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Based on the integral field unit (IFU) data from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we develop a new method to select galaxies with biconical ionized structures, building a sample of 142 edge-on biconical ionized galaxies. We classify these 142 galaxies into 81 star-forming galaxies, 31 composite galaxies, and 30 AGNs (consisting of 23 Seyferts and 7 LI(N)ERs) according to the [N  ii ]-BPT diagram. The star-forming bicones have bar-like structures while AGN bicones display hourglass structures, and composite bicones exhibit transitional morphologies between them due to both black hole and star formation activities. Star-forming bicones have intense star formation activities in their central regions, and the primary driver of biconical structures is the central star formation rate surface density. The lack of difference in the strength of central black hole activities (traced by dust attenuation corrected [O  iii ]λ5007 luminosity and Eddington ratio) between Seyfert bicones and their control samples can be naturally explained as that the accretion disc and the galactic disc are not necessarily coplanar. Additionally, the biconical galaxies with central LI(N)ER-like line ratios are edge-on disc galaxies that show strong central dust attenuation. The radial gradients of H α surface brightness follow the r −2.35 relation, roughly consistent with r −2 profile, which is expected in the case of photoionization by a central point-like source. These observations indicate obscured AGNs or AGN echoes as the primary drivers of biconical structures in LI(N)ERs. [ABSTRACT FROM AUTHOR]

This article aims to propose an evidential reasoning (ER) rule considering the parameter uncertainty. As the essential parameters, the evidence weight and reliability make the ER rule constitute a generalized reasoning framework. Theoretically, the weight is affected by subjective cognition, while the reliability mainly reflects objective variation. However, most of the recent researches have focused on the quantitative calculation methods, which make the differences in the property of the two parameters ignored. In this article, a relatively different idea from previous studies is provided, in which multisource uncertainty of parameters is fully considered. On the one hand, the weight is profiled by interval variable with lower and upper bounds. On the other hand, the reliability is modeled by random variable with probability distribution function. Then, a unified inference model for evidence aggregation is developed based on the inference process of the ER rule. In addition, some basic properties of the model are clearly presented to illustrate the rationality of parameter uncertainty. Finally, a practical example is given to show the potential applications of the proposed model. [ABSTRACT FROM AUTHOR]

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article. [ABSTRACT FROM AUTHOR]

Evidential reasoning (ER) rule has been widely used in addressing uncertainty, ignorance, and vagueness information. To explore its performance measure (PM), the perturbation analysis (PA) for the ER rule (ER rule-PA) is conducted, with perturbation taken into consideration. This article aims to analyze the robustness and stability of the ER rule, serving as theoretical basis and technical support for applied research and applications. The combination of two pieces of independent evidence is discussed, and perturbation is added to one piece of evidence. To represent the expected utility of evidence combination under perturbation, perturbation utility is introduced. The novel concept of perturbation coefficient is proposed to characterize the PM of the ER rule (ER rule-PM). The properties of perturbation coefficient are explored to demonstrate the impact of perturbation. The maximum permissible error (MPE) of perturbation coefficient is defined to characterize the acceptability of perturbation. A numerical study is examined to illustrate the implementation process of ER rule-PA. Moreover, a case study of reliability evaluation of aerospace relay is conducted to show the potential applications of ER rule-PA, which makes the proposed method more practical. [ABSTRACT FROM AUTHOR]

This study aims to compare the levels of NLRP3 inflammasome and its related cytokines (IL-1β, IL-6, and IL-17), in serum and cerebrospinal fluid (CSF) of patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), non-inflammatory chronic polyneuropathy, and functional neurological disorders. The results showed elevated NLRP3 inflammasome, IL-1β, IL-6, and IL-17 levels in serum but not in CSF of CIDP, compared to the other groups. Moreover, there was a positive correlation between NLRP3 inflammasome level and each cytokine. We also found a positive correlation between serum NLRP3 inflammasome level and disease severity in CIDP patients. Taken together, these results suggested that NLRP3 inflammasome could act as a potential biomarker to diagnose CIDP and assess the severity of the disease. [ABSTRACT FROM AUTHOR]

It is important to predict the hidden behavior of a complex system. In the existing models for predicting the hidden behavior, the hidden belief rule base (HBRB) is an effective model which can use qualitative knowledge and quantitative data. However, the frame of discernment (FoD) of HBRB which is composed of some states or propositions and the universal set including all states or propositions is not complete. The global ignorance and local ignorance cannot be considered at the same time, which may lead to the inaccurate forecasting results. To solve the problems, a new HBRB model named as PHBRB in which the hidden behavior is described on the FoD of the power set is proposed in this correspondence paper. Furthermore, by using the evidential reasoning rule as the inference tool of PHBRB, a new projection covariance matrix adaption evolution strategy is developed to optimize the parameters of PHBRB so that more accurate prediction results can be obtained. A case study of network security situation prediction is conducted to demonstrate the effectiveness of the newly proposed method. [ABSTRACT FROM AUTHOR]

Nonlinear complex system modeling has drawn attention from diverse fields and many approaches have been developed. Among those approaches, the advantages of the belief rule base (BRB) expert system have been shown for managing multiple types of information under uncertainty and modeling the nonlinearity present in many theoretical and practical complex systems. However, two challenges still need to be addressed. First, BRB needs to be downsized to conserve modeling and computational effort. For this challenge, a new disjunctive assumption is applied, which can significantly downsize BRB while maintaining its completeness. Second, the structure and parameters of BRB need to be jointly optimized. For this challenge, a new Akaike information criterion (AIC)-based optimization objective is derived to represent both modeling accuracy and modeling complexity. Moreover, a joint bi-level optimization model with an AIC-based objective is constructed for the BRB structure and parameters, and a bi-level optimization algorithm is proposed. Three evolutionary algorithms, namely, the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, are tested in a comparative fashion to determine the best fit for the optimization engine. The results of two practical case studies show that the joint optimization approach can identify an optimal configuration for both its structure and parameters, which is referred to as the best decision structure in this paper. [ABSTRACT FROM AUTHOR]

It is vital to online assess the safety of a complex dynamic system by taking into account the current state, degradation trend, and historical records together. This paper proposes a new safety assessment model with an online algorithm based on the evidential reasoning (ER) approach. It does not only take into account the relative importance of each safety indicator, but also consider the reliability of each indicator. To obtain the integrated safety level, multiple safety indicators are fused at first and the “history,” “current,” and “future” safety states are then integrated. First, a forecasting model based on the third-order Volterra filter is proposed to predict the safety indicators’ information online. Second, an adaptive weighting model is developed to automatically adjust to various conditions and track the characteristics of the dynamic system, and the reliability of each indicator is considered to diminish the influence of inherent disturbance and/or noise. Finally, a safety assessment aggregation scheme based on the ER approach is presented to fuse the history, current, and future safety indicators to obtain the corresponding safety state, and the safety states are then fused synthetically to obtain a comprehensive safety assessment result of the complex dynamic system. A numerical study is examined to demonstrate the implementation and effectiveness. Moreover, a practical example of the inertial navigation system is studied to show the potential applications of the proposed ER-based safety assessment method. [ABSTRACT FROM AUTHOR]

In the current studies on the evidential reasoning (ER) rule, a performance evaluation model that utilizes the ER rule to combine evidence constituted by a single observation indicator (Single indicator-based ER rule, SER) has been developed with excellent scalability. However, the belief distribution (BD) of SER-based evaluation model is composed of a set of single grades that describe the system performance and the corresponding belief degrees. This makes some common uncertain judgments with local ignorance unable to be considered in the inference process, resulting in a decrease in the accuracy and validity of the evaluation results. In this paper, a new SER-based performance evaluation model with extended BD is proposed. Firstly, on the basis of the existing BD in SER, some new elements capable of describing local ignorance are introduced. Secondly, by calculating and optimizing the relevant parameters, the new evaluation model is formed. Thirdly, according to the Stone–Weierstrass theorem and information entropy theory, the approximation ability and uncertainty of the evaluation model are discussed respectively. Finally, a practical example is given to illustrate the potential applications of the proposed model in engineering practice. [ABSTRACT FROM AUTHOR]

It is vital to assess the lives of newly developed products by using failure data from various testing environments. In the current methods, two steps are generally included. The first step is transforming the failure data under one testing environment into the actual working environment, and the second step is integrating all failure data under the actual working environment into a unified result. However, most available methods cannot use information that includes part failure data and part expert knowledge simultaneously. To resolve the above issue, based on the belief rule base (BRB) and the evidential reasoning (ER) approach, a new BRB-ER-based model is proposed, where the BRB is used to transform the failure data from one testing environment into the actual working environment. The ER approach, which is adopted to aggregate the failure data from different testing environments, is used to assess the life of a product. To conclude, the BRB-ER-based model is applied to represent and integrate asynchronous multisource information. In the proposed model, the initial BRB system is constructed based on experts’ knowledge, which results in uncertainty because of the ambiguous nature of human judgment and calls for training the parameters in the BRB-ER-based model. Therefore, an optimal algorithm that employs the differential evolutionary algorithm is proposed. The proposed model and the optimal algorithm operate in an integrated manner to improve the assessment precision by using both failure data and expert knowledge effectively. A case study in three scenarios and use of the conventional approach is examined to demonstrate the capability and potential applications of the new BRB-ER-based model. [ABSTRACT FROM AUTHOR]

It is vital to predicting the hidden behavior of complex systems, which is associated with the operation state of the system and cannot be directly observed. In recent research of hidden behavior prediction, one effective method called the semi-quantitative information-based method has received wide applications and become the main stream, where both the qualitative knowledge and the quantitative data are involved. Although some methods have been effectively applied to predict the hidden behavior, only the single behavior prediction has been achieved. It is shown that multiple hidden behaviors are not considered, which may not meet actual requirements of the system. Besides, the influence of perturbations on the system is not considered, which can indirectly affect the system's hidden behavior and reduce the hidden behavior prediction accuracy. As such, a new hidden behavior prediction model based on the evidential reasoning (ER) rule considering perturbations is proposed in this paper, which is typically a semi-quantitative information-based method and can deal with multiple hidden behaviors. A parameter estimation algorithm is proposed based on the expectation maximization (EM) to obtain more accurate model parameters. A case study is conducted to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

As one of most important aspects in condition-based maintenance (CBM), failure prognosis has attracted an increasing attention with the growing demand for higher operational efficiency and safety in complex engineering systems. Currently there are no effective methods for predicting the failure of a system in real-time by using both expert knowledge and quantitative information (i.e., semi-quantitative information) when degradation failure and shock failure are dependent and competitive. Since belief rule base (BRB) can model the complex system when semi-quantitative information is available, this paper focuses on developing a new BRB based method for online failure prognosis that can deal with this problem. Although it is difficult to obtain accurate and complete quantitative information, some expert knowledge can be collected and represented by a BRB which is an expert system essentially. As such, a new BRB based prognosis model is proposed to predict the system failure in real-time when two failure modes are dependent and competitive. Moreover, a recursive algorithm for online updating the parameters of the failure prognosis model is developed. Equipped with the recursive algorithm, the proposed prognosis model can predict the failure in real-time when two failure modes are dependent and competitive. An experimental case study is examined to demonstrate the implementation and potential applications of the proposed online failure prognosis method. [ABSTRACT FROM AUTHOR]

The quantitative real-time polymerase chain reaction (qRT-PCR) is one of the most widely used methods to study gene expression profiles, and it requires appropriate normalization for accurate and reliable results. Although several genes are commonly used as reference genes (such as GAPDH, ACTB, and 18S rRNA), they are also regulated and can be expressed at varying levels. In this study, we evaluated twelve well-known reference genes to identify the most suitable housekeeping gene for normalization of qRT-PCR in human lumbar vertebral endplate with Modic changes, by using the geNorm, NormFinder, and BestKeeper algorithms. Our results showed that the rarely-used SDHA was the most stable single reference gene, and a combination of three, SDHA, B2M, and LDHA, was the most suitable gene set for normalization in all samples. In addition, the commonly-used genes, GAPDH, ACTB and 18S rRNA, were all inappropriate as internal standards. The rankings of reference genes for the three types of Modic change differed, although SDHA and RPL13A uniformly ranked in the first and last position, respectively. Further simulated expression analysis validated that the arbitrary use of a reference gene could lead to the misinterpretation of data. Our study confirmed the necessity of exploring the expression stability of potential reference genes in each specific tissue and experimental situation before quantitative evaluation of gene expression by qRT-PCR. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate early ASD at short-term follow-up in fused and unoperated patients with degenerative disc disease, using quantitative magnetic resonance imaging (MRI) analysis of the area, signal intensity and their product, i.e., MRI index of the central bright area of the disc as well as measures of intervertebral disc height and Pfirrmann grading scale. The further purpose was to determine whether fusion accelerates ASD compared with non-surgical treatment in short-term follow-up. Methods: One hundred and eight chronic low back patients diagnosed as L4/L5 degeneration undertook either one-level instrumented posterior lumbar interbody fusion or conservative treatment. They were followed up for about 1 year. Finally 46 fused and 45 conservatively treated patients with MRI follow-up were included. Pre- and post-treatment MRIs were compared to determine the progression of disc degeneration at the two cranial adjacent segments. Results: The area, signal intensity and MRI index of the central bright area of the adjacent discs decreased in the operated and unoperated groups from pre-treatment to follow-up, except for an insignificant decrease of signal intensity at the second adjacent segment in the unoperated group. The changes in these parameters were statistically greater at the first than the second adjacent segment in the fused group, but not in the unoperated group. And the changes in the fused group were more pronounced than those at both neighbouring levels in the unoperated group. However, the Pfirrmann grading scale and intervertebral disc height did not detect any changes at adjacent discs in either group. Conclusions: Decrease in the parameters of quantitative MRI analysis indicated early degeneration at discs adjacent to lumbar spinal fusion. Fusion had an independent effect on the natural history of ASD during short-term follow-up. Continued longitudinal follow-up is required to determine whether these MRI changes lead to pathologic changes. [ABSTRACT FROM AUTHOR]

Abstract: Condition-based maintenance has attracted an increasing attention both academically and practically. If the required physical models to describe the dynamic systems are unknown and the monitored information only reflects part of the state of the dynamic systems, expert knowledge is a source of valuable information to be used. However, expert knowledge is usually in a qualitative form, and therefore, needs to be transformed and combined with the measured characteristic information to provide effective prognosis. As such, this paper focuses on developing a novel approach to deal with the problem. In the proposed approach, a belief rule base (BRB) for the failure prognostic model is constructed using the expert knowledge and the analysis of the failure mechanism. An online failure prognostic algorithm is then proposed on the basis of the currently available characteristic variable information. The failure prognostic model is finally used in a condition based decision model to support the replacement decision of the dynamic systems. A case example is examined to demonstrate the implementation and potential applications of the proposed failure prognostic algorithm and the condition-based replacement model. [Copyright &y& Elsevier]

Abstract: Rapid pyrolysis of three typical Chinese coals, lignite from Inner Mongolia, bituminous from Shenfu coalfield, and anthracite from Guizhou, as well as a petroleum coke were carried out in a drop-style high-frequency furnace. The reactor was induction coil heated and had a very small high-temperature zone, which could restrain secondary conversions of nitrogen products. The effects of temperature and coal rank on conversions of fuel-N to primary nitrogen products (char-N, HCN–N, NH3–N and (tar+N2)–N) have been investigated. The results showed that, the increasing temperature reduced the yields of char-N and promoted the conversion of fuel-N to N2. Char-N yields increased, while volatile-N yields decreased as the coal rank increased. In most of the conditions, NH3–N yields were higher than HCN–N yields during rapid pyrolysis of coal. In the case of petroleum coke, NH3–N yields increased gradually with the increasing temperature, but no HCN was detected. We argue that NH3–N can be formed directly through the primary pyrolysis without secondary reactions. Although volatile-N yields of lignite were higher than those of bituminous, yields of (HCN+NH3)–N in volatile-N of lignite were lower than those of bituminous. While the (HCN+NH3)–N yields of anthracite were the lowest of the three coals. Both of the (HCN+NH3)–N yields and (HCN+NH3)–N proportions in volatile-N of petroleum coke were lower than the three coals. [Copyright &y& Elsevier]

In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis. [ABSTRACT FROM PUBLISHER]

Abstract: Rapid pyrolysis of 6 biomass/coal blends (1:4, wt) including rice straw+bituminous (RS+B), rice straw+anthracite (RS+A), chinar leaves+bituminous (CL+B), chinar leaves+anthracite (CL+A), pine sawdust+bituminous (PS+B), and pine sawdust+anthracite (PS+A) was carried out in a high-frequency magnetic field based furnace at 600–1200°C. The reactor could not only achieve high heating rates of fuel samples but also make biomass and coal particles contact well; secondary reactions of primary products during rapid pyrolysis can also be efficiently reduced. By comparing nitrogen distributions in products of blends (experimental values) with those of the sums of individual biomass and coal (weighted values), nitrogen conversion characteristics under rapid pyrolysis of biomass/coal blends were investigated. Results show that, biomass particles in blends lead to higher experimental char-N yields than the weighted values during rapid pyrolysis of biomass/anthracite blends. The decreased heating rates of both biomass and coal particles caused by the low packing densities of biomass may be the reason. For blends of CL+B in which packing density of chinar leaves is high, and for PS+B during pyrolysis of which melting and shrinkage happen to pine sawdust, both biomass and coal particles can obtain high heating rates, synergies can be found to promote nitrogen release from fuel samples and decrease char-N yields under all the conditions. But the low fluidity and not easily collapsed carbon skeletons of rice straw make the heating rates of rice straw and bituminous particles in RS+B lower than those of CL+B and PS+B, and weaker synergies can be found from char-N yields of RS+B. The synergies can obviously be found to decrease the (NH3 +HCN)-N yields and make more nitrogen convert to N2 except for those of several low-temperature conditions (600–700°C). Under the low-temperature (600–700°C) condition, synergies make molar ratios of HCN-N/NH3-N higher than those of the weighted values. [Copyright &y& Elsevier]

The gasification reactivity of petroleum coke, which was treated by microwave at different times, powers, and temperatures, was investigated by using thermogravimetric analysis (TGA) at 1473 K at atmospheric pressure. The results showed that the gasification rate of petroleum coke increased with the increase of conversion and then decreased after reaching its maximum; the conversion corresponding to the maximal gasification rate is about x= 0.1. The gasification rate of petroleum coke increased with the increase of microwave irradiation time and irradiation power, but as the irradiation temperature increased, the gasification rate of petroleum coke first increased and then decreased. Petroleum coke before and after microwave treatment was analyzed by X-ray diffraction (XRD), infrared absorption spectroscopy (IR), and the accelerated surface area and porosimetry system (ASAP). The results showed that the crystalline structure of petroleum coke becomes amorphous and the BET surface area and pore volume become larger with increase of microwave irradiation time and irradiation power. As the irradiation temperature increased, the crystalline ordering degree of petroleum coke became weakened and then strengthened and the BET surface area and pore volume increased and then decreased, which agrees with the change in the petroleum coke gasification reaction rate. Results show that microwave treatment is an effective way to change the structure of petroleum coke and promote its gasification reactivity. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a recursive algorithm based on the Bayesian reasoning approach is proposed to update a belief rule based (BRB) expert system for pipeline leak detection and leak size estimation. In addition to using available real time data, expert knowledge on the relationships of the parameters among different rules is incorporated into the updating process so that the performance of the expert system can be improved. Experiments are carried out to compare the newly proposed algorithm with the previously published algorithms, and results show that the proposed algorithm can update the BRB expert system faster and more accurately, which is important for real-time applications. The BRB expert systems can be automatically tuned to represent complex real world systems, and applied widely in engineering. [ABSTRACT FROM AUTHOR]

Abstract: A belief rule base inference methodology using the evidential reasoning (RIMER) approach has been developed recently. A belief rule base (BRB), which can be treated as a more generalized expert system, extends traditional IF-THEN rules, but requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees. These parameters need to be determined with care for reliable system simulation and prediction. Some off-line optimization models have been proposed, but it is expensive to train and re-train these models in particular for large-scale systems. Moreover, the recursive algorithms are also proposed to fine tune a BRB online, which require less calculation time and satisfy the real-time requirement. However, the earlier mentioned learning algorithms are all based on a predetermined structure of the BRB. For a complex system, prior knowledge may not be perfect, which leads to the construction of an incomplete or even inappropriate initial BRB structure. Also, too many rules in an initial BRB may lead to over fitting, whilst too few rules may result in under fitting. Consequently, such a BRB system may not be capable of achieving overall optimal performance. In this paper, we consider one realistic and important case where both a preliminary BRB structure and system parameters assigned to given rules can be adjusted online. Based on the definition of a new statistical utility for a belief rule as investigated in this paper, a sequential learning algorithm for online constructing more compact BRB systems is proposed. Compared with the other learning algorithms, a belief rule can be automatically added into the BRB or pruned from the BRB, and our algorithm can also satisfy the real-time requirement. In addition, our algorithm inherits the feature of RIMER, i.e., only partial input and output information is required, which could be either incomplete or vague, either numerical or judgmental, or mixed. In order to verify the effectiveness of the proposed algorithm, a practical case study about oil pipeline leak detection is studied and examined to demonstrate how the algorithm can be implemented. [Copyright &y& Elsevier]

A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for pipeline leak detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the pipeline leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for pipeline leak detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and pipeline operating data, and validated by testing data. All training and testing data are collected from a real pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system. [Copyright &y& Elsevier]

Abstract: Kinetics characteristics of petroleum coke gasification with CO2 at 1248–1323K and 0.1MPa were studied using a pressurized thermogravimetric analyzer (TGA). It is shown that gasification rate increases with increasing conversion and then decreases after reaching a peak value around x =0.3. The poor initial porosity is the main reason of the occurrence of maximal gasification rate. A normal distribution function model was proposed to fit the kinetics data. Results show it can describe well the variation of gasification rate at different temperatures. Relationship between the parameter r m in the model and temperature T obeys to the Arrhenius law. Activation energy of petroleum coke CO2 gasification is 198kJmol−1 and the reaction order is 0.54–0.88 in the investigated temperature range, which are in accordance with reported data. [Copyright &y& Elsevier]

The hidden belief rule base model (HBRB) which can utilize both hybrid expert's experience and experimental data has become a useful method for hidden behavior forecasting of complex system in many applications. But the traditional HBRB model is one-step forecasting model, which means that it can only utilize the limited information in very short time instant. In fact, one-step forecasting method is incomplete because the future behavior of a complex system is generated through multiple historical stats in different time instant. Therefore, a time-delay hidden BRB forecasting model (THBRB) is designed, where the input with multiple time instant of the HBRB model is considered, and the corresponding reasoning process is also designed. Further, an optimization method based on projection covariance matrix adaption evolution strategy (P-CMA-ES) algorithm is used to train the initial THBRB model. A case study is established to prove the advantage of the proposed method, and the experiment results show that the proposed THBRB model can predict the future hidden behavior of complex system effectively. [ABSTRACT FROM AUTHOR]

Objective: Turnover of cartilage endplate extracellular matrix (ECM) may play an important role in disc degeneration and low back pain (LBP). However, the expression pattern of pro‐inflammatory factors, matrix metalloproteinases (MMP), and tissue inhibitors of metalloproteinases (TIMP) in the cartilage endplates (CEP) of intervertebral discs (IVD) is not understood. We aimed to examine the transcriptional levels of MMP, TIMP, and interleukins (IL), and the correlations between them. Methods: Thirty degenerated cartilage endplate samples from patients with LBP who underwent lumbar fusion surgery were included in the degenerated group. Ten patients without LBP history who underwent lumbar surgery because of vertebral burst fractures were included in the control group. The degenerative severity of the samples was evaluated by MRI, and hematoxylin–eosin and safranin O‐fast green (SO‐FG) staining. Real‐time polymerase chain reaction (RT‐PCR) was used to detect the mRNA levels of MMP‐1, MMP‐3, MMP‐9, MMP‐13, TIMP‐1, TIMP‐2, TIMP‐3, IL‐1α, IL‐1β, and IL‐6. The correlations between the levels of these genes were tested using Spearman's rho test. Results: Hematoxylin–eosin and SO‐FG staining confirmed a decrease in cell number and proteoglycans in the degenerated cartilage endplate. MRI showed significant signal changes in degenerated cartilage endplates. Patients in the degenerated group showed a higher rate of endplate Modic changes when compared with the control group. MMP‐3, MMP‐9, TIMP‐3, IL‐1α, and IL‐1β were elevated with statistical significance, while MMP‐1, MMP‐13, TIMP‐1, TIMP‐2, and IL‐6 were changed without statistical significance or remained unchanged. Expression of MMP‐3 was positively correlated with IL‐1α (Spearman coefficient, 0.486; P < 0.05); expression of TIMP‐3 was positively correlated with MMP‐9, IL‐1α, and IL‐1β (Spearman coefficient, 0.577, 0.407, and 0.571, respectively; P < 0.05). Conclusion: MMP‐3, MMP‐9, TIMP‐3, IL‐1α, and IL‐1β may play a role in the process of cartilage endplate degeneration. MMP‐3 may be regulated by IL‐1α, and TIMP‐3 might be associated with MMP‐9 and regulated by IL‐1α and IL‐1β. [ABSTRACT FROM AUTHOR]

Objective: To compare postoperative imaging results, clinical outcomes and complications between the multifidus muscle bundle (MMB) approach and the conventional open (CO) approach in one‐level posterior lumbar interbody fusion (PLIF). Methods: Based on the inclusion and exclusion criteria, 201 of 351 patients in our hospital were enrolled in this prospective study and underwent MMB‐PLIF or CO‐PLIF randomly: 111 patients in the MMB‐PLIF group and 90 patients in the CO‐PLIF group. A total of 100 patients failed to be followed up in the following 7–9 years. Therefore, in this study, 52 patients of the MMB group and 49 patients of the CO group were included. We evaluated the differences in terms of multifidus atrophy rate, intervertebral disc height and segmental lordosis restoration of the operation segment, lumbar lordosis restoration, fusion rate, visual analogue scale (VAS) for back and leg pain, Oswestry disability index (ODI), complication rates, and patient satisfaction rates between the two groups. Correlation between multifidus muscle degeneration and the incidence of complications was investigated, and we compared the multifidus muscle degeneration rate between patients with or without intractable back pain or adjacent segment degeneration. Results: There were no significant differences in age, sex, body mass index (BMI), diagnosis, segments distribution, and mean follow‐up time between the MMB‐PLIF group and the CO‐PLIF group. In addition, no differences regarding sex, age, or BMI were found between the lost follow‐up group and the successful follow‐up group. In regard to imaging and clinical evaluation, at the final follow‐up, there were significant differences in multifidus atrophy rates (27.0% ± 6.8% vs 38.7% ± 10.9%), lumbar lordosis restoration (4.6° ± 2.5° vs 3.0° ± 1.9°), postoperative VAS for back pain (1.1 ± 0.9 vs 1.8 ± 1.2), ODI (7.7 ± 5.0 vs 12.4 ± 6.7), and patient satisfaction rates (86.5% vs 61.2%) between MMB‐PLIF and CO‐PLIF groups. However, there were no significant differences in segmental lordosis, intervertebral height restoration, postoperative VAS for leg pain or fusion rate between the two groups. In regards to complications, there were significant differences in the incidence of adjacent segment degeneration (3.8% vs 14.3%), intractable back pain (3.8% vs 22.4%), and residual neurological symptoms (5.8% vs 20.4%) between the two groups (P < 0.05) at the final follow‐up. In addition, patients with adjacent segment degeneration and intractable back pain were observed with more significant multifidus muscle atrophy than those without these two complications (31.9% ± 1.1% vs 39.6% ± 2.1% and 30.9% ± 1.1% vs 42.8% ± 2.1%). Conclusion: Compared with CO‐PLIF, MMB‐PLIF had advantages in relation to protection of the multifidus muscle, better maintenance of lumbar lordosis, reduced lower back pain and ODI score, fewer complications, and a higher patient satisfaction rate. Protection of the multifidus muscle in lumbar surgery is an important aspect of minimally invasive surgery. [ABSTRACT FROM AUTHOR]

Objective: This study was designed to investigate the role of the nucleotide-binding-domain -and leucine-rich repeat -containing (NLR) family, pyrin-domain-containing 3 (NLRP3) inflammasome in the pathogenesis of polymyositis (PM). Methods: Immunochemistry was performed to analyze the NLRP3, caspase-1 and interleukin-1 beta (IL-1β) expression in the muscle tissue of PM patients. Rat model of PM and C2C12 cell were used to investigate the potential role of NLRP3 inflammasome in PM. Results: The percentage of CD 68+ macrophages, and the expression levels of NLRP3, caspase-1 and IL-1β in the muscle tissue were elevated in 27 PM patients. LPS/ATP treatment resulted in activation of NLRP3 inflammasome and secretion of IL-1β as well as interferons (IFNs) and monocyte chemotactic protein-1 (MCP-1) in the Raw 264.7 macrophages. Meanwhile, LPS/ATP challenged activation of NLRP3 inflammasome induced overexpression of major histocompatibility complex class I (MHC-I), a key molecular of PM in the co-cultured C2C12 cells. The effect was decreased by treatment of NLRP3 inflammasome inhibitor MCC950 or siRNA of NLRP3 inflammasome. These findings suggested certain levels of IL-1β rather than IFNs up-regulated MHC-I expression in C2C12 cells. IL-1β blockade using neutralizing IL-1β monoclonal antibody or siRNA of IL-1β suppressed MHC-I overexpression. In vivo, NLRP3 inflammasome inhibition by MCC950 reduced the expression of NLRP3, IL-1β and MHC-I in the muscle tissue of PM modal rats. Also, it attenuated the intensity of muscle inflammation as well as the CRP, CK, and LDH levels in the serum. Conclusion: NLRP3/caspase-1/IL-1β axis may play an important role in the development of PM. Inhibition of NLRP3 activation may hold promise in the treatment of PM. [ABSTRACT FROM AUTHOR]

Objective: This study was designed to investigate the role of the nucleotide-binding-domain -and leucine-rich repeat -containing (NLR) family, pyrin-domain-containing 3 (NLRP3) inflammasome in the pathogenesis of polymyositis (PM). Methods: Immunochemistry was performed to analyze the NLRP3, caspase-1 and interleukin-1 beta (IL-1β) expression in the muscle tissue of PM patients. Rat model of PM and C2C12 cell were used to investigate the potential role of NLRP3 inflammasome in PM. Results: The percentage of CD 68+ macrophages, and the expression levels of NLRP3, caspase-1 and IL-1β in the muscle tissue were elevated in 27 PM patients. LPS/ATP treatment resulted in activation of NLRP3 inflammasome and secretion of IL-1β as well as interferons (IFNs) and monocyte chemotactic protein-1 (MCP-1) in the Raw 264.7 macrophages. Meanwhile, LPS/ATP challenged activation of NLRP3 inflammasome induced overexpression of major histocompatibility complex class I (MHC-I), a key molecular of PM in the co-cultured C2C12 cells. The effect was decreased by treatment of NLRP3 inflammasome inhibitor MCC950 or siRNA of NLRP3 inflammasome. These findings suggested certain levels of IL-1β rather than IFNs up-regulated MHC-I expression in C2C12 cells. IL-1β blockade using neutralizing IL-1β monoclonal antibody or siRNA of IL-1β suppressed MHC-I overexpression. In vivo, NLRP3 inflammasome inhibition by MCC950 reduced the expression of NLRP3, IL-1β and MHC-I in the muscle tissue of PM modal rats. Also, it attenuated the intensity of muscle inflammation as well as the CRP, CK, and LDH levels in the serum. Conclusion: NLRP3/caspase-1/IL-1β axis may play an important role in the development of PM. Inhibition of NLRP3 activation may hold promise in the treatment of PM. [ABSTRACT FROM AUTHOR]

Abstract: Rapid pyrolysis of rice straw (RS) and Shenfu bituminous coal (SB) separately, and rapid co-pyrolysis of RS/SB blends (mass ratio 1:4, 1:4, and 4:1), were carried out in a high-frequency furnace which can ensure both high heating rate and satisfying contact of fuel particles. Synergies between RS and SB during rapid co-pyrolysis were investigated. Intrinsic and morphological structures of residual char from co-pyrolysis, and their effects on gasification characteristics were also studied. Synergies occurred during rapid co-pyrolysis of RS and SB (RS/SB=1:4) resulting in decreasing char yields and increasing volatile yields. Synergies also happened during gasification of the char derived from co-pyrolysis of RS and SB with mass ratio of 1:4. The increased mass ratio of RS to SB did not only weaken synergies during co-pyrolysis, but significantly reduced the gasification rates of the co-pyrolysis char compared to the calculated values. Results can help to optimize co-conversion process of biomass/coal. [Copyright &y& Elsevier]

Abstract: Weapon System Capability Assessment (WSCA) is the initial point of quantification of capabilities in the military capability planning (MCP). WSCA is often a multiple criteria decision making (MCDM) problem with both quantitative and qualitative information under uncertain environment. In this paper, the analysis process and algorithm for WSCA problem is proposed on the basis of belief structure (BS) model and evidential reasoning (ER) approach which were developed to deal with various types of uncertainties such as ignorance and subjectiveness. First of all, the WSCA criteria hierarchy is built by analyzing how the capability is measured. Secondly, a weapon system capability model is formulated using BS. Thirdly, both qualitative and quantitative information involved in capability measure are transformed into BSs by the data transformation algorithm based on rules. Then, the analytical ER approach is used to aggregate the capability measurement information from sub-capability criteria to top-capability criterion, and the assessed weapon systems are ranked and analyzed according to utility intervals. Finally, a case study of real Main Battle Tank capability assessment is explored to show the proposed process for WSCA. [Copyright &y& Elsevier]

With the wide applications of wireless sensor network (WSN), its reliability evaluation has been attracted more attention. The reliability of a WSN is affected mainly by internal and external factors, which include internal faults and external attacks. In this paper, a reliability evaluation method based on a hierarchical belief rule base (BRB) method is proposed for the reliability evaluation of the WSN. First, the factors affecting the reliability of a WSN are analysed, and the reliability evaluation process that considers the WSN fault evaluation and WSN security evaluation is described. Second, the reliability evaluation model is constructed based on the hierarchical BRB model. The qualitative knowledge is used by the BRB model to build initial belief rules, and the quantitative data are used to optimize the initial parameters of the BRB model, which can utilize various types of uncertainty information effectively. Therefore, the proposed method can be applied to the WSN reliability evaluation, which is a complex and uncertain problem. Finally, a simulation case study and an actual case study of wellhead blowout monitoring are conducted to verify the effectiveness of the proposed method. The reliability results of actual WSN are obtained by the standard testing method, where the loss and accuracy rates of the collected data are treated as the observation factors for obtaining the actual reliability values. The estimated results of hierarchical BRB model are very close to the actual reliability values, which shows that the proposed method can be used for evaluating the reliability of the actual WSN accurately. [ABSTRACT FROM AUTHOR]

Objective: To analyze the early complications and causes of oblique lateral interbody fusion, and put forward preventive measures. Methods: There were 235 patients (79 males and 156 females) analyzed in our study from October 2014 to May 2017. The average age was 61.9 ± 0.21 years (from 32 to 83 years). Ninety‐one cases were treated with oblique lateral interbody fusion (OLIF) alone (OLIF alone group) and 144 with OLIF combined with posterior pedicle screw fixation through the intermuscular space approach (OLIF combined group). In addition, 137/144 cases in the combined group were primarily treated by posterior pedicle screw fixation, while the treatments were postponed in 7 cases. There were 190 cases of single fusion segments, 11 of 2 segments, 21 of 3 segments, and 13 of 4 segments. Intraoperative and postoperative complications were observed. Results: Average follow‐up time was 15.6 ± 7.5 months (ranged from 6 to 36 months). Five cases were lost to follow‐up (2 cases from the OLIF alone group and 3 cases from the OLIF combined group). There were 7 cases of vascular injury, 22 cases of endplate damage, 2 cases of vertebral body fracture, 11 cases of nerve injury, 18 cases of cage sedimentation or cage transverse shifting, 3 cases of iliac crest pain, 1 case of right psoas major hematoma, 2 cases of incomplete ileus, 1 case of acute heart failure, 1 case of cerebral infarction, 3 case of left lower abdominal pain, 9 cases of transient psoas weakness, 3 cases of transient quadriceps weakness, and 8 cases of reoperation. The complication incidence was 32.34%. Thirty‐three cases occurred in the OLIF alone group, with a rate of 36.26%, and 43 cases in the group of OLIF combined posterior pedicle screw fixation, with a rate of 29.86%. Fifty‐seven cases occurred in single‐segment fusion, with a rate of 30.0% (57/190), 4 cases occurred in two‐segment fusion, with a rate of 36.36% (4/11), 9 cases occurred in three‐segment fusion, with a rate of 42.86% (9/21), and 6 cases occurred in four‐segment fusion, with a rate of 46.15% (6/13). Conclusion: In summary, OLIF is a relatively safe and very effective technique for minimally invasive lumbar fusion. Nonetheless, it should be noted that OLIF carries the risk of complications, especially in the early stage of development. [ABSTRACT FROM AUTHOR]

Lymph Node Metastasis (LNM) has become one of the most important prognostic factors regarding long-term survival in gastric cancer. As it is difficult for doctors to integrate multiple factors for a comprehensive analysis, Clinical Decision Support System (CDSS) is used to help the analysis. In this paper, a new Cooperative Belief Rule Based (CBRB) prototype CDSS is proposed. CBRB consists of two independent Belief Rule Base (BRB) systems and the final output is combined by the Evidential Reasoning (ER) approach. A corresponding new Cooperative CoEvolutionary Algorithm (CCEA) is proposed to train the proposed CBRB model that is nonlinear. A case study demonstrates that the proposed CDSS prototype can obtain the better performance than other CDSS. [ABSTRACT FROM AUTHOR]

Subdural haematoma (SDH) is a potentially life-threatening complication in patients with spontaneous intracranial hypotension (SIH). In serious cases, SIH patients who present with SDHs develop neurological deficits, a decreased level of consciousness, or cerebral herniation, and may even require an urgent neurosurgical drainage. Despite numerous publications on SDHs, few report its potential risk factors in patients with SIH. In this study, we retrospectively investigated 93 consecutive SIH patients and divided them into an SDH group (n = 25) and a non-SDH (NSDH) group (n = 68). The clinical and radiographic characteristics of these 93 patients were analyzed, and then univariate analysis and further multiple logistic regression analysis were performed to identify the potential risk factors for the development of SDHs. The univariate analysis showed that advanced age, male gender, longer clinical course, dural enhancement, and the venous distension sign were associated with the development of SDHs. However, multivariate analysis only included the latter three factors. Our study reveals important radiological manifestations for predicting the development of SDHs in patients with SIH. [ABSTRACT FROM AUTHOR]

The objective of this paper is to construct reliable belief rule-based (BRB) models for the identification of uncertain nonlinear systems. The BRB methodology is developed from the evidential reasoning (ER) approach and traditional IF–THEN rule based system. It can be used to model complicated nonlinear causal relationships between antecedent attributes and consequents under different types of uncertainty. In a BRB model, various types of information and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first introduce the BRB methodology for modelling uncertain nonlinear systems. Then we present a comparative analysis of three BRB identification models through combining the BRB methodology with nonlinear optimisation techniques. The novel BRB identification models using l ∞ -norm and minimising mean uncertainties in belief rules (MUBR) show remarkable capabilities of capturing the lower and upper bounds of the interval outputs of uncertain nonlinear systems simultaneously. Trade-off analysis between identification accuracy and interval credibility are briefly discussed. Finally, a numerical study of a simplified car dynamics is conducted to demonstrate the capability and effectiveness of the BRB identification models for the modelling and identification of uncertain nonlinear systems. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of how to estimate remaining useful life (RUL) for a class of systems with high fluctuating dagradation caused by time-varying mean and variance. In engineering practice, the fluctuation of the degradation data can reflect the system stability, and hence it can serve as an additional indicator for the system's health state in addition to the degradation observations. To estimate the RUL for this class of systems, three issues should be considered jointly: (i) how to model the degradation data with high fluctuation, (ii) how to define an indicator denoting the fluctuation's level, and (iii) how to combine the degradation path and the fluctuation for the RUL estimating task. In responses to the above issues, this paper characterizes the degree of the fluctuation by the standard deviation of the stochastic degradation process and defines the standard deviation as an additional performance variable. Moreover, a stochastic degradation model is proposed to reflect the high fluctuating degradation data, in which the standard deviation is time dependent. To obtain the marginal distribution of the RUL derived by the standard deviation, the failure threshold of the standard deviation is given according to its influence on the degradation process. Another marginal distribution derived by the degradation data is also obtained by the time distribution of the degradation process crossing the degradation threshold. Then, through deriving the correlation between the two marginal distributions based on the probability theory, the joint distribution of the RUL is obtained. Finally, a practical case study for gyro is conducted. The results demonstrate the feasibility and applicability of the proposed model. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] A bi-level belief rule based model is proposed for diagnosing gastric cancer. [•] A clonal selection algorithm based method is proposed for optimizing the model. [•] A case study demonstrates the effectiveness of the proposed method. [•] Comparative studies show the better performance of the proposed method. [Copyright &y& Elsevier]

Study Design: This study investigated the expression of 2 types of ADAMTS in human intervertebral cartilage endplate (CEP) and related mechanisms concerning tumor necrosis factor α (TNF-α)-induced nuclear factor κB signaling pathway. Objective: To determine which type of ADAMTS is more strongly expressed and the role of TNF-α in CEP. Summary Of Background Data: ADAMTS-4 and ADAMTS-5 were proven to be essential in the degeneration of articular cartilage and intervertebral disc. CEP is an important structure adjacent to the disc. However, the activities of ADAMTS in CEP are unclear. Methods: CEPs were obtained from subjects after spinal surgery and categorized as members of either the Modic change group or the control group. Sections of these tissues were stained with hematoxylin-eosin, safranin O, and immunohistochemistry procedures for ADAMTS-4, ADAMTS-5, and TNF-α. Transcriptional levels of aggrecan, type I collagen, type II collagen, type X collagen, ADAMTS-4, ADAMTS-5, and TNF-α were investigated by quantitative real-time polymerase chain reaction. In addition, the effect of TNF-α on ADAMTS-5 and its potential mechanisms are investigated in cultured bovine endplate chondrocytes in vitro. Results: Our data demonstrated that the degenerative changes associated with the expression of extracellular matrix proteins were correlated with increased levels of ADAMTS-5, but not ADAMTS-4, in the CEP of patients with Modic changes. The expression levels of TNF-α in the Modic change group were significantly increased, which was correlated with the enhanced expression of ADAMTS-5. Additional in vitro investigation confirmed that TNF-α could upregulate the expression of ADAMTS-5 by activating nuclear factor κB pathway in cultured bovine endplate chondrocytes. Conclusion: We conclude that the upregulation of TNF-α and ADAMTS-5, but not ADAMTS-4, may play an important role in degenerative CEP-induced low back pain. Level Of Evidence: N/A. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate early ASD at short-term follow-up in fused and unoperated patients with degenerative disc disease, using quantitative magnetic resonance imaging (MRI) analysis of the area, signal intensity and their product, i.e., MRI index of the central bright area of the disc as well as measures of intervertebral disc height and Pfirrmann grading scale. The further purpose was to determine whether fusion accelerates ASD compared with non-surgical treatment in short-term follow-up.Methods: One hundred and eight chronic low back patients diagnosed as L4/L5 degeneration undertook either one-level instrumented posterior lumbar interbody fusion or conservative treatment. They were followed up for about 1 year. Finally 46 fused and 45 conservatively treated patients with MRI follow-up were included. Pre- and post-treatment MRIs were compared to determine the progression of disc degeneration at the two cranial adjacent segments.Results: The area, signal intensity and MRI index of the central bright area of the adjacent discs decreased in the operated and unoperated groups from pre-treatment to follow-up, except for an insignificant decrease of signal intensity at the second adjacent segment in the unoperated group. The changes in these parameters were statistically greater at the first than the second adjacent segment in the fused group, but not in the unoperated group. And the changes in the fused group were more pronounced than those at both neighbouring levels in the unoperated group. However, the Pfirrmann grading scale and intervertebral disc height did not detect any changes at adjacent discs in either group.Conclusions: Decrease in the parameters of quantitative MRI analysis indicated early degeneration at discs adjacent to lumbar spinal fusion. Fusion had an independent effect on the natural history of ASD during short-term follow-up. Continued longitudinal follow-up is required to determine whether these MRI changes lead to pathologic changes. [ABSTRACT FROM AUTHOR]

Recently, a novel prediction model based on the evidential reasoning (ER) approach is developed to forecast reliability in engineering systems. In order to determine the parameters of the ER-based prediction model, some optimization models have been proposed to train the ER-based prediction model. However, these models are implemented in an offline fashion and thus it is very expensive to train and retrain them when new information is available. This correspondence paper is concerned with developing the recursive algorithms for updating the ER-based prediction model from the probability-based point of view. Using the recursive expectation maximization algorithm, two recursive algorithms are proposed for updating the parameters of the ER-based prediction model under judgmental and numerical outputs, respectively. As such, the proposed algorithms can be used to fine tune the ER-based prediction model online once new information becomes available. We verify the proposed method via a realistic example with missile reliability data. [ABSTRACT FROM PUBLISHER]

Abstract: Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts’ subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique. [Copyright &y& Elsevier]

Abstract: In a bench-scale opposed multi-burner (OMB) gasifier, the temperatures of gasification chamber and quench chamber are measured by thermocouples, and the temperature distributions of flame sections are reconstructed by the Filtered back-projection method. The results show that the temperature of gasification chamber increases slowly as the inserted distance increases in both diesel and coal–water slurry (CWS) tests. The syngas temperature decreases rapidly when it passes through the inlet of quench chamber. The impinging flames of diesel or CWS gasification all focus on the gasifier center due to restraining by each other, and can avoid scouring the refractory wall and prolong the lives of refractory. At the test conditions, the temperature distributions of diesel flames are 1650–2100 °C and those of CWS flames are 1500–2000 °C. The flame temperature distributions appear to be a typical simple peak. The investigations can provide some information for the industrial gasifier. [Copyright &y& Elsevier]

Osteoclasts play an important role in diseases involving bone loss. In this study, we assessed the effect of a plant-derived natural alkaloid (lycorine, or LY) on osteoclastogenesis in vitro and in vivo. Our in vitro study showed that receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis could be inhibited by LY; this effect was due to inhibition of mitogen-activated protein kinase (MAPK) signalling via MAP kinase kinases (MKKs). The MAPK agonist anisomycin could partially rescue the inhibitory effect of LY. Furthermore, LY also played a protective role in both a murine ovariectomy (OVX)-induced osteoporosis model and a titanium particle-induced osteolysis model. These results confirmed that LY was effective in preventing osteoclast-related diseases in vivo. In conclusion, our results show that LY is effective in suppressing osteoclastogenesis and therefore could be used to treat OVX-induced osteoporosis and wear particle-induced osteolysis. [ABSTRACT FROM AUTHOR]