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
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
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
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
This paper investigates the problem of dynamic event-triggered control for a class of large-scale nonlinear systems.In particular,both neutral delays and unknown backlash-like hysteresis are considered.This requires t...This paper investigates the problem of dynamic event-triggered control for a class of large-scale nonlinear systems.In particular,both neutral delays and unknown backlash-like hysteresis are considered.This requires to integrate a compensation mechanism into the event-triggered control architecture.To this end,dynamic gain and adaptive control techniques are introduced to address the effects of neutral delays,unknown hysteresis and parameter uncertainties simultaneously.By introducing a non-negative internal dynamic variable,a dynamic event-triggered controller is designed using the hyperbolic tangent function to reduce the communication burden.By means of the Lyapunov–Krasovskii method,it is demonstrated that all signals of the closed-loop system are globally bounded and eventually converge to a tunable bounded region.Moreover,the Zeno behavior is avoided.Finally,a simulation example is presented to verify the validity of the control scheme.展开更多
Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncerta...Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.展开更多
BACKGROUND Pelvic squamous cell carcinoma of unknown primary(CUP)is extremely rare,accounting for less than one percent of all CUP cases,and its infrequency has lim-ited the development of standardized diagnostic and ...BACKGROUND Pelvic squamous cell carcinoma of unknown primary(CUP)is extremely rare,accounting for less than one percent of all CUP cases,and its infrequency has lim-ited the development of standardized diagnostic and treatment guidelines.CASE SUMMARY A 77-year-old female with a history of resected lung adenocarcinoma presented with worsening constipation.Imaging revealed a 2.5 cm mass adjacent to the right levator ani muscle.Biopsy confirmed poorly differentiated squamous cell carcinoma,positive for pancytokeratin and p40,and negative for p16,cytokeratin 7,cytokeratin 20,and neuroendocrine markers.No primary lesion was identified despite extensive evaluation.She underwent five cycles of 5-fluorouracil(1000 mg/m^(2) continuous infusion,days 1-4)and mitomycin-C(10 mg/m^(2) on day 1)with concurrent pelvic radiotherapy(50.4 Gy in 28 fractions).Follow-up imaging demonstrated complete remission sustained for 12 months.Electrocorticography performance status improved from 2 at diagnosis to 1 during follow-up.CONCLUSION This case highlights the potential role of chemoradiotherapy in managing pelvic squamous cell CUP,achieving durable remission in selected patients.展开更多
Most of the existing non-line-of-sight(NLOS)localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios.To solve the problem,a...Most of the existing non-line-of-sight(NLOS)localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios.To solve the problem,an NLOS target localization method in unknown L-shaped corridor based ultra-wideband(UWB)multiple-input multiple-output(MIMO)radar is proposed in this paper.Firstly,the multipath propagation model of Lshaped corridor is established.Then,the localization process is analyzed by the propagation characteristics of diffraction and reflection.Specifically,two different back-projection imaging processes are performed on the radar echo,and the positions of focus regions in the two images are extracted to generate candidate targets.Furthermore,the distances of propagation paths corresponding to each candidate target are calculated,and then the similarity between each candidate target and the target is evaluated by employing two matching factors.The locations of the targets and the width of the corridor are determined based on the matching rules.Finally,two experiments are carried out to demonstrate that the method can effectively obtain the target positions and unknown scene information even when partial paths are lost.展开更多
Primary of Unknown Origin Cancer(CUP)is a metastatic tumor whose origin remains undetermined.The reason for this ambiguity in identifying the primary site remains unclear,possibly due to the tumor being too small or g...Primary of Unknown Origin Cancer(CUP)is a metastatic tumor whose origin remains undetermined.The reason for this ambiguity in identifying the primary site remains unclear,possibly due to the tumor being too small or growing too slowly,or because the immune system has destroyed the tiny primary lesion.Most CUP patients receive only localized treatment or empirical systemic chemotherapy,leading to poor prognosis and shorter average overall survival.There is currently insufficient evidence-based medical support for the diagnosis and treatment of CUP.This study retrospectively analyzed clinical characteristics,diagnostic methods,treatment approaches,and prognostic outcomes of newly diagnosed CUP patients treated in our department.The findings aim to provide clinical guidance for diagnosis and treatment of CUP,with the goal of reducing diagnostic delays and improving patient outcomes.展开更多
The dinuclear system approach,coupled with the statistical decay model GEMINI++,was used to investigate multinucleon transfer reactions.Experimental production cross-sections in the reaction^(129)Xe+^(248)Cm were repr...The dinuclear system approach,coupled with the statistical decay model GEMINI++,was used to investigate multinucleon transfer reactions.Experimental production cross-sections in the reaction^(129)Xe+^(248)Cm were reproduced to assess the reliability of these theoretical models.The production of neutron-deficient transcalifornium nuclei with Z=99-106 was examined in multinucleon transfer reactions,including^(124)Xe+^(248)Cm,^(124)Xe+^(249)Cf,and^(129)Xe+^(249)Cf.Both the driving potential and the neutron-to-proton equilibration ratio were found to dominate the nucleon transfer process.The reaction^(124)Xe+^(249)Cf is proposed as a promising projectile-target combination for producing neutron-deficient isotopes with Z=99-106,with the optimal incident energy identified as E_(c.m.)=533.64 MeV.Production cross-sections of 25 unknown neutron-deficient trancalifornium isotopes with cross-sections greater than 1 pb were predicted.展开更多
In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extract...In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.展开更多
This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters,mismatched and matched disturbances.Different from the traditional adaptive results ...This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters,mismatched and matched disturbances.Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only,the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters.Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way.With skillful integration of the backstepping technique and the adaptive control,a synthesized controller framework is successfully developed for electro-hydraulic axis systems,in which the coupled interaction between parameter estimation and disturbance estimation is avoided.Accordingly,this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances.Also,a nonlinear filter is designed to eliminate the“explosion of complexity”issue existing in the classical back-stepping technique.The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured.Finally,contrastive experiment results validate the superiority of the developed method as well.展开更多
With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detectin...With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.展开更多
This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown cova...This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.展开更多
In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of ...In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.展开更多
Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications i...Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.展开更多
This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separati...This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separation technique and signal replacement mechanism,the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems.By incorporating a Nussbaum-type function,the proposed approach can deal with the unknown control direction of the nonlinear systems.Based on a Lyapunov-Krasovskii-like composite energy function,the convergence of tracking error sequence is achieved in the iteration domain.Finally,two simulation examples are provided to illustrate the feasibility of the proposed control method.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 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.
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.
基金supported by the National Natural Science Foundation of China under Grant 62073190the Science Center Program of National Natural Science Foundation of China under Grant 62188101.
文摘This paper investigates the problem of dynamic event-triggered control for a class of large-scale nonlinear systems.In particular,both neutral delays and unknown backlash-like hysteresis are considered.This requires to integrate a compensation mechanism into the event-triggered control architecture.To this end,dynamic gain and adaptive control techniques are introduced to address the effects of neutral delays,unknown hysteresis and parameter uncertainties simultaneously.By introducing a non-negative internal dynamic variable,a dynamic event-triggered controller is designed using the hyperbolic tangent function to reduce the communication burden.By means of the Lyapunov–Krasovskii method,it is demonstrated that all signals of the closed-loop system are globally bounded and eventually converge to a tunable bounded region.Moreover,the Zeno behavior is avoided.Finally,a simulation example is presented to verify the validity of the control scheme.
基金National Natural Science Foundation of China(62373102)Jiangsu Natural Science Foundation(BK20221455)Anhui Provincial Key Research and Development Project(2022i01020013)。
文摘Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.
文摘BACKGROUND Pelvic squamous cell carcinoma of unknown primary(CUP)is extremely rare,accounting for less than one percent of all CUP cases,and its infrequency has lim-ited the development of standardized diagnostic and treatment guidelines.CASE SUMMARY A 77-year-old female with a history of resected lung adenocarcinoma presented with worsening constipation.Imaging revealed a 2.5 cm mass adjacent to the right levator ani muscle.Biopsy confirmed poorly differentiated squamous cell carcinoma,positive for pancytokeratin and p40,and negative for p16,cytokeratin 7,cytokeratin 20,and neuroendocrine markers.No primary lesion was identified despite extensive evaluation.She underwent five cycles of 5-fluorouracil(1000 mg/m^(2) continuous infusion,days 1-4)and mitomycin-C(10 mg/m^(2) on day 1)with concurrent pelvic radiotherapy(50.4 Gy in 28 fractions).Follow-up imaging demonstrated complete remission sustained for 12 months.Electrocorticography performance status improved from 2 at diagnosis to 1 during follow-up.CONCLUSION This case highlights the potential role of chemoradiotherapy in managing pelvic squamous cell CUP,achieving durable remission in selected patients.
基金supported by National Natural Science Foundation of China(U20B2070,62001091)Sichuan Science and Technology Program(2022YFS0531).
文摘Most of the existing non-line-of-sight(NLOS)localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios.To solve the problem,an NLOS target localization method in unknown L-shaped corridor based ultra-wideband(UWB)multiple-input multiple-output(MIMO)radar is proposed in this paper.Firstly,the multipath propagation model of Lshaped corridor is established.Then,the localization process is analyzed by the propagation characteristics of diffraction and reflection.Specifically,two different back-projection imaging processes are performed on the radar echo,and the positions of focus regions in the two images are extracted to generate candidate targets.Furthermore,the distances of propagation paths corresponding to each candidate target are calculated,and then the similarity between each candidate target and the target is evaluated by employing two matching factors.The locations of the targets and the width of the corridor are determined based on the matching rules.Finally,two experiments are carried out to demonstrate that the method can effectively obtain the target positions and unknown scene information even when partial paths are lost.
文摘Primary of Unknown Origin Cancer(CUP)is a metastatic tumor whose origin remains undetermined.The reason for this ambiguity in identifying the primary site remains unclear,possibly due to the tumor being too small or growing too slowly,or because the immune system has destroyed the tiny primary lesion.Most CUP patients receive only localized treatment or empirical systemic chemotherapy,leading to poor prognosis and shorter average overall survival.There is currently insufficient evidence-based medical support for the diagnosis and treatment of CUP.This study retrospectively analyzed clinical characteristics,diagnostic methods,treatment approaches,and prognostic outcomes of newly diagnosed CUP patients treated in our department.The findings aim to provide clinical guidance for diagnosis and treatment of CUP,with the goal of reducing diagnostic delays and improving patient outcomes.
基金supported partly by the National Key R&D Program of China(No.2023YFA1606401)the National Natural Science Foundation of China(Nos.12135004,11635003,11961141004,12105019,and 12047513)+1 种基金the Open Project of Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology(No.NLK2023-05)the Central Government Guidance Funds for Local Scientific and Technological Development,China(No.Guike ZY22096024)。
文摘The dinuclear system approach,coupled with the statistical decay model GEMINI++,was used to investigate multinucleon transfer reactions.Experimental production cross-sections in the reaction^(129)Xe+^(248)Cm were reproduced to assess the reliability of these theoretical models.The production of neutron-deficient transcalifornium nuclei with Z=99-106 was examined in multinucleon transfer reactions,including^(124)Xe+^(248)Cm,^(124)Xe+^(249)Cf,and^(129)Xe+^(249)Cf.Both the driving potential and the neutron-to-proton equilibration ratio were found to dominate the nucleon transfer process.The reaction^(124)Xe+^(249)Cf is proposed as a promising projectile-target combination for producing neutron-deficient isotopes with Z=99-106,with the optimal incident energy identified as E_(c.m.)=533.64 MeV.Production cross-sections of 25 unknown neutron-deficient trancalifornium isotopes with cross-sections greater than 1 pb were predicted.
基金This work is supported by the National Key R&D Program of China(2017YFB0802900).
文摘In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.
基金supported in part by the National Key R&D Program of China(No.2021YFB2011300)the National Natural Science Foundation of China(No.52075262,51905271,52275062)+1 种基金the Fok Ying-Tong Education Foundation of China(No.171044)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22_0471)。
文摘This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters,mismatched and matched disturbances.Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only,the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters.Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way.With skillful integration of the backstepping technique and the adaptive control,a synthesized controller framework is successfully developed for electro-hydraulic axis systems,in which the coupled interaction between parameter estimation and disturbance estimation is avoided.Accordingly,this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances.Also,a nonlinear filter is designed to eliminate the“explosion of complexity”issue existing in the classical back-stepping technique.The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured.Finally,contrastive experiment results validate the superiority of the developed method as well.
基金The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research atMajmaah University for funding this research work through the project number(R-2024-920).
文摘With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.
文摘This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.
基金supported by the National Science Foundation of China(Nos.62271293,61903238)the Natural Science Foundation of Shandong Province,China(No.ZR2021MF035)the Social Science Planning Project of Shandong Province,China(No.22CYYJ13).
文摘In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.
基金This research was partly supported by the National Science and Technology Council,Taiwan with Grant Numbers 112-2221-E-992-045,112-2221-E-992-057-MY3 and 112-2622-8-992-009-TD1.
文摘Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.
基金supported by National Natural Science Foundation of China (No. 60974139)Fundamental Research Funds for the Central Universities (No. 72103676)
文摘This paper proposes a new adaptive iterative learning control approach for a class of nonlinearly parameterized systems with unknown time-varying delay and unknown control direction.By employing the parameter separation technique and signal replacement mechanism,the approach can overcome unknown time-varying parameters and unknown time-varying delay of the nonlinear systems.By incorporating a Nussbaum-type function,the proposed approach can deal with the unknown control direction of the nonlinear systems.Based on a Lyapunov-Krasovskii-like composite energy function,the convergence of tracking error sequence is achieved in the iteration domain.Finally,two simulation examples are provided to illustrate the feasibility of the proposed control method.
