Operational Method Research Articles

Food Waste Management Strategies suitable for households as sustainable food

Lack of knowledge, attitude, and behavior in managing leftover foods in households impacts the natural ecosystem and food chain, particularly in developing countries. This research aims to analyze appropriate methods for reducing and processing food waste produced in household areas. This research method uses qualitative research with operational research methods carried out for 6 months on 25 housewives in Pondok Labu Village in South Jakarta, Indonesia. The research was carried out in 3 stages, the first stage before the intervention, the second stage providing the intervention, and the third stage after the intervention. Results showed that before the intervention, on average each respondent produced 351 g of food waste each day. This amount decreased to 8.43 g/day after respondents participated in socialization to reduce food waste and training to manage food waste. The concluded that a combination of education and training improves knowledge, attitude, and behavior in household food waste management and helps moderate food waste generation.

The outcomes of corneal sight rehabilitating surgery in Stevens-Johnson syndrome: case series

PurposeTo summarize the outcomes of corneal sight rehabilitating surgery in Stevens-Johnson syndrome (SJS).MethodsThis is a retrospective analysis of a consecutive case series. Twenty-four eyes of 18 SJS patients were included in this study. The ocular parameters, surgical procedures, postoperative complications, and additional treatments of the cases were reviewed.ResultsA total of 29 corneal sight rehabilitating surgeries, which consists of 9 keratoplasties, 8 Keratolimbal allograft (KLAL) and 12 combined surgeries (keratoplasty and KLAL simultaneously) were performed on the 24 eyes. All patients were treated with glucocorticoid eyedrops and tacrolimus eyedrops for anti-rejection treatment without combining systemic immunosuppression, except two patients who were prescribed prednisone tablets for the management of systemic conditions. The mean follow-up period was 50.6 ± 28.1 months. The optimal visual acuity (VA) (0.74 ± 0.60 logarithm of the minimum angle of resolution [logMAR]) and endpoint VA (1.06 ± 0.82 logMAR) were both significantly better than the preoperative VA (1.96 ± 0.43 logMAR) (95% CI, p = 0.000). 57.1% patients (8/14) were no longer in the low vision spectrum, and 88.9% patients (8/9) were no longer blind. The mean epithelialization time was 7.1 ± 7.6 weeks. The success rate was 86.7%. Additional treatments for improving epithelialization included administration of serum eyedrops (n = 10), contact lens (n = 15), amniotic membrane transplantation (n = 6), and tarsorrhaphy (n = 8). Complications included delayed epithelialization (n = 4, over 12 weeks), glaucoma (n = 11), and severe allograft opacity (n = 4). Only one graft rejection was observed.ConclusionsKeratoplasty and KLAL can remarkably enhance VA and improve low vision or even eliminate blindness for ocular complications of SJS. The outcome of the surgeries was correlated with the preoperative ocular situation and choice of operative methods.

IMPROVING THE FINANCIAL INDICATORS OF CREDIT ORGANIZATIONS USING MACHINE LEARNING

The aim of this article is to identify resource management issues related to the operational goal of the internal control system and to provide effective operational goal management methods with machine learning models. Machine learning algorithms were utilised to develop prediction models of capital, liabilities, income and profit based on the interdependencies of the indicators. Based on the intended profit/income ratio and the numerical quantities of assets, the optimal balance of credit organisations was predicted as a consequence of the models that were generated and used to solve the problem of financial indicator optimisation. The model proposed by us allows for the identification and avoidance of unnecessary concentrations of assets and liabilities, allowing both supervisory authorities and business owners to understand the alignment of the business with its vision, as well as identify problems and implement appropriate solutions, define new ambitious plans, and use resources more effectively.

Legendre wavelet integration operational matrix method for the numerical solution of unsteady MHD nanofluid flow between moving parallel plates

This study proposes a novel method based on Legendre wavelet integration operational matrix for solving a system of nonlinear ODE’s to model the two-phase, unsteady nanofluid flow and heat transfer between parallel plates in presence of magnetic field. To validate the applicability of proposed method, system of nonlinear BVP with exact solutions is employed as a test problem. Test problem results are compared with exact solution, and other numerical methods. The governing equations were then changed using similarity transformation into a set of nonlinear ODE’s. . Following this, the LWOMM is applied for the proposed problem. Findings demonstrated that, for a constant Reynolds number, skin friction coefficient value increases with rising Hartmann and squeezing numbers. Furthermore, it is demonstrated that Eckert number is a reducing function of the squeeze number and Nusselt number is an increasing function of Hartmann number.

Pulse pile-up correction by auto-regression on linear operations (ARLO) method: A comparison with integration-based algorithms

Radiation detection at high count rate suffers from pulse pile-up, where the counting data and energy information of the system are affected by the overlapping of the system output pulses. There exist various pile-up correction strategies to recover the true information of the pulses, among which pulse-tail extrapolation is a well-known method focused on in this study.Present work aims to use a mono-exponential model for extrapolating the pileup-distorted trailing edge of a pulse, to provide a reference line for calculating the true amplitude of its subsequent overlapping pulse. To this goal, the auto-regression on linear operations (ARLO) method is examined and compared with two integration-based methods (the Foss and the Matheson methods), as well as the non-linear least squares (NLS) method.Despite a higher sensitivity to noise, the ARLO method was able to provide a simple, non-iterative solution with a performance over 400 times faster than the NLS algorithm, according to the analysis of a high count rate set of experimental pulses from a NaI(Tl) detection system. Foss and Matheson methods also provided solutions reasonably faster than NLS (but not surpassing ARLO), performing exactly the same as each other with results very close to NLS, benefiting from their non-iterative nature.

Simulation of the cool-down process for the ESS cryogenic moderator system

The ESS cryogenic Moderator System (CMS) has been designed to supply a subcooled liquid hydrogen (17.5 K and 1.0 MPa) to two hydrogen moderators where the nuclear heating is estimated to be 6.7 kW (to be increased to four in the future). The CMS is cooled by a helium refrigerator system with a cooling power of 30.3 kW at 15 K. In this study, a one-dimensional simulation code has been developed to understand temperature behaviors during the cool-down process. The operational methods and parameters have been optimized. The CMS cool-down process is divided into three phases (I: gaseous hydrogen above 36 K, II: condensation from 36 to 31 K, and III: liquid hydrogen below 31 K). It turns out from the simulation result that the CMS would be able to be cooled down to the nominal condition of 17 K within 27 hours.

TPMGD: A genomic database for the traditional medicines in Pakistan

ObjectiveIn Pakistan, traditional medicines are an important component of the medical system, with numerous varieties and great demands. However, due to the scattered resources and the lack of systematic collection and collation, adulteration of traditional Pakistani medicine (TPM) is common, which severely affects the safety of their medicinal use and the import and export trades. Therefore, it is urgent to systematically organize and unify the management of TPM and establish a set of standards and operable methods for the identification of TPM. MethodsWe collected and organized the information on 128 TPMs with regard to their medicinal parts, efficacy, usage, and genetic material, based on Pakistan Hamdard Pharmacopoeia of Eastern Medicine: Pharmaceutical Codex. The genetic information of TPM is summarized from national center for biotechnology information (NCBI) and global pharmacopoeia genome database (GPGD). Furthermore, we utilized bioinformatics technology to supplement the chloroplast genome (cp-genome) data of 12 TPMs. To build the web server, we used the Linux + Apache + MySQL + PHP (LAMP) system and constructed the webpage on a PHP: Hypertext Preprocessor (PHP) model view controller (MVC) framework. ResultsWe constructed a new genomic database, the traditional Pakistani medicine genomic database (TPMGD). This database comprises five entries, namely homepage, medicinal species, species identification, basic local alignment search tool (BLAST), and download. Currently, TPMGD contains basic profiles of 128 TPMs and genetic information of 102 TPMs, including 140 cytochrome c oxidase subunit I (COI) sequences and 119 mitochondrial genome sequences from Bombyx mori, 1 396 internal transcribed spacer 2 (ITS2) sequences and 1 074 intergenic region (psbA-trnH) sequences specific to 92 and 83 plant species, respectively. Additionally, TPMGD includes 199 cp-genome sequences of 82 TPMs. ConclusionTPMGD is a multifunctional database that integrates species description, functional information inquiry, genetic information storage, molecular identification of TPM, etc. The database not only provides convenience for TPM information queries but also establishes the scientific basis for the medication safety, species identification, and resource protection of TPM.

MODERN ASPECTS OF REHABILITATION OF PATIENTS WITH POSTOPERATIVE CESAREAN SECTION SCAR

The number of caesarean sections around the world is increasing every year. This is influenced by the rapid development of modern medical science, the improvement of surgical techniques, the emergence of new suture materials and broad-spectrum antibiotics, pain relief, and the change in society's attitude to childbirth. According to the World Health Organization (WHO), caesarean section (CS) is the most widely used operative method of childbirth today. Determining the role of CS practice in the search for ways to reduce perinatal and maternal morbidity and mortality is essential. In some cases, this method of delivery is the only way to reduce maternal mortality: premature separation of the normally located placenta and the appearance of signs of its partial separation, severe hypertensive conditions, including a severe degree of preeclampsia and eclampsia, somatic complications that lead to complications in the mother's natural delivery pathologies.

MODERN CRITERIA FOR DIAGNOSTICS OF PATIENTS AFTER CESAREAN SECTION OPERATION

The number of caesarean sections around the world is increasing every year. This is influenced by the rapid development of modern medical science, the improvement of surgical techniques, the emergence of new suture materials and broad-spectrum antibiotics, pain relief, and the change in society's attitude to childbirth. According to the World Health Organization (WHO), caesarean section (CS) is the most widely used operative method of childbirth today. Determining the role of CS practice in the search for ways to reduce perinatal and maternal morbidity and mortality is essential. In some cases, this method of delivery is the only way to reduce maternal mortality: premature separation of the normally located placenta and the appearance of signs of its partial separation, severe hypertensive conditions, including a severe degree of preeclampsia and eclampsia, somatic complications that lead to complications in the mother's natural delivery pathologies. Thus, the tactics of taking women with a scar on the uterus through natural childbirth, with adequate prenatal preparation, the development of criteria for assessing the adjustment of the surgical scar, the lack of special criteria for predicting the probability of natural childbirth in pregnant women who underwent cesarean section, the special urgency of finding a solution to these issues defines. Therefore, it is necessary to study the condition of women with uterine scars, to improve the tactics of pregnancy and childbirth management, as well as the methods of preventing and predicting complications.

Micro Grid Integration of Distributed Power Generation Optimal Operating Methods

Microgrids, as localized energy systems comprising distributed power generation sources, offer resilience, flexibility, and efficiency to the modern power grid. Integrating various distributed energy resources (DERs) within microgrids poses challenges in optimizing their operation to ensure reliable and cost-effective energy supply. This paper presents a comprehensive review and analysis of optimal operating methods for the integration of distributed power generation within microgrids.The study explores various aspects of microgrid operation, including optimal dispatch strategies, demand-side management techniques, energy storage utilization, and renewable energy source integration. It investigates the application of advanced optimization algorithms, such as linear programming, mixed-integer linear programming, and metaheuristic algorithms, to address the complex optimization problems inherent in microgrid operation. Furthermore, the paper examines the role of smart grid technologies, advanced control systems, and communication infrastructure in facilitating the efficient operation of microgrids with distributed power generation. It discusses the importance of real-time monitoring, data analytics, and predictive modeling for enhancing decision-making processes and optimizing microgrid performance.

Incident Response Simulation Framework

Incident response is a pivotal security role in organizations that aimed to manage incidents quickly and economically. Cybersecurity Incident Response is a valuable development in the mitigation and operational security and data protection methods to safeguard employees and their assets. Cybersecurity awareness training is a powerful approach that leverage companies to cultivate employees into developing their knowledge and awareness of best practices and strategies for safeguarding sensitive data. Incident response is an emerging field due to the exceptional rise in the security breaches. The organizations need to implement such incident responses for the data breaches. The operational teams should be equipped with the hands-on experience of the incidents. The real-world scenarios must be used as a method to enhance the knowledge of the security team. The proposed system responds to such security demands generating an environment to test the security architecture, measure, track and report Cybersecurity KPIs as overall threat exposure to reduce exposure risk. The system is an educative framework for giving a real-world experience to the users and also test their ability and agility to respond to the incidents in the given time and correctly. Keywords—Incident response, Cybersecurity, data breaches, security

A Lateral Flow-Recombinase Polymerase Amplification Method for Colletotrichum gloeosporioides Detection.

The greater yam (Dioscorea alata), a widely cultivated and nutritious food crop, suffers from widespread yield reduction due to anthracnose caused by Colletotrichum gloeosporioides. Latent infection often occurs before anthracnose phenotypes can be detected, making early prevention difficult and causing significant harm to agricultural production. Through comparative genomic analysis of 60 genomes of 38 species from the Colletotrichum genus, this study identified 17 orthologous gene groups (orthogroups) that were shared by all investigated C. gloeosporioides strains but absent from all other Colletotrichum species. Four of the 17 C. gloeosporioides-specific orthogroups were used as molecular markers for PCR primer designation and C. gloeosporioides detection. All of them can specifically detect C. gloeosporioides out of microbes within and beyond the Colletotrichum genus with different sensitivities. To establish a rapid, portable, and operable anthracnose diagnostic method suitable for field use, specific recombinase polymerase amplification (RPA) primer probe combinations were designed, and a lateral flow (LF)-RPA detection kit for C. gloeosporioides was developed, with the sensitivity reaching the picogram (pg) level. In conclusion, this study identified C. gloeosporioides-specific molecular markers and developed an efficient method for C. gloeosporioides detection, which can be applied to the prevention and control of yam anthracnose as well as anthracnose caused by C. gloeosporioides in other crops. The strategy adopted by this study also serves as a reference for the identification of molecular markers and diagnosis of other plant pathogens.

Road infrastructure maintenance: Operative method for interventions’ ranking

Planned maintenance of transportation infrastructure is a milestone in maintaining the functionality and safety of road networks. This paper is part of this process and aims to propose an easily applicable and versatile operational methodology for prioritizing maintenance interventions generally scheduled on road networks of considerable extent. An operational methodology for ranking and prioritizing maintenance interventions is proposed; this method is applicable to road networks of such size that direct inspection of each situation is impossible. The core of the paper is the analytical description of this methodology, based on a weighted sum function of three blocks: i) Category − is a score related to the purpose of the intervention; ii) Asset − is a score due to the road on which the intervention is located (considering Average Daily Traffic, Accidentality, Social Cost, Road Type, Routes); iii) Typology − is a score related to the element on which the intervention is being carried out. After describing the parameters and the implementation procedure for each block, the calibration of the relative weights is described, and a sensitivity analysis is performed. In the case study, the proposed methodology is applied to the ANAS corporate network in Italy. This case study will highlight the usefulness, versatility, and operability of the methodology with GIS (Geographic Information System) tools for implementing thematic maps. A concluding section concerns the addition of a scoring block − Spatial Context − as an additional differentiating factor due to the spatial context of each road.

Trends and determinants of mental illness in humanitarian migrants resettled in Australia: Analysis of longitudinal data.

Given the frequent exposure of humanitarian migrants to traumatic or stressful circumstances, there exists a potential predisposition to mental illness. Our objective was to pinpoint the trends and determinants of mental illness among humanitarian migrants resettled in Australia. This study considered five waves of longitudinal data involving humanitarian migrants resettled in Australia. Post-traumatic stress disorder (PTSD) and psychological distress were assessed using PTSD-8 and Kessler-6 screening tools. Through a Generalised Linear Mixed model (GLMM), variables displaying a 95% CI that excluded the value of 1.0 for the odds ratio were identified as associated factors for both PTSD and elevated psychological distress. The selection of multivariable covariates was guided by causal loop diagrams and least absolute shrinkage and selection operators methods. At baseline, there were 2399 humanitarian migrants with 1881 retained and at the fifth yearly wave; the response rate was 78.4%. PTSD prevalence decreased from 33.3% (95% CI: 31.4-35.3) at baseline to 28.3% (95% CI: 26.2-30.5) at year 5. Elevated psychological distress persisted across all waves: 17.1% (95% CI: 15.5-18.6) at baseline and 17.1% (95% CI: 15.3-18.9) at year 5. Across the five waves, 34.0% of humanitarian migrants met screening criteria for mental illness, either PTSD or elevated psychological distress. In the multivariate model, factors associated with PTSD were loneliness (AOR 1.5, 95% CI: 1.3-1.8), discrimination (AOR 1.6: 1.2-2.1), temporary housing contract (AOR 3.7: 2.1-6.7), financial hardship (AOR 2.2:1.4-3.6) and chronic health conditions (AOR 1.3: 1.1-1.5), whereas the associated factors for elevated psychological distress were loneliness (AOR 1.8: 1.5-2.2), discrimination (AOR 1.7: 1.3-2.2) and short-term lease housing (AOR 1.6: 1.0-1.7). The prevalence, persistence and consequential burden of mental illness within this demographic underscore the urgent need for targeted social and healthcare policies. These policies should aim to mitigate modifiable risk factors, thereby alleviating the significant impact of mental health challenges on this population.

Fully automated operational modal identification based on scale-space peak picking algorithm and power spectral density estimation

Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.

Deep operational audio-visual emotion recognition

Emotions play a large role in interpersonal communication, marketing, healthcare and the service industry. For this reason, much research has been carried out on emotion classification until today. Audio-visual emotion recognition is a field within artificial intelligence and machine learning that focuses on recognizing and understanding human emotions from both visual and audio cues. It combines computer vision and audio processing techniques to analyze and interpret emotional states expressed by individuals. This paper presents a deep learning model developed over an operational neural network using multiple inputs and aimed at audio-visual emotion recognition. The proposed network utilizes both visual and audio information in an end to end approach. The primary objective of this work is to demonstrate that multi-input models can produce more efficient outcomes compared to single-input models in emotion classification. Another objective is to demonstrate the superior performance of weight calculation methods employed in operational neural networks compared to the conventional weight calculation methods used in convolutional neural networks. Therefore, we want to demonstrate that substituting convolutional neural network approaches with operational neural network methods can yield superior outcomes in emotion categorization models. In the proposed architecture, regular convolutional layers are replaced with operational layers. The experimental results demonstrate that the operational convolutional architecture performs better compared to the classical convolutional neural network architecture.

The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

Improvement of Cross-border Bonded Goods Supply Chain by Optimizing Operational Methods

Based on the existing mode of the supply chain of imported bonded commodities for cross-border e-commerce enterprises, this paper studies and discusses the online operation method of bonded commodities, warehousing management, logistics and transportation, as well as consumer satisfaction, and draws on the operation mode of the supply chain of domestic general trade goods, and tries to make innovations to the operation and management of the whole cross-border bonded commodities supply chain based on this model. The authors of this paper found that updating the sales strategy and cooperation between the supply chain could help merchants accurately predict the positioning of commodities and market demand, which is helpful for cross-border e-commerce enterprises to grasp the market trends and consumption trends and avoid the inventory backlog in bonded warehouses. In addition, the innovation of bonded merchandise sales could increase the consumer attention of different groups of consumers, accelerate the circulation of goods and increase the sales of goods. It helps cross-border e-commerce enterprises to enhance their market competitiveness. Finally, merchants, through the management of logistics and transportation, through cooperation and data sharing, can better reduce logistics and transportation costs and rapid delivery.

MARKERS OF COMPLICATIONS AFTER LAPAROSCOPIC GASTRIC BYPASS IN PATIENTS WITH OBESITY

Introduction. Bariatric surgery is one of the most effective methods of treating morbid obesity, which allows for a long-lasting result. Along with the increase in the number of operative methods of obesity treatment, the relevance of issues related to postoperative complications is increasing. In recent years, the issue of using special checklists after surgery has been actively investigated. This will help optimize the process of monitoring patients' condition in the postoperative period.
 The aim of the study: to develop a check-list model to prevent the detection of complications after laparoscopic gastric bypass in obese patients.
 Materials and methods. On the basis of the developed checklist, a study of the condition of obese patients, who underwent laparoscopic gastric bypass during 2019-2023, was conducted on the basis of the Department of General Surgery No. 2 of the Bogomolets National Medical University. The checklist included the following criteria: the level of pain on the VAS scale, the level of nausea, eating, mobility, whether there is a desire to be discharged home, abdominal muscle tension, pain in the calf muscles, heart rate, oxygen saturation in the blood, the number drainage discharge within 24 hours, temperature, systolic blood pressure, central venous pressure, laboratory diagnosis, decrease in hemoglobin concentration, leukocyte level after surgery, C-reactive protein level after surgery, blood procalcitonin level, erythrocyte sedimentation rate level, the presence of a shift in the leukocyte formula to the left. The presence of complications was assessed, the parameters of the checklist were analyzed using univariate logistic regression and ROC analysis for early detection of complications and determination of the chance of complications in the future.
 Results. Complications occurred in 18/218 (8.2%). The most frequent complication was bleeding in 6/18 (33.3%). Reoperation was performed in 12 cases of 12/18 (66.6%), endoscopic treatment was performed in 1 case, and conservative methods were used in 5 cases. According to the checklist, 31.81% had a positive predictive value (PPV) and 99.56% had a negative predictive value (NPV). The optimal cut-off point of the total score of the checklist was 6. Patients with a positive prognosis of the checklist have a higher chance of developing complications of VSH – 4.5 (95% CI 1.8-9.72), p <0.001.
 Conclusions. The complication rate was 18/218 (8.2%). According to the checklist, heart rate, systolic blood pressure and mobility are very good predictors of postoperative complications. The checklist is the best option for determining the readiness of patients to be discharged from the hospital due to a high prognostic negative result. Due to the low prognostic positive result, the evaluated checklist needs additional modification and optimization taking into account the results already obtained.

Theory and practice for assessing structural integrity and dynamical integrity of high-speed trains

PurposeThe safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.Design/methodology/approachFirst, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.FindingsVehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.Originality/valueThe practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.