Search Results (613)

Hepatitis E, caused by the hepatitis E virus (HEV), is a zoonotic disease that extends beyond hepatocellular necrosis to replicate in multiple organs. While most infections are self-limiting, HEV infection during pregnancy is associated with severe outcomes, including acute liver failure, preterm delivery, and miscarriage, with the mechanisms underlying this high pathogenicity remaining poorly understood. This study established a pregnant tree shrew model with a late-stage HEV infection and a cellular model using zoonotic HEV genotypes GT3 and GT4 to investigate the effects of estrogen on HEV replication. Results showed that negative-strand RNA detection revealed replicative intermediates in feces and tissues during the acute phase, with peak viral loads occurring within one week and the highest titers in bile. Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels rose at 3 days post-inoculation (DPI), peaking at 7 DPI. Elevated estrogen levels post-miscarriage correlated with increased viral loads, a trend mirrored in cell culture models showing linear relationships between estrogen and viral replication. Histopathology demonstrated viral hepatitis lesions in liver tissues and abnormalities in the uterus, ovaries, and brain, including hydropic degeneration, neuronal disruption, and granulosa cell necrosis. This study developed a pregnant tree shrew model for HEV infection, providing a robust tool for exploring pathogenic mechanisms during pregnancy and genotype-specific differences in zoonotic HEV pathogenicity. These findings offer new insights into the role of estrogen in HEV replication and its contribution to adverse pregnancy outcomes. Full article

The operational efficiency and reliability of the ship’s lubrication oil system directly impact the vessel’s safety. Traditional reliability analysis methods struggle to effectively handle the system’s dynamic characteristics and multi-source data analysis. To address these issues, this study proposes an innovative method that integrates feature dimensionality reduction, a dynamic Bayesian network of gravity model to improve the accuracy of system reliability analysis. First, the proportional hazards model is used to evaluate the operational reliability of each component, providing a quantitative basis for assessing the system’s health status through failure rate estimation. Then, a dynamic Bayesian network model is employed for overall system reliability analysis, fully considering the impact of multi-state devices and different maintenance strategies. The proposed DBN-based reliability assessment method achieves significant improvements over the traditional Fault Tree Analysis (FTA). The reliability of the main lubrication oil system (GUB) increases from 0.169 to 0.261, representing a 9.2% improvement; under scheduled maintenance conditions, the system reliability stabilizes at approximately 0.9873 after $0.4\times {10}^5$ h, compared to only 0.24 without maintenance. The proposed method effectively evaluates the reliability of the lubrication oil system, and the maintenance strategy using this method can greatly improve the reliability, providing strong support for scientifically guiding maintenance decisions. Full article

The role of nitric oxide (NO), soluble guanylate cyclase (sGC), and the cyclic guanosine monophosphate (cGMP) pathway in cardiovascular and renal health and disease is a complex issue. The impact of these biochemical pathways on the vascular tree is well established: the activation of sGC by NO promotes vasodilation and modulates vascular tone. Indeed, additional characteristics exist that lead physicians to believe there is a pleiotropic influence of this pathway on the functional activities and structural characteristics of human tissues and cells. Recently, sGC stimulators have demonstrated clinical efficacy in patients with worsening heart failure with reduced ejection fraction, improving cardiovascular death risk, re-hospitalization for HF, and all-cause mortality. These new outcome data have increased interest in understanding the potential pathophysiological mechanisms. The NO-sGC-cGMP axis may influence endothelial function, kidney performance, and cardiac muscle cell activity. The synergy of these actions could explain the positive effects of vericiguat on worsening HF. The aim of this narrative review was to provide a comprehensive insight into the pathophysiological mechanisms of action of NO-sGC-cGMP axis stimulators on cardiac muscle, endothelial cells, and kidneys. Full article

In operational testing contexts, testers face dual challenges of constrained timeframes and limited resources, both of which impede the generation of reliability test data. To address this issue, integrating data from similar systems with test data can effectively expand data sources. This study proposes a systematic approach wherein the mission of the system under test (SUT) is decomposed to identify candidate subsystems for data combination. A phylogenetic tree representation is constructed for subsystem analysis and subsequently mapped to a mixed-integer programming (MIP) model, enabling efficient computation of similarity factors. A reliability assessment model that combines data from similar subsystems is established. The similarity factor is regarded as a covariate, and the regression relationship between it and the subsystem failure-time distribution is established. The joint posterior distribution of regression coefficients is derived using Bayesian theory, which are then sampled via the No-U-Turn Sampler (NUTS) algorithm to obtain reliability estimates. Numerical case studies demonstrate that the proposed method outperforms existing approaches, yielding more robust similarity factors and higher accuracy in reliability assessments. Full article

Porcine epidemic diarrhea virus (PEDV) is a major pathogen responsible for viral diarrhea in pigs, causing particularly high mortality in neonatal piglets. In recent years, genetic variations in PEDV have resulted in alterations in both its virulence and antigenicity, leading to a reduced efficacy of existing vaccines. In this study, diarrheal samples were collected from four commercial pig farms in the Hubei, Guangxi, and Jiangxi provinces, China, which experienced vaccine failure. RT-qPCR confirmed PEDV infection, and three PEDV strains, 2021-HBMC, 2024-JXYX, and 2024-JXNC, were successfully isolated. Sequence analysis and phylogenetic tree construction classified these strains into the G2c genotype, the predominant subtype in China. The neutralization assays revealed a significant reduction in the neutralizing titers of these strains against the immune serum compared with the AJ1102 reference strain. Further amino acid sequence analysis of the spike (S) protein identified several mutations in key neutralizing epitopes compared with the AJ1102 strain, including S27L, E57A, N139D, M214T, and P229L in the S-NTD epitope; A520S, F539L, K566N, D569E, G612V, P634S, E636V/K in the COE epitope; and Y1376H in the 2C10 epitope, along with several deletions at N-glycosylation sites (347NSSD and 510NITV). Additionally, whole-genome sequencing and recombination analysis indicated that the 2021-HBMC strain may have resulted from a recombination event. The findings of this study underscore the challenge posed by the continuous genetic evolution of PEDV to vaccine efficacy and provide valuable insights for future vaccine development and control strategies. Full article

In human–robot collaborative environments, predicting and explaining robotic grasp failures is crucial for effective operation. While machine learning models can predict failures accurately, they often lack transparency, limiting their utility in critical applications. This paper presents a comparative analysis of three post hoc explanation methods—Tree-SHAP, LIME, and TreeInterpreter—for explaining grasp failure predictions from white-box and black-box models. Using a simulated robotic grasping dataset, we evaluate these methods based on their agreement in identifying important features, similarity in feature importance rankings, dependency on model type, and computational efficiency. Our findings reveal that Tree-SHAP and TreeInterpreter demonstrate stronger consistency with each other than with LIME, particularly for correctly predicted failures. The choice of ML model significantly affects explanation consistency, with simpler models yielding more agreement across methods. TreeInterpreter offers a substantial computational advantage, operating approximately 24 times faster than Tree-SHAP and over 2000 times faster than LIME for complex models. All methods consistently identify effort in joint 1 across fingers 1 and 3 as critical factors in grasp failures, aligning with mechanical design principles. These insights contribute to developing more transparent and reliable robotic grasping systems, enabling better human–robot collaboration through improved failure understanding and prevention. Full article

To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. After iterative training of the BPDM-GCN algorithm, the comprehensive link weights within the network topology are generated. Then, according to the comprehensive link weight and taking the shortest path as the optimization objective, a backup path implementation method based on the incremental shortest path tree is designed to reduce the phasor data transmission delay in the backup path. In conclusion, the experimental results show that the backup path formulated by this algorithm exhibits reduced data transmission delay, minimal path extension, and a high success rate in recovering failed links. Compared to the superior NRLF-RL algorithm, the BPDM-GCN algorithm achieves a reduction of approximately 14.29% in the average failure link recovery delay and an increase of approximately 5.24% in the failure link recovery success rate. Full article

The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and operational modes. A dataset comprising 2.2 million measurements from a laboratory-based PV model, covering seven fault categories—including inverter failures, partial shading, and sensor faults—is used for training and validation. Models are assessed under both Maximum Power Point Tracking (MPPT) and Limited Power Point Tracking (LPPT) conditions to determine their adaptability. The results indicate that the ensemble bagged tree classifier achieves the highest accuracy (92.2%) across all fault scenarios, while neural network-based models perform better under MPPT conditions. Additionally, the study highlights variations in model performance based on power mode, suggesting the potential for adaptive diagnostic approaches. The findings reinforce the feasibility of machine learning for predictive maintenance in PV systems, offering a cost-effective, sensor-free method for real-time fault detection. Future research should explore hybrid models that dynamically switch between classifiers based on system conditions, as well as validation using real-world PV installations. Full article

Mining companies are increasingly being motivated to become High Reliability Organisations (HROs) in order to achieve better results in critical areas such as safety, environment management, and loss avoidance despite their complex environments. High Reliability Organisations are recognised by their abilities to effectively anticipate failures and disasters, including use of lessons learnt from previous failures. This paper seeks to demonstrate how designers for systems in the mining industry can learn from failures to anticipate failures and effectively manage them. It also demonstrates the applicability of a hybrid model which incorporates and integrates Fault Tree Analysis (FTA), Reliability Block Diagram (RBD) analysis, Risk Priority Number (RPN) concepts, and Analytical Hierarchy Processes (AHPs) in a case study for a High Potential Incident (HPI) at an underground hard rock mine. It shows how valuable lessons can be extracted and how these lessons can be used in decision making to prevent and manage future failures. The main contribution of this work is the demonstration of incorporating HRO principles with a hybrid modelling framework for learning from failures. Full article

The topic of hydrogen as an energy vector is widely discussed in the present literature, being one of the crucial technologies aimed at human carbon footprint reduction. There are different hydrogen production methods. In particular, this paper focuses on Steam Methane Reforming (SMR), which requires a source of high-temperature heat (around 900 °C) to trigger the chemical reaction between steam and CH₄. This paper examines a plant in which the reforming heat is supplied through a helium-cooled high-temperature nuclear reactor (HTR). After a review of the recent literature, this paper provides a description of the plant and its main components, with a central focus on the safety and reliability features of the combined nuclear and chemical system. The main aspect emphasized in this paper is the assessment of the hydrogen production reliability, carried out through Failure Modes and Effects Analysis (FMEA) with the aid of simulation software able to determine the quantity and origin of plant stops based on its operational tree. The analysis covers a time span of 20 years, and the results provide a breakdown of all the failures that occurred, together with proposals aimed at improving reliability. Full article

The maritime industry is rapidly advancing toward the initial stages of the digitised era of shipping, characterised by considerable advances in maritime autonomous technology in recent times. This study examines the effectiveness of training packages and the impact of rank during the failure of a sophisticated autopilot control system. For this study, the fault recognition and diagnostic skills of 60 navigational seafarers conducting a navigational watch in a full mission bridge watchkeeping simulator were analysed. Participants had either significant experience as qualified navigational officers of the watch or were navigational officers of the watch cadets with 12 months’ watchkeeping experience. These groups were subdivided into those who were given a training package focused on behavioural aspects of managing automation, such as maintaining situational awareness, and those given a technical training package. The findings were analysed using an Event Tree Analysis method to assess the participants’ performance in diagnosing a navigation fault. Additionally, the fault recognition skills were assessed between groups of training and rank. The study found that participants who received the behavioural training were more successful in both recognising and diagnosing the fault during the exercise. Behavioural training groups outperformed technical training groups, even when technical training participants were experienced seafarers. This difference in performance occurred without any apparent differences in workload or secondary task performance. Understanding the data gathered from the study could lead to the development of future training regimes for navigational officers of the watch and help to optimise the evolution of the seafaring role. Full article

In the present paper, the subject of investigation is the reliability assessment of the single-stage reversible Hydropower Unit No. 3 (HU3) in the Bulgarian Pumped Hydro-Electric Storage (PHES) plant “Chaira”, which processes the waters of the “Belmeken” dam and “Chaira” dam. Preceding the destruction of HU4 and its virtual simulation, an analysis and its conclusions for rehabilitation and safety provided the information required for the reliability assessment of HU3. Detailed analysis of the consequences of the prolonged use of HU3 was carried out. The Supervisory Control and Data Acquisition (SCADA) system records were studied. Fault Tree Analysis (FTA) was applied to determine the component relationships and subsystem failures that can lead to an undesired primary event. A Failure Modes and Effect Analysis methodology was proposed for the large-scale hydraulic units and PHES. Based on the data of the virtual simulation and the investigations of the HU4 and its damages, as well as on the failures in the stay vanes of HU3, it is recommended to organize the monitoring of crucial elements of the structure and of water ingress into the drainage holes, which will allow for detecting failures in a timely manner. Full article

Paeonia ludlowii is a threatened and valuable germplasm in the cultivated tree peony gene pool, with distinctive traits such as tall stature, pure yellow flowers, and scarlet foliage in autumn. However, the crossability barrier limits gene transfer from P. ludlowii to cultivated tree peony. Therefore, our study investigated the reasons for the lack of crossability between P. ludlowii and Paeonia ostii ‘Fengdan’. Distant cross pollination (DH) resulted in the formation of many calloses at the ends of the pollen tubes, which grew non-polar, twisted, entangled, and often stopped in the style. Pollen tubes elongated the fastest in self-pollination (CK), and pollen tubes elongated faster and fewer pollen tube abnormalities were observed in stigmas treated with KCl solution before pollination (KH) than in DH. During pollen–pistil interactions, the absence of stigma exudates, high levels of H₂O₂, O₂⁻, MDA, ^•OH, ABA, and MeJA, and lower levels of BR and GA₃ may negatively affect pollen germination and pollen tube elongation in the pistil of P. ostii ‘Fengdan’. Pollen tubes in CK and KH penetrated the ovule into the embryo sac at 24 h after pollination, whereas only a few pollen tubes in DH penetrated the ovule at 36 h after pollination. Pre-embryo abnormalities and the inhibition of free nuclear endosperm division resulted in embryo abortion in most of the fruits of DH and many fruits of KH, which occurred between 10 and 20 days after pollination, whereas embryos in CK developed well. Early embryo abortion and endosperm abortion in most of the fruits of DH and KH led to seed abortion. Seed abortion in KH and DH was mainly due to an insufficient supply of auxins and gibberellins and lower content of soluble protein and soluble sugars. The cross failure between P. ludlowii and P. ostii ‘Fengdan’ is mostly caused by a pre-fertilization barrier. KH treatment can effectively promote pollen tube growth and facilitate normal development of hybrid embryos. These findings provide new insights into overcoming the interspecific hybridization barrier between cultivated tree peony varieties and wild species. Full article

Aiming at the problems of complex calculation processes, insufficient risk data, and reliance on experts’ subjective judgments that exist in traditional probability analysis methods, this paper proposes a probability analysis method for hazardous chemical storage tank leakage accidents based on neural networks and fuzzy dynamic fault trees (Fuzzy DFT). This method combines fuzzy set theory (FST) and Bootstrap technology to accurately quantify the failure probabilities of basic events (BEs) and reduce the dependence on experts’ subjective judgments. Furthermore, an artificial neural network (ANN) model for tank failures is constructed. This model can accurately calculate the probability of tank leakage accidents by taking into account the dependency relationships among basic events. Finally, a long short-term memory (LSTM) network is utilized to analyze the dynamic evolution trend of the probability of storage tank accidents over time. In this paper, this method is applied to the case of the “11.28” Shenghua vinyl chloride leakage accident. The results show that the calculation results of this method are highly consistent with the actual situation of the accident, indicating that it is a scientific and effective method for analyzing the probability of hazardous chemical storage tank leakage accidents. Full article

Several machine learning classification algorithms were evaluated using a dataset focused on heart failure. Results obtained from logistic regression, random forest, decision tree, K-nearest neighbors, and multilayer perceptron (MLP) were compared to obtain the best model. The random forest method obtained specificity = 0.93, AUC = 0.97, and Matthews correlation coefficient (MCC) = 0.83. The accuracy was high; therefore, it was considered the best model. On the other hand, K-nearest neighbors and MLP (multi-layer perceptron) showed lower accuracy rates. These results confirm the effectiveness of the random forest method in identifying heart failure cases. This study underlines that the number of features, feature selection and quality, model type, and hyperparameter fit are also critical in these studies, as well as the importance of using machine learning techniques. Full article