Prognostic Method Research Articles

Multi-modal fusion network with channel information guided module for prognosis prediction in patients with anti-N-methyl-D-aspartate receptor encephalitis

As the most common type of autoimmune encephalitis, the pathological mechanism of anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis has been gradually clarified, but the optimal treatment has not yet been clarified. Accurate early prognostic assessment is of great significance for reversing the symptoms of anti-NMDAR encephalitis. Compared with the expensive costs of human evaluation, the prognostic evaluation method based on the deep learning diagnosis model has obvious advantages. In addition, full consideration of multi-modal information, such as multi-sequence magnetic resonance imaging (MRI) features and clinical variables, has a positive impact on the accurate prediction of patient prognosis. In this paper, a multimodal fusion network is proposed to predict the prognosis of patients with anti-NMDAR encephalitis. The proposed network contains three key substructures. First of all, the channel information guided module is designed to constrain the heterogeneity between the features of different modes through the attention mechanism to achieve more effective feature fusion. Second, we design a backbone network for feature extraction of brain MRI, which is based on a dual-branch residual structure and utilizes the channel information guided module to ensure that the information of features on different scales is complementary. Finally, a feature fusion network is proposed, which uses channel information guided module and dynamic normalization weighting to control the fusion of clinical variables and MRI features. Different from the existing multi-modal methods, our method avoids the huge model structure and uses an end-to-end structure to predict the prognosis of patients with multi-modal features. The above method was applied to the dataset proposed by a dual-center study on anti-NMDAR encephalitis in Southwest China, achieving excellent performance in terms of AUC (0.9799) and accuracy (0.9512). We further validate the model on an independent external validation dataset, and the results show that the model has good generalization.

A fusion method for capacity estimation of fast-charging lithium-ion battery under varied degradation mode

Recently, lithium-ion batteries with fast-charging capability have been gradually equipped in electric products. Capacity estimation specially developed for fast-charging batteries is still an open question, and most of the existing works are proposed for batteries charged under the 1C rate. However, incorrect fast-charging strategies may result in Li plating leading to battery capacity aging rapidly and degradation mode variation. Moreover, unprecise sampling by BMS increases the difficulty of accurately estimating battery capacity in such scenarios. Thus, a fusion prognostic method based on ensemble learning is proposed for the above issues in fast-charging batteries. Firstly, measurement-based health features are extracted from the portion charging phase. Then, the accuracy of the validation dataset-based strategy is proposed to achieve time-varying weight allocation. Finally, a fusion prognostic model is constructed based on the above weight allocation strategy and health features. The effectiveness of the proposed fusion method is verified by a self-design fast-charging battery dataset that maintains stable and reliable performance under sparse sampling and degradation mode variation conditions. In addition, the robustness and adaptability are validated by the comparison experiments with traditional ensemble learning-based methods, in which accuracy is improved by 27.5% minimally.

Mechanical properties of CTCs in patients with diagnosed ovarian cancer

The incidence and mortality of gynecologic cancers have been constantly increasing in China over the last 2 decades, which become a major health concern for women. Survival rates of gynecologic cancers are generally not satisfactory and decrease along with the advancing stage, this is mainly due to the lack of a clear prognostic evaluation during the treatment, which brings difficulties to the treatment. Therefore, more accurate prognostic evaluation methods are urgently needed. To solve this problem, this article explores the changes in the biomechanical properties of cells. Changes in the biomechanical properties of circulating tumor cells (CTCs) were explored by nano detection technology. The reference criteria for clinical evaluation of ovarian cancer (Age, FIGO stage, Histologic type, CA-125, Ascites, Single/Double, Residual lesion, and Chemotherapy) were compared and analyzed. The results showed that the average cell height of CTCs was 4.12 ± 0.83 μm before chemotherapy and 4.87 ± 0.71 μm after chemotherapy, with an average increase of 18.203 %. The apparent Young's modulus (E) was 3.884 ± 0.045 kPa before chemotherapy and 4.514 ± 0.025 kPa after chemotherapy, which increased by 0.63 kPa. The ROC analysis of FIGO stage of ovarian cancer patients showed that Young's modulus of cells could better reflect the accuracy of the evaluation of FIGO stage of patients, with the accuracy reaching 76.7 %, which was higher than the detection accuracy of CA-125 (72.6 %). In conclusion, the mechanical properties of CTCs can indicate the FIGO stage and diagnosis of patients and predict the prognostic risk of patients.

Neural Process for Health Prognostics With Uncertainty Estimations

Gaussian processes (GP) and neural networks (NN) are two kinds of function approximation models which are widely used for health prognostics. As an emerging meta-learning method, neural processes (NP) combine the advantages of GP and NN. Similar to GP, NP can quickly adapt to new observations, and can estimate the uncertainty in their prediction. Similar to NN, NP are scalable for complex functions and large datasets. However, NP does not have the capability to handle the prognostics in the time series prevailing in health prognostics. Inspired by these, an NP-based health prognostics toward prognostic uncertainty is presented. The method is validated by datasets which are sourced from real industrial case. Meanwhile, the proposed method is compared with the state-of-the-art NN-based and GP-based health prognostics methods, which are also developed for prognostics with uncertainty estimations. The results show that the proposed method performs the prognostics with superior accuracy and quality.

Spherical-dynamic time warping – A new method for similarity-based remaining useful life prediction

Machinery prognostics and health management (PHM) plays a key role in the reliable and efficient operation of industrial processes. With the emerging big data era, data-driven prognostic methods which avoid considering complicated system models have attracted growing research interest. Among many data-driven models, similarity-based prediction methods have been popular due to their strong interpretability and relatively simple implementation process. Nevertheless, when quantifying the similarity between two trajectories, most existing similarity measures neglect the nonlinearity of the distance measurement at different degradation stages and degradation alignments with timing difference, which may not be sufficient to retrieve the most suitable trajectories for remaining useful life (RUL) prediction. To overcome these limitations, a spherical-Dynamic Time Warping (spherical-DTW) algorithm is put forward to find an optimal match between the test and training trajectories at the retrieval step. Dynamic Time Warping allows degradation alignments with timing difference through stretching or compressing the trajectories with regard to time, thereby the data in similar degradation levels can be well aligned across different units. Moreover, a newly defined nonlinear spherical distance method is introduced and incorporated into the retrieval process to account for the nonlinearity of the damage propagation process. The significance of this study is that the newly proposed spherical-DTW algorithm goes one step further to consider the nonlinearity of fault evolutions and allow degradation pattern alignments with timing difference when performing similarity-based prognostics. Two run-to-failure cases, involving a real-world industrial compressor failure case and a gas turbine engine failure dataset, are investigated to demonstrate the effectiveness and superiority of the proposed algorithm.

Эра судов в политико-правовой трансформации России: идеи и реалии

Abstract: Introduction. The author, proceeding from the understanding that the discussion and construction of the image of the future makes it possible to find answers to the challenges of the time, invites you to discuss the idea of the «era of courts» expressed in Russia. The development of the conceptual foundations of the judiciary development is an element of the formation of the law of the future, the contours of the future political and legal sphere of the country.
 The discussion of the era of courts in Russia is carried out in the context of the analysis of the complex and contradictory process of institutionalization of the judiciary. The author understands this process as the acquisition by the judiciary of its inherent legal nature within the framework of filling it with adequate legal content of the organizational forms and legal statuses enshrined in this area and identifies a number of stages in the process. The era of courts is considered as a new promising stage in the institutionalization of the judiciary, within the framework of which it’s legal nature will be most fully realized, and its place and role in the transformation of the political, legal and socio-economic spheres of Russia have been changed.
 Methods. The research is based on the use of general scientific, private scientific and special methods of cognition. The work uses historical-legal, comparative-legal, formal-legal methods. In the course of the study, a rather rarely used prognostic method was applied.
 Results. The paper examines the factors underlying the formation of a new stage in the institutionalization of the judiciary, its substantive characteristics are predicted, in particular, derivation from institutional and political factors, basing the judiciary in the eyes of society and power structures on high public authority the courts going beyond their traditionally understood jurisdiction, including the referral to the courts of controversial issues of an overtly political nature and significance, transformation of the existing model for resolving social conflicts by supplementing the traditional political model with a significant judicial element. 
 The article characterizes the author’s understanding of the role of the judiciary in social development as a driver of growth, as an institution, ensuring the stability and efficiency of the functioning of the established legal forms of social and economic turnover, and legal limits to the activity of power structures.
 The author carries out a formal legal measurement of the steps taken to institutionalize the Russian judiciary, primarily within the framework of the updated Constitution of the Russian Federation. It is stated that the judiciary in modern conditions does not fully manifest the legal nature inherent in it, ensuring sustainable and dynamic development of the country.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [18F]FDG PET/CT examinations.

To create the 3D convolutional neural network (CNN)-based system that can use whole-body [18F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC). In this study, 1224 image sets from OC patients who underwent whole-body [18F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation. New deep neural network algorithms, the OCDAc-Net, and the OCDAs-Net were developed for diagnostic classification and staging of OC patients using [18F]FDG PET/CT images. Examinations were divided into independent training (75%), validation (10%), and testing (15%) subsets. This study included 37 women (mean age 56.3years; age range 36-83years). Data augmentation techniques were applied to the images in two phases. There were 1224 image sets for diagnostic classification and staging. For the test set, 170 image sets were considered for diagnostic classification and staging. The OCDAc-Net areas under the receiver operating characteristic curve (AUCs) and overall accuracy for diagnostic classification were 0.990 and 0.92, respectively. The OCDAs-Net achieved areas under the receiver operating characteristic curve (AUCs) of 0.995 and overall accuracy of 0.94 for staging. The proposed 3D CNN-based models provide potential tools for recurrence/post-therapy surveillance in OC. The OCDAc-Net and the OCDAs-Net model provide a new prognostic analysis method that can utilize PET images without pathological findings for diagnostic classification and staging.

Consultation on UTUC II Stockholm 2022: diagnostic and prognostic methods—what’s around the corner?

PurposeTo map current literature and provide an overview of upcoming future diagnostic and prognostic methods for upper tract urothelial carcinoma (UTUC), including translational medical science.MethodsA scoping review approach was applied to search the literature. Based on the published literature, and the experts own experience and opinions consensus was reached through discussions at the meeting Consultation on UTUC II in Stockholm, September 2022.ResultsThe gene mutational profile of UTUC correlates with stage, grade, prognosis, and response to different therapeutic strategies. Analysis of pathway proteins downstream of known pathogenic mutations might be an alternative approach.Liquid biopsies of cell-free DNA may detect UTUC with a higher sensitivity and specificity than urinary cytology. Extracellular vesicles from tumour cells can be detected in urine and may be used to identify the location of the urothelial carcinoma in the urinary tract. 3D microscopy of UTUC samples may add information in the analysis of tumour stage. Chemokines and chemokine receptors were linked to overall survival and responsiveness to neoadjuvant chemotherapy in muscle-invasive bladder cancer, which is potentially also of interest in UTUC.ConclusionCurrent diagnostic methods for UTUC have shortcomings, especially concerning prognostication, which is important for personalized treatment decisions. There are several upcoming methods that may be of interest for UTUC. Most have been studied for urothelial carcinoma of the bladder, and it is important to keep in mind that UTUC is a different entity and not all methods are adaptable or applicable to UTUC.

Combined Use of Tumor Markers in Gastric Cancer: A Novel Method with Promising Prognostic Accuracy and Practicality.

The effect of a single tumor marker on the prognosis of gastric cancer patients is not ideal. This study explored a novel prognostic assessment method for gastric cancer (GC) patients using a combination of three important tumor markers (CEA, CA72-4, and CA19-9). Data from 1966 GC patients who underwent curative gastrectomy at Sun Yat-Sen University Cancer Center (Guangzhou, China) were included. Hazard ratios (HR) for all factors for overall survival (OS) were analyzed by Cox regression. A nomogram and calibration curve were used to establish the survival prediction model. The prediction accuracy was evaluated with the concordance index (C-index). All patients were divided into four groups (C0-C3) according to the number of elevated tumor markers. The 5-year OS rates of the patients in preoperative groups C0-C3 were 83.8% (81.3-86.4%), 72.8% (68.5-77.4%), 58.9% (50.4-68.9%), and 18.5% (4.0-33.0%), respectively, and those in postoperative groups C0-C3 were 82.1% (79.4-84.8%), 76.1% (72.2-80.3%), 57.6% (48.4-68.5%), and 16.8% (5.1-28.5%), respectively, with significant differences between each C0-C3 subgroup in both preoperative and postoperative cohorts. Multivariate analysis showed that preoperative (HR: 6.001, 95% CI: 3.523-10.221) and postoperative (HR: 8.149, 95% CI: 4.962-13.528) elevated tumor markers were independent risk factors for GC patients. The C-index for the combined use of tumor markers was 0.65-0.66, which was higher than that for using a single tumor marker (0.53-0.56). The combined use of tumor markers significantly improved the prognostic value compared with using a single tumor marker. The survival prediction model including the combined tumor markers was accurate and effective.

Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

AbstractPrognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464–75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.

EBV-Associated Hub Genes as Potential Biomarkers for Predicting the Prognosis of Nasopharyngeal Carcinoma.

This study aimed to develop a model using Epstein-Barr virus (EBV)-associated hub genes in order to predict the prognosis of nasopharyngeal carcinoma (NPC). Differential expression analysis, univariate regression analysis, and machine learning were performed in three microarray datasets (GSE2371, GSE12452, and GSE102349) collected from the GEO database. Three hundred and sixty-six EBV-DEGs were identified, 25 of which were found to be significantly associated with NPC prognosis. These 25 genes were used to classify NPC into two subtypes, and six genes (C16orf54, CD27, CD53, CRIP1, RARRES3, and TBC1D10C) were found to be hub genes in NPC related to immune infiltration and cell cycle regulation. It was shown that these genes could be used to predict the prognosis of NPC, with functions related to tumor proliferation and immune infiltration, making them potential therapeutic targets. The findings of this study could aid in the development of screening and prognostic methods for NPC based on EBV-related features.

Prognosis using Bayesian Method by Incorporating Physical Constraints

Accurately predicting the remaining useful life (RUL) of industrial machinery is crucial for ensuring their reliability and safety. Prognostic methods that rely on Bayesian inference, such as Bayesian method (BM), Kalman and Particle filter (KF, PF), have been extensively studied for the RUL prognosis. However, these algorithms can be affected by noise when training data is limited, and the uncertainty associated with empirical models that are used in place of expensive physics models. As a result, this can lead to significant prediction errors or even infeasible RUL prediction in some cases. To overcome this challenge, three different approaches are proposed to guide the Bayesian framework by incorporating low-fidelity physical information. The proposed approaches embed inequality constraints to reduce sensitivity to local observations and achieve robust prediction. To determine an appropriate approach and its advantageous features, performance is evaluated by both numerical example and real case study for drone motor degradation.

Predictive value of the Oxford Acute Severity of Illness Score in acute stroke patients with stroke-associated pneumonia.

Stroke-associated pneumonia (SAP) is associated with a poor prognosis and a high mortality rate in stroke patients. However, the accuracy of early prediction of SAP is insufficient, and there is a lack of effective prognostic evaluation methods. Therefore, in this study, we investigated the predictive value of the Oxford Acute Severity of Illness Score (OASIS) in SAP to provide a potential reference index for the incidence and prognosis of SAP. We recruited a total of 280 patients with acute ischemic stroke who had been diagnosed and treated in the Zhumadian Central Hospital between January 2021 and January 2023. These patients were divided into an SAP group (86 cases) and a non-SAP group (194 cases) according to SAP diagnostic criteria by expert consensus on the diagnosis and treatment of SAP. We collated general and clinical data from all patients, including the survival of SAP patients during the follow-up period. Multivariate logistic regression was used to analyze the risk factors for SAP. Kaplan-Meier and multivariate COX regression analyses were used to investigate the relationship between OASIS and the prognosis of SAP, and a receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of OASIS for SAP. Our analyses identified body temperature, C-reactive protein, procalcitonin, OASIS, and a prolonged length of intensive care unit (ICU) stay as the main risk factors for SAP (all Ps < 0.05). Advanced age and an elevated OASIS were identified as the main risk factors for death in SAP patients (all Ps < 0.05). The risk of death in patients with OASIS of 31-42 points was significantly higher than that in patients with OASIS of 12-20 points (HR = 5.588, 95% CI = 1.531-20.401, P = 0.009). ROC curve analysis further showed that OASIS had a high predictive value for morbidity and the incidence of death in SAP patients. OASIS can effectively predict the onset and death of SAP patients and provides a potential reference index for early diagnosis and the prediction of prognosis in patients with SAP. Our findings should be considered in clinical practice.

Simple Hybrid Model for Estimating Remaining Useful Life of SiC MOSFETs in Power Cycling Experiments

Recording and prediction of the accumulated damage, which will eventually lead to the failure of power electronic modules, is an aspect of high importance for power electronic systems design and, in particular, for development of Prognostic and Health Management (PHM) schemes for in-field applications. To this end, this paper presents a simple and cost-effective prognostic method for predicting the remaining useful life (RUL) of TO-247 packaged silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs) subjected to power cycling experiments. The model assumes that the major failure mode is bond-wire lift-off and uses a damage accumulation scheme based on Paris’ crack law. The only inputs to the model are historical data on the average junction temperature swing and the temperature-compensated drain-source ON-state resistance at the peak temperature of the current cycle. Using only these two input values, the model is shown to predict RUL with surprising accuracy for the range of constant current loads determining cycling conditions under which the test data series have been acquired. This work is a first step in an ongoing project towards building more elaborate prognostic schemes for RUL-determination of SiC power MOSFETs in actual working conditions, using physics-informed neural networks (PINNs).

A new insight updates in diagnosis and management of acute lymphoblastic leukemia, cytogenetics, immunophenotyping, and proteomic profile

Acute lymphoblastic leukemia (ALL) is a severely invasive hematological cancer that results from the rapid increase and accumulation of lymphoid blasts in the blood, bone marrow (BM), and other organs. The pervasiveness of ALL has made it the prevalent childhood acute leukemia making up approximately 80% of leukemia in children and about 20% in adults. Due to the prevalence of ALL, more accurate diagnosis and prognostic methods are required. Proteome analysis uses a variety of analytical techniques, including protein sequencing, structural or expression proteomics, protein modification, sub-cellular protein localization, protein-protein interactions, and biological functional proteomics. Analysis of cell-signaling pathways and activation/deactivation are crucial to follow up the development, remission, or relapse of ALL. As a result, this review emphasizes cytogenetics and immunophenotyping while also highlighting the proteomic profile, clinical symptoms, diagnosis, and management of ALL. Also, it evaluates the procedures and techniques for the testing of bodily fluids (peripheral blood, cerebrospinal fluid, and BM) from ALL patients at various stages of disease, as well as the use of proteomic platforms in discovering sensitive and specific biomarkers for ALL.

On certain terms and definitions in the legislation on gender equality

In this article, the author examines the principle of equality and emphasizes the importance of using various research methods to understand this principle. The author considers the system-structural method, the phenomenological method, the method of comparative analysis, the prognostic method and the historical-legal method as tools that help to get a more complete picture of the problem. The study of the principle of equality helps to understand how to ensure equality and justice in society, to identify inequalities and discrimination, as well as to improve legislation and create equal conditions for all citizens. The article is devoted to the study of the principle of equality and the use of various research methods for its better understanding. The author notes that the principle of equality is a fundamental concept in various fields of knowledge, and its understanding is key to creating a fair and equal society. The article discusses various research methods that can be used to study the principle of equality. For example, the system-structural method allows you to analyze the problem in the context of the system, identify relationships and the influence of various factors on equality. The phenomenological method focuses on researching the experiences of individuals who have experienced discrimination, which helps to gain a deeper understanding of the problem and its consequences. The method of comparative analysis of legal norms and legislation of different countries makes it possible to identify the best practices and experience of other countries, which can be used to improve legislation in a specific country. Forecasting and modeling allow us to predict future outcomes and evaluate the effectiveness of different measures to achieve equality. The historical-legal method of research allows analyzing the past and finding the causes and consequences of discrimination in the past, which can be useful for developing strategies to combat discrimination in modern society. The use of various research methods allows obtaining detailed information about the problem of discrimination and inequality. This allows to analyze the problem from different criteria, taking into account social, economic, cultural and other aspects. Studying the principle of equality is of great importance for society. It helps to reveal systemic inequalities and discrimination that may exist in various areas of life, including employment, education, health care and others. Equality research helps identify negative trends and develop strategies to counter discrimination and ensure equal opportunities for all. Overall, this article highlights the importance of using a variety of research methods to understand the principle of equality. This allows you to get a comprehensive picture of the problem of discrimination and inequality, to reveal their causes and consequences, as well as to develop effective strategies for improving society and creating more equal and fair conditions for all citizens. The use of various research methods allows collecting various data, analyzing them from different perspectives and obtaining a more objective understanding of the problem. Key words: women from vulnerable groups, methodological principles, vulnerable categories of persons, research on the principle of equality.

New Short-Long-Term Degradation Model for Precise Battery Health Prognostics

Battery health prognostic plays a crucial role in safe operation of electrical devices. Battery degradation modeling is a crucial step for battery health prognostics. Currently, since most existing battery prognostic models only consider entire long-term degradation trends during modeling, battery degradation modeling is still not precise and lacks short-term deviations to sufficiently reflect strong complexity and volatility of degradation processes. To solve this problem, a new battery prognostic method is proposed by simultaneously considering long-term degradation tendency and short-term volatility. Because Wiener process and Ornstein-Uhlenbeck process have ability to respectively model long-term degradation tendency and short-term volatility, a short-long-term degradation model is accordingly proposed by linearly fusing two stochastic processes. Additionally, the proposed model can characterize modeling uncertainty and observation uncertainty. Besides, this paper proves that the proposed model follows a log-normal distribution, which provides solid foundations for state-space modeling. Here, the purpose of formulating the proposed model to a state-space model with multiple cross-correlation hidden state variables is to better describe individual battery degradation using on-line battery capacity data. Health prognostics can be easily realized by extrapolating the updated model to a failure threshold. Results show that our proposed method performs well than contrast methods for battery health prognostics.

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

Degradation modeling and prognostics serve as the basis for system health management. Recently, various sensors provide plentiful monitoring data that can reflect the system status. A multitude of feature fusion techniques based on multisensor data have been proposed to generate a composite health index (HI) for prognostics, which can represent the underlying degradation mechanism. Most existing methods have used linear fusion models and neglected the practical requirements for HI construction, which are insufficient to reveal the nonlinear relations among features and difficult to obtain accurate HIs for complicated systems. This study proposes a novel feature fusion-based HI construction method with deep learning and multiobjective optimization. Multiple degradation features are fused by a deep neutral network (DNN). Several desired properties that the HIs should have for prognostics are adopted to formulate the objective functions of DNN training. To balance the spatial complexity and performance of the fusion model, a multiobjective optimization model is generated for training the DNN. Then, a generalized nonlinear Wiener process model is used to predict the remaining useful life with the resulted HIs. Finally, two cases are analyzed to illustrate the effectiveness and robustness of the proposed method.

A Hybrid Health Prognostics Method for Proton Exchange Membrane Fuel Cells With Internal Health Recovery

Existing health prognostics methods often omit the internal health recovery of proton exchange membrane fuel cells (PEMFCs), although this phenomenon commonly exists, especially in the long-term usage of PEMFCs for hydrogen fuel cell vehicles. To this end, a novel hybrid method for PEMFCs is proposed, and internal recovery effects and external health data are collaboratively leveraged to achieve high-accuracy health prognostics. Aiming at characterizing health degradation in detail, the health prognostics of PEMFCs is addressed as voltage prediction with recovery identification, trend prediction and fluctuation prediction. Notably, the internal impedance extracted from electrochemical impedance spectroscopy is used to identify the internal recovery effects. This model-based recovery identification is further incorporated with particle filter for the trend prediction and with random forest regression for the fluctuation prediction by using external health data. Equipped with this hybrid method, simultaneous long-term and short-term health assessment and prognostics are realized. Durability test data of two PEMFCs are used to demonstrate the proposed method. The RMSE of the proposed method can reach 0.0090, 0.0088, and 0.0094 for the long-term predictions at 550 h, 600 h, and 650 h, respectively, which are smaller than conventional model-based, data-driven and EKF-LSTM hybrid methods.

The influence of U.S.-China relations on the current geopolitical situation

The complex interaction between the USA and China, which has developed into a real confrontation, greatly affects global politics and economics, significantly influencing the geopolitical situation. The political behaviour of most countries, delicate geopolitical balance, and the solution to global problems are now directly dependent on changes in U.S.-China relations. The study aims to determine the consequences of U.S.-China relations for global politics. The methodology of the study involved using a comparison method, graphical analysis of the characteristics of the role of the U.S. and China for the respective countries (adversary, rival, ally, partner), prognostic method and content analysis of German political declarative documents. Establishing optimal U.S.-China relations would help solve the problems of global recession, mass migration, environmental issues, Russian military aggression in Ukraine, and food security. The article reveals the pragmatic approaches of most countries to the competition between the U.S. and China. Identifying the critical goals of U.S. and China relations will help establish possible prospects for cooperation or increased conflict due to changes in the geopolitical situation. The study proves the need to develop and implement strategies to support stability, find standard solutions to global problems, and cooperate between the U.S. and China.