Search

In engineering applications, the simultaneous suspension and corrosion of pipelines under unstable geological conditions present significant challenges, leading to large deformations, stress concentrations, and potentially catastrophic failures. Previous research often overlooked the simultaneous impact of these factors, resulting in inaccuracy in the failure analysis and further maintenance strategies. To fill the gap, this paper employs numerical analysis to create and simulate a series of pipe–soil coupling models uniquely considering internal corrosion for a more precise investigation of mechanical and failure behaviors in high-strength spanning pipelines. This research pioneers a comprehensive parametric analysis, exploring key factors such as operating conditions and geometric features on deformation, stress, strain, and stress concentration factor (SCF), all vital for failure determination. The results offer crucial insights, showing that maximum stresses in corroded spanning pipelines occur within the corrosion area, unlike in intact pipelines, where they typically appear at the span end. Increases in spanning length, corrosion dimensions, and internal pressure lead to upward trends in the maximum longitudinal stress, strain, and vertical displacement. The axial location of the internal corrosion also significantly affects the pipeline's mechanical state. The longitudinal strain escalates along the axial location, being 1.97 times smaller at 2/10ls compared with 5/10ls. This study diverges from standard analyses by innovatively spotlighting the significant impact of corrosion width on spanning pipeline safety, particularly affecting longitudinal SCF. Maximum SCF increases by 36.1% with the corrosion width expanding from 5° to 50°, showcasing a more pronounced effect than that of corrosion length. This study advances pipeline integrity knowledge and lays the groundwork for future research, offering crucial tools for life span prediction and failure prevention in spanning pipelines. Practical Applications: Our study reveals a more accurate way to simulate and predict when and where pipelines might fail due to the combination of internal corrosion and spanning. Traditionally, it was thought that the ends of suspended pipelines were most at risk, but our research shows the weak spots are within the corrosion area. The obtained stress concentration factor can be further used to estimate the fatigue life of a spanning pipeline. The simulation results also suggest that corrosion width is vital for spanning pipelines' mechanical behaviors. [ABSTRACT FROM AUTHOR]

• A novel method to conduct waste characterization for RDF3 production application. • Detect and classify single and multi-family waste using video data. • Smart Sight uses computer vision for frame extraction & enhancement. • Solution's waste estimates compared with lab results, showing high accuracy. A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@0.5 of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition. [ABSTRACT FROM AUTHOR]

In order to clarify effects of different fungal strains on fermented tiger nut paste enzyme activities and volatile flavor compounds composition, koji enzyme activities and volatile flavor substances of six tiger nut paste preparations (S1–S6) were compared. The results revealed that koji enzyme activities of S4, S5 and S6 were significantly higher than corresponding activities of S1, S2 and S3. Among them, S4 exhibited the highest enzyme activity levels for cellulase, xylanase, acid protease, glucosidase, and amylase. GC-MS analysis of the six paste preparations detected 43 volatile components, including 17 aldehydes, 8 alkenes, 4 heterocyclics, 2 ketones, 2 phenols and 7 other types of compounds. Notably, S4 had the most volatile compounds (28), including 5 volatile compounds which were not detected in the other tiger nut pastes. Results of principal component analysis and partial least squares discriminant analysis (PLS-DA) revealed differences in levels of 17 volatile flavor substances among the six tiger nut paste preparations. Notably, PLS-DA results achieved more significant separation and stronger clustering trends when used to discriminate among volatile flavor substances of paste preparations fermented using single-strain starters (S1, S2, S3) versus double-strain starters (S4, S5, S6) consisting of Aspergillus oryzae, Aspergillus niger and/or Mucor. The tiger nut paste (S4) prepared by mixed fermentation of A. oryzae and A. niger had a unique flavor, producing the highest enzyme activity and a wider variety of volatile flavor compounds. Therefore, the production of tiger nut paste through compound strain koji fermentation was a feasible strategy. [ABSTRACT FROM AUTHOR]

Although essential contributions have been made to the reliability analysis of corroded pipelines, the interacting effect between adjacent corrosion defects is rarely considered, let alone the effects of the corrosion depth and steel grade on the interacting effect. This paper proposes a new reliability method to fill the gap. First, the finite-element method and regression analysis were applied to investigate how the corrosion depth and steel grade impact the interacting effect and develop new interaction rules. Second, the new interaction rule, burst pressure model, Monte Carlo simulation (MCS), sensitivity analysis, feature scaling, and artificial neural network (ANN) were integrated to predict reliability. The proposed method combines several approaches to achieve a more accurate and efficient reliability estimation of pipelines with multiple corrosion defects than conventional assessment methods. An example is given to demonstrate the method. Results show that the limit spacing distance grows as the corrosion depth increases. The growth of the limit spacing distance of the X80 pipeline is more significant than that of the X65 pipeline. Existing interaction rules introduce conservatism to the prediction of the limit spacing distance. Two new interaction rules were developed and can realize better prediction accuracy by considering the corrosion depth and steel grade. Besides, the interacting effect significantly affects the maintenance time. The maintenance time lag between the X65 pipeline ignoring and considering the interacting effect is about 7.5 years. Different interaction rules result in different reliability descending paths. Because the new interaction rule was developed for this case, it could provide a more accurate reliability analysis. The trained ANN shows excellent prediction accuracy and high computing efficiency. The mean squared error in the reliability predicted by the ANN is 2.4×10−6. The elapsed time of the ANN prediction is about 50 times shorter than that of the MCS. In engineering practice, corrosion defects usually occur in patches, and the interacting effect between adjacent defects impairs the structural reliability of corroded pipelines. The corrosion depth and steel grade are found to significantly affect the interacting effect and limit spacing distance. The new interaction rules can be applied to achieve a more accurate limit spacing distance estimation by considering the effects of the corrosion depth and steel grade. The interacting effect plays a vital role in the reliability analysis of corroded pipelines. The integrated reliability method proposed in this paper considers the interacting effect and interaction rule. In practical application, once the pipeline and defect information, such as wall thickness and corrosion depth, is gathered, the proposed method can be used to realize a more accurate and effective reliability analysis of pipelines with multiple corrosion defects. Thus, the proposed reliability method and results of this paper are beneficial for the integrity management of corroded pipelines. [ABSTRACT FROM AUTHOR]

Existing studies on wind farm predictive maintenance mostly incorporated repairs on the level of wind turbine (WT) subassemblies, e.g. drive train, gearbox and generator, including major repairs and minor repairs. The economic dependency among subassemblies was also considered. But assumptions needed to be made on major and minor repairs regarding their costs and impact on system functions. Besides, existing studies considered only fixed costs on the WT and farm levels without differentiating fixed costs that apply to a subset of components, such as crane costs, resulting in over-simplification compared to wind farm practices. In this work, a wind farm predictive maintenance approach is developed considering component level repairs and economic dependency. There are multiple components in a subassembly, e.g. the generator consists of components like generator rotor, generator bearings, contactor, etc. These components' failures lead to major or minor repairs, downtime, and demand on repair resources, particularly cranes. With the proposed wind farm predictive maintenance approach, component level major and minor repairs and their costs can be modeled explicitly in a more realistic and accurate way. Economic dependency can also be modeled more accurately by incorporating the downtime, i.e. revenue loss, and repair resource requirement caused by different component failures. A predictive maintenance optimization model is developed to find the optimal maintenance policy. The number of WTs eligible for preventive maintenance is introduced as a new decision variable, which also captures the economic dependency among WTs. A simulation-based method is developed for maintenance cost evaluation. Examples are used to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

The lung microbiota have been found to be substantially altered in numerous pulmonary disorders, and crosstalk between the host pathophysiology and lung microbiota plays critical roles in the regulation of disease states. The aim of this study was to investigate dynamic changes in the lung microbiota during different stages of acute lung injury and acute respiratory distress syndrome (ALI/ARDS). Rats receiving an intraperitoneal administration of lipopolysaccharide (LPS) were sacrificed at 12 and 48 h after injection, and the hematological parameters, serum cytokine levels, and histological characteristics of the lung tissue and lung microbiota were assessed. After LPS injection, along with fluctuations of systemic cytokine levels and the onset and regression of pulmonary edema, the diversity, components, and functionalities of the pulmonary microbiota underwent significant dynamic changes. The volatility of the α-diversity indices narrowed after LPS injection, and the indices significantly decreased 48 h later. The abundance of 18 genera and functionality of adenosine triphosphate–binding cassette (ABC) transporters, pentose phosphate, and bacterial chemotaxis pathways were found to significantly differ between specified time points. Several significant correlations between the components and functionalities of the lung microbiota and indicative symptoms of ALI/ARDS were also observed. Brevibacterium was correlated with cytokines tumor necrosis factor (TNF)-α, interleukin (IL)-10, and IL-6 and with hematological percentage of neutrophils (NEU%); Wnt, Notch, and chronic myeloid leukemia signaling pathways were correlated with IL-1β; mitogen-activated protein kinase (MAPK) signaling pathway–yeast was correlated with IL-10; and the pathways of ascorbate and aldarate metabolism and basal transcription factors were correlated with platelet-related indicators. The correlations between the lung microbiota and indicative symptoms of ALI/ARDS identified in this study support further investigation into the underlying mechanism of host–microbiota interactions during lung injury and repair. [ABSTRACT FROM AUTHOR]

The pipeline is a major approach to achieving large-scale hydrogen transportation. Hydrogen damage can deteriorate the material performance of the pipe steel, like ductility and plasticity reduction. Corrosion is dominating damage that impairs a pipeline's bearing capacity and structural reliability. However, previous research barely investigated the effect of hydrogen damage on failure behaviors, residual strength and interacting effect between adjacent corrosions of corroded high-strength pipelines transporting hydrogen. Besides, hardly any burst pressure model considers hydrogen damage. In this paper, several approaches, including the finite element method (FEM), regression analysis, the orthogonal test method, and the artificial neural network method, are applied to fill the gap. First, a series of finite element models with different geometric features and hydrogen damage is established to investigate the effects of hydrogen damage and corrosion on failure behaviors and residual strength. The results show that hydrogen damage can change the corroded pipeline's failure behaviors and reduce the residual strength. Second, based on the simulation results and regression analysis, a new burst model is developed to consider the hydrogen damage and improve the estimation accuracy. Third, based on the genetic algorithm (GA), a GA-BP neural network is established and trained for accurate and efficient residual strength estimation considering hydrogen damage. Furthermore, an orthogonal test is designed and performed to investigate the effects of critical parameters on the burst pressure of the corroded pipeline after hydrogen damage. The results indicate that hydrogen damage and corrosion length have similar contributions to the residual strength. Finally, the simulation results of pipelines with multiple corrosions show that hydrogen damage has a significant impact on the interacting effect between adjacent corrosions. The results obtained are valuable for further integrity management of steel pipelines carrying hydrogen. • Failure analysis of corroded high-strength pipeline subject to hydrogen damage. • FEM and artificial neural network are used. • The interacting effect between adjacent corrosions is considered. • A new burst model considering hydrogen damage is developed. [ABSTRACT FROM AUTHOR]

Purpose: Wind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction. Design/methodology/approach: Wind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs. Findings: The wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities. Originality/value: This paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used. [ABSTRACT FROM AUTHOR]

Let ℱ be a set of countable many functions, where every function f ∈ ℱ satisfies f : ℤ → ℤ and f (n) → + ∞ as n → + ∞. This paper studies the size of the set consisting of those numbers whose Lüroth digit sequence is strictly increasing and contains any finite pattern of ℱ. We prove that the Hausdorff dimension of such set is 1 / 2 and give several applications. [ABSTRACT FROM AUTHOR]

Background: Polydactyly is one of the most common congenital hand/foot malformations in humans. Mutations in GLI3 have been reported to cause syndromic and non-syndromic forms of preaxial and postaxial polydactylies. Case presentation: The patient was a 2-year-old boy who underwent surgery in our hospital. The right hand and left foot of the patient were labelled as postaxial polydactyly type B, and there was cutaneous webbing between the 3rd and 4th fingers of the left hand. We identified a novel c. 1622C > T variant in GLI3 leading to an isolated postaxial synpolydactyly. Conclusions: The patient carries a novel autosomal dominant heterozygous missense mutation. This mutation c.1622C > T;p.(Thr541Met) in the GLI3 gene may affect the normal function of the zinc finger domain (ZFD) in a different way. However, it seems that more research is needed to determine the exact effects of this mutation. [ABSTRACT FROM AUTHOR]

A hybrid model with multiple sub-classifier is a powerful classifier for intelligent fault diagnosis of gearboxes. Each sub-classifier is used to deal with different gear or bearing faults, and thus each should have its own optimal feature group. However, these sub-classifiers in the hybrid model used the same feature group in most reported feature selection methods. To overcome this problem, this study brings some insights into optimization on sub-classifiers. This proposed method, known as block feature selection (BFS), optimizes the feature group for only one fault type in each sub-classifier. The selected groups are then amalgamated to form the optimal features for all types of gearbox failures, with corresponding labels in the hybrid model. Each sub-classifier is considered to be one block. In each block, the concept of NSGA-II is used to select features for its corresponding sub-classifier. Then, a new sorting algorithm is proposed to find the best feature group of sub-classifier. In general, BFS can optimize features of each sub-classifier to find the best feature group for hybrid model. The effectiveness of BFS is validated by experimental data from bearing and gear faults in a planetary gearbox rig. The robustness of BFS is verified by incorporating white noise at different levels. [ABSTRACT FROM AUTHOR]

• Stochastics simulation modeling for RDF production. • Biomass to biofuel supply chain gets impacted by decision making at strategic,tactical and operation levels. • Application of network flow modeling technique for designing RDF production line. • Measuring impact of varied uncertainties on the quality of produced RDF. • Support classifying an RDF as an SRF quality fuel based on EN-5539 and ISO-21640. Municipal solid waste management requires intelligent and integrated decision-making to achieve sustainable waste treatment processes. In particular, a waste treatment system that transforms municipal solid wastes into a commodity called refuse-derived fuel (RDF) is being researched as a promising waste to energy solution. This waste processing faces limitations in maintaining consistent production and quality control standards of RDF. In this context, a network flow modeling technique is used to design a stochastic discrete-event simulation model for the production in a general material recovery facility (MRF) to evaluate its performance. The developed model supports revisions in the strategic, tactical, and operational decision levels and is integrated with varied uncertainties like probability distributions of in-feed waste compositions, moisture content, and calorific value of individual waste components, affecting the energy performance of a MRF. The model provides improvements to operating conditions and enables prediction for quality standards of RDF, enabling the waste management authority to meet their outlined quality specification for the final product. The validation of the model is conducted in a way, where the quality measures of the final product collected from an MRF are compared with the estimated values those from the simulation. The comparison reveals that precision in results from the developed model, in all performed tests is consistent with actual observed results, inferring the developed simulation model as a viable tool for estimating quality measures for RDF. The foundations of the model are based of assumptions like emphasis on general representation but not physical properties of MRF, only selected sets of uncertainties are studied, and variations in the operating conditions can affect estimated quality of RDF. [ABSTRACT FROM AUTHOR]

Wind power is a significant clean energy source. Operation & maintenance (O&M) costs account for about 25% of the cost of wind power, and it is critical to improve the reliability of wind power generators to reduce the overall cost and increase wind power competitiveness comparing to other power sources. Wind turbines are subject to instantaneously varying load due to wind turbulence, which challenges the prognostic study for predicting equipment future health conditions and remaining useful lives. With existing prognostics methods, the average constant load is typically used to approximate the varying external load. In this paper, an integrated varying-load approach is proposed for predicting wind turbine gearbox remaining useful life by specifically considering instantaneously varying external load, which is more realistic. Fatigue crack damage is focused on. The method integrates gear physical models and available health condition data, and the distribution of uncertain material parameter modeled in crack degradation process is updated via Bayesian inference once new health condition data become available. Examples are provided to demonstrate the effectiveness of the proposed varying-load approach. A comparative study is conducted between the proposed approach and existing constant-load approximation method, and the results show that the proposed varying-load approach can provide more accurate prediction. [ABSTRACT FROM AUTHOR]

Pipelines are widely used in transporting large quantities of oil and gas products over long distances due to their safety, efficiency and low cost. Integrity is essential for reliable pipeline operations, for preventing expensive downtime and failures resulting in leaking or spilling oil or gas content to the environment. Pipeline integrity management is a program that manages methods, tools and activities for assessing the health conditions of pipelines and scheduling inspection and maintenance activities to reduce the risks and costs. A pipeline integrity management program mainly consists of three major steps: defect detection and identification, defect growth prediction, and risk-based management. In-line inspections (ILI) are performed periodically using smart pigging tools to detect pipeline defects such as corrosion and cracks. Significant advances are needed to accurately evaluate defects based on ILI data, predict defect growth and optimize integrity activities to prevent pipeline failures, and pipeline integrity management has drawn extensive and growing research interests. This paper provides a comprehensive review on pipeline integrity management based on ILI data. Signal processing methods for defect evaluation for different types of ILI tools are presented. Physics-based models and data-driven methods for predicting defect growth for pipelines with different categories of defects are discussed. And models and methods for risk-based integrity management are reviewed in this paper. Current research challenges and possible future research trends in pipeline integrity management are also discussed. [ABSTRACT FROM AUTHOR]

Clinical efficacy of meniscal plasty and total meniscectomy under arthroscopy in treating middle-aged and elderly patients with meniscus injury were explored. One hundred and fifteen elderly patients with meniscal injuries treated in the First People's Hospital of Wujiang District Suzhou from May 2012 to October 2014 were retrospectively analyzed. They were randomly divided into meniscal plasty group (experimental group) and total meniscectomy group (control group). Length of stay, operation time and VAS between the two groups were compared. Knee function was evaluated before the surgery and at the final follow-up using IKDC 2000 and Lysholm score. The differences of postoperative clinical efficacy and complications in both groups were observed. Postoperative IKDC 2000 and Lysholm score in the experimental group were superior to those of control group (P<0.001). There was no significant difference in the relieving effect of knee pain between the two types of procedures (P>0.05). Shorter operation time was found in control than that of experimental group. We did not observe remarkable differences in length of stay and postoperative complication rate between the two groups (P>0.05). Both meniscal plasty and total meniscectomy under arthroscopy can effectively relieve knee pain in a short time. Meniscal plasty could remarkably alleviate the progression of knee osteoarthritis, maintain knee function and improve daily life of affected population. We considered that meniscal plasty should be served as the preferred approach in treating middle-aged and elderly patients with meniscus injury. [ABSTRACT FROM AUTHOR]

• Joint CBM optimization method for multi-component systems. • RUL of only some components can be continuously monitored and predicted. • Loss of efficiency due to system degradation is considered in benefit assessment. • Insights derived on the effects of the prognostic performance on the benefit of CBM. Recent developments in prognostic models have led to increased use of prognostic information to improve the benefits of condition-based maintenance (CBM). Existing CBM decision-making methods are often limited by the assumption that prognostics information is available for all components and by the neglect of economic loss due to system degradation in assessing benefits. This paper aims to find optimal maintenance decisions with maximum net revenue for multi-component systems using prognostics information. In contrast to the reported works, three main contributions are made. First, considering that the remaining useful life (RUL) of only some components in the system can be predicted continuously, maintenance decisions, including planned inspection intervals and prognostic thresholds, are optimized jointly at the system-level. Second, the loss of efficiency due to system degradation is investigated when assessing the benefits of maintenance decisions. A degradation-related system efficiency model is proposed, and its parameters can be determined from historical data. Third, we extend our model with prognostic error modeling to derive insights on the effects of the prognostic model on the relative benefit of CBM. A case of maintenance optimization of wind turbine farms is provided to demonstrate and validate the proposed method. [ABSTRACT FROM AUTHOR]

Surface wear is one of the main failure modes that gears suffer from due to the sliding contact in the mesh process. However, the existing gear prognostics methods mainly focused on the fatigue cracking failure mode and the existing prediction methods considering surface wear are physics based without utilizing condition monitoring data. This paper proposes the first integrated prognostics method for failure time prediction of gears subject to the surface wear failure mode, utilizing both physical models, i.e., Archard's wear model and condition monitoring data, i.e., inspection data on gear mass loss in this study. By noticing the importance of the wear coefficient in Archard's model, the proposed method can result in a more accurate value of the wear coefficient so that the wear evolution in the future is forecasted with more accuracy. To achieve this, a Bayesian update process is implemented to incorporate the mass loss observation at an inspection point to determine the posterior distribution of the wear coefficient. With more mass loss data available, this posterior distribution gets narrower and its mean approaches the actual value of the coefficient. To use Archard’ model, the gear mesh geometry and Hertz contact theory are applied to compute the sliding distance and the contact pressure for different points on the tooth flank. The proposed method is validated using run-to-failure experiments with a planetary gearbox test rig. [ABSTRACT FROM PUBLISHER]

Natural killer (NK) cells are lymphocytes of the innate immune system. They not only exert cell-mediated cytotoxicity against tumor cells or infected cells, but also play regulatory role through promoting or suppressing functions of other immune cells by secretion of cytokines and chemokines. However, overactivation or dysfunction of NK cells may be associated with pathogenesis of some diseases. NK cells are found to act as a two edged weapon and play opposite roles with both regulatory and inducer activity in autoimmune diseases. Though the precise mechanisms for the opposite effects of NK cells has not been fully elucidated, the importance of NK cells in autoimmune diseases might be associated with different NK cell subsets, different tissue microenvironment and different stages of corresponding diseases. The local tissue microenvironment, unique cellular interactions and different stages of corresponding diseases shape the properties and function of NK cells. In this review, we focus on recent research on the features and function of different NK cell subsets, particularly tissue-resident NK cells in different tissues, and their potential role in autoimmune diseases. [ABSTRACT FROM AUTHOR]

• Discovery of potential risks for the gas transmission station. • Structure mapping method based on FMEA. • GTS's object-oriented bayesian network (OOBN) framework. • Accident precursor identification approach based on PAA-CUSUM. Gas transmission stations (GTS) are significant infrastructure for cities and critical components of natural gas delivery, with severe consequences in the case of an accident. As a result, it necessitates the importance of potential risk discovery and accident precursor identification. However, existing models for risk analysis of GTS systems are too complex and only periodically update the risk of GTS, making it difficult to discover its potential risk in time. Some data used as input to the models are not from the system under consideration, leading to results inconsistent with the actual working conditions. This study proposes a structure mapping method based on failure modes and effects analysis (FMEA) to form the GTS's object-oriented Bayesian network (OOBN) framework, making the model more user-friendly. An accident precursor identification approach is proposed based on the piecewise aggregate approximation-cumulative sum (PAA-CUSUM) algorithm, which can better discover the potential risks in real-time. The proposed method identifies process anomalies through monitoring data and analyzes the events and propagation patterns with the highest potential risk. A case study of a GTS in China is conducted. The results demonstrate that the proposed method is beneficial for assisting station operators in identifying possible hazards and providing a foundation for daily risk mitigation. [ABSTRACT FROM AUTHOR]

Although natural killer (NK) cells were initially named for their spontaneous tumor-killing capacity, their concept has been greatly expanded with more than 40 years of extensive investigation. Currently, NK cells are known as a heterogeneous population of innate lymphoid cell (ILC) family, consisting of different subsets with unique phenotypic and functional features. Recent studies have shown that tissue-resident NK (trNK) cells, which are distinct from conventional NK (cNK) cells, preferentially distribute in non-lymphoid tissues, such as the liver, uterus, salivary gland, and adipose. In this review, we provide a comprehensive overview of the current knowledge about the phenotype, function and development of trNK cells across different tissues and describe the similarities and differences between diverse trNK cells and cNK cells, with a particular focus on the tissue-specific characteristics of different trNK cells. [ABSTRACT FROM AUTHOR]

Prognostics based on the deep learning model often assume that their training and testing data come from the same equipment with similar working conditions. However, a machine often has specific operating conditions for different tasks, which will cause significant divergence in its measurements. Generally, planned maintenance or model-based fault detections can be done first to collect very few suspension histories when the machine works in a new environment. Few suspension histories can help the prognostic model generalize to new environments. This paper proposes a transductive method to use limited suspension histories in transfer prognostics. Different from reported cross-domain prognostics that only align two-domain histories in a holistic manner, the proposed domain adaptation strategy simultaneously minimizes the distance between both marginal and conditional probability distributions in different domains. If the measurements have a clear degradation manifold, iterative learning will allow the model to get better and better pseudo predictions, thus guiding the prognostic model to learn generalized domain invariant features to deal with different working conditions. A heuristic method and a parallel framework are proposed to verify model parameters and uncertainties. The prognostic performance of the proposed approach is validated by using two case studies. [ABSTRACT FROM AUTHOR]

Prognostics based on deep learning models usually assume that training and testing data come from the same equipment with similar working conditions. In practice, some new working conditions of test equipment may not be recorded in the training dataset, and thus the learned remaining useful life (RUL) prediction model may not work well. This work is the first to propose a novel prognostic model based on state-space modeling and reinforcement learning to predict the RUL of equipment operating under extrapolated new conditions without corresponding training data. Instead of directly constructing a supervised model that relates monitoring measurements to their RUL, a discrete-time state-space model is built using possible failure histories. The contribution of this paper is twofold. First, on the basis of the state-space model, an interpretable prognostic model that combines Lyapunov constraint and reinforcement learning is proposed to predict the equipment RUL. Second, an adversarial training method based on the idea of H∞ robustness is integrated to reduce the effect of state-space modeling errors. The proposed model aims to reduce the causality between RUL and operating parameters and increase the causality between RUL and unobserved degradation characteristics. Consequently, the proposed model is interpretable and has the potential to predict RUL for the equipment operating under conditions beyond the record. The proposed approach is demonstrated and validated using a simulation study and an experimental dataset. [ABSTRACT FROM AUTHOR]

Minimal paths (MPs) play an important role in network reliability evaluation. In this paper, we report an efficient recursive algorithm for finding all MPs in two-terminal networks, which consist of a source node and a sink node. A linked path structure indexed by nodes is introduced, which accepts both directed and undirected form of networks. The distance between each node and the sink node is defined, and a simple recursive algorithm is presented for labeling the distance for each node. Based on the distance between each node and the sink node, additional conditions for backtracking are incorporated to reduce the number of search branches. With the newly introduced linked node structure, the distances between each node and the sink node, and the additional backtracking conditions, an improved backtracking algorithm for searching for all MPs is developed. In addition, the proposed algorithm can be adapted to search for all minimal paths for each source–sink pair in networks consisting of multiple source nodes and/or multiple sink nodes. Through computational experiments, it is demonstrated that the proposed algorithm is more efficient than existing algorithms when the network size is not too small. The proposed algorithm becomes more advantageous as the size of the network grows. [ABSTRACT FROM AUTHOR]

In this paper, we develop an integrated prognostics method considering a time-varying operating condition, which integrates physical gear models and sensor data. By taking advantage of stress analysis in finite element modeling (FEM), the degradation process governed by Paris’ law can adjust itself immediately to respond to the changes of the operating condition. The capability to directly relate the load to the damage propagation is a key advantage of the proposed integrated prognostics approach over the existing data-driven methods for dealing with time-varying operating conditions. In the proposed method, uncertainties in material parameters are considered as sources responsible for randomness in the predicted failure life. The joint distribution of material parameters is updated as sensor data become available. The updated distribution better characterizes the material parameters, and reduces the uncertainty in life prediction for the specific individual unit under condition monitoring. The update process is realized via Bayesian inference. To reduce the computational effort, a polynomial chaos expansion (PCE) collocation method is applied in computing the likelihood function in the Bayesian inference and the predicted failure time distribution. Examples based on crack propagation in a spur gear tooth are given to demonstrate the effectiveness of the proposed method. In addition, the example also shows that the proposed approach is effective even when the current loading profile is different from the loading profile under which historical data were collected. [ABSTRACT FROM AUTHOR]

Hemorrhagic complications of anti-neutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis are life-threatening conditions. Of these, alveolar hemorrhage is the most common and well-described hemorrhagic complication and is a prominent component of the pulmonary-renal syndrome. The clinical presentation of alveolar hemorrhage is highly variable ranging from mild to serious disease that may require respiratory support. Less attention has been directed at hemorrhagic complications that have been reported in other organs and which may be as severe or more severe with regard to morbidity and mortality. For example, among the most serious non-pulmonary complications are those related to cerebral and gastrointestinal hemorrhage. Cerebral hemorrhage is the clearly the most critical issue with a persistent high mortality rate. The prognosis of gastrointestinal hemorrhage, once an ominous complication has improved with medical and surgical intervention. Not to be dismissed are the consequences of hemorrhagic issues related to skin, soft tissue or muscle involvement. Muscle hematomas, while rare, also accompanied by significant morbidity. [ABSTRACT FROM AUTHOR]

Recent studies have identified a population of liver-resident innate lymphoid cells (ILCs) that, based on the expression of certain phenotypic markers, were termed ‘liver-resident NK cells’ and considered to be a new subset of conventional natural killer (cNK) cells. However, different transcriptional networks control the development of liver-resident NK cells and cNK cells and, furthermore, these cells exhibit features that characterize mucosal ILC1s. Here, we review findings providing insight into the origin, phenotype, and function of liver-resident NK cells, and discuss these in the context of the current understanding of lineage relations of ILC subsets. We propose that the similarities between liver-resident NK cells and mucosal ILC1s should be considered when revising the categorization framework for these cells, and discuss implications of this revision for other tissue-specific NK cells. [ABSTRACT FROM AUTHOR]

Deep learning methods have shown great potential to provide reliable remaining useful life (RUL) predictions in Prognostics and Health Management applications. However, deep learning models, particularly supervised learning methods, are strongly dependent on a large number of failure histories. In practice, engineering assets are generally replaced by new ones before failure during planned maintenance, resulting in a small number of failure histories and often times more than twice as many suspension histories. In this paper, a semi-supervised generative adversarial network (GAN) regression model is developed to consider both failure and suspension histories for RUL predictions. The proposed GAN model utilizes conditional multi-task objective functions to capture useful information from suspension histories to improve prediction accuracy, instead of simply treating them as unlabeled data. The method will not directly predict the failure times of suspension histories, but match the statistical information between similar failure and suspension histories to the greatest extent for model training. As a result, the failure information of suspension histories will not only rely on the failure histories but also on the generated data, thereby improving the model generalization, especially when the amount of data is limited. In addition, a robustness evaluation method is proposed to assess the uncertainty of the prognostic model caused by the scarce failure data. The accuracy and credibility of the proposed approach are validated by using two case studies. [ABSTRACT FROM AUTHOR]

Highlights: [•] Mouse splenic conventional natural killer (cNK) cells have been primarily studied. [•] The thymus contains NK cells that can be distinguished from cNK cells. [•] The liver has cNK cells and tissue-resident (trNK) NK cells. [•] Other organs may also have trNK cells, adding their potential diversity. [•] We review the features of these NK cells. [ABSTRACT FROM AUTHOR]

Human natural killer ( NK) cells have distinct functions as NKtolerant, NKcytotoxic and NKregulatory cells and can be divided into different subsets based on the relative expression of the surface markers CD27 and CD11b. CD27+ NK cells, which are abundant cytokine producers, are numerically in the minority in human peripheral blood but constitute the large population of NK cells in cord blood, spleen, tonsil and decidua tissues. Recent data suggest that these NK cells may have immunoregulatory properties under certain conditions. In this review, we will focus on these new NK cell subsets and discuss how regulatory NK cells may serve as rheostats or sentinels in controlling inflammation and maintaining immune homeostasis in various organs. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN)-based methods have been extensively investigated for equipment health condition prediction. However, effective condition-based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above-mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real-world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The evolution of two-dimensional dispersive focusing wave groups in deep water under wind forcing and wave breaking effects is investigated numerically and measurements collected from wind–wave experiments are used to evaluate the numerical simulations. Wind forcing is modeled by introducing into the dynamic boundary condition a surface slope coherent pressure distribution, which is expressed through Miles’ shear instability theory and Jeffreys’ sheltering model. To activate Jeffreys’ model in simulating waves evolving under wind forcing, an air flow separation criterion depending on wind speed and wave steepness is proposed. Direct comparisons of the measurements and the simulations are made by including the wind-driven current in the simulations. To simulate breaking waves, an eddy viscosity model is incorporated into a system of nonlinear evolution equations to dissipate wave energy and to predict surface elevation after breaking. For wave groups under no wind action, the eddy viscosity model simulates well the energy dissipation in breaking waves and predicts well the surface elevation after breaking. Under the weaker wind forcing condition, after consideration of the wind-driven current, the numerical model produces satisfying predictions. As the wind forcing becomes stronger, the disparity between the experiments and the simulations becomes more evident while the numerical results are still regarded as acceptable. The relative importances of the Miles’ and the Jeffreys’ models for waves under wind forcing are discussed through additional numerical tests. [Copyright &y& Elsevier]

Product reliability in the field is important for a wide variety of critical applications such as manufacturing, transportation, power generation, and health care. In particular, the propensity of achieving zero-downtime emphasizes the need for Remaining Useful Life (RUL) prediction for a single unit. The task is quite challenging when the unit is subject to time-varying operating conditions. This article provides a framework for predicting the RUL of a single unit under time-varying operating conditions by incorporating the results of both accelerated degradation testing andin situcondition monitoring. For illustration purposes, the underlying degradation process is modeled as a Brownian motion evolving in response to the operating conditions. The model is combined within situdegradation measurements of the unit and the operating conditions to predict the unit's RUL through a Bayesian technique. When the operating conditions are piecewise constant, statistical approaches using a conjugate prior distribution and Markov chain Monte Carlo approach are developed for cases involving linear and non-linear degradation–stress relationships, respectively. The proposed framework is also extended to handle a more complex case where the projected future operating conditions are stochastic. Simulation experiments and a case study for ball bearings are used to verify the prediction capability and practicality of the framework. In the case study, a quantile regression technique is proposed to handle load-dependent failure threshold values in RUL prediction. [ABSTRACT FROM PUBLISHER]

Abstract: Uncertainty quantification in damage growth is critical in equipment health prognosis and condition based maintenance. Integrated health prognostics has recently drawn growing attention due to its capability to produce more accurate predictions through integrating physical models and real-time condition monitoring data. In the existing literature, simulation is commonly used to account for the uncertainty in prognostics, which is inefficient. In this paper, instead of using simulation, a stochastic collocation approach is developed for efficient integrated gear health prognosis. Based on generalized polynomial chaos expansion, the approach is utilized to evaluate the uncertainty in gear remaining useful life prediction as well as the likelihood function in Bayesian inference. The collected condition monitoring data are incorporated into prognostics via Bayesian inference to update the distributions of uncertainties at given inspection times. Accordingly, the distribution of the remaining useful life is updated. Compared to conventional simulation methods, the stochastic collocation approach is much more efficient, and is capable of dealing with high dimensional probability space. An example is used to demonstrate the effectiveness and efficiency of the proposed approach. [Copyright &y& Elsevier]

Accurate health prognosis is critical for ensuring equipment reliability and reducing the overall life-cycle costs. The existing gear prognosis methods are primarily either model-based or data-driven. In this paper, an integrated prognostics method is developed for gear remaining life prediction, which utilizes both gear physical models and real-time condition monitoring data. The general prognosis framework for gears is proposed. The developed physical models include a gear finite element model for gear stress analysis, a gear dynamics model for dynamic load calculation, and a damage propagation model described using Paris' law. A gear mesh stiffness computation method is developed based on the gear system potential energy, which results in more realistic curved crack propagation paths. Material uncertainty and model uncertainty are considered to account for the differences among different specific units that affect the damage propagation path. A Bayesian method is used to fuse the collected condition monitoring data to update the distributions of the uncertainty factors for the current specific unit being monitored, and to achieve the updated remaining useful life prediction. An example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Abstract: As major components of innate immunity, NK cells not only exert cell-mediated cytotoxicity against tumor cells or infected cells, but also act to regulate the function of other immune cells by secretion of cytokines and chemokines, thus providing surveillance in early defense against viruses, intracellular bacteria and cancer cells. However, the effector function of NK cells must be exquisitely controlled in order to prevent inadvertent attack against self normal cells. The activity of NK cells is defined by integration of signals coming from inhibitory and activation receptors. Inhibitory receptors not only distinguish healthy from diseased cells by recognize self-MHC class I molecules on cell surfaces with “missing-self” model, but also provide an educational signal that generates functional NK cells. NK cells enrich in immunotolerance organ and recent findings of different regulatory NK cell subsets have indicated the unique role of NK cells in maintenance of homeostasis. Once the self-tolerance is broken, autoimmune response may occur. Although data has demonstrated that NK cells play important role in autoimmune disorders, NK cells seemed to act as a two edged weapon and play opposite roles with both regulatory and inducer activity even in the same disease. The precise role and regulatory mechanisms need to be further determined. In this review, we focus on recent research on the association of NK cells and antoimmune diseases, particularly the genetic correlation, the immune tolerance and misrecognition of NK cells, the regulatory function of NK cells, and their potential role in autoimmunity. [Copyright &y& Elsevier]

Abstract: Few methods are available for optimizing corrective maintenance and time-based maintenance for wind farms, although these strategies are currently widely used in practice. Economic dependencies exist among wind turbine systems and their components in the wind farm. That is, it may be more economical to maintain multiple turbines or turbine components when a corrective or preventive maintenance opportunity presents. In this paper, opportunistic maintenance approaches are developed for wind farms to take advantage of the maintenance opportunities. Imperfect maintenance actions are considered, which addresses the practical issue that preventive maintenance does not always return components to as-good-as-new status. The proposed opportunistic maintenance policies are defined by the component''s age threshold values, and different imperfect maintenance thresholds are introduced for failure turbines and working turbines. Three types of preventive maintenance actions are considered, including perfect, imperfect and two-level action. Simulation methods are developed to evaluate the costs of proposed opportunistic maintenance policies. Numerical examples are provided to illustrate the proposed approaches. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance methods in significantly reducing the maintenance cost. The developed methods are expected to bring immediate benefits to wind power industry. [Copyright &y& Elsevier]

Purpose: Postoperative delirium is a state of acute brain dysfunction characterized by fluctuating mental status that affects millions of patients each year. We used prophylactic inhalation of hydrogen gas in elderly patients undergoing elective surgery to compare their occurrence of postoperative delirium with that of controls. Methods: A total of 184 patients aged ≥ 65 years were enrolled and randomized into either a control group or a hydrogen inhalation group. The quality of sleep was assessed 1 day before and 1, 3, and 7 days after surgery at 8 A.M. The Confusion Assessment Method (CAM) was used as a screening tool for delirium and assessed the patients' state of consciousness 1–7 days after surgery. Results: Postoperative delirium occurred in 17 (24%) of 70 patients without hydrogen inhalation and in 10 (12%) of 83 patients after hydrogen inhalation. The incidence of delirium was decreased in the hydrogen group. No significant differences were found between length of stay in hospital after surgery and sleep quality at 1, 3, and 7 days postoperatively between the two groups. The numerical rating scale (NRS) pain scores were higher in the hydrogen group (4.08 ± 1.77) than the control group (3.54 ± 1.77) on day 1 (p < 0.05); however, the mean difference between the two groups was small (1 to 1.6). There were no significant differences on day 3 and 7. The postoperative C-reactive protein level was significantly lower in the hydrogen group than the control group. Conclusions: This study suggests that hydrogen inhalation can prevent postoperative delirium in elderly noncardiac patients by reducing the inflammatory response. [ABSTRACT FROM AUTHOR]

An eddy viscosity model to describe energy dissipation in two-dimensional breaking waves in deep water is implemented in a numerical model for the evolution of nonlinear surface waves and evaluated with experimental results. In the experiments, to develop a reliable eddy viscosity model, breaking waves are generated by both energy focusing and modulated wave groups. Local wave parameters prior to and following breaking are defined and then determined. Significant correlations between the pre-breaking and post-breaking parameters are identified and adopted in the eddy viscosity model. The numerical model detects automatically wave breaking onset based on local surface slope, determines pre-breaking local wave parameters, predicts post-breaking time and length scales, and estimates eddy viscosity to dissipate energy in wave breaking events. Numerical simulations with the model are performed and compared to the experiments. It is found that the model predicts well the total energy dissipation due to breaking waves. In addition, the computed surface elevations after wave breaking agree reasonably well with the measurements for the energy focusing (plunging) wave groups. However, for breaking wave groups due to modulational instability (plunging and spilling), a relatively large discrepancy between the surface elevation predictions and the experimental measurements is observed, in particular, at the downstream wave probe locations. This is possibly due to wave reflection and three-dimensionality in the experiments. To further validate the eddy viscosity model, the evolution of highly nonlinear irregular waves is studied numerically and the numerical solutions are compared with additional independent laboratory experiments for long-crested irregular waves. It is shown that the numerical model is capable of predicting the wave evolution subsequent to wave breaking. [ABSTRACT FROM AUTHOR]

Tanker vessels used for offshore oil production and storage are kept in station by turret mooring systems, enabling the vessels to weathervane in the direction of the dominant environmental loads. These passive weathervaning systems have been observed to be ineffective in swell-dominated long wave conditions. A notable feature of poor heading stability is the existence of a stable equilibrium at a large heading angle (50 - 60°) with respect to the direction of oncoming waves. Model, experiments on a 1:60 scaled FPSO have confirmed this behavior, particularly when wave length is comparable to the ship length. A detailed study of the dynamics of the prototype FPSO was conducted using frequency domain (quasi-dynamic) and time domain (dynamic) analyses. The quasi-dynamic study shows the importance of the balance between the yaw drift moment and sway induced yaw drift moment on the final heading angle. A pitchfork bifurcation of the equilibrium is seen to occur when wavelength/ship length ratio equals 0.73. The time domain analysis provides data consistent with model test findings. The viscous damping of various modes on yaw motion is shown to be important and can significantly alter the heading instability. With lack of heading control, the ship motions, principally roll, can increase thus affecting onboard operations. [ABSTRACT FROM AUTHOR]

Abstract: This research represents the first scientific publication reporting barley protein as a wall material to encapsulate fish oil. Solid microcapsules were able to form in aqueous solution by pre-emulsifying barley proteins with a homogenizer followed a microfluidizer system. No organic solvent or cross-linking reagents were used in the preparation process. The wet status microcapsules were converted into free-flowing powder (dry status microcapsules) by a spray-drying process. The optimum microcapsule preparation conditions were 15% protein, oil/protein ratio=1.0, and an inlet temperature of 150°C. These microcapsules exhibited high encapsulation efficiency (EE: 92.9–100.2%), loading efficiency (LE: 46.5–50.1%) and low moisture content (0.75–0.90%). The oxidative stability of microencapsulated fish oil was tested at both dry status and in aqueous solutions (pH 7.0 and pH 2.0) in an accelerated storage test (40°C, 8weeks). These barley protein microcapsules possessed a strong ability to protect fish oil against oxidation, making them ideally suited for use in liquid/semi-liquid food systems. Food formulation tests confirmed their successful application in milk and yogurt for their respective shelf lives. [Copyright &y& Elsevier]

Abstract: The objective of condition based maintenance (CBM) is typically to determine an optimal maintenance policy to minimize the overall maintenance cost based on condition monitoring information. The existing work reported in the literature only focuses on determining the optimal CBM policy for a single unit. In this paper, we investigate CBM of multi-component systems, where economic dependency exists among different components subject to condition monitoring. The fixed preventive replacement cost, such as sending a maintenance team to the site, is incurred once a preventive replacement is performed on one component. As a result, it would be more economical to preventively replace multiple components at the same time. In this work, we propose a multi-component system CBM policy based on proportional hazards model (PHM). The cost evaluation of such a CBM policy becomes much more complex when we extend the PHM based CBM policy from a single unit to a multi-component system. A numerical algorithm is developed in this paper for the exact cost evaluation of the PHM based multi-component CBM policy. Examples using real-world condition monitoring data are provided to demonstrate the proposed methods. [Copyright &y& Elsevier]

Abstract: By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost. [ABSTRACT FROM AUTHOR]

Abstract: Novel microparticles (3–5μm) were created by pre-emulsifying barley proteins with a homogenizer followed a microfluidizer system. These microparticles exhibited a high oil carrying capacity (encapsulation efficiency, 93–97%; loading efficiency, 46–49%). Microparticle degradation and bioactive compound release behaviours were studied in the simulated gastro-intestinal (GI) tract. The data revealed that nano-encapsulations (20–30nm) were formed as a result of enzymatic degradation of barley protein microparticle bulk matrix in the simulated gastric tract. These nano-encapsulations delivered β-carotene to a simulated human intestinal tract intact, where they were degraded by pancreatic enzymes and steadily released the β-carotene. These uniquely structured microparticles may provide a new strategy for the nutraceutical and pharmaceutical industries to develop targeted delivery systems for lipophilic bioactive compounds. [Copyright &y& Elsevier]

The development of accurate health condition prediction approaches has been a key research topic in condition based maintenance (CBM) in recent years. However, current health condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. Neural network based methods have been considered to be a very promising category of methods for equipment health condition prediction. In this paper, we propose a neural network prediction model called extended recurrent neural network (ERNN). An ERNN based approach is developed for health condition prediction of gearboxes based on the vibration data collected from a gearbox experimental system. The results demonstrate the capability of the ERNN based approach for producing satisfactory health condition prediction results. A comparative study based on the gearbox experiment data further establishes ERNN as an effective recurrent neural network model for equipment health condition prediction. [ABSTRACT FROM PUBLISHER]

Abstract: Artificial neural network (ANN) methods have shown great promise in achieving more accurate equipment remaining useful life prediction. However, most reported ANN methods only utilize condition monitoring data from failure histories, and ignore data obtained from suspension histories in which equipments are taken out of service before they fail. Suspension history condition monitoring data contain useful information revealing the degradation of equipment, and will help to achieve more accurate remaining useful life prediction if properly used, particularly when there are very limited failure histories, which is the case in many applications. In this paper, we develop an ANN approach utilizing both failure and suspension condition monitoring histories. The ANN model uses age and condition monitoring data as inputs and the life percentage as output. For each suspension history, the optimal predicted life is determined which can minimize the validation mean square error in the training process using the suspension history and the failure histories. Then the ANN is trained using the failure histories and all the suspension histories with the obtained optimal predicted life values, and the trained ANN can be used for remaining useful life prediction of other equipments. The key idea behind this approach is that the underlying relationship between the inputs and output of ANN is the same for all failure and suspension histories, and thus the optimal life for a suspension history is the one resulting in the lowest ANN validation error. The proposed approach is validated using vibration monitoring data collected from pump bearings in the field. [Copyright &y& Elsevier]

Abstract: In this paper, we present a practical approach for the joint reliability–redundancy optimization of multi-state series–parallel systems. In addition to determining the optimal redundancy level for each parallel subsystem, this approach also aims at finding the optimal values for the variables that affect the component state distributions in each subsystem. The key point is that technical and organizational actions can affect the state transition rates of a multi-state component, and thus affect the state distribution of the component and the availability of the system. Taking this into consideration, we present an approach for determining the optimal versions and numbers of components and the optimal set of technical and organizational actions for each subsystem of a multi-state series–parallel system, so as to minimize the system cost while satisfying the system availability constraint. The approach might be considered to be the multi-state version of the joint system reliability–redundancy optimization methods. [Copyright &y& Elsevier]

The discovery of innate immune receptors and the emergence of liver immunology (high content of NK and NKT cells in liver) led to the second research summit in innate immunity since the finding of NK cells in the middle 1970s. Liver disease is one of the most dangerous threats to humans, and the progress in innate immunology and liver immunology made it possible to re-explain the cellular and molecular immune mechanisms of liver disease. In the past ten years, we have found that innate recognilion of hepatic NK and NKT subsets were involved in murine liver injury. We established a novel NK cell-dependent acute murine hepatitis model by activating Toll-like receptor-3 (TLR-3) with an injection of poly I:C, which may mimic mild viral hepatitis (such as Chronic Hepatitis B). We observed that a network of innate immune cells including NK, NKT and Kupifer cells is involved in liver immune injury in our established NK cell-dependent murine model. We noted that TLR-3 on Kupffer cells activated by pretreatment with poly I: C might protect against bacterial toxin (LPS)-induced fulminant hepatitis by down-regulating TLR-4 function, while TLR-3 pre-activation of NK cells might reduce Con A-induced NKT cell-mediated fulminant hepatitis by blocking NKT cell recruitment to the liver. We also found that the oversensitivity to injury by immune stimulation in HBV (hepatitis B virus) transgenic mice (full HBV gene-tg or HBs-tg) correlated to the over-expression of Real, an NKG2D (natural killer cell group 2D) ligand of NK cells or CD1d, a ligand of TCR-V14 of NKT cells, on HBV[sup+] hepatocytes, which leads to an innate immune response against hepatocytes and is critical in liver immune injury and regeneration. [ABSTRACT FROM AUTHOR]

The k-out-of-n system structure is a very popular type of redundancy in fault-tolerant systems, with wide applications in both industrial and military systems. In this paper, the modeling, application and reliability evaluation of k-out-of-n systems are studied for the case where the components and the system have multiple performance levels. A multi-state k-out-of-n system model is proposed that allows different requirements on the number of components for different state levels, and, very importantly, more practical engineering systems can fit into this model. The multiple states in the model can be interpreted in two ways: (i) multiple levels of capacity; and (ii) multiple failure modes. Application examples of the proposed multi-state k-out-of-n system model are given under each of the interpretations. An approach is presented for efficient reliability evaluation of multi-state k-out-of-n systems with identically and independently distributed components. A recursive algorithm is proposed for reliability evaluation of multi-state k-out-of-n systems with independent components. Efficiency investigations show that both of the reliability evaluation approaches are efficient. The multi-state k-out-of-n system model with a constant k value, which is a special case of the general multi-state k-out-of-n system model, has been studied for a long time, but only on the theoretical stage. A practical application of this model is presented in this paper as well. [ABSTRACT FROM AUTHOR]

The multistate networks under consideration consist of a source node, a sink node, and some independent failure-prone components in between the nodes. The components can work at different levels of capacity. For such a network, we are interested in evaluating the probability that the flow from the source node to the sink node is equal to or greater than a demanded flow of d units. A general method for reliability evaluation of such multistate networks is using minimal path (cut) vectors. A minimal path vector to system state d is called a d-MP. Approaches for generating all d-MPs have been reported. Given that all d-MPs have been found, the issue becomes how to evaluate the probability of the union of the events that the component state vector is greater than or equal to at least one of the d-MPs. There is a need for a more efficient method of determining the probability of this union of events. In this paper, we report an efficient recursive algorithm for this union probability evaluation based on the Sum of Disjoint Products (SDP) principle, and name it the Recursive Sum of Disjoint Products (RSDP) algorithm. The basic idea is that, based on the SDP principle and a specially defined "maximum" operator, "⊕", the probability of a union with L vectors can be calculated via calculating the probabilities of several unions with L-1 vectors or less. The correctness of RSDP is illustrated. The efficiency of this algorithm is investigated by comparing it with an existing algorithm that is generally accepted to be efficient. It is found that RSDP is more efficient than the existing algorithm when the number of components of a system is not too small. RSDP provides us with an efficient, systematic and simple approach for evaluating multistate network reliability given all d-MPs. [ABSTRACT FROM AUTHOR]

In different species of mammal, uterine natural killer (uNK) cells are massively recruited and presented at the fetal maternal interface with a spatio-temporal pattern, and regarded as a constructive element to support reproductive development. Recent insights highlight the uNK cells activation, function and interaction with local compartments, which all contribute to the initiation of vascular structural changes. New trends of uNK cells research will benefit the diagnosis, management and test treatment strategy of preeclampsia. Furthermore, we suggest that more efforts and specific studies are needed to further explore the role of uNK cells at the unique micro-environment. [ABSTRACT FROM AUTHOR]