,POH+NO,P_(emi),and_(Paerosol+hv)during EP,respectively.The missing source has a high correlation with solar radiation,there might be other photo-related HONO sources or the contributions of photosensitized reactions were underestimated.In the extremely underestimated cases,HONO production rates fromthe P_(nitrate),P_(ground+hv),and Paerosol+hv increased by 0.17,0.10,and 0.10 ppb/hr during PEP,0.23,0.13,and 0.16 ppb/hr during EP,and P_(nitrate)was still the primary source during both PEP and EP.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However...Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However,their adoption is hindered by the challenges of autonomous navigation in unknown environments,exacerbated by their limited onboard computational resources and demanding flight dynamics.This work addresses these challenges by presenting a lightweight,vision-based autonomous navigation system weighing 26.0 g,enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s.Central to this system is a novel end-toend Bi-level Cooperative Policy(BCP)that significantly improves flight efficiency and safety.BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning(HRL)for robust and efficient training.Quantitative evaluations show that BCP achieves up to 6.5%shorter path lengths,11.2%faster task completion time,and improved explainability compared to state-of-the-art reinforcement learning algorithms.Additionally,BCP demonstrates 35.7%more efficient and stable training,reducing computational overhead while maintaining high performance.The system design incorporates optimized lightweight components,including a 4.0 g customized stereo camera,a 6.0 g 3D-printed camera mount,and a 16.0 g onboard computer,all tailored to FWAV applications.Real-flight experiments validate the sim-toreal transferability of the proposed navigation system,demonstrating its readiness for real-world deployment in challenging scenarios.This research advances the practicality of FWAVs,paving the way for their broader adoption in critical missions where compact,agile aerial robots are indispensable.展开更多
The first-ever synthesis of the unknown furo[2,3:4,5]pyrimido[1,2-b]indazole skeleton was demonstrated based on the undiscovered tetra-functionalization of enaminones,with simple substrates and reaction conditions.The...The first-ever synthesis of the unknown furo[2,3:4,5]pyrimido[1,2-b]indazole skeleton was demonstrated based on the undiscovered tetra-functionalization of enaminones,with simple substrates and reaction conditions.The key to realizing this process lies in the multiple trapping of the in situ generated ketenimine cation by the 3-aminoindazole,which results in the formation of four new chemical bonds and two new rings in one pot.Moreover,the products of this new reaction were found to exhibit aggregationinduced emission(AIE)without modification.展开更多
BACKGROUND Fever of unknown origin(FUO)remains a diagnostic challenge and was originally defined in 1961.Its classic criteria include fever≥38.3°C(≥101°F)on multiple occasions,fever lasting three weeks or ...BACKGROUND Fever of unknown origin(FUO)remains a diagnostic challenge and was originally defined in 1961.Its classic criteria include fever≥38.3°C(≥101°F)on multiple occasions,fever lasting three weeks or longer,and a diagnosis after one week of inpatient evaluation.However,these criteria may not fully encompass the varied clinical presentations seen in resource-limited settings such as India.The adaptation of FUO definitions to local healthcare contexts is crucial for enhancing diagnostic accuracy and optimizing patient outcomes.AIM To investigate the applicability of revised FUO criteria in a tertiary care setting in India.METHODS This longitudinal-exploratory study at All India Institute of Medical Sciences Rishikesh(January 2018–December 2022)analyzed 228 adult patients with fever≥99.1°F lasting over three days.Patients diagnosed within three days of admission were excluded.Data were collected retrospectively and prospectively using predefined FUO definitions based on durations of nondiagnosis(3-21 days,>21 days),temperature ranges(99.1°F-100.9°F,≥101°F),and hospitalization durations(3-7 days,>7 days).Descriptive statistics and comparative tests(Fisher's exact test,χ2 test)evaluated outcomes across definitions.RESULTS Among the proposed FUO definitions,Definition B(fever lasting 3-21 days,temperatures between 99.1°F-100.9°F,hospitalization>7 days)predominated(40.8%),while only 2.2%met the classical criteria.Notably,36.5%of Definition B patients remained undiagnosed after 7-10 days,despite 94%undergoing diagnostic workups within 21 days.Infection emerged as the leading etiology across definitions,without significant variation in outcomes or mortality during hospitalization(χ2=27.937,P=0.142).CONCLUSION Adapting FUO criteria to local contexts improves diagnostic accuracy and treatment.Definition B(40.8%prevalence)showed practical utility,with higher mortality in patients discharged on empirical'Anti-tuberculosis therapy'.展开更多
The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative po...The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.展开更多
BACKGROUND Nosocomial fever of unknown origin(nFUO)is a frequent and challenging diagnostic entity,encompassing diverse infectious and non-infectious etiologies.Timely identification is crucial,yet evidence on the dia...BACKGROUND Nosocomial fever of unknown origin(nFUO)is a frequent and challenging diagnostic entity,encompassing diverse infectious and non-infectious etiologies.Timely identification is crucial,yet evidence on the diagnostic accuracy of commonly employed sepsis screening tools and biomarkers remains sparse.We hypothesized that these tools and biomarkers measured at fever onset could distinguish infectious from non-infectious causes of nFUO in critically ill adults.AIM To evaluate the diagnostic utility of sepsis tools and biomarkers in identifying infectious causes of nFUO.METHODS This prospective observational study included patients admitted to the Acute Care Emergency Medicine Unit,Postgraduate Institute of Medical Education and Research,Chandigarh,India(July 2023 to December 2024).nFUO was defined by Durack and Street criteria.Diagnostic performance of sepsis screening tools(systemic inflammatory response syndrome,Sequential Organ Failure Assessment,quick Sequential Organ Failure Assessment,National Early Warning Score,and Modified Early Warning Score)and biomarkers[procalcitonin(PCT),C-reactive protein(CRP)]at fever onset was assessed using receiver operating characteristic curve analysis.RESULTS Of 80 cases(mean age 42.9±16.5 years;80% male),42.5% had infectious causes,38.7% non-infectious,and 18.8% remained undiagnosed.Pneumonia(26.2%)and bloodstream infections(11.2%)were the most common infectious etiologies,while central fever and thrombophlebitis(each 7.5%)were predominant among non-infectious causes.Sepsis tools showed poor diagnostic accuracy,with area under the receiver operating characteristic curve(AUC)values close to 0.5.PCT demonstrated modest performance(AUC=0.61;optimal cut-off:0.85μg/L),while CRP was paradoxically higher in non-infectious cases(AUC=0.45).Overall mortality was 20% and was highest among undiagnosed patients(33.3%).Fever duration and hospitalization length were significantly greater in infectious cases.CONCLUSION Sepsis tools,PCT,and CRP have limited utility in identifying infectious causes of nFUO in critically ill adults and should not solely guide initial decision-making.展开更多
With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown ma...With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.展开更多
BACKGROUND Leuconostoc garlicum is commonly found in fermented foods and very few infected patients have been reported,who typically present symptoms such as fever and fatigue.Conventional clinical examinations often ...BACKGROUND Leuconostoc garlicum is commonly found in fermented foods and very few infected patients have been reported,who typically present symptoms such as fever and fatigue.Conventional clinical examinations often struggle to identify this bacterium,and routine anti-infective treatments are generally ineffective.Both diagnostic challenges and therapeutic limitations pose significant difficulties for clinicians.CASE SUMMARY We report a patient ultimately diagnosed with Leuconostoc garlicum infection.The primary manifestations included persistent fever,cough and fatigue.These symptoms lasted for 2 months.He received anti-infective treatment at a community hospital,but this was ineffective.After inquiring about the patient's medical history and conducting a physical examination,the patient underwent laboratory tests.Complete blood count tests revealed that the patient had a high proportion of neutrophils,C-reactive protein level was 235.9 mg/L,erythrocyte sedimentation rate was 67 mm/h,respiratory pathogen testing was negative,and he was then thought to have an infectious disease.However,conventional anti-infective treatments were ineffective.After excluding infectious neurological diseases,urologic diseases and digestive problems,we ultimately focused our attention on the lungs.A lung computed tomography scan indicated pulmonary inflammation.Bronchoalveolar lavage fluid for next-generation sequencing suggested lung infection with Leuconostoc garlicum.The patient's symptoms gradually improved following treatment with piperacillin tazobactam and linezolid.During the follow-up period,the patient's temperature remained normal.CONCLUSION For patients with suspected bacterial infection and experiencing fever,conventional anti-infective treatment can be ineffective in controlling their symptoms,and an infection due to rare bacteria or drug-resistant bacteria should be considered.Next-generation sequencing enables rapid and precise identification of infection-related pathogens in febrile patients.展开更多
This paper discusses the design of event-triggered output-feedback controller for a class of nonlinear time-delay systems with multiple uncertainties. In sharp contrast to previous works, the considered systems posses...This paper discusses the design of event-triggered output-feedback controller for a class of nonlinear time-delay systems with multiple uncertainties. In sharp contrast to previous works, the considered systems possess two important characteristics: (i) The uncertain nonlinear terms meet the linearly unmeasurable-states dependent growth with the growth rate being an unknown function of the input and output. (ii) There exist input matching uncertainty and unknown measurement sensitivity. By introducing a single dynamic gain and employing a cleverly devised event-triggering mechanism (ETM), we design a new gain-based event-triggered output-feedback controller, which globally regulates all states of the considered systems and maintains global boundedness of the closed-loop system. Furthermore, the estimation of input matching uncertainty achieves convergence towards its actual value, and Zeno behavior does not happen. Two simulation examples including a practical one show that the proposed approach is effective.展开更多
Hydraulic-electric systems are widely utilized in various applications.However,over time,these systems may encounter random faults such as loose cables,ambient environmental noise,or sensor aging,leading to inaccurate...Hydraulic-electric systems are widely utilized in various applications.However,over time,these systems may encounter random faults such as loose cables,ambient environmental noise,or sensor aging,leading to inaccurate sensor readings.These faults may result in system instability or compromise safety.In this paper,we propose a fault compensation control system to mitigate the effects of sensor faults and ensure system safety.Specifically,we utilize the pressure sensor within the system to implement the control process and evaluate performance based on the piston position.First,we develop a mathematical model to identify optimal parameters for the fault estimation model based on the Lyapunov stability principle.Next,we design an unknown input observer that estimates the state vector and detects pressure sensor faults using a linear matrix inequality optimization algorithm.The estimated pressure faults are incorporated into the fault compensation control system to counteract their effects via a fault residual coefficient.The discrepancy between the feedback state and the estimated state determines this coefficient.We assess the piston position’s performance through pressure control to evaluate the proposed model’s effectiveness.Finally,the system simulation results are analyzed to validate the efficiency of the proposed model.When a pressure sensor fault occurs,the proposed approach effectively minimizes position control errors,enhancing overall system stability.When a pressure sensor fault occurs,the proposed model compensates for the fault to mitigate the impact of pressure problem,thereby enhancing the position control quality of the EHA system.The fault compensation method ensures over 90%system performance,with its effectiveness becoming more evident under pressure sensor faults.展开更多
This paper studies motor joint control of a 4-degree-of-freedom(DoF)robotic manipulator using learning-based Adaptive Dynamic Programming(ADP)approach.The manipulator’s dynamics are modelled as an open-loop 4-link se...This paper studies motor joint control of a 4-degree-of-freedom(DoF)robotic manipulator using learning-based Adaptive Dynamic Programming(ADP)approach.The manipulator’s dynamics are modelled as an open-loop 4-link serial kinematic chain with 4 Degrees of Freedom(DoF).Decentralised optimal controllers are designed for each link using ADP approach based on a set of cost matrices and data collected from exploration trajectories.The proposed control strategy employs an off-line,off-policy iterative approach to derive four optimal control policies,one for each joint,under exploration strategies.The objective of the controller is to control the position of each joint.Simulation and experimental results show that four independent optimal controllers are found,each under similar exploration strategies,and the proposed ADP approach successfully yields optimal linear control policies despite the presence of these complexities.The experimental results conducted on the Quanser Qarm robotic platform demonstrate the effectiveness of the proposed ADP controllers in handling significant dynamic nonlinearities,such as actuation limitations,output saturation,and filter delays.展开更多
A spontaneous splenic rupture is one of the rarest encounters in our field. It is a potentially fatal condition if not diagnosed early and treated promptly. Moreover, several preexisting diseases contribute to the occ...A spontaneous splenic rupture is one of the rarest encounters in our field. It is a potentially fatal condition if not diagnosed early and treated promptly. Moreover, several preexisting diseases contribute to the occurrence of spontaneous splenic rupture, which includes hematological disease, infectious, malignancy, and immune-compromised disease. In our case, we report a 37-year-old male with a known case of diabetes mellitus who presented with generalized abdominal pain and was diagnosed with spontaneous splenic rupture. He was treated with splenic artery embolization and discharged with a good outcome. Despite the rarity of the disease, it is important to keep it in mind when a patient presents to you with abdominal pain.展开更多
There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are di...There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.展开更多
基金supported in part by the National Natural Science Foundation of China(61821004,62033007)Major Fundamental Research Program of Shandong Province(ZR2023ZD37)
文摘In this paper, the multi-agent systems(MASs) typically with heterogeneous unknown nonlinearities and nonidentical unknown control coefficients are studied. Although the model information of MASs is coarse, the leader-following consensus is still pursued, with a prescribed performance and zero consensus errors. Leveraging a powerful funnel control strategy, a fully distributed and completely relative-state-dependent protocol is designed. Distinctively, the time-varying function characterizing the performance boundary is introduced, not only to construct the funnel gains but also as an indispensable part of the protocol,enhancing the control ability and enabling the consensus errors to converge to zero(rather than a residual set). Remark that when control directions are unknown, coexisting with inherent system nonlinearities, it is essential to incorporate an additional compensation mechanism while imposing a hierarchical structure of communication topology for the control design and analysis. Simulation examples are given to illustrate the effectiveness of the theoretical results.
文摘In this paper we make use of a special procedure on the repro ducing kernel space to give an expansion theorem for the function with two unkno wns and a surface approximation formula. The error of the surface possesses mono tonically decreasing and uniformly convergent characteristics in the sense of t he norm on the space.
基金This project is partially supported by Natural Science Foundation of Gansu Province under Grant 3ZS041-A25-011 by National Natural Science Foundation under Grant 10471056.
文摘Based on the finite difference discretization of partial differential equations, we propose a kind of semi-implicit θ-schemes of incremental unknowns type for the heat equation with time-dependent coefficients. The stability of the new schemes is carefully studied. Some new types of conditions give better stability when θ is closed to 1/2 even if we have variable coefficients.
基金supported by the Natural Science Foundation of China No.62303126the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater penetration,power grids are becoming increasingly vulnerable to cyber threats,potentially leading to frequency instability and widespread disruptions.We model two significant attack vectors:load-altering attacks(LAAs)and false data injection attacks(FDIAs)that corrupt frequency measurements.These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models,incorporating generation rate constraints and nonlinear loads.A coordinated attack strategy is presented,combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators,thereby maximizing disruption while evading traditional detection.To counteract these threats,we propose an Unknown Input Observer(UIO)-based detection framework for linear and nonlinear LFCs.The UIO is designed using linear matrix inequalities(LMIs)to estimate system states while isolating unknown attack inputs,enabling attack detection through monitoring measurement residuals against a predefined threshold.For mitigation,we leverage BESS capabilities with two adaptive strategies:dynamic mitigation for dynamic LAAs,which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium;and staticmitigation for static LAAs and FDIAs.Simulations show that the UIO achieves high detection accuracy,with residuals exceeding thresholds promptly under coordinated attacks,even in nonlinear models.Mitigation strategies reduce frequency deviations by up to 80%compared to unmitigated cases,restoring stability within seconds.
基金supported by the National Natural Science Foundation of China(Grant Nos.32270688,31801117,and 82430107 to X.L.,and 32500589 to H.S.)the China Postdoctoral Science Foundation(Grant Nos.BX20240253 and 2024M762384 to H.S.)+1 种基金the Natural Science Foundation of Tianjin(Grant No.24JCQNJC01280 to H.S.)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-3-003A).
文摘For decades,the central dogma of oncology has been that a cancer’s identity is inextricably linked to its anatomical origin.This principle underpins the entire diagnostic and therapeutic framework,from histology-based classification to site-specific treatment guidelines.Yet,this framework catastrophically fails for a substantial population of patients diagnosed with cancer of unknown primary(CUP).These patients present metastatic disease,yet their primary tumors remain elusive despite exhaustive clinical workup1.CUP,accounting for 1%-3%of all cancer diagnoses,is an enigma with devastating consequences;the median overall survival is only 2-12 months2-4.The inability to pinpoint an origin forces clinicians to rely on broad-spectrum empirical chemotherapy,such as taxane-carboplatin regimens,which have limited efficacy and exclude patients from the promise of targeted therapies and clinical trials5.CUP is not only a diagnostic challenge but also an indictment of the siloed approach to understanding malignancy:this cancer highlights the limitations of origin-based diagnostic frameworks.However,the confluence of high-dimensional biological data and advanced artificial intelligence(AI)is now poised to address this long-standing diagnostic limitation and to herald a new era for not only CUP but also oncology as a whole(Figure 1).
文摘The novel coronavirus disease(COVID-19)outbreak is a major public health crisis unseen in about 100 years.There is also a possibility that the disease may continue to exist for a long time to come.One of the most effective approaches to tackling the spread of the virus is to adopt widespread lockdowns.
基金supported by China Postdoctoral Science Foundation(2023M733220)Zhengzhou PM_(2.5)and O_(3)Collaborative Control and Monitoring Project(20220347A)the National Key Research and Development Program of China(No.2017YFC0212403).
文摘HONO is a critical precursor of•OH,but its sources are controversial due to its complex formation mechanism.This study conducted comprehensive observations in Zhengzhou from April 26 to May 11,2022.Low NO_(x)concentrations were observed during the Covid epidemic period(EP)(10.4±3.0 ppb),compared to the pre-epidemic period(PEP)(12.5±3.8 ppb).The mean HONO concentration during EP(0.53±0.34 ppb)was 0.09 ppb lower than that during PEP(0.62±0.53 ppb).The decrease in HONO concentration during EP came mainly at night due to the reduction in the direct emission(P_(emi))(0.03 ppb/hr),the homogeneous reaction between•OHandNO(P_(OH+NO))(0.02 ppb/hr),and the heterogeneous conversion of NO_(2)on the ground(0.01 ppb/hr).Notably,there was no significant change in daytime HONO concentration.The daytime HONO budget indicated that the primary HONO sources during PEP were the nitrate photolysis(P_(nitrate)),followed by the P_(OH+NO),Pemi,the photo-enhanced reaction of NO_(2)on the ground(P_(ground+hv))and aerosol surface(Paerosol+hv).The primary HONO sources were Pnitrate,POH+NO,P_(emi),and_(Paerosol+hv)during EP,respectively.The missing source has a high correlation with solar radiation,there might be other photo-related HONO sources or the contributions of photosensitized reactions were underestimated.In the extremely underestimated cases,HONO production rates fromthe P_(nitrate),P_(ground+hv),and Paerosol+hv increased by 0.17,0.10,and 0.10 ppb/hr during PEP,0.23,0.13,and 0.16 ppb/hr during EP,and P_(nitrate)was still the primary source during both PEP and EP.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金supported by the Fundamental Research Funds for the Central Universities,China。
文摘Flapping Wing Aerial Vehicles(FWAVs)hold immense potential for applications such as search-and-rescue missions in complex terrains,environmental monitoring in hazardous areas,and exploration in confined spaces.However,their adoption is hindered by the challenges of autonomous navigation in unknown environments,exacerbated by their limited onboard computational resources and demanding flight dynamics.This work addresses these challenges by presenting a lightweight,vision-based autonomous navigation system weighing 26.0 g,enabling FWAVs to achieve obstacle-avoidance flight at a speed of 9.0 m/s.Central to this system is a novel end-toend Bi-level Cooperative Policy(BCP)that significantly improves flight efficiency and safety.BCP employs lightweight neural networks for real-time performance and leverages Hierarchical Reinforcement Learning(HRL)for robust and efficient training.Quantitative evaluations show that BCP achieves up to 6.5%shorter path lengths,11.2%faster task completion time,and improved explainability compared to state-of-the-art reinforcement learning algorithms.Additionally,BCP demonstrates 35.7%more efficient and stable training,reducing computational overhead while maintaining high performance.The system design incorporates optimized lightweight components,including a 4.0 g customized stereo camera,a 6.0 g 3D-printed camera mount,and a 16.0 g onboard computer,all tailored to FWAV applications.Real-flight experiments validate the sim-toreal transferability of the proposed navigation system,demonstrating its readiness for real-world deployment in challenging scenarios.This research advances the practicality of FWAVs,paving the way for their broader adoption in critical missions where compact,agile aerial robots are indispensable.
基金supported by the National Natural Science Foundation of China(Nos.21971080,22171098)supported by Chengdu Guibao Science&Technology Co.,Ltd.This work was also supported by the 111 Project(No.B17019)。
文摘The first-ever synthesis of the unknown furo[2,3:4,5]pyrimido[1,2-b]indazole skeleton was demonstrated based on the undiscovered tetra-functionalization of enaminones,with simple substrates and reaction conditions.The key to realizing this process lies in the multiple trapping of the in situ generated ketenimine cation by the 3-aminoindazole,which results in the formation of four new chemical bonds and two new rings in one pot.Moreover,the products of this new reaction were found to exhibit aggregationinduced emission(AIE)without modification.
文摘BACKGROUND Fever of unknown origin(FUO)remains a diagnostic challenge and was originally defined in 1961.Its classic criteria include fever≥38.3°C(≥101°F)on multiple occasions,fever lasting three weeks or longer,and a diagnosis after one week of inpatient evaluation.However,these criteria may not fully encompass the varied clinical presentations seen in resource-limited settings such as India.The adaptation of FUO definitions to local healthcare contexts is crucial for enhancing diagnostic accuracy and optimizing patient outcomes.AIM To investigate the applicability of revised FUO criteria in a tertiary care setting in India.METHODS This longitudinal-exploratory study at All India Institute of Medical Sciences Rishikesh(January 2018–December 2022)analyzed 228 adult patients with fever≥99.1°F lasting over three days.Patients diagnosed within three days of admission were excluded.Data were collected retrospectively and prospectively using predefined FUO definitions based on durations of nondiagnosis(3-21 days,>21 days),temperature ranges(99.1°F-100.9°F,≥101°F),and hospitalization durations(3-7 days,>7 days).Descriptive statistics and comparative tests(Fisher's exact test,χ2 test)evaluated outcomes across definitions.RESULTS Among the proposed FUO definitions,Definition B(fever lasting 3-21 days,temperatures between 99.1°F-100.9°F,hospitalization>7 days)predominated(40.8%),while only 2.2%met the classical criteria.Notably,36.5%of Definition B patients remained undiagnosed after 7-10 days,despite 94%undergoing diagnostic workups within 21 days.Infection emerged as the leading etiology across definitions,without significant variation in outcomes or mortality during hospitalization(χ2=27.937,P=0.142).CONCLUSION Adapting FUO criteria to local contexts improves diagnostic accuracy and treatment.Definition B(40.8%prevalence)showed practical utility,with higher mortality in patients discharged on empirical'Anti-tuberculosis therapy'.
基金co-supported by the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3023)the National Natural Science Foundation of China(No.12272404)。
文摘The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.
文摘BACKGROUND Nosocomial fever of unknown origin(nFUO)is a frequent and challenging diagnostic entity,encompassing diverse infectious and non-infectious etiologies.Timely identification is crucial,yet evidence on the diagnostic accuracy of commonly employed sepsis screening tools and biomarkers remains sparse.We hypothesized that these tools and biomarkers measured at fever onset could distinguish infectious from non-infectious causes of nFUO in critically ill adults.AIM To evaluate the diagnostic utility of sepsis tools and biomarkers in identifying infectious causes of nFUO.METHODS This prospective observational study included patients admitted to the Acute Care Emergency Medicine Unit,Postgraduate Institute of Medical Education and Research,Chandigarh,India(July 2023 to December 2024).nFUO was defined by Durack and Street criteria.Diagnostic performance of sepsis screening tools(systemic inflammatory response syndrome,Sequential Organ Failure Assessment,quick Sequential Organ Failure Assessment,National Early Warning Score,and Modified Early Warning Score)and biomarkers[procalcitonin(PCT),C-reactive protein(CRP)]at fever onset was assessed using receiver operating characteristic curve analysis.RESULTS Of 80 cases(mean age 42.9±16.5 years;80% male),42.5% had infectious causes,38.7% non-infectious,and 18.8% remained undiagnosed.Pneumonia(26.2%)and bloodstream infections(11.2%)were the most common infectious etiologies,while central fever and thrombophlebitis(each 7.5%)were predominant among non-infectious causes.Sepsis tools showed poor diagnostic accuracy,with area under the receiver operating characteristic curve(AUC)values close to 0.5.PCT demonstrated modest performance(AUC=0.61;optimal cut-off:0.85μg/L),while CRP was paradoxically higher in non-infectious cases(AUC=0.45).Overall mortality was 20% and was highest among undiagnosed patients(33.3%).Fever duration and hospitalization length were significantly greater in infectious cases.CONCLUSION Sepsis tools,PCT,and CRP have limited utility in identifying infectious causes of nFUO in critically ill adults and should not solely guide initial decision-making.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB4500800)the National Science Foundation of China(No.42071431).
文摘With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.
文摘BACKGROUND Leuconostoc garlicum is commonly found in fermented foods and very few infected patients have been reported,who typically present symptoms such as fever and fatigue.Conventional clinical examinations often struggle to identify this bacterium,and routine anti-infective treatments are generally ineffective.Both diagnostic challenges and therapeutic limitations pose significant difficulties for clinicians.CASE SUMMARY We report a patient ultimately diagnosed with Leuconostoc garlicum infection.The primary manifestations included persistent fever,cough and fatigue.These symptoms lasted for 2 months.He received anti-infective treatment at a community hospital,but this was ineffective.After inquiring about the patient's medical history and conducting a physical examination,the patient underwent laboratory tests.Complete blood count tests revealed that the patient had a high proportion of neutrophils,C-reactive protein level was 235.9 mg/L,erythrocyte sedimentation rate was 67 mm/h,respiratory pathogen testing was negative,and he was then thought to have an infectious disease.However,conventional anti-infective treatments were ineffective.After excluding infectious neurological diseases,urologic diseases and digestive problems,we ultimately focused our attention on the lungs.A lung computed tomography scan indicated pulmonary inflammation.Bronchoalveolar lavage fluid for next-generation sequencing suggested lung infection with Leuconostoc garlicum.The patient's symptoms gradually improved following treatment with piperacillin tazobactam and linezolid.During the follow-up period,the patient's temperature remained normal.CONCLUSION For patients with suspected bacterial infection and experiencing fever,conventional anti-infective treatment can be ineffective in controlling their symptoms,and an infection due to rare bacteria or drug-resistant bacteria should be considered.Next-generation sequencing enables rapid and precise identification of infection-related pathogens in febrile patients.
基金supported by the fund of Beijing Municipal Commission of Education(KM202210017001 and 22019821001)the Natural Science Foundation of Henan Province(222300420253).
文摘This paper discusses the design of event-triggered output-feedback controller for a class of nonlinear time-delay systems with multiple uncertainties. In sharp contrast to previous works, the considered systems possess two important characteristics: (i) The uncertain nonlinear terms meet the linearly unmeasurable-states dependent growth with the growth rate being an unknown function of the input and output. (ii) There exist input matching uncertainty and unknown measurement sensitivity. By introducing a single dynamic gain and employing a cleverly devised event-triggering mechanism (ETM), we design a new gain-based event-triggered output-feedback controller, which globally regulates all states of the considered systems and maintains global boundedness of the closed-loop system. Furthermore, the estimation of input matching uncertainty achieves convergence towards its actual value, and Zeno behavior does not happen. Two simulation examples including a practical one show that the proposed approach is effective.
基金supported by Nguyen Tat Thanh University,Ho Chi Minh City,Vietnam,provided with the facilities required to carry out this work.
文摘Hydraulic-electric systems are widely utilized in various applications.However,over time,these systems may encounter random faults such as loose cables,ambient environmental noise,or sensor aging,leading to inaccurate sensor readings.These faults may result in system instability or compromise safety.In this paper,we propose a fault compensation control system to mitigate the effects of sensor faults and ensure system safety.Specifically,we utilize the pressure sensor within the system to implement the control process and evaluate performance based on the piston position.First,we develop a mathematical model to identify optimal parameters for the fault estimation model based on the Lyapunov stability principle.Next,we design an unknown input observer that estimates the state vector and detects pressure sensor faults using a linear matrix inequality optimization algorithm.The estimated pressure faults are incorporated into the fault compensation control system to counteract their effects via a fault residual coefficient.The discrepancy between the feedback state and the estimated state determines this coefficient.We assess the piston position’s performance through pressure control to evaluate the proposed model’s effectiveness.Finally,the system simulation results are analyzed to validate the efficiency of the proposed model.When a pressure sensor fault occurs,the proposed approach effectively minimizes position control errors,enhancing overall system stability.When a pressure sensor fault occurs,the proposed model compensates for the fault to mitigate the impact of pressure problem,thereby enhancing the position control quality of the EHA system.The fault compensation method ensures over 90%system performance,with its effectiveness becoming more evident under pressure sensor faults.
基金supported by the DEEPCOBOT project under Grant 306640/O70 funded by the Research Council of Norway.
文摘This paper studies motor joint control of a 4-degree-of-freedom(DoF)robotic manipulator using learning-based Adaptive Dynamic Programming(ADP)approach.The manipulator’s dynamics are modelled as an open-loop 4-link serial kinematic chain with 4 Degrees of Freedom(DoF).Decentralised optimal controllers are designed for each link using ADP approach based on a set of cost matrices and data collected from exploration trajectories.The proposed control strategy employs an off-line,off-policy iterative approach to derive four optimal control policies,one for each joint,under exploration strategies.The objective of the controller is to control the position of each joint.Simulation and experimental results show that four independent optimal controllers are found,each under similar exploration strategies,and the proposed ADP approach successfully yields optimal linear control policies despite the presence of these complexities.The experimental results conducted on the Quanser Qarm robotic platform demonstrate the effectiveness of the proposed ADP controllers in handling significant dynamic nonlinearities,such as actuation limitations,output saturation,and filter delays.
文摘A spontaneous splenic rupture is one of the rarest encounters in our field. It is a potentially fatal condition if not diagnosed early and treated promptly. Moreover, several preexisting diseases contribute to the occurrence of spontaneous splenic rupture, which includes hematological disease, infectious, malignancy, and immune-compromised disease. In our case, we report a 37-year-old male with a known case of diabetes mellitus who presented with generalized abdominal pain and was diagnosed with spontaneous splenic rupture. He was treated with splenic artery embolization and discharged with a good outcome. Despite the rarity of the disease, it is important to keep it in mind when a patient presents to you with abdominal pain.
基金fully supported by National Natural Science Foundation of China(61871422)Natural Science Foundation of Sichuan Province(2023NSFSC1422)Central Universities of South west Minzu University(ZYN2022032)。
文摘There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.
