High-risk Components Research Articles

Utilization of Machine Learning and Explainable Artificial Intelligence (XAI) for Fault Prediction and Diagnosis in Wafer Transfer Robot

Faults in the wafer transfer robots (WTRs) used in semiconductor manufacturing processes can significantly affect productivity. This study defines high-risk components such as bearing motors, ball screws, timing belts, robot hands, and end effectors, and generates fault data for each component based on Fluke’s law. A stacking classifier was applied for fault prediction and severity classification, and logistic regression was used to identify fault components. Additionally, to analyze the frequency bands affecting each failed component and assess the severity of faults involving two mixed components, a hybrid explainable artificial intelligence (XAI) model combining Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) was employed to inform the user about the component causing the fault. This approach demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity.

Professional Competency Requirements for Maintenance and Operation of Power Battery Systems

The power battery system is one of the high-risk components for new energy vehicles, and standardized operation and maintenance is one of the effective measures to improve the safety of new energy vehicles. The operation and maintenance of the power battery system includes daily operation, first level maintenance, and second level maintenance. A combination of practical research and theoretical analysis is adopted to confirm different maintenance tasks, job contents, and technical requirements, in order to improve the standardization and safety of operations. Fault mode analysis is a continuation of fault maintenance and provides practical basis for the design and process improvement of power battery systems.

Machine learning-aided risk-based inspection strategy for hydrogen technologies

Although technically challenging, effective, safe, and economical transport is crucial for enabling a widespread rollout of hydrogen technologies. A promising option to transport large amounts of hydrogen lies in employing retrofitted natural gas pipelines. Nevertheless, H2-rich environments tend to degrade pipeline steels, reducing their load-bearing capability and accelerating crack propagation. Regular inspection and maintenance activities can preserve the pipelines’ integrity and guarantee safe operations. The risk-based inspection (RBI) approach is based on estimating the risk for each component item. It focuses most inspection activities on high-risk components to reduce costs while maximizing the plant’s safety and availability. However, the RBI standards do not consider hydrogen-induced degradations and cannot be adopted for industrial equipment operating in H2 environments. This study proposes a novel ad-hoc methodology for the risk-based inspection planning of hydrogen handling equipment. A machine-learning model to predict the fatigue crack growth in gaseous hydrogen environments is developed and integrated with the conventional RBI approach. The proposed methodology is validated on three pipelines transporting hydrogen and natural gas in different concentrations. The results show how similar operating conditions can determine different degradation rates depending on the environment and highlight how hydrogen-enhanced fatigue can reduce the pipelines’ lifetime.

Outcome of early versus late primary embolization in ruptured brain arteriovenous malformations.

To determine whether patients with a ruptured brain arteriovenous malformation (rBAVM) would benefit from an early embolization. rBAVM treated first by embolization between March 2002 and May 2022 were included. Embolization was defined early (Group 1) when performed within 10 days postbleeding. If later, embolization was considered late (Group 2). Demographic and rBAVM data were compared between the groups. High-risk bleeding components and reasons for deferring embolization were retrieved. Primary endpoint was rebleeding. Secondary endpoints were good functional outcome (FO, modified Rankin Scale mRS ≤ 2) and angiographic occlusion. Predictors of rebleeding and FO were determined by multivariate analysis. 105 patients were recruited (N = 34 in Group 1; N = 71 in Group 2). No rebleeding was noted before, during or after the first embolization session in the early embolization group. Late embolization depended on missed diagnosis and referral pattern. Eleven patients (10.5%) suffered a rebleeding, of whom N = 3 before embolization (only in Group 2), N = 5 periembolization (N = 2 at the second embolization session in Group 1) and N = 3 spontaneous more than 30 days postembolization. More high-risk components were embolized in Group 1 (19/34; 55.9 vs 17/71; 23.9%; p = .011). Rebleeding rates, FO at last FU (90.9% vs 74.3%) and occlusion rates (80.8% vs 88.5%) did not differ between the groups. Glasgow coma scale ≤ 8 predicted rebleeding, rebleeding correlated with poor FO. Early embolization did prevent rebleeding. The overall rebleeding risk was linked to bleeding before late embolization and bleeding at the second embolization. Rebleeding predicted the final FO.

A Transformer Heavy Overload Spatiotemporal Distribution Prediction Ensemble under Imbalanced and Nonlinear Data Scenarios

As a crucial component of power systems, distribution transformers are indispensable to ensure the sustainability of power supply. In addition, unhealthy transformers can lead to wasted energy and environmental pollution. Thus, accurate assessments and predictions of their health statuses have become a top priority. Unlike assumed ideal environments, however, some complex data distributions in practical scenarios lead to more difficulties in diagnosis. One challenge here is the potential imbalanced distribution of data factors since sparsely occurring factors along with some Unusual High-Risk (UHR) components, whose appearance may also damage transformer operations, can easily be neglected. Another is that the importance weight of data components is simply calculated according to their frequency or proportion, which may not always be reasonable in real nonlinear data scenes. With such motivations, this paper proposes a novel integrated method combining the Two-fold Conditional Connection Pattern Recognition (TCCPR) and Component Significance Diagnostic (CSD) models. Initially, the likely environmental factors that could result in distribution transformer heavy overloads were incorporated into an established comprehensive evaluation database. The TCCPR model included the UHR time series and factors that are associated with heavy overload in both spatial and temporal dimensions. The CSD model was constructed to calculate the risk impact weights of each risky component straightforwardly, in line with the total risk variation levels of the whole system caused by them. Finally, the results of one empirical case study demonstrated their adaptation capability and enhanced performance when applied in complex and imbalanced multi-source data scenes.

Group Maintenance for Numerical Control Machine Tools: A Case Study

The high-reliability requirement of numerical control (NC) machine tools emphasizes the effectiveness of preventive maintenance (PM). However, most existing PM decisions are made by treating them as single-component systems, resulting in the problems of “excessive maintenance” or “insufficient maintenance.” Moreover, real failure data has rarely been utilized for maintenance decision-making, which leads to idealized policies far from practice. This article presents a framework to derive the optimal group maintenance (GM) policy from real failure histories for an NC machine tool comprised of heterogeneous components. Criticality analysis is first conducted to identify high-risk and low-risk components. The lifetime of each high-risk component is characterized by a two-parameter Weibull model or by a Weibull competing-risk model according to whether its failures are independent of the other components. High-risk components with strictly increasing or bathtub-shaped failure rates are selected for PM decision-making. This article introduces a multilevel PM decision-making approach: 1) a repair-replacement model is developed at the component level to derive the optimal PM interval and the maximum number of PM actions; and 2) at the system level, a GM procedure is presented to obtain the optimal PM interval of the system. The failure histories of an NC machine tool with 13 components are used to illustrate the effectiveness of the proposed approach. A maintenance plan is finally made based on optimization results to guide maintenance implementation.

Almost 1 in 2 patients with acute coronary syndrome are extremely high-risk and potential candidates for in-hospital triple lipid-lowering therapy: data from the CALLINICUS-Hellas Registry

Abstract Introduction The great heterogeneity of acute coronary syndrome (ACS) patients in the risk for cardiovascular events recurrence, prompted a recent position paper endorsed by the International Lipid Expert Panel to propose criteria, i.e. coexistence of multivessel coronary artery disease (CAD), polyvascular disease, heterozygous familial hypercholesterolemia (heFH), diabetes mellitus (DM) with estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 and/or lipoprotein (a) [Lp(a)] >50 mg/dL etc., which define the extremely high-risk ACS patients for whom an low-density lipoprotein cholesterol (LDL-C) <40 mg/dL was proposed. Purpose To estimate the proportion of patients with ACS that fulfill the criteria of the extremely high-risk group and are potential candidates for "extremely" intensive early lipid-lowering therapy (LLT) aiming at an LDL-C <40 mg/dL. Methods We recruited 780 consecutive patients with ACS who were participants of the CALLINICUS-Hellas Registry which is a prospective multicenter observational study that explores the adherence to LLT, the achievement of LDL-C targets and the cardiovascular events at 6- and 24-months after hospital discharge of patients with ACS. Results The prevalence of high risk components was: heFH 13.5%, multivessel CAD 44%, polyvascular disease 3.2%,and DM with eGFR <60 ml/min/1.73 m2 and/or Lp(a) >50 mg/dL 8.1%. By excluding the overlapping between these conditions, 430 ACS patients (55.1%) had at least one of the characteristics which define the extremely high-risk patients (Figure). Presuming a 65% LDL-C reduction of the levels (the pretreatment if on LLT prior admission or the admission if not on prior LLT) of the extremely high-risk ACS patients by taking a maximum dual LLT (intense statin plus ezetimibe), 312 patients, i.e., 40% of all initially recruited patients would fail to achieve an LDL-C <40 mg/dL suggesting that they require additional LLT, namely, a proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i). Conclusions Almost 1 in 2 patients with ACS have extremely high-risk characteristics and are candidates for in-hospital triple LLT (statin plus ezetimibe plus PCSK9i) aiming at an LDL-C <40 mg/dL, a strategy shown to improve plaque stability and possibly to reduce the recurrence rate of early coronary events.Figure

Risk assessment for biopharmaceutical single-use manufacturing: A case study of upstream continuous processing

In the current transition to intensified upstream processing, the risks of adopting traditional single-use systems for high-titer, long-duration perfusion cultures, have thus far not been considered. This case study uses the Failure Modes and Effects Analysis (FMEA) method to evaluate the risks associated with implementing upstream single-use technology. The simulated model process was used to compare the risk level of single-use technology for a traditional fed-batch cell culture with that for perfusion culture, under the same annual protein production conditions. To provide a reasonable source of potential risk for FMEA, all single-use upstream operations for both fed-batch and perfusion processes were investigated using an analytical method developed to quantify the impact of process parameters and operating conditions on single-use system specifications and to ensure objectivity. Many of the risks and their levels, were similar in long-duration perfusion cultures and fed-batch cultures. However, differences were observed for high-risk components such as daily sampling and installation. The result of this analysis indicates that the reasons for risk are different for fed-batch cultures and perfusion cultures such as larger bioreactors in fed-batch and longer runs in perfusion, respectively. This risk assessment method could identify additional control measures and be part of a holistic contamination control strategy and help visualize their effectiveness.

When CEO compensation plan based on risk changes firm strategic variation and strategic deviation? The moderating role of shareholder return

ObjetivoOs autores estendem a pesquisa recente usando o componente de risco dos planos de remuneração de RH para examinar os efeitos dos componentes de risco em dois resultados estratégicos: mudança temporal dentro da empresa, ou variação estratégica, e divergência estratégica da empresa do setor, ou desvio estratégico. Além disso, examinamos o papel do desempenho financeiro anterior como uma condição moderadora dos efeitos dos componentes de risco no plano de remuneração e nos resultados estratégicos da empresa.Projeto/metodologia/abordagemPara examinar os efeitos dos componentes de baixo e alto risco da remuneração executiva na variação e desvio estratégico ao longo do tempo, coletamos dados de 2.510 empresas listadas no índice Standard & Poor's 500 em um formato de dados de painel de um período de 12 anos. Os autores coletaram dados financeiros e de outros níveis da empresa da COMPUSTAT, remuneração executiva e dados de nível executivo da EXECUCOMP.ResultadosNossos resultados suportam os principais efeitos dos componentes de risco na mudança estratégica, enquanto os componentes de alto e baixo risco atuam no desvio estratégico contingente ao papel moderador do Retorno Total ao Acionista. No referencial teórico, os autores testam o papel moderador do Total Shareholder Return como condição limite dos efeitos dos componentes de risco no plano de remuneração. Ao fazer isso, fornecemos uma compreensão refinada da influência dos componentes de risco do plano de remuneração nos resultados próximos aos executivos, como a manutenção do status quo e a busca por ganhos financeiros.Originalidadeao considerar a influência dos componentes de baixo e alto risco dos planos de remuneração nos resultados estratégicos -em vez do desempenho da empresa- este estudo expande a literatura de estratégia que apoia a influência do esquema de remuneração nos resultados de uma empresa. Esse caminho é novo porque oferece uma perspectiva moderadora para entender os desvios e mudanças estratégicas que os CEOs imprimem em suas empresas.Limitações/implicações da pesquisaNovos estudos podem explorar um efeito moderador de três vias em indicadores de desempenho, como TSR, Q de Tobin e ROA. Abordamos essa limitação e fizemos uma análise comparativa, mas não incluímos mecanismos moderadores adicionais nesses efeitos interativos.Implicações práticasAo desagregar a remuneração do executivo com base nos componentes de risco, os conselhos de administração podem mitigar possíveis vieses indesejados na relação entre principal e agente.

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

A Closed Form Method Combined with the Steinberg’s Solution to Estimate Number of Fatigue Cycles for Lead-Free Solders

Solder joints of components are normally the most susceptible to the vibration loading conditions. The Steinberg’s closed form solution has been widely used in industries to identify high risk components to be candidates for detailed finite element (FE) durability analysis under vibration loads. Unfortunately, the Steinberg’s closed form solution is only applicable to the SnPb, not the lead-free materials (SAC); therefore, it would be troublesome to identify high risk SAC components, especially if there are numerous SAC components in the BOM. This paper is to present a method that would be able to combine with the Steinberg’s closed form solution to identify high risk SAC components. This method is developed from comparison between SnPb and SAC fatigue vs. strain range relationship using the high and low cycles of fatigue Coffin Manson closed form equation. In addition, this method can also predict solder joint fatigue cycles of one material using the already-derived fatigue cycles from another material without re-running detailed FE analysis. This additional feature would help, for example, to make prediction if re-ball from SAC to SnPb or vice versa would pose any risks. It is strongly recommended to utilize this method when assessing lead-free components under vibration as currently it is only available method to serve this purpose.

Risk Identification of Building Construction Projects in Egypt

Risk identification is integral to construction management. The severe consequences of identified risk factors on time and cost performance can be addressed by improved risk management. The identification and preparation of responses to risk factors affects the risk management process directly and indirectly. Recent changes in the valuation of the Egyptian currency against foreign currencies during the last year and measures to cut down on the importation of engineering materials and equipment have had significant effects on the existing and recognized regulations concerning construction in Egypt. A pilot survey with expert engineers is a crucial step in completing research. In this research study, 15 experts were asked to discuss the collected risk factors from previous studies to verify the workability of these factors in Egypt. Thirty-five risk factors were selected during the pilot survey, which was distributed to 95 participants. To facilitate the analysis process, the collection was based on a five-point Likert scale. Therefore, redefining and arranging the risks according to the current circumstances is the main objective of this study. The proposed model identified different high-risk factors that could cumulatively affect overall performance, such as funding problems from contractors, material price fluctuations, unrealistic estimates of the duration of project activities, and shortages of construction materials in the market. Thus, to help stakeholders achieve project success, these high-risk factor components should be identified and controlled duly.

Concept of Contamination Control Door for DEMO and Proof of Principle Design

During the maintenance period of a future fusion reactor power plant, called DEMOnstration Power Plant (DEMO), remotely handled casks are required to confine and handle DEMO in-vessel components during their transportation between the reactor and the active maintenance facility. In order to limit the dispersion of activated dust, a Contamination Control Door (CCD) is designed to be placed at an interface between separable containments (e.g., vacuum vessels and casks) to inhibit the release of contamination at the interface between them. The remotely operated CCD—technically, a double lidded door system—consists of two separable doors (the cask door and port door) and three different locking mechanisms: (i) between the cask door and cask, (ii) between the cask door and port door and (iii) between the port door and port. The locking mechanisms are selected and assessed according to different criteria, and the structure of the CCD is optimized using an Abaqus Topology Optimization Module. Due to the elastic properties of the CCD, deflections will occur during the lifting procedure, which may lead to malfunctions of the CCD. A test rig is developed to investigate the performance of high-risk components in the CCD in the case of deflections and also malpositioning. Misalignment can be induced along three axes and three angles intentionally to test the single components and items. The aim is to identify a possible range of operating in the case of misalignments. It is expected that the proposed CCD design should be able to operate appropriately in the case of ±3 mm translational misalignments and ±1° rotational misalignments.

Bridge Network Seismic Risk Assessment Using ShakeMap/HAZUS with Dynamic Traffic Modeling

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. This paper brings about seismic performance assessment for the Cyprus transportation network from which the decision-making platform can be modeled and implemented. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of the transportation road network is carried out by utilizing the graph theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a dynamic traffic assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the HAZUS loss estimation tool is used. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region.

Reduction of Work-Related Musculoskeletal Disorders in Plastic Surgeons by Introduction of a Posture Training Device.

Plastic surgeons have an increased risk for the development of musculoskeletal disorders because of frequent poor ergonomics of the operating room. This study characterizes selected plastic surgery procedures, with an attempt to identify high-risk procedures and procedural components as well as the impact of biofeedback on surgical ergonomics. A commercially available posture training device was used to initially record neck and spine positioning and later to send biofeedback to prompt surgeons to correct posture. Device data were correlated with in-person observations to characterize factors associated with more time spent in the slouched/nonneutral cervical and thoracic spine posture. The proportion of time spent in the upright position during surgery was significantly different among male and female participants, level of training, participant height, in the sitting versus nonsitting positioning (P < 0.001), with loupes use, and if there was more than an 8-inch height difference between 2 participants (mean, 0.70 ± 0.285). Using the device intervention, all participants spent a larger proportion of operating time upright. Half of these improvements in posture were statistically significant. While in feedback mode, participants experienced shorter and more frequent periods of slouching/nonneutral posture. When comparing the same participant performing the same procedure with and without device biofeedback, 72.2% of participants spent more time in the upright/neutral posture during the surgery when the device was sending feedback. Biofeedback devices used in the operating room can lead to improved surgical posture, which may translate to reduction of workplace injuries, and overall physician health. This study found that a commercially available posture training device and sitting stools in the operating room could significantly improve physician cervical and thoracic spine posture.

Low-intensity exercise improves cardiac tolerance to ischemia/reperfusion injury in aged female rats with metabolic syndrome.

Metabolic syndrome (MetS) is a group of cardiovascular risk factors which has higher prevalence in the elderly population. Being an aged obese woman with raised arterial blood pressure and blood glucose level might increase the risk of having cardiovascular morbidity or mortality during an attack of myocardial infarction. The present study was designed to evaluate the cardioprotective efficacy of a low intensity swim-exercise program on ischemic reperfusion injury in isolated perfused hearts from aged female rats with MetS. Wistar rats were allocated into three groups: controls, MetS-sedentary, and MetS-exercised. Body weight (BW) and waist circumference were measured with calculation of body mass index (BMI). Blood pressure, fasting blood glucose (FBG) and lipid profile were determined. Cardiac activities of isolated hearts were studied on Langendorff preparation under basal conditions and following ischemia/reperfusion (I/R), and the level of cardiac tissue nitrite was also assessed. MetS-exercised rats showed restoration of control values for BW, BMI, waist circumference, visceral fat, FBG, plasma lipid profile and left ventricular nitrite content. Meanwhile, arterial blood pressure was significantly lower than both controls and MetS-sedentary rats. These findings suggest that a short-term, low-intensity exercise program can improve cardiac tolerance to I/R injury possibly by ameliorating the high-risk cardiovascular components of the MetS.

Vulnerable Carotid Artery Plaques in Asymptomatic Patients—A Narrative Review

AbstractOver the last two decades, medical management of carotid artery patients has improved significantly. Most patients remain stable on best medical therapy (BMT), making interventions unnecessary in all patients. “Selective” intervention is advocated for only those few patients who are having vulnerable or unstable carotid artery plaques. Literature search was done to explore current concept and role of available investigations to identify vulnerable carotid plaques. Vulnerable plaque is defined as those plaques having active inflammation, high large necrotic lipid content, neovascularity, thin capsule, surface irregularity, or intraplaque hemorrhage. Ultrasound (US) is the simple, noninvasive, cost-effective investigation to differentiate soft (echolucent) from fibrocalcified (echogenic) plaques. It can also comment on other high-risk plaque features such as plaque volume and area. Contrast-enhanced US can visualize neovascularization and plaque surface irregularities better than conventional US. Computed tomography is limited in identifying most high-risk plaque features and is not useful. High-resolution magnetic resonance imaging is the most accurate and externally validated investigation to characterize most high-risk plaque components. Positron emission tomography has emerged as the most promising dynamic investigation to identify and quantify inflammatory plaques and will be clinically very useful in decision making.

Assessment of fatigue crack growth based on 3D finite element modeling approach

Fatigue crack growth life assessment enables manufacturers to quantify damage tolerance capability of high-risk components. Reduced order models that are based on simple geometries (i.e. surface crack in a plate, corner crack at a bolt hole) and on the assumption that cracks maintain an elliptical shape during propagation, are commonly employed in the damage assessment. A more comprehensive modeling process should consider the component geometry, service loading conditions and, eliminate assumptions related to crack front shape or planarity of the crack growth path. A 3D finite element-based approach is evaluated in this study as a more accurate alternative to reduced order models. For verification purposes, an analytical solution-based model was developed and implemented in MATLAB to predict fatigue crack growth life and crack front evolution for three different crack types: surface, corner and internal. The analytical model solutions are compared against 3D finite element (FE) based approach implemented in SimModeler Crack. The 3D FE modelling approach has been further tested and validated against experimental fatigue crack growth measurements from two Al 2024-T3 specimens containing multiple cracks. The verification and validation data presented herein show that the 3D FE-based modelling approach provides an accurate and effective modelling tool for crack propagation life assessment of structural components.

Prognostic Nomograms Based on Ground Glass Opacity and Subtype of Lung Adenocarcinoma for Patients with Pathological Stage IA Lung Adenocarcinoma.

The value of lung adenocarcinoma (LUAD) subtypes and ground glass opacity (GGO) in pathological stage IA invasive adenocarcinoma (IAC) has been poorly understood, and reports of their association with each other have been limited. In the current study, we retrospectively reviewed 484 patients with pathological stage IA invasive adenocarcinoma (IAC) at Sun Yat-sen University Cancer Center from March 2011 to August 2018. Patients with at least 5% solid or micropapillary presence were categorized as high-risk subtypes. Independent indicators for disease-free survival (DFS) and overall survival (OS) were identified by multivariate Cox regression analysis. Based on these indicators, we developed prognostic nomograms of OS and DFS. The predictive performance of the two nomograms were assessed by calibration plots. A total of 412 patients were recognized as having the low-risk subtype, and 359 patients had a GGO. Patients with the low-risk subtype had a high rate of GGO nodules (p < 0.001). Multivariate Cox regression analysis showed that the high-risk subtype and GGO components were independent prognostic factors for OS (LUAD subtype: p = 0.002; HR 3.624; 95% CI 1.263–10.397; GGO component: p = 0.001; HR 3.186; 95% CI 1.155–8.792) and DFS (LUAD subtype: p = 0.001; HR 2.284; 95% CI 1.448–5.509; GGO component: p = 0.003; HR 1.877; 95% CI 1.013–3.476). The C-indices of the nomogram based on the LUAD subtype and GGO components to predict OS and DFS were 0.866 (95% CI 0.841–0.891) and 0.667 (95% CI 0.586–0.748), respectively. Therefore, the high-risk subtype and GGO components were potential prognostic biomarkers for patients with stage IA IAC, and prognostic models based on these indicators showed good predictive performance and satisfactory agreement between observational and predicted survival.

Refrigerant distributions and dynamic migration characteristics of the transcritical CO2 air conditioning system

Research on refrigerant mass migration and distribution could not only provide deep understanding of the system performance enhancement, but also contribute to searching the high risk components caused by the Boiling Liquid Expanding Vapor Explosion (BLEVE) due to the high operation pressure of transcritical CO2 system. An experimental bench and a mathematical model of the transcritical CO2 automotive air-conditioning system were set up to explore the refrigerant migration characteristics at different operation conditions. Simulated results of the system were validated by experiments. The distribution characteristics of the refrigerant under different charge were studied and the refrigerant charge suggestions were given. Then, under the appropriate refrigerant charge, the migration characteristics of the refrigerant in the automobile air conditioning system were simulated in the process of startup and shutdown of the compressor and conversion of cooling mode. Furthermore, influence of the parameters, such as ambient temperature, air flow rate, compressor speed on the characteristics of the refrigerant distribution in the air-conditioning system were studied. The results could provide reference for the system design and system safety.