Current System Research Articles

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Multidecadal Phase Changes in the Thermodynamic State of the System: Ocean–Atmosphere–Continent

The present-day climate (the recent 100–150 years) obviously constitutes the structure of a global intra-system rhythmic process with an individual rhythm of about 60 years. In turn, each of the rhythms is presented by the two climate phases of about 25–35 years characterized by qualitative differences: one phase is relatively continental, while the other is humid. Globality and quasi-synchronism of environmental changes are accompanied by planetary structures: the Global Atmospheric Oscillation (GAO) in the atmosphere and the Multidecadal Oscillation of the Heat content in the Ocean (MOHO) discovered relatively recently. Unexpected and rapid qualitative phase changes in the climate, which first focused attention in the mid-1970s of the last century, were titled “climate shifts”. The revealed features of the present-day climate are of exceptional scientific and practical interest and deserve the development of methods for predicting the timing of the forthcoming climate shift. Arising unexpectedly and accompanied by rapid significant changes, these shifts identified the problem of understanding the nature and establishing the processes and mechanisms causing them. First of all, of interest are phase changes in the thermodynamic state of the climate system components: the ocean, atmosphere, and continents. As a result of the World Ocean (WO) thermohydrodynamics numerical modelling, it is shown that MOHO is localized in the layer of the main thermocline, where the most important elements of the WO circulation are located. The performed study based on observational data allows us to conclude that, during the phase of the WO thermal discharge (1975–1999), the two key systems of currents, the Kuroshio and the Gulf Stream, were under similar thermodynamic conditions.

Data-Driven Modeling and Operation Optimization With Inherent Feature Extraction for Complex Industrial Processes

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

Design of Real Time Intelligent System for Women Safety

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women.

Quantitative evaluation of crowd intelligence innovation system health: An ecosystem perspective

Crowd intelligence innovation (CII) is an open problem-solving model, typically performs on a crowdsourcing-based online innovation platform, which is highly dependent on the engagement and high-quality work of participants. On an online platform, many demanders publish tasks, and innovators submit schemes, which forms a complex CII system. To ensure such a CII system operates sustainably, it is critical for the platform operators to know the current system’s health statuses before taking proper management actions. However, the large influx of participators with varied behaviors makes it extremely difficult to evaluate the health statuses of the system and capture its characteristics. Practicable method for quantitatively evaluating the health status of a CII system is still missing. Therefore, this study proposed a new quantitative health status evaluation method from an ecosystem perspective. First, the crowd intelligence innovation ecosystem (CIIE) was established by mapping the CII system onto the natural ecosystem based on ecological theory. Second, the health status of CIIE was described by its internal health status and external health status using a mixed method. The internal health status of the CIIE was defined and evaluated based on its health characteristics in terms of stability, ascendency, development capacity, and redundancy measurements using ecological network analysis. The external health status was evaluated using the ecological entropy index system. As the primary verification of the feasibility of our proposed approach, a case study of Zhubajie was conducted. These results confirmed the feasibility of our evaluation method. With this method, the health status of CIIEs could be accurately measured, which supported the management and organization the CII systems. Future work will focus on adapting suitable measures to improve the operation of CII.

A Novel Lightweight Navigation System for Oral and Maxillofacial Surgery Using an External Curved Self-Identifying Checkerboard

This paper presents a novel lightweight navigation system for oral and maxillofacial surgery (OMS). An external curved checkerboard with self-identifying markers is set as the reference object around the surgical scene. A customized oral clip with a micro camera is designed for oral localization by tracking the external checkerboard. Similarly, the dental handpiece is also equipped with a micro camera, which can be localized like the clip. The spatial model of the markers is provided by binocular stereoscopic reconstruction. A front surface mirror is taken up for the registration between the oral cavity and the camera on the clip. The pivot calibration of the dental handpiece is accomplished by our proposed calibration method. We set up an experimental group and a control group for evaluation. The surgical tools of the experimental group were approximately 30% lighter, 35% less bulky, and 90% cheaper than those of the control group. Our system yielded the comprehensive navigation accuracy of 0.92 mm whereas the accuracy of the control group was 0.87 mm. Results revealed that our system can achieve similar accuracy compared with a prevailing system at a lighter weight, a more compact volume, and a lower cost. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to reduce the burden on patients and surgeons during OMS. Current commercial navigation systems are still limited by the burden of extra cumbersome fiducial markers and high hardware costs. Their high accuracy benefits from the large size of fiducial markers. To take full advantage of the camera’s localization effect, we propose the concept of “marker-camera inverse projection”, i.e., reversing the roles of the camera and the markers. In this way, cameras on surgical tools detect more points with a more uniform distribution. Our proposed system achieves a decent balance between navigation accuracy and hardware cost, which facilitates the development of surgical tools to be lighter and more economical, and involves tremendous potential for commercialization.

Indoor pollution control based on surrogate model for residential buildings

Individuals typically spend most of their lives indoors, predominantly in spaces like offices and residences. Consequently, prolonged indoor exposure underscores the critical significance of maintaining optimal indoor air quality (IAQ) to safeguard one's health. The primary impediment to attaining efficient regulation of Indoor Air Quality (IAQ) is the challenge of monitoring the IAQ parameters, particularly within the immediate vicinity of an individual's breathing space. Current heating, ventilation, and air conditioning systems lack the ability to rapidly predict and optimize the quality of indoor air. The objective of this study is to acquire the distribution features of indoor pollutants and precise indoor environment data in order to efficiently forecast and enhance the IAQ. To achieve this objective, a proposed surrogate model was developed using computational fluid dynamics (CFD). Notably, the Kriging surrogate model can rapidly predict IAQ while using a limited number of CFD runs. CFD are widely used as numerical simulation methods to obtain the accurate information. Surrogate models can rapidly forecast indoor environmental conditions using CFD simulation data simultaneously. Optimization algorithms can efficiently achieve desirable indoor ambient conditions, offering highly effective and intelligent control techniques for indoor atmospheric ventilation.

Implementing Blockchain in the Management of Organ Donation and Transplantation

Abstract: Current organ donation and transplantation systems suffer from vulnerabilities to scams and lack of transparency, hindering trust and efficiency. This proposal introduces a groundbreaking blockchain-based solution that combats both issues. By leveraging the blockchain's security and immutability, the system detects and predicts potential scams through AI analysis of realtime data, notifying authorities immediately. Additionally, it strengthens security and trust by ensuring secure participant identities, controlled access to sensitive information, and automated processes via smart contracts. This not only reduces the risk of illegal activity but also fosters fairness and participation, ultimately transforming the system into a life-saving model of transparency and trust. But that's not all. Blockchain securely stores identities, encrypts sensitive information, and automates tasks with smart contracts, eliminating the human error and shady dealings that plague the current system. This creates a web of trust, empowering donors, recipients, and medical professionals alike. The result? A system that's not just secure, but fair and efficient, saving lives and restoring faith in this vital field. It's time to transplant trust into organ donation, and blockchain is the fertile ground for its growth

Air Writing Word Recognition and Translation

Abstract: Project develops a system that can recognize the air-written words in free space, and then classify the recognized word. Air-writing is the new way of writing the linguistic words in free area using hand or finger movements. This project could be a combination of computer vision hand chase and handwriting recognition machine learning. The air writing recognition system uses the web camera of a pc to trace word written within the air by the user and then uses a convolutional neural network to classify the word into one of the possible classes. Several current systems use advanced and high-priced chase setups to realize gesture recognition, however, we tend to get to form a system that may attain a similar work with a far cheaper setup.

A review of wastewater-based epidemiology for antimicrobial resistance surveillance

Antimicrobial resistance (AMR) is recognized as one of the most serious threats to public health. Unparalleled population growth and accelerated rates of AMR emergence and dissemination have resulted in both novel resistance in pathogenic organisms and the re-appearance of infections that were formerly under control. Consequently, this has led to an increased quantity of infectious diseases. One of the main drivers of antimicrobial overuse is inappropriate prescribing in human and veterinary medicine. The ability to rapidly survey the spread of antimicrobial resistance within human populations is key for its prevention, intervention, and control. However, many constraints are present for current clinical surveillance systems and their capacity to determine AMR dynamics in the microbiome of healthy individuals as well as in clinical pathogens causing infections. Wastewater-based epidemiology (WBE) is an emergent technique that has the capacity to act as a supplementary measure for current infectious disease surveillance systems and as an early warning system for infectious disease outbreaks. The development of disease outbreaks to the community level can be monitored in real time through the analysis of population pooled wastewater. This review provides an introduction to using wastewater-based epidemiology to monitor AMR bacteria, as well as an overview of wastewater-based epidemiology and its components.

Light-Mediated Polymerization Catalyzed by Carbon Nanomaterials.

Carbon nanomaterials, specifically carbon dots and carbon nitrides, play a crucial role as heterogeneous photoinitiators in both radical and cationic polymerization processes. These recently introduced materials offer promising solutions to the limitations of current homogeneous systems, presenting a novel approach to photopolymerization. This review highlights the preparation and photocatalytic performance of these nanomaterials, emphasizing their application in various polymerization techniques, including photoinduced i) free radical, ii) RAFT, iii) ATRP, and iv) cationic photopolymerization. Additionally, it discusses their potential in addressing contemporary challenges and explores prospects in this field. Moreover, carbon nitrides, in particular, exhibit exceptional oxygen tolerance, underscoring their significance in radical polymerization processes and allowing their applications such as 3D printing, surface modification of coatings, and hydrogel engineering.

FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Predicting traffic flow is vital for optimizing intelligent transportation systems (ITS) and reducing congestion by forecasting traffic patterns accurately. However, current centralized TFP systems have limitations, e.g., slow training, high communication costs, and privacy concerns. To address these challenges, this study proposes FedAGAT, a system for short-term traffic flow prediction (TFP) based on federated community and adaptive spatial-temporal graph attention networks (AGAT). The FedAGAT system allows for local data processing and sharing only model updates with the server. This enables real-time, scalable, and secure TFP - essential capabilities for efficient ITS. AGAT is employed to capture intricate spatial-temporal dependencies in traffic flow, while federated learning facilitates decentralized learning, enhancing privacy. The FedAGAT prediction process involves four steps: dividing the local subnetwork using spectral community detection, locally training based on global parameters, uploading updated parameters, and creating a global model prediction based on the aggregated parameters. To evaluate the performance of FedAGAT, two real-world traffic datasets were utilized for benchmarking against seven statistical and deep learning models. Results demonstrate that FedAGAT provides relatively higher accuracy for short- and mid-term forecasting horizons. Moreover, FedAGAT predictions closely match real traffic flow values, and the overall performance is comparable to a global model, while requiring less time.

Analisis Sistem Informasi Akuntansi Penggajian Dalam Upaya Mendukung Pengambilan Keputusan Pada Rumah Sakit Umum Haji Medan

Current economic developments have been encouragingcompany growth to enlarge and expand spacescope of business. Likewise, current information systems havemakes it easier for companies to deal with problems that may occur in the payroll process. Objective This research is to find out how the system is implemented payroll accounting at the Medan Haji General Hospital and for find out whether the payroll accounting system is implemented by Companies can help management in decision making towards salary. This type of research is a type of qualitative research with a descriptive approach that uses primary data such as results of interviews with the company and secondary parties such as structureorganization. Data collection techniques were carried out using techniques observation, interviews, and literature, data analysis methods used is a descriptive method. This research was conducted in Medan Haji General Hospital which is located on Jl. Hajj Hospital – Estate terrain and research time starts in January 2024 until the completion of this thesis. The results of this research show that the accounting system implemented by the Hajj General Hospital to handle payroll has been quite adequate in helping company management in making decisions regarding salaries, p This is good because the company employs employees from a wide range levels and in very large numbers and provide rewards for their services in the form of salaries, so the company must have effective accounting system.

[Infectious Diseases - a new specialty for postgraduate training in Germany].

Medicine in Germany is currently facing major structural and economic challenges. Infectious Diseases, with the recent introduction of a new specialty in "Internal Medicine and Infectious Diseases" and with the existing additional training for almost all specializations, will make an important contribution to overcoming these challenges. Expertise in infectious diseases has to be very broad and requires high interdisciplinarity, which makes infectious diseases an attractive and demanding specialty. The complex fundamentals of infectious diseases must now be quickly conveyed to as many physicians as possible in a short period of time, as part of their specialization or as additional training. Until this is achieved, transitional solutions will be necessary for some time. The adaptation of the current billing and reimbursement system for infectious diseases services and improved intersectoral cooperation are of the utmost importance for the further development of the specialty.

Transcendental philosophy and logic diagrams

AbstractLogic diagrams have seen a resurgence in their application in a range of fields, including logic, biology, media science, computer science and philosophy. Consequently, understanding the history and philosophy of these diagrams has become crucial. As many current diagrammatic systems in logic are based on ideas that originated in the 18th and 19th centuries, it is important to consider what motivated the use of logic diagrams in the past and whether these reasons are still valid today. This paper proposes that transcendental philosophy was a key inspiration for the development of logic diagrams and that such diagrams can be employed in transcendental arguments, even after the linguistic turn.

Safety of Perception Systems for Automated Driving: A Case Study on Apollo

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle’s own as well as surrounding objects’ relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe.

Early Validation and Verification of System Behaviour in Model-based Systems Engineering: A Systematic Literature Review

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&amp;V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&amp;V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&amp;V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&amp;V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&amp;V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.

High normal alanine aminotransferase is an indicator for better response to antiviral therapy in chronic hepatitis B.

Evidence shows people living with CHB even with a normal ALT (40U/L as threshold) suffer histological disease and there is still little research to evaluate the potential benefit of antiviral benefits in them. We retrospectively examined 1352 patients who underwent liver biopsy from 2017 to 2021 and then obtained their 1-year follow-up data to analyze. ALT levels were categorized into high and low, with thresholds set at >29 for males and >15 for females through Youden's Index. The high normal ALT group showed significant histological disease at baseline (56.43% vs 43.82%, p< 0.001), and better HBV DNA clearance from treatment using PSM (p=0.005). Similar results were obtained using 2016 AASLD high normals (male >30, female >19). Further multivariate logistic analysis showed that high normal ALT (both criterias) was an independent predictor of treatment (OR 1.993, 95% CI 1.115-3.560, p=0.020; OR 2.000, 95% CI 1.055-3.793, p=0.034) Both of the models had higher AUC compared with current scoring system, and there was no obvious difference between the two models (AUC:0.8840 vs 0.8835). Male >30 or female >19 and Male >29 or female>15 are suggested to be better thresholds for normal ALT. Having a high normal ALT in CHB provides a potential benefit in antiviral therapy.

Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements

Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.

A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems

Based on cluster system theory and the Markov process, a performance prediction method utilizing time-dependent subsystem transfers between family systems is proposed in this paper. The family system is divided via the mean clustering method, with the key performance parameters of subsystems utilized as identification parameters. According to the transition quantity of subsystems in the family systems, the transition probability of subsystems between family systems is described via the Markov process. The transition matrix between subsystems is established by dividing multiple intervals of key performance states. The inter-family transfer matrix and the current family system label of the subsystem are updated in real time. Thus, the transition probability of any subsystem and the total number of subsystems to be transferred to the failure-state family system can be judged, and the remaining life can be further determined. Using the real-world monitoring dataset from the FAST Telescope, the effectiveness and accuracy of the method are verified. Due to the representativeness of family systems to subsystems and the powerful transfer-describing ability of Markov processes, the proposed method shows superiority in online prediction and performance evaluation compared to the fault data-based method, such as improvements in rapidity and accuracy. In addition, the proposed method can be used to evaluate overall reliability without reference samples, thus making the prediction method more practical in complex, large systems with small or even zero sample conditions.