Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

An Improved Distributed Query for Large-Scale RDF Data

The rigid structure of the traditional relational database leads to data redundancy,which seriously affects the efficiency of the data query and cannot effectively manage massive data.To solve this problem,we use distributed storage and parallel computing technology to query RDF data.In order to achieve efficient storage and retrieval of large-scale RDF data,we combine the respective advantage of the storage model of the relational database and the distributed query.To overcome the disadvantages of storing and querying RDF data,we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform.We conduct the LUBM query statements respectively with the selected data sets.In experiments,we compare query response time in different conditions to evaluate the feasibility and correctness of our approaches.The results show that the proposed scheme can reduce the storage cost and improve query efficiency.

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Background:The prognosis of breast cancer is often unfavorable,emphasizing the need for early metastasis risk detection and accurate treatment predictions.This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival(DFS).Methods:We retrospectively collected pathology imaging,molecular and clinical data from The Cancer Genome Atlas and one independent institution in China.We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal(DeepClinMed-PGM)model for DFS prediction,integrating clinicopathological data with molecular insights.The patients included the training cohort(n=741),internal validation cohort(n=184),and external testing cohort(n=95).Result:Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve(AUC)values.In the training cohort,AUC values for 1-,3-,and 5-year DFS predictions increased to 0.979,0.957,and 0.871,while in the external testing cohort,the values reached 0.851,0.878,and 0.938 for 1-,2-,and 3-year DFS predictions,respectively.The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts,including the training cohort[hazard ratio(HR)0.027,95%confidence interval(CI)0.0016-0.046,P<0.0001],the internal validation cohort(HR 0.117,95%CI 0.041-0.334,P<0.0001),and the external cohort(HR 0.061,95%CI 0.017-0.218,P<0.0001).Additionally,the DeepClinMed-PGM model demonstrated C-index values of 0.925,0.823,and 0.864 within the three cohorts,respectively.Conclusion:This study introduces an approach to breast cancer prognosis,integrating imaging and molecular and clinical data for enhanced predictive accuracy,offering promise for personalized treatment strategies.

An Angle Structure Descriptor for Image Retrieval

This paper presents an efficient image feature representation method, namely angle structure descriptor(ASD), which is built based on the angle structures of images. According to the diversity in directions, angle structures are defined in local blocks. Combining color information in HSV color space, we use angle structures to detect images. The internal correlations between neighboring pixels in angle structures are explored to form a feature vector. With angle structures as bridges, ASD extracts image features by integrating multiple information as a whole, such as color, texture, shape and spatial layout information. In addition, the proposed algorithm is efficient for image retrieval without any clustering implementation or model training. Experimental results demonstrate that ASD outperforms the other related algorithms.

Federated Learning for 6G Communications:Challenges,Methods,and Future Directions

As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G communications.It is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale networks.However,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns.Federated learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G.In this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G.We then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.

Multivariate time series prediction based on AR_CLSTM

Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.

Proton pump inhibitors increase the severity of hepatic encephalopathy in cirrhotic patients

BACKGROUND Liver cirrhosis is the late stage of hepatic fibrosis and is characterized by portal hypertension that can clinically lead to decompensation in the form of ascites,esophageal/gastric varices or encephalopathy.The most common sequelae associated with liver cirrhosis are neurologic and neuropsychiatric impairments labeled as hepatic encephalopathy(HE).Well established triggers for HE include infection,gastrointestinal bleeding,constipation,and medications.Alterations to the gut microbiome is one of the leading ammonia producers in the body,and therefore may make patients more susceptible to HE.AIM To investigate the relationship between the use of proton pump inhibitors(PPIs)and HE in patients with cirrhosis.METHODS This is a single center,retrospective analysis.Patients were included in the study with an admitting diagnosis of HE.The degree of HE was determined from subjective and objective portions of hospital admission notes using the West Haven Criteria.The primary outcome of the study was to evaluate the grade of HE in PPI users versus non-users at admission to the hospital and throughout their hospital course.Secondary outcomes included rate of infection,gastrointestinal bleeding within the last 12 mo,mean ammonia level,and model for end-stage liver disease scores at admission.RESULTS The HE grade at admission using the West Haven Criteria was 2.3 in the PPI group compared to 1.7 in the PPI nonuser group(P=0.001).The average length of hospital stay in PPI group was 8.3 d compared to 6.5 d in PPI nonusers(P=0.046).Twenty-seven(31.8%)patients in the PPI user group required an Intensive Care Unit admission during their hospital course compared to 6 in the PPI nonuser group(16.7%)(P=0.138).Finally,10(11.8%)patients in the PPI group expired during their hospital stay compared to 1 in the PPI nonuser group(2.8%)(P=0.220).CONCLUSION Chronic PPI use in cirrhotic patients is associated with significantly higher average West Haven Criteria for HE compared to patients that do not use PPIs.

MXene Ti_3C_2T_x saturable absorber for pulsed laser at 1.3 μm

The excellent optical properties of MXene provide new opportunities for short-pulse lasers. A diode-pumped passively Q-switched laser at 1.3 μm wavelength with MXene Ti3C2Tx as saturable absorber was achieved for the first time. The stable passively Q-switched laser has 454 ns pulse width and 162 kHz repetition rate at 4.5 W incident pumped power. The experimental results show that the MXene Ti3C2Tx saturable absorber can be used as an optical modulator to generate short pulse lasers in a solid-state laser field.

Aspect-Level Opinion Mining of Online Customer Reviews

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.

A network security situation awareness method based on layered attack graph

The real-time of network security situation awareness(NSSA)is always affected by the state explosion problem.To solve this problem,a new NSSA method based on layered attack graph(LAG)is proposed.Firstly,network is divided into several logical subnets by community discovery algorithm.The logical subnets and connections between them constitute the logical network.Then,based on the original and logical networks,the selection of attack path is optimized according to the monotonic principle of attack behavior.The proposed method can sharply reduce the attack path scale and hence tackle the state explosion problem in NSSA.The experiments results show that the generation of attack paths by this method consumes 0.029 s while the counterparts by other methods are more than 56 s.Meanwhile,this method can give the same security strategy with other methods.

Optical waveguide in Nd:Bi_(12)SiO_(20) crystal produced by multi-energy C ion implantation

We report the fabrication of a planar waveguide in the Nd：Bi_（12）SiO_（20） crystal by multi-energy C ions at room temperature. The waveguide is annealed at 200℃, 260℃, and 300℃ in succession each for 30 min in an open oven. The effective refractive index profiles at transverse electric（TE） polarization are stable after the annealing treatments. Damage distribution for multi-energy C ion implanted in Nd：Bi_（12）SiO_（20） crystal is calculated by SRIM 2010. The Raman and fluorescence spectra of the Nd：Bi_（12）SiO_（20） crystal are collected by an excitation beam at 633 nm and 473 nm, respectively. The results indicate the stabilization of the optical waveguide in Nd：Bi_（12）SiO_（20） crystal.

An application of insurance mechanism in risk management for energy internet

As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.

Tunable Nd,La:SrF2 laser and passively Q-switched operation based on gold nanobipyramids saturable absorber

A novel Nd, La：SrF_2 disordered crystal is prepared, and its continuous-wave wavelength tuning operation is performed for the first time. Employing a surface plasmon resonance（SPR） based gold nanobipyramids（G-NBPs） saturable absorber,we obtain a compact diode-pumped passively Q-switched Nd, La：SrF_2 laser. The stable Q-switched pulse operates with the shortest pulse duration of 1.15 μs and the maximum repetition rate of 41 k Hz. The corresponding single pulse energy is 2.24 μJ. The results indicate that G-NBPs could be a promising saturable absorber applied to the diode-pumped solid state lasers（DPSSLs）.

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

In this paper,we study the dynamics of the CoVid-19 outbreak in Semarang,Indonesia,using a fractional CoVid-19 model.We first determine the effects of the isolation rateand infection rate b on the reproduction number R0 and infected number V.We find that R0 is directly proportional to b and inversely proportional to.For V,the effect of physical distancing is not as significant as changing.Asincreases,V decreases,the number of susceptible individuals increases,the number of quarantined individuals decreases sharply,and the number of recovered individuals decreases.Moreover,the effect of vaccination is also considered.The combination of physical distancing,isolation,and vaccination has a significant impact on reducing the number of infected individuals.Analysis of dynamical systems allows us to understand the characteristics of our model,such as its boundedness and non-negativity,the existence of equilibrium points,the existence and uniqueness of solutions,and the local and global stability.To validate our fractional CoVid-19 model,we introduce the fractional extended Kalman filter(FracEKF)as a prediction method and compare the results against reported CoVid-19 data.FracEKF is a modified version of the basic extended Kalman filter with a time-fractional memory effect.The prediction results illustrate the accuracy of this model in terms of the root mean square error(RMSE),normalized root mean square error(NRMSE),and mean absolute percentage error(MAPE)for each fractional-order.Varyingreproduces the trends observed in the reported data for the number of infected individuals,i.e.,whenincreases,the infected number decreases.Moreover,a higher fractional-order results in higher model accuracy.Furthermore,higher values of the process noise Qf give smaller errors,whereas higher values of the observation noise Rf produce higher errors.Qf and the fractional-order a are inversely proportional to RMSE;NRMSE,and MAPE,whereas Rf is directly proportional to RMSE;NRMSE,and MAPE.

Identity-Preserving Adversarial Training for Robust Network Embedding

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

The challenges of a Big Data Earth

The potential of big data fused with the vision of a digital Earth offers powerful opportunities to deepen understanding of the whole Earth system and the management of a sustainable planet.It is important to stand back from often confusing detail to clarify what those opportunities are and how they might be seized.The essential scientific potential of data,big or small,is to reveal patterns,which have often been the fundamental first step in stimulating inquiry,leading to new questions,new perspectives and potentially to new answers.The digital revolution has created a“digital microscope”that permits us to see patterns that have not been seen before,and when coupled with machine learning technologies to analyse them in creating statistical predictions of the behaviour of both human and non-human systems.These potentials converge with the imperative to represent an Earth system with interacting non-human and human components,as a vital contribution to the understanding and actions required in working towards planetary sustainability.But a digital Earth is also capable of being represented mathematically as a digitally networked phenomenon,analogous to an analogue computer,and should be an important target for a Big Earth Data Journal.We should also return to Al Gore’s vision of an accessible digital Earth with wide usability.Pre-determining the separate functions of parallel digital Earths risks losing one of the great potentials of big data and learning algorithms,the identification and analysis of unanticipated relationships and processes.

A commentary of Digital contact tracing in MIT Technology Review 2021

In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact tracing.Through this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance pressure.The practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.

Passively Q-switched operation of a 1.94μm thulium-doped solid-state laser based on MXeneV_(2)CT_(x)

MXene V_(2)CT_(x) has great practicability because it is not easy to degrade under ambient conditions.In this paper,a V_(2)CT_(x) saturable absorber(SA)was firstly applied to a passively Q-switched(PQS)laser,to the best of our knowledge.The V_(2)CT_(x)-SA was prepared by the spin-coating method.The linear absorption of the V_(2)CT_(x)-SA in the 1000-2200 nm region and the nonlinear absorption near 2μm were studied.With the V_(2)CT_(x)-SA,a typical PQS operation at 1.94μm was realized in a Tm:YAlO3laser.The minimum pulse width produced by the PQS laser was 528 ns,and the peak power,repetition rate,and average output power were 10.06 W,65.9 kHz,and 350 mW,respectively.Meanwhile,the maximum pulse energy was 6.33μJ.This work demonstrates that the V_(2)CT_(x) can be used as an effective SA to obtain nanosecond pulses with high peak power and high repetition rate simultaneously.

Assessing the effectiveness of the intervention measures of COVID-19 in China based on dynamical method

Normalized interventions were implemented in different cities in China to contain the outbreak of COVID-19 before December 2022.However,the differences in the intensity and timeliness of the implementations lead to differences in final size of the infections.Taking the outbreak of COVID-19 in three representative cities Xi'an,Zhengzhou and Yuzhou in January 2022,as examples,we develop a compartmental model to describe the spread of novel coronavirus and implementation of interventions to assess concretely the effectiveness of Chinese interventions and explore their impact on epidemic patterns.After applying reported human confirmed cases to verify the rationality of the model,we apply the model to speculate transmission trend and length of concealed period at the initial spread phase of the epidemic(they are estimated as 10.5,7.8,8.2 days,respectively),to estimate the range of basic reproduction number(2.9,0.7,1.6),and to define two indexes(transmission rate vt and controlled rate vc)to evaluate the overall effect of the interventions.It is shown that for Zhengzhou,vc is always more than v t with regular interventions,and Xi'an take 8 days to achieve vc>v t twice as long as Yuzhou,which can interpret the fact that the epidemic situation in Xi'an was more severe.By carrying out parameter values,it is concluded that in the early stage,strengthening the precision of close contact tracking and frequency of large-scale nucleic acid testing of non-quarantined population are the most effective on controlling the outbreaks and reducing final size.And,if the close contact tracking strategy is sufficiently implemented,at the late stage largescale nucleic acid testing of non-quarantined population is not essential.