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With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.

With the rise of the Internet of Things, edge computing has become one of the key technologies in Internet of Things solutions. In the context of the Industrial Internet of Things, hierarchical edge computing shows its advantages. This article focuses on hierarchical edge computing in the industrial Internet of Things scene, and studies the dynamic resource allocation of hierarchical edge computing networks. When using a hierarchical edge computing network with existing equipment, it is difficult to make changes to existing equipment. Therefore, this article uses queuing theory modeling analysis and proposes Dynamic Link Model based on Nodes Relation. Aiming at the hierarchical edge computing network, this model uses a method based on node connection relationship transfer to achieve load balancing of task flow and completes the dynamic allocation of computing resources in the network, and proposes a time experienced priority queue offloading strategy. The paper uses Java to achieve a dynamic link model experiment based on the connection relationship between nodes. The results show that this scheme has significant advantages in the global average delay of the system, and ensure the loss probability is reasonable within a certain limit.

Modeling of manufacturing processes using artificial intelligence-based techniques has recently received considerable attention. The current investigation introduces a new artificial intelligence-based predictive model for friction stir welding of dissimilar polymeric materials. The welded joint is made of acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) sheets. The proposed model is used to correlate the joint characteristics (tensile strength, joint efficiency and extensibility) with the welding variables (rotational tool speed, welding speed and tool tilt angle). The model consists of a random vector functional link (RVFL) model optimized by Hunger games search (HGS) optimizer. HGS optimizer is utilized to find out the optimal internal parameters of RVFL that boost the model accuracy. The proposed model is compared with two other optimized models in which RVFL is integrated with the sine cosine algorithm (SCA) or ecosystem-based optimizer (AEO). RVFL-HGS outperformed RVFL-SCA and RVFL-AEO based on different statistical measures. For all investigated joint characteristics, the determination coefficient ranges between 0.907 and 0.993 for RVFL-HGS, 0.869 and 0.972 for RVFL-AEO, 0.829 and 0.983 for RVFL-SCA.

The article presents innovations of the Code of Criminal Procedure of the Republic of Kazakhstan which, in pre-trial investigations, play a major role in protecting the rights and freedoms of citizens. The article justifies the need for special legal regulation of these objects of citizens ‘ rights. The author considers positions of scientists, judicial practice and legislation of Kazakhstan and foreign countries. It identifies shortcomings and contradictions of legal legislation. In accordance with the requirements of the new legislation, the transition to a three-link model under the pilot program will protect the rights of many citizens in the country, which will practically reduce the responsibility of persons who are not involved in crimes in the course of investigations. As a result of the analysis of the legislation of Kazakhstan, the author concluded that it is necessary to change the legal regime and recently adopted Criminal Procedure Code, which is more or less beneficial for citizens.

This paper presents a new method for predicting unimpeded taxi time based on the airport node–link model through statistical analysis of the airport surface detection equipment surveillance data. T...

Online measuring of component concentrations in sodium aluminate liquor is essential and important to Bayer alumina production process. They are the basis of closed-loop control and optimization and affect the final product quality. There are three main components in sodium aluminate liquor, termed caustic hydroxide, alumina and sodium carbonate (their concentrations are represented by $$c_\mathrm{K}$$ , $$c_\mathrm{A}$$ and $$c_\mathrm{C}$$ , respectively). They are obtained off-line by titration analysis and suffered from larger time delays. To solve this problem, a hybrid model for $$c_\mathrm{K}$$ and $$c_\mathrm{A}$$ is proposed by combining a mechanism model and a stochastic configuration network (SCN) compensation model. An SCN-based model for $$c_\mathrm{C}$$ is also proposed using the estimated values of $$c_\mathrm{K}$$ and $$c_\mathrm{A}$$ from the hybrid model. A real-world application conducted in Henan Branch of China Aluminum Co. Ltd demonstrates the effectiveness of the proposed modelling techniques. Experimental results show that our proposed method performs favourably in terms of the prediction accuracy, compared against the regress model, BP neural networks, RBF neural networks and random vector functional link model.

Frequently, we encounter the phenomenon of hysteresis in kinetic-dynamic modeling. The hysteresis loop in the concentration-effect curve suggests a time discrepancy caused by various pharmacokinetic and pharmacodynamic factors. To collapse the hysteresis loop and to simplify the concentration-effect relationship, several kinetic-dynamic modeling approaches including the effect compartment link model, turnover model (indirect response model), and tolerance/rebound model, have been used. The semicompartmental model is one method to describe the hysteresis of the pharmacokinetic-pharmacodynamic relationship. Furthermore, this semi-compartmental model differs from other models (full parametric approaches) as it does not require pharmacokinetic parameters to estimate pharmacodynamic parameters and ke0 . Therefore, we could employ a semi-compartmental approach in case it is difficult to apply the compartment model to pharmacokinetic data, as required for the pharmacodynamic analysis of inhalational anesthetics.

Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively. The adaptive weighted link model is completely depends on the semi-local topological structure and thus can be calculated not only faster but also under the case where the global topology is not known, especially when the network is sparse, the time complexity is approximate linear. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks show that the adaptive weighted link model always perform the best in comparison with well-known state-of-the-art methods.

© 2019 Elsevier Ltd This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage capacities. The model is a quasi-dynamic model. The link model at the heart of this quasi-dynamic equilibrium model is a spatial queueing model, which takes account of the space taken up by queues both when there is no blocking back and also when there is blocking back. The paper shows that if this quasi-dynamic model is utilised then for any feasible demand there is an equilibrium solution, provided (i) queue storage capacities are large or (ii) prices are used to help impose capacity restrictions; the prices either remove queueing delays entirely or just reduce spatial queues sufficiently to ensure that blocking back does not occur at equilibrium. Similar results, but now involving the P 0 control policy (introduced in Smith (1979a, 1987)) and two new variations of this policy (i.e., the spatial P 0 control policy, and the biased spatial P 0 control policy) are obtained. In these results, the control policies allow green-times to vary in response to prices as well as spatial queueing delays. These three policies are also tested on a small simple network. In these tests, the biased spatial version of P 0 is much the best in reducing equilibrium delays (on this simple network). The paper further illustrates how the spatial queueing model works on simple networks with different merge models; it is demonstrated that equilibrium may be prevented by certain (fixed ratio) merge models. It is also shown in this case that equilibrium may be imposed on just the controlled area itself by a variety of (merge model, gating strategy) combinations. Opportunities for developing such combined gating and merging control strategies are finally discussed. ispartof: TRANSPORTATION RESEARCH PART B-METHODOLOGICAL vol:122 pages:140-166 status: published

Summary This study is extended to construct the network model, the node model, and the link model of complex communication network for satellite navigation system (CCN‐SNS) based on the hierarchica...

In this article the research results of steady-state and dynamic performance of asynchronous back-to-back HVDC link in Tomsk electric power system are presented. It is proposed to use the back-to-back HVDC link to provide an asynchronous link for parallel operation of the North and South parts of Tomsk electric power system. The steady-state stability limit and maximum allowed power flow were defined. Based on this the more effective location and power of back-to-back HVDC link were determined. Therefore, it is shown the application of back-to-back HVDC link decreases the level of short-circuit current, power fluctuations and increases the stability of load operation, especially of motor load at substation.

Methodological and applied tools based on the use of fuzzy set theory are considered to evaluate the achievement of goals in the context of the sustainable development model implementation, the technology of implementation of the link model between its individual indicators, which should be used in the development of strategic development scenarios in Ukraine. They were tested for rank assessment of achievement of the 47 European countries according to the 2017 UN Repository.

The Coronavirus disease 2019 (COVID-19) pandemic and its associated governmental recommendations and restrictions have influenced many aspects of human life, including exercise and mental health. This study aims to explore the influence of COVID-19 on exercise behavior and its impact on mood states, as well as predict changes in exercise behavior during a similar future pandemic in Taiwan. A cross-sectional online survey was conducted between 7 April and 13 May 2020 (n = 1114). Data on exercise behavior pre and during the pandemic and mood states were collected. A cumulative link model was used to predict changes in exercise frequency during a similar future pandemic by exercise frequency during the pandemic. A linear model was used to predict the influence of exercise frequency before and during the pandemic on mood states during the pandemic. A total of 71.2%, 67.3%, and 58.3% of respondents maintained their exercise intensity, frequency, and duration, respectively, during the pandemic. Frequent exercisers are more likely to maintain their exercise frequency during a similar pandemic (p &lt, 0.001). Higher exercise frequencies during the pandemic were associated with better mood states (p &lt, 0.05). Moreover, the effects of prepandemic exercise frequency on mood states are moderated by changes in exercise frequency during the pandemic (p &lt, 0.05). Additionally, maintenance of exercise frequency during a pandemic specifically for frequent exercisers are recommended to preserve mood states. These results may provide evidence for health policies on exercise promotion and mental health before and during a future pandemic.

Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between parent webpage and child webpages, the visual similarity, and the semantics of webpages. Additionally, this paper provides an information service based on a main content extraction algorithm for extracting the lecture information. Stable and invariant features enable the proposed method to adapt to various kinds of campus websites. The experiments conducted on 50 websites show the effectiveness and efficiency of the provided service.

Mortality forecasting has crucial implications for insurance and pension policies. A large amount of literature has proposed models to forecast mortality using cross-sectional (period) data instead of longitudinal (cohort) data. As a consequence, decisions are generally based on period life tables and summary measures such as period life expectancy, which reflect hypothetical mortality rather than the mortality actually experienced by a cohort. This study introduces a novel method to forecast cohort mortality and the cohort life expectancy of non-extinct cohorts. The intent is to complete the mortality profile of cohorts born up to 1960. The proposed method is based on the penalized composite link model for ungrouping data. The performance of the method is investigated using cohort mortality data retrieved from the Human Mortality Database for England & Wales, Sweden, and Switzerland for male and female populations.

To establish relationships between the molecular structure of polyolefines and their physical characteristics which determine possible commercial applications, structural changes and tensile deformation response up to deformations beyond the natural draw ratio were investigated using a variety of experimental approaches. True stress-strain curves were measured at different temperatures so as to estimate the available effective network density, which will eventually define the failure mode of the material under investigation. Analysis of the deformation by means of tensile strain hardening, assuming the Haward-Thackray spring dashpot decoupling assumption by means of Edward-Vilgis' non-Gaussian rubber-elastic slip-link model, reveals the role of transient and fixed network nodes.It was established by differential scanning calorimetry and X-ray diffraction analysis that the transformation from lamellar to fibrillar morphology passes through the several pronounced stages: deformation of initial lamellae (lambda Distinction between fixed and transient slip link network contributions reveals neatly that although there is a slight drop in the fixed link network density with increasing temperature, this contribution remains of the same order of magnitude and predominantly related to the molecular mass. This observation enhances the idea that the network of entanglements that remain fixed on the time scale of the measurement is actually entropic in nature and does not depend greatly on temperature. Considering slip link contributions, one can find them to be prominently present at ambient temperature and their importance becomes negligible at elevated temperature, except for the case of the most crystalline material.

We present a more efficient way to predict the dynamic modulus of a broadly disperse, entangled polymer melt that uses the discrete slip-link model. Polydisperse blends provide a computational challenge because of the large number of molecular weights that contribute to the modulus. Here, we simulate the order 10 probes to capture the sliding dynamics of chains, but a continuous background to capture constraint dynamics. Contributions of other molecular weights can be interpolated from the simulated probes. Since we use the continuous background, additional probes can be added as needed without re-doing the prior simulations. We show that the method is capable of predicting the modulus well for two different chemistries. Comparison with a simplified double reptation model for a third chemistry shows agreement between the two approaches for frequencies near and below the crossover point of the dynamic modulus even though the distribution is rather narrow.We present a more efficient way to predict the dynamic modulus of a broadly disperse, entangled polymer melt that uses the discrete slip-link model. Polydisperse blends provide a computational challenge because of the large number of molecular weights that contribute to the modulus. Here, we simulate the order 10 probes to capture the sliding dynamics of chains, but a continuous background to capture constraint dynamics. Contributions of other molecular weights can be interpolated from the simulated probes. Since we use the continuous background, additional probes can be added as needed without re-doing the prior simulations. We show that the method is capable of predicting the modulus well for two different chemistries. Comparison with a simplified double reptation model for a third chemistry shows agreement between the two approaches for frequencies near and below the crossover point of the dynamic modulus even though the distribution is rather narrow.

Link Model, a participatory rural appraisal (PRA) programme for micro-infrastructure construction funding aid, has received much attention in Bangladesh over the last few decades. This study aims to contribute to the latest phase of the Link Model project, launched in July 2016 by the Bangladeshi government. To this end, the authors conducted a field survey in Kalihati Upazila, which involved 102 villagers and 153 local representatives. The authors used the survey to examine the efficacy of the programme and to identify the key factors that contribute to its success. Results obtained from the questionnaire survey showed that the programme had high degrees of satisfaction and penetration in the surveyed villages. Moreover, the degree of satisfaction was unaffected by gender, religion, income or education background, which is notably consistent with the ideals of PRA. Villagers who understood the policy better were more satisfied with the micro-infrastructures developed through the Link Model programme. The ratings of union development officers (UDO) strongly correlated with the number of micro-infrastructures. Therefore, this study concludes that the key success factors of Link Model are (a) the understanding of villagers on the programme for quality and (b) the capability of UDO for quantity.

This paper proposes the Topical Link Model (TLM), which can be used as a framework to integrate information of a specific decision topic in financial reports formatted in the eXtensible Business Reporting Language (XBRL). Building on link analysis and Topic Maps concepts, this study demonstrates that the TLM allows users and preparers of XBRL-formatted financial reports to operationalize the Generic Linkbase, an existing XLink semantic tool embedded in XBRL, to integrate topic-centric information. To validate the merits of integrating information in financial reports based on TLM, this study implements a preliminary evaluation exercise that integrates quantitative and qualitative information in XBRL-formatted financial reports. The results from the exercise demonstrates that XLink, as a semantic tool, can have broader applications because it provides detailed and insightful “links” among elements in financial reports.

Many aspects of gauge theories — such as the one underlying quantum chromodynamics, which describes quark physics — evade common numerical methods. Tensor networks are getting closer to a solution, having successfully tackled the related problem of a three-dimensional quantum link model.

The outcome of high-throughput biological experiments is affected by many operational factors in the experimental and data-analytical procedures. Understanding how these factors affect the reproducibility of the outcome is critical for establishing workflows that produce replicable discoveries. In this article, we propose a regression framework, based on a novel cumulative link model, to assess the covariate effects of operational factors on the reproducibility of findings from high-throughput experiments. In contrast to existing graphical approaches, our method allows one to succinctly characterize the simultaneous and independent effects of covariates on reproducibility and to compare reproducibility while controlling for potential confounding variables. We also establish a connection between our model and certain Archimedean copula models. This connection not only offers our regression framework an interpretation in copula models, but also provides guidance on choosing the functional forms of the regression. Furthermore, it also opens a new way to interpret and utilize these copulas in the context of reproducibility. Using simulations, we show that our method produces calibrated type I error and is more powerful in detecting difference in reproducibility than existing measures of agreement. We illustrate the usefulness of our method using a ChIP-seq study and a microarray study.

The master equation underlying ecoSLM, an ultra-coarse-grained slip link model, is presented. In the absence of constraint release, the equilibrium and dynamic properties of the discrete master equation for large chains are found to be virtually identical to the continuous Fokker-Planck equation for Brownian particles diffusing in a potential. A single-chain microscopic model with repulsion between adjacent slip links is described. It is approximately consistent with the quadratic fluctuation potential used in ecoSLM. Mapping ecoSLM with fine-grained slip link models or experiments requires specification of an effective friction as a function of molecular weight. Methods to accomplish this are discussed. Collectively, the mathematical framework described provides an interface for fine-grained slip link models to potentially use ecoSLM for extreme coarse-graining.

3D coverage is not only closer to the actual application environment, but also a research hotspot of sensor networks in recent years. For this reason, a node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network is proposed in this article. According to wireless link-aware area, the link coverage model in three-dimensional wireless sensor network is constructed, and the cube-based network coverage is used to represent the quality of service of the network. This model takes advantage of the principle that the presence of human beings can change the transmission channel of the link. On this basis, the intruder is detected by the data packets transmitted between the wireless links, and then the coverage area is monitored by monitoring the received signal strength of the wireless signal. Based on this new link awareness model, the problem of optimal coverage deployment of the receiving node is solved, that is, how to deploy the receiving node to achieve the optimal coverage of the monitoring area when the location of the sending node is given. In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. Based on the genetic algorithm, the particle swarm optimization algorithm which integrates the idea of simulated annealing is regarded as an important operator of the genetic algorithm, which can converge to the optimal solution quickly. The simulation results show that the proposed method can improve the network coverage, converge quickly, and reduce the network energy consumption. In addition, we set up a real experimental environment for coverage verification, and the experimental results verify the feasibility of the proposed method.

Experimental data have made it abundantly clear that the strength of polycrystalline silicon (poly-Si) microelectromechanical systems (MEMS) structures exhibits significant variability, which arises from the random distribution of the size and shape of sidewall defects created by the manufacturing process. Test data also indicated that the strength statistics of MEMS structures depends strongly on the structure size. Understanding the size effect on the strength distribution is of paramount importance if experimental data obtained using specimens of one size are to be used with confidence to predict the strength statistics of MEMS devices of other sizes. In this paper, we present a renewal weakest-link statistical model for the failure strength of poly-Si MEMS structures. The model takes into account the detailed statistical information of randomly distributed sidewall defects, including their geometry and spacing, in addition to the local random material strength. The large-size asymptotic behavior of the model is derived based on the stability postulate. Through the comparison with the measured strength distributions of MEMS specimens of different sizes, we show that the model is capable of capturing the size dependence of strength distribution. Based on the properties of simulated random stress field and random number of sidewall defects, a simplified method is developed for efficient computation of strength distribution of MEMS structures.

A new dynamic model of a distributed real-time computing system is investigated. The model is embedded in the system software and is intended for test diagnostics of violations in addressing exchanges between the system software modules. The questions of observability and controllability of the proposed model are discussed for the case when models of links are used without loss of information.

This study presents a probabilistic modeling of size effect of 30NiCrMoV12 steel in a low cycle fatigue (LCF) regime. Three types of fatigue tests have been conducted on three geometrical specimens. Results indicate that nearly all of the LCF lifetime of 30NiCrMoV12 consists of the development of multiple surface short cracks, which has shown statistical aspects of their mutual interactions and coalescence. This gives rise to an approach of LCF damage based on statistical physics. A statistical Weibull’s weakest link model has been verified and a procedure for surface multiple fracture simulation is then established by including the random processes of initiation, propagation and coalescence of dispersed surface cracks. Using this procedure, the life of a component can be predicted from the criterion of critical cracks formation by coalescence of dispersed defects.